DEFORESTATION AROUND THE WORLD - India Environment Portal

DEFORESTATION 

AROUND THE WORLD 

Edited by Paulo Moutinho

Deforestation Around the World 

Edited by Paulo Moutinho 

Published by InTech 

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Copyright © 2012 InTech 

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Statements and opinions expressed in the chapters are these of the individual contributors 

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Publishing Process Manager Maja Bozicevic 

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First published March, 2012 

Printed in Croatia 

A free online edition of this book is available at www.intechopen.com 

Additional hard copies can be obtained from orders@intechopen.com 

Deforestation Around the World, Edited by Paulo Moutinho 

p. cm. 

ISBN 978-953-51-0417-9

Contents 

Preface IX 

Part 1 Deforestation Impacts 1 

Chapter 1 The Climatic Effects of Deforestation 

in South and Southeast Asia 3 

Rachindra Mawalagedara and Robert J. Oglesby 

Chapter 2 Impacts of Deforestation on Climate 

and Water Resources in Western Amazon 21 

Ranyére Silva Nóbrega 

Chapter 3 Deforestation and Water Borne Parasitic Zoonoses 35 

Maria Anete Lallo 

Chapter 4 Impact of Deforestation on the Sustainability 

of Biodiversity in the Mesoamerican Biological Corridor 49 

Vani Starry Manoharan, John Mecikalski, 

Ronald Welch and Aaron Song 

Chapter 5 Dinaric Karst – An Example of Deforestation 

and Desertification of Limestone Terrain 73 

Andrej Kranjc 

Chapter 6 Landslides Caused Deforestation 95 

Diandong Ren, Lance M. Leslie and Qingyun Duan 

Chapter 7 Deforestation Dynamics: A Review and Evaluation 

of Theoretical Approaches and Evidence from Greece 123 

Serafeim Polyzos and Dionysios Minetos 

Part 2 Mapping Deforestation 143 

Chapter 8 Geospatial Analysis of Deforestation and Land Use 

Dynamics in a Region of Southwestern Nigeria 145 

Nathaniel O. Adeoye, Albert A. Abegunde and Samson Adeyinka

VI Contents 

Chapter 9 Unsupervised Classification 

of Aerial Images Based on the Otsu’s Method 171 

Antonia Macedo-Cruz, I. Villegas-Romero, 

M. Santos-Peñas and G. Pajares-Martinsanz 

Chapter 10 Deforestation and Waodani Lands in Ecuador: 

Mapping and Demarcation Amidst Shaky Politics 187 

Anthony Stocks, Andrew Noss, 

Malgorzata Bryja and Santiago Arce 

Chapter 11 Sustainable Forest Management Techniques 203 

K.P. Chethan, Jayaraman Srinivasan, Kumar Kriti and Kaki Sivaji 

Chapter 12 Bunjil Forest Watch 

a Community-Based Forest Monitoring Service 229 

Chris Goodman 

Chapter 13 Remnant Vegetation Analysis 

of Guanabara Bay Basin, Rio de Janeiro, Brazil, 

Using Geographical Information System 253 

Luzia Alice Ferreira de Moraes 

Part 3 Preventing Deforestation 281 

Chapter 14 Preserving Biodiversity and Ecosystems: 

Catalyzing Conservation Contagion 283 

Robert H. Horwich, Jonathan Lyon, Arnab Bose and Clara B. Jones 

Chapter 15 Efficiency of the Strategies to Prevent 

and Mitigate the Deforestation in Costa Rica 319 

Óscar M. Chaves 

Chapter 16 Agroforestry Systems 

and Local Institutional Development 

for Preventing Deforestation in Chiapas, Mexico 333 

Lorena Soto-Pinto, Miguel A. Castillo-Santiago 

and Guillermo Jiménez-Ferrer 

Chapter 17 Economic Models of Shifting Cultivation: A Review 351 

Yoshito Takasaki

Preface 

Forests are giant reservoirs of carbon and biodiversity that must remain largely intact 

if we want to bring global warming under control and preserve life on earth. 

However, over the last decade, 13 million ha/year of forests have been deforested in 

the word. South America and Africa present the highest deforestation rates (> 3.4 

million ha/yr) and forest losses were also intense in many places of Asia. Although 

studies don’t always agree, they generally indicate that deforestation causes are 

related to infrastructure investments, expansions of agricultural and pasture frontiers, 

land tenure issues, absence of adequate surveillance by the government, high demand 

for forest products (wood), rural settlements, mining, and logging. International 

growing demand for commodities and global land crises can also affect deforestation. 

Consequently, the impacts resulting from deforestation can include changes in the 

rainfall regime - increasing the risk of forest fires during dry seasons - negative 

changes on river level and on the quality of water, as well as enormous biodiversity 

losses, considering that more than half of the animals and plants living on the planet 

live in forests. Deforestation also dramatically affects human population living in or 

from the forests. Indigenous peoples and traditional communities -the natural 

guardians of the forests - have seen their territories been reduced or invaded by 

deforesters. However, they are the most promising alternative to protect the forests 

and simultaneously create a sustainable economy based on forest products. 

Despite global consequences, deforestation presents local/regional-based dynamics. 

This book provides a general view about this dynamics, incorporating the diversity of 

causes, impacts and actions to prevent deforestation in several places around the 

world. The chapters are divided in three sections: (I) Deforestation Impacts, (III) 

Mapping Deforestation, and (II) Preventing Deforestation. 

Chapter 1 (Oglesby) discusses the impacts of land cover changes on the climate in 

South and Southeast Asia, and Chapter 2 (Nóbrega) presents the effects of 

deforestation on the climate and hydrological cycle in Rondonia state, Brazil, that can 

have potential effects on the biodiversity, as indicated by Chapter 3’s (Lallo) study on 

parasites population in Brazil. Chapter 4 (Manoharan and colleagues), describes how 

deforestation and changes in physical and climate parameters could affect the 

potential of environmental protection provided by the Mesoamerican Biological 

Corridor (Central America). Then, Chapter 5 (Kranjc) exemplifies the large

X Preface 

environmental consequences of deforestation in the Dinaric Karst region, Slovenia, 

and finally, Chapter 6 (Diandong et al.) shows how disturbs caused by landslides can 

provoke vegetation losses at landscape scale. 

The first section is closed by Chapter 7 (Polyzos et al.) and relates the forest land use 

changes in Greece and how it has affected the regional and economic development. 

The next section, Mapping Deforestation, begins with Chapter 8 by Adeoye et al. They 

use a set of images data to analyze the historical deforestation in southwestern Nigeria 

demonstrating the potential of the remote-sensing and GIS technology for forest 

conservation. Chapter 9 (Macedo-Cruz, et al.) suggests a new remote-sensing 

methodology, an unsupervised classification method, with potential to map 

deforestation. Stocks et al. (Chapter 10) demonstrate how deforestation mapping is 

important for forest protection in the Waodani indigenous land, Ecuador, and Chapter 

11 (Jayaraman et al.) discusses the potential of the Wireless Sensor Networks (WSNs) 

as an accessible and low cost technology that could be useful to monitor the 

environment in a large area. Chapter 12 (Goodman) proposes a free public online 

service to map the recent forest disturbances in one particular area and explains how it 

could attend local communities. Finally, Chapter 13 (De Moraes) provides a timeseries 

of land-cover in the Guanabara Bay Basin, Brazil, to demonstrate how remote 

sensing data is essential to measure the critical changes in vegetation remnants of 

Atlantic Forest, the most deforested forest in Brazil, to offer better information for 

decision-makers. 

On the last section, Preventing Deforestation, Chapter 14 (Horwich et al.) 

demonstrates how the community-based initiatives positively affect the conservation 

of forests in three different countries, Belize, Namibia and India. Chapter 15 (Chaves) 

bring us a review on the potential of different conservation strategies (ecotourism, 

payments for environmental services, private reserves, and environmental education) 

to mitigate deforestation in Costa Rica. Chapter 16 (Soto-Pinto et al.) reveals how a 

sustainable production (agroforestry), reforestation and forest conservation - 

developed by the Scolel Té project is dealing with deforestation in Chiapas State, 

Mexico. Finally, the Chapter 17 (Taksaki) provides a critical review on how economic 

models for shifting cultivation and deforestation are presented by economists and how 

these models can be related to deforestation dynamics. 

I hope the study-cases reported here may call attention for the velocity we are losing 

our forests in a planetary scale and for inestimable impact that will have in human life 

quality, in wild life, in water, soil and air and in the world economy. To keep it short, 

it won’t be a surprise if the cost to fix the losses would overcome the investments we 

have done to achieve the present unsustainable development. 

I am grateful to InTech for the opportunity to edit this volume and the authors with 

whom I learned so much about the issues of deforestation around the world. I am also

Preface XI 

thankful to Maja Bozicevic, the InTech’s Publishing Manager, who assisted me in the 

editing process. 

Paulo Moutinho 

IPAM - Instituto de Pesquisa Ambiental da Amazônia 

Brazil

In Memoriam of Cyrene Moutinho

Part 1 

Deforestation Impacts

1. Introduction 

The Climatic Effects of Deforestation in 

South and Southeast Asia 

1 

Rachindra Mawalagedara and Robert J. Oglesby 

University of Nebraska, Lincoln 

USA 

Deforestation is the removal of the existing natural vegetation cover, especially where the 

native cover is largely forest. The growth in the world population has increased the clearing 

of forests to obtain fuel and building material, to grow crops and to raise livestock. Over the 

past 300 years, 7-11 million km 2 of forest has been cleared (Foley et al. 2005). Deforestation 

can have a devastating impact on biodiversity as about 70% of land dwelling animals and 

plants are found in forests. Impacts such as land degradation in the absence of forest 

regrowth, soil erosion and sedimentation in rivers can have a negative impact on the 

environment. These impacts are discussed in greater detail in the other chapters of the book. 

Importantly, deforestation can also have strong effects on climate. 

In the past it was assumed that the local climate determined the vegetation type in a region 

(Nobre et al. 1991) with the amount of incoming solar radiation, precipitation and soil type 

determining the vegetation cover of the region. But studies have shown that the atmosphere 

and the vegetation interact with each other, exchanging energy, moisture and momentum 

(Zeng et al. 1999) and are in a dynamic equilibrium (Nobre et al., 1991). Therefore any 

change in vegetation cover can potentially lead to a change in the climate. As deforestation 

is a pressing problem in most parts of the world it is important to understand the possible 

consequences of deforestation and the mechanisms by which the change in land cover can 

alter the climate. 

The impacts of land cover changes on the atmosphere have been studied extensively using 

both observations and computer models (Suh and Lee, 2004; Lean and Warrilow, 1989; 

Kanae et al., 2001; Clark et al., 2001). Previous studies have shown that deforestation can 

change the surface albedo, surface roughness and the amount of evapotranspiration 

(evapotranspiration is the combined effect of evaporation from the surface and the 

transpiration from vegetation) (Gibbard et al., 2005; Oglesby et al., 2010; Hasler et al., 2007) 

thus, leading to a modification of the surface energy and moisture budgets. 

In order to determine the full climatic impact of deforestation, it is necessary to understand 

the behavior of the surface energy and the moisture budgets, as deforestation interacts 

directly or indirectly with all the components of these budgets. The surface energy budget 

looks at all the possible sources and sinks of energy at the surface as well as any possible 

horizontal transport (fluxes) and storage of energy within the seasonally active layer just 

below the surface. Over land, incoming and reflected solar radiation (shortwave radiation),

4 


incoming and outgoing longwave radiation, sensible heat flux, latent heat flux and ground 

storage are the important terms that need to be considered for the surface energy budget. 

Unlike in the oceans where transport of energy (by the oceans) is significant to the climate 

system, horizontal transport of energy in the ground is negligible. The moisture budget is 

related to the hydrologic cycle and takes into account precipitation, evaporation, surface 

runoff (horizontal transport of water) and storage. If the period considered is a year or 

longer, the storage of energy and moisture can be considered negligible. The changes in the 

terms of the two budgets can be used to determine the changes in the climate. 

To understand how deforestation impacts the climate it is important to understand the 

behavior of the terms that make up the surface energy and moisture budgets. The primary 

source of energy that contributes to the surface energy budget is the sun. The energy that 

sustains the Earth and drives the global circulation is acquired from incoming solar 

radiation but is not absorbed directly by the atmosphere. Instead, most of the incoming 

solar radiation is first absorbed by the surface. The amount of solar radiation absorbed by 

the surface depends on the surface albedo, which determines the fraction of solar 

radiation reflected from the surface. The albedo can be expressed either as a percentage or 

a fraction, ranging from 100% (1.0) to 0% (0.0) with the former value indicating that all the 

incoming radiation is reflected (no absorption) and the latter indicating that no reflection 

of radiation takes place (all incoming radiation is absorbed). The surface albedo is related 

to the texture and the colour of the surface, with dark rough surfaces (low albedo) 

absorbing more energy. 

Once the energy is absorbed by the surface, radiative and non-radiative processes transfer it 

from the surface to the atmosphere. Part of the absorbed energy warms the surface and is 

then emitted as longwave radiation from the surface. The magnitude of the emission is 

determined by the temperature and the emissivity of the surface (emissivity depends on the 

type of the surface: vegetated, bare soil etc.) and can be determined by the Stephen- 

Boltzmann Law. Part of the longwave radiation emitted by the surface is absorbed and 

reemitted by the atmosphere (can be calculated using the emissivity and the temperature of 

the atmosphere). A portion of the reemitted longwave radiation then acts as a source of 

energy for the surface. The remaining energy is partitioned between sensible and latent heat 

fluxes which are the non-radiative terms in the surface energy budget. The sensible heat flux 

heats the atmosphere in contact with the surface and is a less efficient method of heat 

transfer compared to the latent heat flux. It is difficult to measure the sensible heat flux 

directly. But it can be calculated easily if the latent heat flux and the Bowen ratio are known. 

The Bowen ratio is a measure of the water availability in a region and is the ratio between 

sensible heat flux and the latent heat flux. The latent heat flux can be calculated using the 

rate of evaporation. 

The latent heat exchange, a significant process in the surface energy budget, is proportional 

to the amount of evaporation, and thereby provides a link to the surface moisture budget. 

The magnitude of the latent energy flux depends both on the amount of moisture available 

at the surface and the energy available for evaporation. In the tropics energy is not usually a 

limiting factor and hence the dependency is on the water availability. Therefore the energy 

that is not emitted as longwave radiation or stored in the ground is partitioned between 

sensible and latent heat and this partitioning is determined by the amount of water available 

at the surface. The latent heat flux also provides a measure of cooling at the surface due to

The Climatic Effects of Deforestation in South and Southeast Asia 

evapotranspiration (i.e. water absorbs energy from the surface and evaporates thus cooling 

the surface). Once the evaporated moisture condenses in the atmosphere the latent energy is 

released. The energy released contributes to convection and helps to drive the local 

circulation. Therefore the latent heat exchange is not only an important cooling mechanism 

but is an important measure of the energy available for regional circulation. 

The surface moisture budget accounts for precipitation, evapotranspiration and surface 

runoff. The evapotranspiration term links this to the surface energy budget. Changes in 

availability and the partitioning of energy can have an impact on evapotranspiration and 

hence other terms in the moisture budget. The information from the surface moisture 

budget namely, precipitation and evaporation can be used to compute the atmospheric 

moisture convergence/divergence which gives the net amount of water vapor transported 

into or out of a region by the regional circulation. If the amount of precipitation is larger 

than the local evapotranspiration this indicates that the moisture transport into the region 

makes a significant contribution to precipitation. Thus the atmospheric moisture 

convergence can be used to determine the relative importance of an external moisture 

source to that of local evaporation. 

As discussed above both surface albedo and emissivity are sensitive to the nature of the land 

surface and hence depend on the type of land-use (forest, water, urban etc). The magnitude 

of the latent heat flux also depends on the land-use category as the water available for 

evapotranspiration changes with land-use. For example a vegetated surface would have 

more moisture available for evapotranspiration than barren land, and, as described more 

fully later in the chapter, forested land will have higher values than grassland or shrubland. 

Therefore it is evident that any change in the land-use in a region will modify both the 

surface energy and the moisture budgets. 

Deforestation alters the land surface properties and the interactions between the surface and 

the atmosphere. Two of the most important changes due to deforestation are the increase in 

surface albedo and the decrease in evapotranspiration. The significance of these changes is 

discussed in more detail under the methods section. 

Deforestation results in two competing effects, warming due to the reduction in 

evapotranspiration and a cooling due to the increase surface albedo. Previous studies have 

shown that in most regions the magnitude of warming is much greater than that of cooling, 

resulting in warmer and drier conditions (Zhang et. al., 1996; Oglesby et al., 2010). But these 

impacts are further modulated or enhanced by the dominant circulation patterns and 

moisture sources of the considered region. For example the change in precipitation due to 

the decrease in evapotranspiration would be more dramatic in a region such as the Amazon 

basin where 50% of the moisture available for precipitation comes local evapotranspiration 

(Lean and Warrilow, 1989). But if the region is close to a water body such as an ocean or a 

lake and the local and/or regional circulations are favorable for moisture transport, the 

contribution from local evapotranspiration would not be as significant. Most coastal regions 

receive abundant moisture from the ocean, carried inland by onshore flow (i.e. sea breeze – 

local circulation) contributing to precipitation. This mechanism alone is not strong enough 

for moisture to be transported to inland regions far from the oceans. But a large scale 

circulation pattern such as the Asian monsoon can penetrate further inland supplying 

moisture to continental regions thus making the impact of reduced evapotranspiration on 

precipitation much smaller. 

5

6 


This study focused on South Asia, Southeast Asia and Sri Lanka, all regions where the Asian 

monsoon plays an important role, and all regions where deforestation is currently or 

potentially a major issue. In these regions, the monsoon flow brings moisture from the ocean 

over land and this together with moisture provided by evapotranspiration produce 

abundant rainfall. The wet season is mostly during the summer monsoon period, but some 

areas experience rainfall during the winter monsoon as well. Any changes in this established 

pattern of rainfall and associated climate could have devastating consequences as this is a 

heavily populated area where agriculture is of great importance. 

The Hadley cell is one of the prominent features that make up the global circulation. The 

Hadley cell consists of two asymmetric cells extending between approximately 15 o in the 

summer hemisphere and 30 o in the winter hemisphere (Lu et al. 2007). This circulation is 

defined by a branch of rising air over the surface low pressure belt (Inter-Tropical 

Convergence Zone- ITCZ) in the tropics, a poleward flow in both hemispheres in the upper 

troposphere, a branch of subsiding air in the subtropics and equator-ward flow (in both 

hemispheres) at the surface that converges at the ITCZ (Mitas et al., 2005). This circulation 

transports both energy and angular momentum and gives rise to many of the climatic 

regions that are present today. For example regions that fall under the ITCZ receive 

abundant rainfall due to the strong convection associated with the rising branch of the 

Hadley cell and either receive rainfall throughout the year or have well defined wet/dry 

seasons. The subtropical deserts are in the regions where the subsiding branches of the 

Hadley cell are found as the sinking of air prevents the formation of clouds and hence 

precipitation. Any changes in the strength or the extension of the Hadley cell will therefore 

impact the climate within the region and have the ability to change the geographical 

boundaries of these climate zones. As the location of Southeast Asia is closely associated 

with the rising branch of the Hadley cell and the poleward transport of energy, 

deforestation can have consequences on both regional and global scales. 

It is evident that tropical deforestation can have an impact on the climate by modifying the 

magnitudes and the spatial and the temporal patterns of temperature and precipitation, 

creating warmer and drier climatic conditions in most regions. These changes might then be 

modulated or enhanced by the dominant circulation patterns such as the Asian monsoon 

leading to additional changes in the climate. The local moisture sources of the considered 

region may also play a significant role in modifying the climatic effects of deforestation 

Therefore, it is important to understand the possible consequences of deforestation and the 

mechanisms by which the change in land cover can alter the monsoonal climate. 

2. Methods 

In order to understand the impacts of tropical deforestation on the climate in South and 

Southeast Asia, a widely-used regional climate model (WRF – Weather Research and 

Forecasting Model) was employed. The Weather Research and Forecasting (WRF) Model is a 

next generation mesoscale numerical weather prediction system that has been developed as 

a collaborative effort by the National Center for Atmospheric Research (NCAR), the 

National Oceanic and Atmospheric Administration (the National Centers for Environmental 

Prediction (NCEP) and the Forecast Systems Laboratory (FSL), the Air Force Weather 

Agency (AFWA), the Naval Research Laboratory, the University of Oklahoma, and the


Federal Aviation Administration (FAA). WRF can be used in a wide scope of spatial scales 

ranging from a few meters to thousands of kilometers and is suitable for both operational 

forecasting and atmospheric research (Skamarock et al., 2008; http://www.wrfmodel.org/index.php). 

The main focus of this study was to identify the impacts of deforestation on the monsoonal 

climate in South and Southeast Asia. Therefore a Regional Climate Model (RCM) was used 

for this study instead of a Global Circulation Model (GCM), so that it was possible to run the 

model at a high resolution. This allowed the model to include the regional features and 

predict the regional climate with more accuracy. The GCM which usually has a horizontal 

resolution of 100 – 250 km (McGuffie and Henderson-Sellers, 2005). can capture the features 

of large and synoptic scale atmospheric circulation, but is too coarse to include small scale 

features such as the effects of topography or land surface effects to simulate the climate on a 

regional scale accurately (Denis et al., 2002). Since GCM simulations are done for the entire 

globe it is not feasible to run it at very high resolution due to limitations in computing 

power. Therefore regional climate models (RCM) are used to study regional climate 

changes. The main difference is a RCM is focused on the region of interest and has a much 

higher resolution. For example WRF simulations can be done at a resolution of 4 km which 

allows many small scale features such as mountains, coastlines and, importantly for our 

purposes, land-use categories to be represented more accurately. 

The model was forced at the lateral boundaries by NCEP/NCAR Reanalysis data (NNRP) 1 . 

Since the model is a regional model the simulations are done for a restricted region. 

Therefore, conditions at the boundaries of the specified domain need to be provided so that 

the model can properly simulate the climate within the domain. Reanalysis data is used to 

provide these boundary conditions as observations alone are not sufficient to describe the 

full state of the atmosphere due to missing or spatially non-uniform data. Reanalysis data 

solves this problem by combining actual observations with a global model of the 

atmosphere to produce a comprehensive data set that serves as a proxy for real 

observations, thus providing better boundary conditions for the regional model. 

The model simulations focused on three specific domains: 1. South Asia (Southern India and 

Sri Lanka with a resolution of 12 km), 2. Southeast Asia (with a resolution of 12 km), 3. High 

resolution focus on Sri Lanka (with a resolution of 4 km). The WRF simulations were done 

for the years 1988, 1991 and 1993. These years represent a strong, weak and normal 

monsoon year with respect to South Asia. For each of these three years, a control run as well 

as two idealized runs (completely deforested and forested situations) were carried out and 

analyzed. The control runs are also compared to actual observations in order to identify 

model strengths and weaknesses, as well as any biases. In the deforested run all the land use 

categories (other than inland water) were replaced with grassland, which has a higher 

albedo than the tropical forests, but much less capability at extracting water from the soil. In 

the forested run evergreen broadleaf forest was used. These land-use changes, while 

extreme, provided the maximum possible range of impacts due to deforestation. 

1The data for this study are from the Research Data Archive (RDA) which is maintained by the 

Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric 

Research (NCAR). NCAR is sponsored by the National Science Foundation (NSF). The original data are 

available from the RDA (http://dss.ucar.edu) in dataset number ds090.2. 

7

8 


In a WRF simulation each grid point has a land-use category (grassland, cropland, 

evergreen broadleaf forest, water etc.) assigned to it based on the land-use data set being 

used for the model run. The properties (surface albedo, surface emissivity, moisture 

availability, surface roughness length) of each land-use category depend on the land surface 

model used in the WRF run. The land surface model is the component that takes care of the 

processes involving land-surface interactions. For the WRF runs, the 5-layer thermal 

diffusion scheme was selected as the land surface model. USGS (winter) data set was used 

to specify land-use categories and their properties. To simulate deforested conditions, all the 

land-use categories other than water were replaced with grassland. Grassland has a higher 

albedo (23%) than most other land-use types and therefore absorbs less energy. The 

specified moisture capacity (0.30) is, however, also low. Water bodies have the lowest 

surface albedo (8%) and a specified moisture availability of 1.0 (saturated surface). Forested 

conditions were simulated by replacing all land-use categories other than water by 

evergreen broadleaf forest which has a very low surface albedo (12%), but much higher 

moisture availability (0.5) compared to grassland, with the former allowing the surface to 

absorb more incoming energy and the latter supporting larger evaporation amounts. 

Before analyzing the results of the WRF simulations it is important to understand the 

possible impacts of the land-use changes made to the deforested and deforested runs (as 

mentioned above), on the surface energy and the moisture budgets and how the changes 

will ultimately affect the regional climate. 

Tropical forests have a low surface albedo throughout the year. This allows the forests to 

absorb a large part of the incoming radiation. Most land-use categories have surface albedos 

that are higher than that of a tropical rainforest. Therefore tropical deforestation leads to an 

increase in the surface albedo, allowing the surface to reflect more radiation. As a result the 

surface absorbs less radiation creating a cooling effect. This also reduces the amount of 

energy available for evapotranspiration. 

On the other hand, the trees found in tropical forests have the capacity to draw water from 

the soil and thereby add a large amount of moisture to the atmosphere via transpiration. 

The large leaf and stem area allows the trees to intercept a significant amount of the rainfall. 

The intercepted water is then evaporated into the atmosphere. Evapotranspiration can be an 

important moisture source for local precipitation, especially in regions that are not in the 

proximity of a water body. Also evapotranspiration helps to lower the surface temperature. 

Deforestation leads to a decrease in evapotranspiration. This removes or reduces the 

capacity of the local moisture source and the cooling effect of evapotranspiration. This also 

alters the energy partitioning between latent and sensible heat fluxes at the surface. Due to 

the reduction in evapotranspiration and hence the latent energy flux, the energy transfer 

between the surface and the atmosphere would be achieved mostly through the exchange of 

sensible heat. As this is a less efficient method of heat transfer (compared to the cooling by 

latent heat transfer), this would lead to an increase in the surface temperature. The 

reduction in the latent energy flux means the energy available for convection is reduced. 

This can potentially lead to a weakening of the local circulation that in turn can have a 

negative impact on the moisture convergence in the area. The reduction of moisture 

convergence and evapotranspiration result in a reduction in precipitation. Therefore the 

reduction in evapotranspiration can result in a warming of the surface and a decrease in 

precipitation. The decrease in precipitation then acts as a positive feedback to further reduce


the evapotranspiration and enhance the warming effect. Thus, deforestation will result in a 

warmer and drier climate. 

The surface energy and the moisture budgets were analyzed to see if the possible changes 

that are discussed above were present in the WRF output. Temperature, precipitation and 

evaporation anomalies (deforested – forested) were also calculated to identify the climatic 

impact of deforestation. 

3. Results 

In response to deforestation, for all three years, there is an increase in temperature over 

majority of the land areas whereas the changes over the oceans are more variable. Figure I 

show the changes in temperature (at 2 m) between the deforested and the forested runs for 

the year 1988. (For brevity, we show changes for one representative year.) Changes in the 

magnitudes in the spatial patterns of both the annual and the seasonal (JJA) temperatures 

are shown in the figure. The period of June, July and August (JJA) was selected to focus on 

the height of the summer monsoon season. Most regions show a clear warming, evident in 

both the annual and the seasonal values. The most prominent warming is seen along the 

west coast of India and Sri Lanka. There is a small region in the central highlands of Sri 

Lanka where the response is a cooling of temperature. Figure II shows the time series of 

temperature over land areas for 1988. The warming is evident in the monthly temperatures 

in all of the domains. The temperature at 2 m (for 1988) for the deforested and the forested 

situations and the corresponding changes are shown in table II. 

The spatial pattern of precipitation over each of the three years shows a decrease over land 

whereas some regions over the oceans experience enhanced rainfall. The annual 

precipitation values show a larger reduction than just the values in the monsoon season. 

This indicates that while the amount of precipitation received during the summer monsoon 

is affected by deforestation so are the other mechanisms such as the winter monsoon and 

convection that provide rainfall during the rest of the year. Figure III shows the 

precipitation anomalies between the deforested and the forested simulations for the 

representative year 1988. Also the increase in Bowen ratio (over land) indicates that 

conditions become drier. The annual precipitation (for 1988) over South Asia decreases by 

19% while the reduction over Sri Lanka is only 10% relative to the forested run. Southeast 

Asia experiences a 53% decrease in precipitation as a result of deforestation (All the percent 

changes provided in this chapter are calculated as the difference between the deforested and 

forested runs with respect to the forested run. i.e. [(D-F)/F]*100%). 

Evaporation over land is much smaller after deforestation but the changes over the ocean in 

the domains over South Asia and Sri Lanka show an increase. (The increase over the ocean 

is likely due to the warmer temperatures due to deforestation. The amount of water vapor 

the atmosphere can hold strongly depends on the temperature with warm air being able to 

hold more moisture than cold air. As deforestation warms the atmosphere, the air over the 

adjacent ocean also warms, gaining the ability to hold more moisture. This implies that more 

evaporation can take place before the air is saturated thus enhancing evaporation over the 

ocean.) Overall, the annual evapotranspiration over all three domains is reduced. This can 

be seen in figure IV which shows the anomalies in evapotranspiration for 1988. The largest 

decrease is seen over Sri Lanka where the (area averaged) annual evaporation decreases by 

9

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465 mm (table I), which is 29% reduction compared to the forested run, over the year. The 

annual evapotranspiration over South Asia decreases by 420 mm (27%) whereas the 

decrease over Southeast Asia is 415 mm (25%). The reduction in evapotranspiration is a 

result of the lower transpiration, reduced interception of precipitation (due to reduced leaf 

and stem area) and smaller roughness length. The roughness length provides a measure of 

the surface friction and the exchange of moisture between the atmosphere and the surface, 

with smaller roughness lengths modulating the exchange. 

The latent heat flux shows a decrease over all domains, consistent with the reduction in 

evaporation. As moisture becomes limited with the change in land-use, less energy is used 

for evapotranspiration. This means the evaporative cooling at the surface is reduced and the 

remaining energy goes into warming the surface. The sensible heat flux, which heats the air 

in contact with the surface, shows an increase due to the change in energy partitioning at the 

surface (i.e. in response to the decrease in latent energy flux). The changes in evaporation, 

latent heat flux and sensible heat flux support the warming observed in response to 

deforestation. 

Precipitation is greater than the evapotranspiration over South Asia and Sri Lanka. This 

indicates that the moisture provided from a moisture source (i.e. moisture transported from 

the Indian Ocean into the area by the regional circulation) other than local 

evapotranspiration makes a significant contribution to the precipitation in the region, 

suggesting that the Indian monsoon plays a dominant role where precipitation is 

considered. Out of the two regions the moisture transport is more significant for Sri Lanka 

as precipitation over the region can be more than twice the amount of the local 

evapotranspiration. 

The decrease in precipitation is much larger than that of evapotranspiration over Sri Lanka. 

The decrease in the local moisture recycling capacity alone cannot explain this strong 

reduction in precipitation. This indicates the moisture transported into the region has 

decreased, signaling a weakening or a change in the regional circulation leading to a 

reduced atmospheric moisture convergence into the area. This shows that while the 

moisture brought in with the monsoon flow may be a prominent factor in determining the 

amount of precipitation, deforestation can apparently weaken the moisture flux so that less 

water is available for precipitation in the region. 

On the other hand, the reduction in precipitation over South Asia is less than that of 

evaporation. This indicates a stronger moisture convergence over the Indian subcontinent. 

As a result deforestation causes two competing effects on precipitation. The decrease in 

evapotranspiration has a negative impact on precipitation whereas the increased moisture 

convergence has a positive effect on it. But the magnitude of the latter is smaller than that of 

the evapotranspiration, therefore ultimately resulting in a decrease in precipitation. 

The difference between precipitation and evapotranspiration is also a measure of surface 

runoff and storage. The changes in surface runoff can be very important for streamflow in 

the region. But further analysis of these terms are not possible because the land surface 

model used in the WRF runs does not have the capability to compute these terms separately. 

Clouds reflect part of the incoming solar radiation back out to space, hence the amount of 

incoming shortwave radiation at the surface can be used as a proxy for cloud cover. The 

incoming shortwave radiation shows a reduction for all three years over all domains. This is 

a indication of reduced cloud cover. The decrease in evaporation (and hence latent heat flux) 

means that there is less moisture.


Table 1. The terms of the surface energy and the moisture budgets for deforested (D) and 

forested (F) WRF simulations as well as the difference between the two simulations (D-F) 

over South Asia, Sri Lanka, and Southeast Asia . These are the areas averaged values for 

1988 and are computed only over land areas. Precipitation and evaporation are yearly totals. 

The units are Wm -2 unless noted otherwise in the table. The Bowen ratio is unitless. Here 

SW – shortwave radiation, LW – longwave radiation, Rnet – net radiation, LE – latent energy 

flux, SH – sensible heat flux, GE – storage, P – precipitation, E – evaporation. The subscripts 

(up/down) give the direction of the radiation. SWnet and LWnet are calculated as the 

difference between downward and upward radiation. A positive net radiation value 

indicates that the radiation is aimed towards the surface (downwards). 

11

12 


Table 2. The area averaged annual temperature at 2 m and surface skin temperature (surface 

temperature) are shown here. Temperature values are in Kelvins (K). Surface emissivity and 

surface albedo for both deforested and forested simulation are included here. All the values 

in the table are for 1988


Fig. 1. The annual and the seasonal (June, July and August) temperature anomalies (i.e. 

deforest –forest) for South Asia (a,d), Sri Lanka (b,e) and Southeast Asia (c,f) for 1988. 

Temperature is the surface air temperature 2m above the surface and is expressed in Kelvins 

(K) with a contour interval of 0.25 K. 

13

14 


Fig. 2. The time series of temperature for 1988. The monthly temperature values over land 

regions (excluding oceans) for South Asia (a), Sri Lanka (b) and Southeast Asia (c) are shown 

here. Temperature is the air temperature 2m above the surface and is measured in Kelvin (K)


Fig. 3. The total annual and the seasonal (June, July and August) precipitation anomalies (i.e 


Precipitation is in millimeters (mm) with a contour interval of 100 mm. 

15

16 


Fig. 4. The annual and the seasonal (June, July and August) evaporation anomalies (i.e 


Evaporation is in millimeters (mm) with a contour interval of 200 mm for annual plot and 50 

mm for the seasonal plot.


4. Discussion 

Deforestation leads to warmer and drier climatic conditions. As a result of deforestation less 

moisture is available at the land surface, leading to a reduction in evapotranspiration. This 

in turn leads to an increase in temperature and a decrease in precipitation over land. These 

changes then work together to alter the moisture convergence in the region. These changes 

are seen not only during the monsoon season but through the entire year. 

The monsoon is primarily driven by the pressure gradient created by the differential heating 

of the ocean and land. Land regions especially the Tibetan plateau warms up more than the 

Indian Ocean during the summer due to the small heat capacity of land. This creates a 

thermal low pressure over land and the resulting pressure gradient between ocean and land 

initiates the monsoon flow. Subsequently, the monsoon is sustained by the release of latent 

heat into the atmosphere. Therefore warmer temperatures should intensify the monsoon, 

increasing precipitation over land. But this is not seen in the results. The reasons for this are 

regionally specific. Over Sri Lanka it is likely that the warming of the land itself is reducing 

the amount of precipitation. The initial step in the formation of precipitation is the rising of 

parcels of warm moist air. The air being warm and moist by itself is not sufficient to cause 

rising motion. For air to rise the density of a parcel of air has to be lower than that of the 

surrounding air, that is the parcel temperature must be warmer than that of the surrounding 

(cooler) air. When deforestation warms the air over land, it reduces this difference, creating 

a more stable atmosphere which inhibits the rising of air and hence also weakens the 

atmospheric moisture convergence. Therefore even if the atmosphere otherwise holds 

plenty of moisture this process reduces the amount of precipitation that can form. Therefore 

the weakened moisture convergence together with the reduced evaporation causes the 

reduction in precipitation. The conditions are different in the Indian subcontinent where the 

summer monsoon is at its’ strongest. The warming of the land region now results in a 

stronger moisture convergence indicating the possibility that the monsoon might become 

stronger over the region. It is possible that the warming over the Indian subcontinent (which 

has a smaller magnitude than the warming in Sri Lanka) is insufficient to stabilize the 

atmosphere. But as discussed in the results section the reduced evapotranspiration at any 

rate plays the dominant role in reducing the annual and the seasonal precipitation. 

The changes in the climatic conditions due to deforestation can have a strong impact on 

society. Warmer and drier conditions can have far reaching consequences in many different 

ways. The elevated temperatures alone can cause life loss especially if the number of heat 

waves increases. India would be more susceptible to this as parts of the country experience 

high summer temperatures prior to the onset of the monsoon, a time during which people 

are vulnerable to the excess heat. The warm temperatures can also kill livestock and destroy 

stored goods. 

The reduction in precipitation in combination with the warmer temperature can have a 

negative impact on agriculture in the region. These climatic changes can result in reduced 

crop yields, a shift in the life cycle of the crop and change the length of the growing season 

and the timing of the harvest. Also the regions that are the best suited for growing crops 

may migrate. As the warm and dry conditions act as positive feedbacks, deforestation can 

lead to persistent droughts. This may then result in desertification making some regions no 

longer viable as agricultural land and marginally habitable regions unsuitable for living. In 

17

18 


addition to this the changes in temperature and precipitation may change the habitats of 

animals and expand or alter the spread of diseases such as malaria. 

Some regions depend largely on rainfall and/or stream flow for drinking water. If the 

decrease in precipitation is large enough, the lack of access to clean drinking water can 

become a major problem. In addition to this the water level in the rivers are important for 

transport along the rivers, generation of hydropower and irrigation of crops. If the regions 

that feed the river flow experience a decrease in precipitation it would lower the water 

levels regardless of what happens downstream causing many economic problems. 

All these changes would lead to a shortage in food and a disruption in the well established 

livelihoods in the region leading to poverty and famine in extreme cases. They would also 

cause many health and safety issues to which the very young, the old and the poor would be 

the most sensitive. 

Deforestation in South and Southeast Asia has an impact on the monsoonal climate. These 

climatic impacts then in turn will cause social, economical, environmental and health 

problems in the region. It is important to notice that Southeast Asia still has a significant 

amount of forest whereas the situation in south Asia is closer to the conditions of the 

deforested simulations. Therefore it is imperative to set in place, policies that would prevent 

or slow down deforestation in the region as well as to implement steps to deal with issues 

that have already arisen due to deforestation. 

This study illustrates the maximum possible range of changes possible due to deforestation 

and shows that the signal due to deforestation can override even a strong monsoon. 

Therefore it is worthwhile to continue this study, focused on more realistic land-use changes 

and to examine the magnitude of the changes. Also future studies would include the 

changes to the Hadley cell and the occurrence of extreme events, especially floods, under 

both realistic and idealized situations. 

5. Conclusion 

Deforestation impacts the climate by modifying surface energy and moisture budgets. These 

modifications are mostly due to the decrease in evapotranspiration and increase in the 

surface albedo. The changes in surface albedo and evapotranspiration have competing 

effects on the temperature, but as seen from the results of the WRF runs the warming effect 

due to the reduced evapotranspiration dominates over the cooling effects of the reduced 

albedo. 

Results show that majority of the land areas would become warmer and drier in response to 

deforestation, with precipitation, evapotranspiration and cloud cover all showing a 

decrease. Atmospheric moisture convergence shows regionally specific changes. These 

changes are seen in both the annual and monsoon seasonal values, suggesting that the 

changes that take place due to deforestation have the ability to override even a strong 

monsoon signal. The changes over the oceans are more variable, with an increase in 

evaporation seen in both seasonal and annual values in the domains over South Asia and Sri 

Lanka. 

These changes due to deforestation can have far reaching social, economic and 

environmental impacts as well as cause serious health issues. Warm temperatures can cause 

illness and even heat related deaths. The decrease in precipitation can lead to the drying of


natural springs and reduced stream flow, cutting of access to clean drinking water in some 

regions. The habitats of plants and animals can change resulting in spread of diseases. 

Further the warm dry conditions can reduce or destroy crops, kill livestock and decrease the 

output from hydro powered electricity plants. 

The climatic changes due to deforestation have the very real potential to impact the 

economic and the social structures of a country. As deforestation has been an ongoing 

problem, a climatic signal may already be present and the consequences of the changes 

already affecting us to some extent. If such a signal is present further deforestation may 

amplify these changes to a level that they would be clearly evident. Therefore it is important 

to understand the impacts of human activity on the climate, and set in place policies not 

only regarding deforestation but also implement steps to understand and deal with the 

consequences of a climatic signal that can result from current land-use practices. 

6. References 

Clark D. B., Xue Y., Harding R. J. & Valdes P.J. (2001). “Modeling the impact of land surface 

degradation on the climate of tropical north Africa”. Journal of Climate, vol 14 

1809-1822 

Denis B., Laprise R., Caya D. & Côté J. (2002). “Downscaling ability of one-way nested 

regional climate models: the Big-Brother Experiment”. Climate Dynamics 18:627- 

646 

Foley J. A., DeFries R., Asner G. P., et al., (2005). “Global consequences of land use”. Science, 

309, 570-574. 

Gibbard S.G., Caldeira K., Bala G., Phillips J.J. & Wickett M., (2005). “Climate effects of globa 

land cover change”. Geophysical research letter. 

Hasler, N. & Avissar R. (2007). "What controls evapotranspiration in the amazon basin?" 

Journal of Hydrometeorology 8(3): 380-395. 

Kanae S., Oki T. & Musiake K. (2001). “Impacts of deforestation on regional precipitation 

over the Indochina peninsula”. Journal of Hydroclimatology, vol 2 51-70 

Lean J. & Warrilow D.A. (1989). “Simulation of the regional climatic impact of Amazon 

deforestation”. Nature 342: 411-413 

Lu J., Vecchi G. A. & Reichler T., (2007). “Expansion of the Hadley cell under global 

warming”. Geophysical Research Letters, Vol 34 

McGuffie K. & Henderson-Sellers A. (2005). A climate modeling primer (third edition). John 

Wiley & Sons, ISBN: 9780470857502 

Mitas C. M. & Clement A., (2005). “Has the Hadley cell been strengthening in recent 

decades?”. Geophysical Research Letters, Vol 32 

Nobre C.A., Sellers P.J. & Shukla J., (1991). “Amazonian deforestation and regional climate 

Change”. Journal of Climate vol 4: 957-988 

Oglesby R. J., Sever T. L., Saturno W., D. J. Erickson, III & Srikishen J. (2010), “Collapse of 

the Maya: Could deforestation have contributed?”, J. Geophys. Res., 115, D12106, 

doi:10.1029/2009JD011942. 

Skamarock W.C., Klemp J.B., Dudhia J., Gill D.O., Barker D.M., Duda M.G., Huang Xiang- 

Yu, Wang W. & Powers J.G., (2008) “A Description of the Advanced Research WRF 

Version 3” 

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Suh M. S., & Lee D. K. (2004). “ Impacts of land use/cover changes on Surface climate over 

east Asia for extreme climate cases using RegCM2”. J. Geophy. Res.,109 

Zeng N. & Neelin J. D., (1999). “A land-atmosphere interaction theory for the tropical 

deforestation problem”. Journal of Climate Vol12: 857-872 

Zhang H. & Henderson-Sellers A. (1996). “Impacts of tropical deforestation. Part 1: Process 

analysis of local climatic change”. Journal of Climate vol19: 1497-1517


Impacts of Deforestation on Climate 

and Water Resources in Western Amazon 

2 

Ranyére Silva Nóbrega 

University Federal of Pernambuco 

Brazil 

The Amazon importance in several areas of research demonstrates how the region affects 

the balance of South America and, depending on the scale used, on the planet. The 

biodiversity, mineral wealth, water resources wealth, carbon sequestration, transport of 

energy in the atmosphere are examples of important aspects of the region. Another 

important phenomenon that occurs in the Amazon are the energy flows between soilvegetation-atmosphere 

dynamics that affect the climate, water resources and the advection 

of moisture to the surrounding parts. 

Deforestation is the major environmental problem in the Amazon River basin nowadays, 

and its impacts affect both the local and global scale. In fact, this region is responsible for 

approximately 13% of all global runoff into the oceans (Foley et al., 2002) and its abundant 

vegetation releases large amounts of water vapor through evapotranspiration leading to a 

recycling in precipitation of about 25-35% (Brubaker et al, 1993; Eltahir and Bras, 1994; 

Trenberth, 1999). 

Rondonia State, located in Western Amazon, already has a large area of vegetation changed 

by deforestation. Historically, there were tax incentives and government so that there was 

an expansion of development. Today the concern with changes in environmental balance of 

the Amazon basin, Rondônia, is justified by the increasing pressure on various forms of 

exploitation of the region, for example, timber extraction and agricultural expansion, the 

construction of hydropower, exploitation of biological and mineral riches. 

Krusche et al. (2005) suggest the following reasons for this progress: between 1970 and 1990 

there was a surge in state occupancy with settlers coming from other regions, extensive 

cattle ranching became the main economic activity and the state ground most is old and 

weathered, with the exception of some basins, promoting agriculture in an appropriate area. 

According to the authors, the pattern of occupancy was observed of the "fishbone" 

associated with the opening of roads. 

The highway BR-364construction, responsible for turning the region with the rest of the 

country, was one of the factors that triggered large projects of colonization / occupation. 

Earlier this deforestation process was seen as boon, as a prerequisite for applying for tenure 

and subsequent legalization of land (Santos, 2001). Fearnside (2007) assert that the main 

aspect of change in land use / land cover in region is deforestation, and that it has grown 

over the years.

22 


Deforestation rates of the State during the period 1988 to 2007 followed, in general, the same 

degradation that deforestation in the Amazon, since it has to be estimated. The most 

relevant peaks occurred in 1994 and 2004, showing a slight decrease for the years 2005 to 

2007, however, leaving the state responsible for a higher percentage than the last 10 years 

earlier, reflecting a greater intensity on change in coverage plant that closed in the rest of the 

Amazon. 

Year 

Deforestation (km2) 

Rondönia Amazon 

1988 2340 21050 11.10 

1989 1430 17770 8.00 

1990 1670 13730 12.20 

1991 1110 11030 10.10 

1992 2265 13786 16.40 

1993 2595 14896 17.40 

1994 2595 14896 17.40 

1995 4730 29059 16.30 

1996 2432 18161 13.40 

1997 1986 13227 15.00 

1998 2041 17383 11.70 

1999 2358 17259 13.70 

2000 2465 18226 13.50 

2001 2673 18165 14.70 

2002 3067 21651 14.50 

2003 3620 25396 14.40 

2004 3834 27772 14.00 

2005 3233 19014 17.00 

2006 2062 14286 14.70 

2007 1611 11651 13.82 

2008 1136 12911 10.00 

2009 482 7464 6.45% 

2010 427 6451 6.6% 

Table 1. Annual deforestation rates - Amazon and Rondônia (Source data: Prodes, Inpe) 

Public policies has been working to combat deforestation across the Amazon, we can 

observe the decrease in the rate since 2007, however, has been observed that in conservation 

areas this rate is increasing. 

The proposal chapter is investigating the impacts that deforestation and climate change can 

lead on hydrological cycle in the region, as well as the feedback system of climate and 

hydrological cycle. 

%

Impacts of Deforestation on Climate and Water Resources in Western Amazon 

2. Metodology 

The study is centered in Rondônia state, whose area of about 234.000 km2. The state's 

network runoff is represented by Madeira river (an important tributary of the Amazonian 

river basin) and its streams that form eight important sub river basins, among them, it is the 

Jamari sub river basin (Fig. 1). About 28% of the Rondônia state have already been 

deforested, because of this, is used as the test catchment study. 

The Jamari river basin has suffered a substantial deforestation due to the advance of the 

agricultural frontier in the Rondônia state. The basin is crossed by two important rivers 

namely Jamari and Candeias. Jamari river has its nascent in the southwest part of “Serra do 

Pacaás Novos”, in Rondônia, and streams northward flowing into the right bank of Madeira 

river, whose river basin is defined by the geographical coordinates 08º 28'S to 11º 07'S of 

latitude and 62º 36'W to 64º 20'W of longitude with about 29.066.68 km² of area. 

The semi-distributed hydrological model SLURP with more detailed input parametric 

information will be used in this research in order to investigate the impacts caused by 

deforestation as well as climate changes on hydrological processes in Jamari River basin. 

Realistic and extremes scenarios of deforestation will be analyzed, and also scenarios of 

temperature rise and precipitation increase/decrease. 

Fig. 1. Localization and drainage network Jamari sub river basin. 

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24 


2.1 Hydrological model 

Semi-distributed Land Use-based Runoff Processes - SLURP is a basin model that simulates 

the hydrological cycle from precipitation to runoff including the effects of reservoirs, 

regulators and water extractions (Kite, 2005). First divides a basin into sub-basins using 

topography from a digital elevation map. These sub-basins are further divided into areas of 

different land covers using data from a digital land cover classification. Each land cover 

class has a distinct set of parameters for the model. 

This model uses basically three types of data: i) digital elevation data (DEM); ii) land cover 

data; and iii) climatic data. The matrix data of both DEM and land cover data must have the 

same dimension. Climatic data should contain: precipitation, air temperature, dew-point 

temperature (or relative humidity), solar radiation and wind intensity. Firstly, it divides a 

hydrological basin into sub-river basins and then divides each sub-river basin into land 

cover components using the public-domain topographic analysis software TOPAZ (and 

Martz and Garbreht, 1999). These homogeneous areas are based on the hydrological 

response unit (HRU) concept described by Kite (2005). SLURP defines these areas as 

Aggregated Simulation Areas (ASA). 

The model has been applied in many countries for small hectares basins (Su et al., 2000) to 

large basins such as Mackenzie (Kite et al., 1994) and it was developed to make maximum 

use of remote sensing data. Applications of the model includes studies of climate change 

(Kite, 1993), hydropower (Kite et al., 1998), water productivity (Kite, 2005), irrigation (Kite 

and Droogers, 1999) and wildlife refuges (de Voogt et al., 1999), contribution of snowmelt to 

runoff (Laurente and Valeo, 2003; Thorne and Woo, 2006), and large mountainous 

catchment (Thorne and Woo, 2006). However, the SLURP model was not used in the 

Amazon, and its conceptual approach allows its use in regions with little data, as well as the 

possibility and direct use of remote sensing data which allows to retrieve physical 

parameters with good accuracy, even in basins with small slopes, as found in some subbasins 

in the Amazon River. 

2.2 Data 

Digital Elevation Model - DEM from the Shuttle Radar Topography Mission (SRTM) with 

90-m resolution horizontal was used to obtain topography. In order to correct failures, it was 

used the technique of space filtering, interactive filling. 

For actual land cover data it was used seven images of Landsat 7 scenes 2007 over the 

Jamari sub-river basin, resolution of 30m, provided by Amazonian Protection System 

(SIPAM). Firstly, the scenes were georeferenced and then a mosaic was composed. Secondly, 

NDVI performed a supervised classification to obtain the land-cover image. Then, the data 

was sampled again to 90m resolution, since the SLURP requires that the matrix of land 

cover has the same size of DEM. Finally, the data was classified into four classes: water, 

forest, non-forest and man-modified (urbanized). The non-forest class includes agricultural 

areas and the savannah. 

2.3 Climatic, rainfall and runoff data 

Climatic, rainfall and runoff data are some of the main difficulties in hydrometeorological 

modeling in the Amazon. The time series available is short and has many flaws. Was used 

data set from four stations with information about precipitation, air temperature and dew


point of the Agency for Environmental Development in Rondônia (SEDAM). We also use 

data from five rainfall gauge of the National Water Agency (ANA). The data sets are from 

the period between 1 January 1999 and December 31, 2007. 

2.4 Model performance evaluation criteria 

Model performance was evaluated by using four different error measures: Nash and 

Sutcliffe (NS), Percent BIAS (PBIAS), Daily Root Mean Square (DRMS) error criteria (Zhi et 

al., 2009; Moriasi et al., 2007), and Deviation Volume (D%) (Kite, 2005). The equations were 

given as showed below: 

n 

(Q Q ) 

2 

 

i 1 

obs 

 

mod 

NS 

1 

n 

(Q Q ) 

2 

 

i 1 

obs 

 

 

obsd 

where Qobs and Qmod are the measured and modeled data, respectively; . Q obsd . is average 

modeled data; and n is the total number of data records. The coefficient can range from -∞ to 

1 and represents the amount of data oscillation that is explained by the model. The model is 

considered optimal if NS = 1, appropriate and good if NS > 0.75, acceptable if 0.36

26 


volumes measured and simulated. A positive value indicates underestimation of the 

simulated volumes (losses in origin). A negative value indicates that the calculated average 

flow is high (losses in sinks) (Kite, 2005). 

3. Simulations 

Based on the percentage of deforestation in the basin obtained from the PRODES data (Table 

2) it was defined two trends scenarios: i) DEFOR+20, 20% more deforestation area, and ii) 

DEFOR+30, 30% deforestation area. It was defined three extreme scenarios of land cover for 

investigating the relationship between soil-cover change and runoff within SLURP model. 

The experiments are: i) 100%FOR, one hundred per cent with forest and water; ii) 

100%NOFOR, one hundred per cent with savannah plus pasture and water; and iii) 

100%MANMODIF, one hundred per cent man-modified area and water. For climatic 

impacts analysis has been used two scenarios. The scenarios were based in climate futures 

projections up to 2050 A2 HadCM3 model, discussed in Marengo (2006b). In both scenarios 

is assumed that the temperature rise 2 oC, and rainfall varies 20%, decreasing and increasing. 

The climate scenarios are: i) P+20, meaning 20% increase in rainfall, with an increase in 

temperature of 2 degrees, and ii) P-20, meaning 20% reduction in rainfall, with an increase in 

temperature of 2 degrees. 

SCENARIO DESCRIPTION 

DEFOR+20 20% more deforestation area 

DEFOR+30 30% more deforestation area 

100%FOR 100% forest area 

100%NOFOR 100% savannah plus pasture 

100%MANMODIF 100% man-modified area 

P+20 20% increase in rainfall, and 2oC temperature increase 

P-20 20% decrease in rainfall, and 2oC temperature increase 

Table 2. Simulations scenarios 

3.1 Problems identified – implementation of necessary remedial measures 

SLURP model needs data from weather stations which contains precipitation, temperature, 

humidity and wind. The average is calculated using the Thiessen polygons method for each 

ASA. If there is no such data, the model does not perform the simulation (for example, when 

there is rainfall data, but there is not temperature). 

In some countries, such as Brazil, it is common to have only rainfall station, instead of 

weather station, making the network rainfall much denser than the weather. But the 

compilation of the model does not allow the use of this data. Aiming to overcome this 

limitation, we tried to develop a methodology that would use the climatic stations without 

changing the source code of the model. Adopted method is based on the concept that the 

spatial variability of precipitation is less than the other data, such as temperature. Then, 

without the model, it was calculated the mean rainfall for each ASA also using the Thiessen 

method, but including the data from rainfall stations. After that, the files of average 

precipitation for each ASA were replaced.


4. Results and discussion 

Jamari sub-river basin was automatically divided by SLURP into five aggregate similar 

areas (ASAs) according to the DEM and land cover data (Fig. 2). For each ASA it was 

obtained the percentile area of land cover occupied for each of the four classes: i) water; ii) 

forest; iii) non-forest; and iv) man-modified (Table 3). The total basin area obtained by the 

model is 28.847 km 2 (~99% of the total area, according to Government State official data). 

The model was calibrated and checked by the two different types of data: i) weather stations 

data (OBS1); and ii) weather station data added rainfall gauge station (OBS2). Obviously it is 

expected that the use of a denser network of precipitation within a basin simulation results 

in improvements, since the data quality is consistent, but it was not clear if the model would 

accept the manual modification. 

Fig. 2. ASAs for Jamari sub river basin; X – weather stations; ● – rainfall station 

27

28 


ASA Name Water Forest Non forest Man modified Total (km 2) 

ASA 01 9.0 63.0 7.8 20.2 3025.81 

ASA 02 3.6 67.3 11.7 17.4 5007.65 

ASA 03 9.3 61.3 7.0 22.4 9239.68 

ASA 04 3.9 62.2 11.4 22.5 10999.55 

ASA 05 4.1 93.8 2.0 0.1 573.94 

Table 3. Land coverage and total area for each ASA (%) 

The NS, RSR, PBIAS and D(%) for OBS2(OBS1) calibration period was 0.88(0.74), 0.31(0.45), - 

7%(-12%) and -0.94%(-10.1%), respectively. The NS, RSR, PBIAS and D(%) for OBS2(OBS1) 

validation period was 0.84(0.71), 0.34(0.48), -8%(-15%) and -13.4(-10.3). The verification of 

the model efficiency criteria indicates that the values are acceptable during both the 

calibration and validation period. (Table 3), but it is clear that the model did better with the 

inclusion of climatic stations. From this point, we used OBS2 data with SLURP climate 

input. 

4.1 Calibration and verification 

Model was calibrated and verified by the two different types of data: i) weather stations 

data (OBS1); and ii) weather station data added rainfall gauge station (OBS2). Obviously it is 

expected that the use of a denser network of precipitation within a basin simulation results 

in improvements, since the data quality is consistent, but it was not clear if the model would 

accept the manual modification. 

The NS, RSR, PBIAS and D(%) for OBS2(OBS1) calibration period was 0.88(0.74), 0.31(0.45), - 

7%(-12%) and -0.94%(-10.1%), respectively. The NS, RSR, PBIAS and D(%) for OBS2(OBS1) 

validation period was 0.84(0.71), 0.34(0.48), -8%(-15%) and -13.4(-10.3). Model efficiency 

criteria verification indicates that the values are acceptable during both the calibration and 

validation period (Table 4), but it is clear that the model did better with the inclusion of 

climatic stations, therefore, used OBS2 data with climate input. 

OBS2 (OBS1) NS RSR PBIAS D(%) 

Calibration 0.88 (0.74) 0.31(0.45) -7%(-12%) -0.94 (-10.1) 

Verification 0.84 (0.71) 0.34(0.48) -8% (-15%) -13.4 (-10.3) 

Table 4. Model performance 

4.2 Deforestation impacts 

Taking into account the current deforestation rate in the area which is being studied, the 

trend scenarios can be designed by 2013 and 2016, respectively. The results for DEFOR+20% 

and DEFOR+30% indicated increased runoff compared to the average from 1999-2007, 825.3 

m 3.s -1, to 1048.1 m 3.s -1 and 1163.7 m 3.s -1, resulting in an increase of 27% and 41%, 

respectively. During the dry season (characterized by a weak runoff), the flow trends to 

increase remarkably, what can be a concern for local population who use these rivers for


human supply, navigation (in some places, the only kind of transportation), and also for 

power generation. If these scenarios become real, the rivers of the basin will be subjected to 

a different runoff pattern that might cause some socioeconomic impact. Although the 

extreme scenarios are not realistic, the results are instructive because they clarify the nonlinear 

response of the hydrological cycle to the progressive changes in land cover. 

When modifying the land cover to 100%FOR, the annual calculated runoff average 

decreased from 825.3m3.s-1 to 329.1 m3.s-1, i.e., a decrease of about 60%. On the other hand, 

for the scenarios with 100%NOFOR and 100%MANMODIF, runoff increased to 2313.1m3.s-1, and 1729.4m3.s-1, an increase of 181%, and 109% of the observed annual runoff average, 

respectively. 

The parameters that most influenced the results in these scenarios were related to the 

amount of available soil water for evapotranspiration and canopy interception, which were 

modified according to the soil cover. The high interception in scenario 100%FOR leads to a 

reduction of precipitation that reaches the soil and thus reduces runoff. It also reduces the 

amount of water available for evaporation. Furthermore, the increase of flowing in scenarios 

100%NOFOREST and 100%MANMODIF is due to the substantial decrease in 

evapotranspiration and rainfall interception by the canopy. It is worth mentioning that this 

study used the same series of precipitation for all scenarios, but the precipitation in the 

region is a variable that has its intensity, largely influenced by local evaporation. Hence, 

decrease in evapotranspiration tends to reduce rainfall. The use of SLURP coupled to an 

atmospheric model can reveal more about this feedback mechanism in further studies. 

The elements of water balance are shown in Fig. 3. It may be noticed that evapotranspiration 

varies slightly between the scenarios, except the 100%NOFOR, where evapotranspiration is 

approximately 90% the value of the other scenarios. However, when the exchange of water 

between the surface and the atmosphere is divided into evaporation and transpiration, the 

peculiarities of each scenario are quite evident. In 100%FOREST, transpiration contributed 

to the increase of evapotranspiration. This is quite obvious, since in this scenario, the 

interaction between the surface and free atmosphere is dominated by the exchange of 

processes in the vegetation canopy. In 100% NOFOREST, the land cover formed by the 

typical savanna and pasture vegetation makes the transpiration to be twice as much as 

evaporation. In 100% MANMODIF, the deforested soil with urban characteristic implies a 

greater contribution in evaporation than in transpiration. 

It can be observed that the evapotranspiration and groundwater reduces slightly with the 

decrease of forest areas when we compare with the trend scenarios. In terms of numbers the 

evapotranspiration decreased from 20% to 30%, respectively; groundwater flow decreased 

20% and 8%, respectively. As long as deforestation leads to less water interception by 

vegetation, the contribution of evaporation increases with the expansion of the deforested 

area. 

Several studies suggest the local contribution of evapotranspiration as responsible for about 

50% of the precipitation that occurs in western Amazonia (Nóbrega, 2005; Marengo, 2006a; 

Nóbrega, 2008). Therefore, a decrease in vegetation cover over a region can alter the 

precipitation regime is this region (and neighborhood), decreasing the amount of water 

vapor originated there, because the evapotranspiration decreased in these simulated 

scenarios, and this trend, combined with the increase in flowing during dry periods, may 

worsen social and environmental problems during more critical periods. 

29

30 


Fig. 3. Observed and Simulated water balance components for scenarios DEFOR+20% and 

DEFOR+30 

4.3 Climate change impacts 

Daily changes in runoff due to temperature and precipitation variations are shown in Fig. 4. 

The effect when the rainfall increases or decreases in 20% was as expected. An increase in 

rainfalls tends to increase runoffs, and a decrease in rainfalls tends to decrease runoffs, 

which is more noticeable during the rainy season, when rains are more significant. For the 

scenario P +20, the runoff increased 31%, and setting P-20 decreased 13%, indicating that a 

increase rainfall will respond more significantly than the decrease in this specific region. 

These results allow an analysis that a decrease in rainfall may have a critical effect especially 

during the rainy season, affecting navigation, agricultural production, human consumption 

and power generation. Fig. 5 shows the seasonal average flow for the investigation period, 

confirming that the impacts are more significant during the rainy season. 

Fig. 4. Observed and Simulated runoff for scenarios P+20 and P-20


mm 3 /s 

2500 

2000 

1500 

1000 

500 

0 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dez 

Observed P+20 P-20 

Fig. 5. Superficial runoff monthly average Observed and simulated for scenarios P+20 and 

P-20 

Water balance changes due to changes in temperature and precipitation, it was observed 

that an increase in temperature tends to increase transpiration in both scenarios, and a 

decrease in rainfall tends to reduce evaporation (Fig. 6). The evapotranspiration increases 

30% and 54%, evaporation decreases 21% and increases 3%, transpiration increases 37% and 

41%, and groundwater decreases 35% and 33% for the sets of P-20 and P +20 respectively. 

(mm/yr).10 2 

160 

140 

120 

100 

80 

60 

40 

20 

0 

Default P-20 P+20 

ETp E T G 

Fig. 6. Observed (Default) and simulated water balance components for scenarios P+20 and 

P-20 

It is important to mention that the simulation did not consider the impacts that deforestation 

would cause on climatic variables. Although with the results obtained from the simulation, 

it is possible to conclude that deforestation modifies substantially physical processes of the 

hydrological cycle. 

31

32 


Impacts are easier to be seen during the rainy season. Furthermore, with less water available 

for evapotranspiration, the vegetation may suffer from water stress. Although some authors 

found a precipitation increase in deforested areas (Li et al., 2007), if the deforestation keeps 

on increasing, the changes in the hydrological cycle might become unsustainable. The 

change in cover / land use can lead the current system to a new dry equilibrium, and 

vegetation should be modified to adapt itself to climate changes. 

5. Conclusions 

Firstly, it was needed to make sure if the model could be used in that region, due to the lack 

of some meteorological data and the small slope of the region. Based on the NASH, RSR, 

PBIAS and D% criteria, the results indicate acceptable values. Furthermore, since it is a 

semi-distributed model, it requires less startup parameters than the distributed models, and 

also is able to calculate results faster. 

Deforestation in Amazonia has been occurring for some decades and the rate of annual 

growth is noticeable. In addition, this might be influenced by climatic and social-economics 

factors. Land cover/use changes simulations indicated that the runoff can be changed. The 

results suggest that there is an increase in runoff when deforestation occurs in the extreme 

and trend scenarios, associated with less interception of water by the canopy. If the average 

rate of deforestation continues to be about 3.45% per year in the basin, our simulations 

predict that the annual runoff will increase about 27% by 2013 and 41% by 2016. Samuel 

Hydropower, located on the Jamari river, began to be built in 1982. Between 2004 and 2006, 

the hydropower floodgates had to be opened because the river level reached its maximum 

level. 

The results make us believe that the ongoing deforestation could be responsible for 

opening these floodgates, since the observed data do not indicate more rain than the 

average. In used model, sediment load that affects the level increase of the river is not 

taken into account, but it is likely to result in a sediment increase due to the silt produced 

by deforestation. 

Evapotranspiration and groundwater tend to decrease with deforestation. Results show that 

the main impact might occur on transpiration, which tends to decrease with deforestation, 

while the evaporation tends to increase. Alterations in water balance in the Amazon can 

result in modifications in the local hydrological cycle, and agreeing with other studies, it 

will affect rain patterns there and close areas, once the water vapor generated goes straight 

to the neighborhood. 

6. References 

Brubaker, L.K., Entekhabi, D., and P.S. Eagleson. (1993). Estimation of continental 

precipitation recycling. J. Climate, Vol. 6, pp. 1077-1089. 

de Voogt, K., Kite, G.W., Droogers, P., and H. Murray-Rust. (1999). Modeling water 

allocation between a wetland and irrigated agriculture in the Gediz Basin, 

Turkey. Research Report, International Water Management Institute, Colombo, 

Sri Lanka


Eltahir, E.A.B., and R.L. Bras. (1994). Precipitation recycling in the Amazon basin. Quart. J. 

R. Met. Soc., Vol. 120, pp. 861-880. 

Fearnside, P.M. (2007). Deforestation in Amazonia. Encyclopedia of Earth. Eds. C.J. 

Cleveland (General Editor) & M. Hall-Beyer (Topic Editor). Environmental 

Information Coalition, National Council for Science and the Environment, 

Washington, D.C., U.S.A. 

Foley, I.A., Botta, A., Coe, M.T., and M.H. Costa. (2002). The El Niño-Southern Oscillation 

and the climate ecosystem and river of Amazonia. G. Biogeochemical Cycles, 

Vol.16, pp.1132-1144. 

Kannan, N. et al., (2007). Hydrological modelling of a small catchment using SWAT-2000 – 

Ensuring correct flow partitioning for contaminant modeling. J. Hydrology, Vol. 

3(34) pp. 64-72. 

Kite, G.W. (2005). Manual for the SLURP hydrological model. 236 p. 

Kite, G.W., and P. Droogers. (1999). Irrigation modeling in the context of basin water 

resources. J. Water Resources Development, Vol.15, pp. 43-54. 

Kite, G.W., Danard, M., and B. LI. (1998). Simulating long series of streamflow using data 

from an atmospheric model. Hydrological Sciences, Vol.43(3). 

Kite, G.W., and P. Droogers. (1999). Irrigation modeling in the context of basin water 

resources. J. Water Resources Development, Vol.15, pp. 43-54. 

Krusche, A.V., Ballester, M.V.R., and R.L. Victoria. (2005). Effects of land use changes in the 

biogeochemistry of fluvial systems of the Ji-Paraná river basin, Rondônia. Acta 

Amazônica, Vol. 35(2), pp. 192-205. 

Laurent, MESt, C. Valeo. (2003). Modeling runoff in the northern boreal forest using 

SLURP with snow ripening and frozen ground. Geophysical Research Abstracts, 

Vol. 5, pp. 06-30. 

Li, K.Y., Coe, M.T., Ramankutty, N., and R. Jong. (2007). Modeling the hydrological impact 

of land-use change in West Africa. J. Hydrology, Vol. 337, pp. 258-268. 

Marengo, J.A., (2006a). On the hydrological cycle of the Amazon basin: a historical review 

and current state-of-the-art. Rev. Brasil. Meteorologia, Vol. 21(3), pp. 1-19. 

Marengo, J.A., (2006b). Global Climate Changesand biodiversity efects. 201 p. 

Martz, W., and J. Garbrecht. (1999). An outlet breaching algorithm for the treatment of 

closed depressions in a raster DEM. Computers & Geosciences, Vol. 25, pp. 835- 

844. 

Moriasi, D.N. et al., (2007). Model evaluation guidelines for systematic quantification of 

accuracy in watershed simulations. Vol. 50(3), pp. 885-900. 

Nóbrega R.S., (2008). Modeling impacts of deforestation in water resources of river basin 

Jamari (RO) using data surface and TRMM. D.Sc. Thesis. Federal University of 

Campina Grande, Paraíba, Brasil. 212p. 

Santos, C. A. (2001). Fronteira do Guaporé. Porto Velho/RO: EDUFRO. 

Su, M, Stolte, W.J., and G. van der Kamp. (2000). Modeling Canadian prairie wetland 

hydrology using a semi-distributed streamflow model. Hydrological Processes, 

Vol.14(14), pp. 2405-2422. 

Thorne R., and M. Woo. (2006). Efficacy of a hydrologic model in simulating discharge 

from a large mountainous catchment. J. of Hydrol, Vol. 30(1-2), pp.301-312. 

Trenberth, K.E. (1999). Atmospheric Moisture Recycling: Role of Advection and Local 

Evaporation. J. Climate, Vol. 12, pp.1368-1381. 

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Zhi, L., et al., (2009). Impacts of land use change and climate variability on hydrology in an 

agricultural catchment on the Loess Plateau of China. J. Hydrology (doi: 

10.116/j.jhydrol.2009.08.007)


3 

Deforestation and 

Water Borne Parasitic Zoonoses 

Maria Anete Lallo 

Universidade Paulista (UNIP), São Paulo 

Brazil 

Disease emergence or re-emergence is often the consequence of the societal and 

technological change and manifests frequently in an unpredictable manner. It has been 

estimated that among emerging diseases, 75% has zoonotic characteristics. Many factors 

influencing the emergence of zoonoses, such as environmental change and land use, 

changes in demographics, changes in technology and industry, increasing international 

travel and commerce, breakdown of public health measures, and microbial adaptation and 

change (Broglia & Kapel, 2011). 

Deforestation is one of the most disruptive changes affecting parasitic and vector 

populations. When the forest is cleared and erosion of the soil strips away the former state, 

if indeed, it is permitted and able to regenerate. The response of tropical forests to 

perturbation is affected by soil type, elevation, mean precipitation, and latitude. Cleared 

tropical forests are typically converted into grazing land for cattle, small-scale agricultural 

plots, human settlements or, left as open areas. Expansion of existing human settlements 

and movement of human populations create a need for increased food supply, leading to 

changes in the types and amounts of vegetation, thereby providing changed ecological 

niches and conditions for proliferation of newly arriving and/ or adaptive existing vectors 

and their parasites (Slifko et al., 2000). Deforestation is one of the changes that most affect 

the ecological niches of the disease, favoring the transmission of them (Patz et al., 2000; 

Slifko et al., 2000). 

The waterborne or food is the main route of transmission of parasitic diseases. Zoonoses 

such as giardiasis, cryptosporidiosis and microsporidiosis are waterborne diseases that 

include the participation of domestic and wild animals and man. Environmental changes 

and ecological disturbances, due to both natural phenomena and human intervention, have 

exerted and can be expected to continue to exert a marked influence on the emergence and 

proliferation of zoonotic parasitic diseases. They change the ecological balance the ecological 

balance and context within which vectors and their parasites breed, develop, and transmit 

disease (Patz et al, 2000). 

Interest in the contamination of drinking water by enteric pathogenic protozoa has increased 

considerably during the past three decades and a number of protozoan parasitic infections of 

humans are transmitted by the waterborne route (Patz et al., 2000; Slifko et al., 2000).

36 


Waterborne transmission is one of the main risk factors for intestinal diseases causing an 

important morbidity and mortality worldwide. Over 50% of the waterborne infections are 

produced by unknown agents. In the last years, for economic and environmental reasons, 

spreading sewage sludge on agricultural lands has increased. This might affect not only the 

circulation of recognized pathogens such as Cryptosporidium and Giardia, but also emerging 

pathogens, such as microsporidia. The general impression is that treatment of water has 

demonstrated a high efficacy of pathogen removal, however, as viable pathogen have been 

detected in water. It is important understand that the presence of human pathogens in 

surface water may suggest the presence of living environmental reservoirs, such as domestic 

and wild animals. Aquatic birds may play an important role in the transmission of different 

pathogens (Izquierdo et al., 2011). 

The parasites Cryptosporidium, Giardia e microsporidia are major of diarrheal disease in 

human, worldwide and have also been recognized as the predominat causes of waterborne 

diseases. Cryptosporidium, Giardia and microsporidia have life cycle wich are suited to 

waterborne and foodborne transmission. Their life cycle are completed within and 

individual host, with transmission by fecal-oral route. The transmissible stages, 

Cryptosporidium oocysts or Giardia cysts or microsporidia spores, are produced in a large 

numbers and are infectious when excreted, a marked resistence to environmental and water 

treatment stresses, wich assists their dissemination, and have the potential to be transmitted 

from non-human to human hosts (zoonoses) and vice-versa, enhancing the reservoir of 

(oo)cysts or spores markedly (Smith et al, 2007). The purpose of this chapter is to show the 

biological and epidemiological aspects most relevant of the parasites Cryptosporidium, 

Giardia and microsporidia responsible for waterborne parasitic diseases most important. 

2. Cryptosporidiosis 

It is a parasitic disease caused by protozoa of the genus Cryptosporidium, which affects 

amphibians, birds, mammals, reptiles and fish and is characterized by impairment of the 

digestive system. Since its first description in the 1970s, cryptosporidiosis has been considered 

an opportunistic infection in immunodeficient individuals, it is known today, however, that it 

is a prevalent disease in immunocompetent individuals also important. Many studies have 

also revealed the prevalence of this protozoan infection in animals, although the zoonotic 

transmission of the disease is not yet fully understood (Fayer et al., 2010). 

2.1 About the agent and the disease 

Cryptosporidium is a protozoan that has about 22 identified species (Table 1). Although many 

species have been described to date, C. parvum is the most widespread species of mammals, 

including man. It is known that C. parvum is not a homogeneous species since the isoenzyme 

analysis and DNA sequencing revealed differences between oocysts isolated from various 

animal species. 7 genotypes have been identified - of cattle, humans, mice, pigs, opossuns, 

dogs and ferrets (Fayer 2010; Plutzer Karanis, 2009). 

As in other coccidian monoxenic its life cycle is not employing intermediate host. 

Cryptospodirium oocysts are small and contain four sporozoites inside free. When they are 

ingested by the host, the oocysts release of sporozoites in the small intestine, which invade 

intestinal cells. The sporozoites begin multiplying to form asexual meront type I and type II

Deforestation and Water Borne Parasitic Zoonoses 

with 4 and 8 merozoites, respectively. The type II merozoites give rise to the sexual phase of 

the cycle or gametogony with differentiation stages in male (microgametes) and female 

(macrogametes). The microgametes penetrates macrogamete leading to the formation of a 

zygote that develops into oocyst. Are two types of oocysts produced - a kind of thin-walled 

autoinfectante able to release within the host, starting a new cycle and a thick-walled, highly 

resistant to environmental conditions, which is eliminated in feces. The cycle time is variable 

and may occur in up to 48 hours or 14 days depending on the host species. Unlike other 

coccidia, which eliminate the non-sporulated oocysts, the oocysts of C. parvum undergo 

sporulation within the host, eliminating the already infective for the environment (Carey, 

2004; Chalmers & Davies, 2009). 

Species Hosts 

Fishes 

Psicicryptosporidium cichlidis Oreochromis miloticus e Tilapia zilli 

Psicicryptosporidium reichenbachklinkei Trichogaster leeri 

Cryptosporidium molnari Sparus auratusDicentrarchus labrax (Gilthead) 

C.scophthalmi Scophthalmus maximus(Turbot) 

Amphibians and reptiles 

C. serpentis Elaphe guttata (Corn snake) 

C. varanii Varanus prasinus (Emerald monitor) 

C. fragile Duttaphrynus melanostictus (Black-spined toad) 

Birds 

C. meleagridis Meleagris gallopavo (turkey) 

C. baileyi Gallus gallus (chicken) 

C. galli Gallus gallus (chicken) 

Mammals 

C. muris Mus musculus (mice) 

C. parvum Mus musculus (mice) 

C. wrairi Cavia porcellus (guinea pig) 

C. felis Felis catis (cat) 

C. andersoni Bos taurus (cattle) 

C. canis Canis familiaris (dog) 

C. hominis Homo sapiens (man) 

C. suis Sus scrofa (pig) 

C. bovis Bos taurus (cattle) 

C. fayeri Macropus rufus (kangaroo) 

C. ryanae Bos taurus (cattle) 

C. macropodum Macropus giganteus (kangaroo) 

Table 1. Species of Cryptosporidium by Fayer (2010). 

The pathogenesis and clinical picture of criptoporidiose are influenced by several factors, 

including animal species, age, immune response and association with other pathogens. The 

infection can range from subclinical to severe, And have more severe disease (Table 2). In 

humans, the incubation period is 20-10 days and the duration of the disease in 

immunocompetent individuals, up to 3 weeks. In immunodeficient or immunosuppressed 

individuals, infection is chronic, with symptoms and elimination of persistent oocysts. The 

37

38 


clinical signs manifested in cryptosporidiosis include profuse watery diarrhea, vomiting, 

anorexia, weight loss, abdominal pain, fever and dehydration. In immunocompetent 

individuals, these symptoms are mild and transient. The spread of infection to the 

gallbladder and bile ducts, pancreas and respiratory system is common in AIDS patients 

(Barr, 1998; Chalmers & davies, 2009). 

The diagnosis of cryptosporidiosis is based on the meeting of the parasite in feces, using 

methods of concentration of oocysts, such as formaldehyde or ether flotation with saturated 

sucrose solution, associated with staining techniques, such as Ziehl- Nielsen, Kinyoun, 

fuchsin or safranin. Different immunological techniques have been used for the diagnosis of 

human cryptosporidiosis. Among them, ELISA or immunofluorescence with monoclonal or 

polyclonal antibodies are used to detect oocysts in the feces. The PCR technique is an 

alternative to both conventional diagnosis of Cryptosporidium in fecal specimens and in 

environmental samples. Although PCR is rapid, sensitive and accurate, has limitations as 

the detection of nucleic acid of viable organisms, naked nucleic acid and the possibility of 

laboratory contamination. We recommend its use for oocysts in water samples (Fayer et al., 

2000; Marquardt et al., 2000; Xiao, 2002). 

Characteristics Immunodeficient individuals 

Immunocompetent 

individuals 

Susceptible population 

Immunocompromised persons 

of all ages, especially with AIDS 

Children, first with less than 

1 year of age and adults of all 

ages 

Infection sites Intestinal or extraintestinal Intestinal usually 

Enteric form 

Asymptomatic or transient or 

chronic diarrhea or fulminant 

Asymptomatic, acute and 

persistent 

Clinic form 

Diarrhea, fever, abdominal 

Diarrhea, fever, abdominal pain, 

cramps, weight loss, nausea 

weight loss e vômitos 

and vomiting 

Table 2. Characteristics of cryptosporidiosis in humans. 

2.2 Epidemiology and prophylaxis 

Cross-transmission studies revealed that oocysts obtained from humans are infective to 

other mammals, as oocysts from animals that are infectious to other species, it is a zoonotic 

disease potential. The epidemiology of cryptosporidiosis is influenced by the capacity of 

thick-walled oocysts survive in the environment (Table 3) (Xiao, 2002). 

Temperature Survival Time 

25 e 30C 3 months 

20C 6 months 

15C 7 months 

-20 e -70C Feel hours 

Table 3. Survivel condition of Cryptosporidium oocysts 

The oocyst is the oral-fecal route and occurs until now, have been described various forms 

of transmission, can be highlighted - from person to person by direct or indirect contact,


including sexual activities, from animal to animal, animal to man, by drinking water or 

recreation, from food and air. The number of oocysts required to establish an infection is small, 

it is estimated that the infectious dose varies from 9 to 1,000 oocysts (Fayer et al., 2010). 

Feces containing oocysts contaminate soil, food and water. The movement of oocysts in the 

environment is favored by the winds, the rain water, the movement of animals and the actions 

of man himself. The major outbreaks of cryptosporidiosis reported in HIV-negative people are 

linked to the ingestion of water contaminated with oocysts derived from cattle or sheep. This 

water containing oocysts also contaminated foods, especially vegetables and fruits, and is 

another important way of transmission in outbreaks of cryptosporidiosis. Additionally, food 

can be contaminated by the hands of manipulators (Robinson et al., 2010). 

A variety of risk factors are associated with infection by Cryptosporidium, among them stand 

out from the deficiency of the immune response, the presence of concomitant infections, 

ingestion of contaminated food and water, poor sanitary conditions and occupational 

exposure, is the contact with animals or infected humans (Plutzer et al., 2009). 

From the earliest descriptions of cryptosporidiosis in humans, the number of cases has 

continued to grow, in the case of individual reports or outbreaks, such infection is attributed to 

C. parvum. The average prevalence in industrialized countries by 2.2% in immunocompetent 

individuals and 14% in HIV-positive. Already in developing countries, these numbers increase 

and may reach 8.5% in immunocompetent patients and 24% in HIV-positive. This means that 

proper sanitary conditions and a rigorous treatment of the water, as seen in developed 

countries, the spread of the disease can be decreased (Hajdušek et al., 2004). 

In the United States, an estimated 50% of the animals from cattle herds eliminate oocysts of 

C. parvum, however, the disease is preferentially observed in calves that manifest from the 

4th day of life until the fourth week. In other domestic animals, the prevalence of 

cryptosporidiosis is less valued, however it is known that predominates in neonates and 

young people (Thompson et al., 2009). 

Human or animal cryptosporidiosis can only be controlled if the oocysts of the parasite is 

eliminated or destroyed. The oocysts can spread and persist in the environment for a long 

time. Moreover, it is known that this parasitic form also resists water to conventional 

treatments such as chlorination and filtration. Giardia is 14 to 30 times more susceptible to 

water treatment with chlorine or ozone. Either way, ozone is the most effective chemical 

agent in the inactivation of Cryptosporidium oocysts (Thompson et al., 2008). 

To reduce the risk of infection of individuals more susceptible to cryptosporidiosis, such as 

HIV-positive, immunosuppressed individuals and children, it is recommended that drinking 

water be boiled for about 1 minute before ingestion. The same recommendation should be 

made for the young or immunodeficient animals (Fayer et al., 2010; Thompson et al., 2008). 

Others include general health care and proper cleaning of the hands of food handlers, 

proper disposal of animal waste, sewage treatment, washing litter boxes with boiling water, 

among others (Chalmers & Davies, 2009). 

One should keep susceptible individuals, HIV-positive or immunosuppressed, have contact 

with the feces of pets, especially if they have less than 6 months old. If this is not possible, 

you should recommend the appropriate use of gloves. Not recommended the removal of 

this person's contact with your pet because of the strong emotional bond that unites them. 

Immunosuppressed patients should ideally acquire an animal older than 6 months, you do 

not have diarrhea and it has been previously examined by a veterinarian (Fayer et al, 2000; 

Xiao, 2010). 

39

40 


Of the various species of Cryptosporidium, C. parvum has been observed in a larger number of 

human infections, and reaches a large number of animal species. Within this species, two 

genotypes are, most of the time, described the infection - the human (38% of infections) and 

cattle (62% of infections). Thus, for the human cryptosporidiosis occurring bovine genotype 

is necessary to contact with infected animals such as cattle, sheep and goats, or that there is 

environmental contamination, especially water and food. Several cases of cryptosporidiosis 

have been reported in veterinary students and animal handlers, resulting probably from 

contact with infected animals, especially cattle, which reinforces the possibility of such 

transmission. Recently, the dog genotype of C. parvum has also been identified in human 

infections(Fayer et al, 2000; Xiao, 2010). 

All these evidences indicate that a large number of hosts and genotypes may be involved in 

cryptosporidiosis and molecular characterization of this parasite should facilitate 

understanding of the epidemiology of the disease. While these points are not completely 

understood, it is recommended the adoption of control measures in situations of potential 

risk (Xiao, 2010). 

3. Giardiasis 

It is a disease caused by the flagellate protozoan Giardia, intestinal parasite of a wide variety 

of animals, including man, constituting one of the most prevalent intestinal parasites, 

known and described throughout the world (Marquardt et al.,2000). 


The most accepted classification of the genus Giardia is based on its morphological 

characteristics, with six described species - G. agilis, G. Muris, G. psittaci, G. ardeae, G. microti 

and G. duodenalis (Table 4). G. duodenalis is also known as G. intestinalis or G. lamblia. This 

protozoan has two simple forms of life - the trophozoite and cyst. The trophozoite lives in 

the small intestine where they act by the scourges, but many are adhering to the intestinal 

mucosa. The cyst is ovoid and is surrounded by a proteinaceous fibrous wall that confers 

resistance to the environment conditions (Hopkins et al., 1997; Volotão et al, 2007). 

Giardia has a direct life cycle and its transmission is oro-faecal route. Drinking water 

contaminated with cysts represents a major cause of giardiasis in humans and other 

animals, which is therefore considered a waterborne disease (Kulda & Nohýnková, 1995). 

Inside the host, the cyst release two trophozoites that attach to the small intestine. The 

trophozoites start their asexual multiplication by binary fission and by action of bile salts 

and alkaline pH suffer encystment, but the mechanism and where this occurs remain 

unknown (Bogitsh & Cheng, 1998). 

Giardiasis may present as an asymptomatic or symptomatic, Acute or chronic disease. In 

adult animals, the infection is usually asymptomatic and is rarely detected. Already in 

young animals aged less than one year, clinical signs and symptoms may be present and 

identification of the parasite is more easily obtained (Thompson, 2000). 

In general, clinical signs and symptoms observed in giardiasis include diarrhea, acute or 

chronic, steatorrhea, abdominal pain, lethargy, anorexia, flatulence, fatigue, abdominal 

distension, nausea, mucus in stools, growth deficits and weight loss (Bogitsh & Cheng, 

1998).


The elimination intermittent cysts and lack of specific clinical signs makes the diagnosis of 

Giardia more difficult and requires multiple fecal examinations are carried out within 4 to 5 

days (Kulda & Nohýnková, 1995). 

Species and genotypes Hosts 

G. microti Muskrat and voles 

G. agilis Amphibians 

G. muris Rodents 

G. psittaci Birds 

G. ardeae Birds 

G. duodenalis (G. intestinalis ou G. 

lamblia) 

Genotypes of G. duodenalis 

Mammals 

Genotype A 

Humans, primates, dogs, cats, cattle, rodents, wild 

animals 

Genotype B Humans, primates, dogs, horse, cattle 

Genotype C Dogs 

Genotype D Dogs 

Genotype E Ungulates 

Genotype F Cats 

Genotype G Rodents 

Table 4. Species and genotypes of Giardia. 

Techniques that promote the fluctuation of the cysts, using saturated solutions of zinc 

sulphate and sugar, are methods that allow diagnosis of most cases. Another method of 

diagnosis is an ELISA assay, which detects Giardia antigen in faeces preserved in formalin or 

kept under refrigeration. In humans, the sensitivity and specificity of this test is high (100% 

and 96%, respectively), allowing quick diagnosis, however, its use in dogs and cats revealed 

similar results flotation techniques, which are preferred for their low cost and ease of 

implementation.The indirect immunofluorescence and polymerase chain reaction (PCR) 

have been used for epidemiological studies or as research tools (Kulda & Nohýnková, 1995). 


Giardiasis is spread throughout the world being described more than 250 million 

symptomatic cases in humans for years and is now included as the list of neglected diseases 

made by the World Health Organization in humans, its prevalence depends on the level of 

hygiene and sanitary facilities, ranging from 2% to 43%. Children are more susceptible to 

infection because of its low immunity and their lack of hygiene. In adults, infection may 

provide a certain degree of resistance to subsequent infections, reducing its prevalence in 

this age group (Mohammed Mahdy et al, 2008; Monis &Thompson, 2003). 

Although giardiasis is classified as a zoonosis by the World Health Organization, it is 

unclear the exact participation of animals in the epidemiological chain of transmission of 

this disease. However one must consider that the interaction of multiple hosts associated 

with environmental conditions are essential links to propitiate the occurrence of giardiasis, 

which just as cryptosporidiosis, are the waterborne disease (Mohammed Mahdy et al, 2008; 

Monis &Thompson, 2003). 

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Of all the species of Giardia only G. duodenalis has been observed in humans, livestock and 

pets. Many of these animals possess a particular genotype, but they all have infections with 

genotypes A and B also found in humans. The number of molecular studies involving the 

infection observed in man with concomitant infections in animals is very restricted in 

aboriginal Australians were detected 13 human cases and 9 dogs in the genotype A, as 

found cases of giardiasis by genotypes A and B simultaneously in dogs and humans in 

Bangkok. Some wild animals such as beavers and rats, which have a high prevalence of 

giardiasis by genotype B, has historically been considered important sources of water 

contamination (Cacció & Ryan 2008). 

The intake of only 10 cysts of Giardia is sufficient to determine the disease and an infected 

person can eliminate up to 300 million cysts per ml of feces (Inpankaew et al, 2007). 

The infection is acquired by ingesting contaminated food or water. Cysts remain viable for 

weeks in water, which facilitates its transmission. The survival of cysts in the water the same 

temperature dependent and may remain viable for up to 2 months in water at 8 ºC and for 

only 4 hours in water at 37 °C (Xiao & Fayer, 2008). 

Water contamination is through human sewage or feces from infected animals. The 

occurrence of giardiasis in sparsely populated areas, such as the Arctic region of Canada, 

reinforces the hypothesis that wild animals such as beavers, constitute important sources of 

infection. The beavers have 44% prevalence of Giardia and therefore are reservoirs for 

human infection. As the transmission of this disease occurs by contamination of water, food 

and environment as described in Table 5, the prevention of giardiasis include environmental 

sanitation (Hunter &Thompson, 2005). 

Lack of hygiene of food handlers 

Use of contaminated feces, sewage and manure as fertilizer for agriculture 

Grazing cattle close to agriculture 

Defecation of infected wild hosts in plantations 

Vector-borne contamination of food contaminated by sewage 

Use of animal manure or soil contaminated with human feces in agriculture 

Use of contaminated water for irrigation 

Use of contaminated water to dilute insecticides or fungicides 

Wash salads and raw foods in contaminated water 

Use of contaminated water to make ice or frozen foods 

Use of contaminated water to make food that they receive the least amount of heating or 

treatment with preservatives 

Table 5. Possible sources of food contamination. 

The water should be protected from possible contamination by proper disposal and 

treatment of sewage from humans or animals. Because Giardia is resistant to routine 

chlorination of water, it is recommended that the flocculation, sedimentation and filtration 

of water are carried out before use. When the water is not subjected to such treatment, there 

should be boiling water, which promotes the complete removal of Giardia cysts. The greatest 

risk of zoonotic transmission is related to the genotype of G. duodenalis, which has been 

described in humans, farm animals, dogs, cats, beavers and in rodents (Hunter & 

Thompson, 2005; Jerlström-Hultqvist, 2010).


Farm animals, especially cattle, are an important source of infection for man. Cattle with 

giardiasis can eliminate up to one million cysts per gram of feces, so few infected animals 

are a major risk to public health (Thompson et al, 2008). 

Although the clinical consequences of infection with Giardia in dogs and cats seem to be 

minimal, there are grounds for believing that such animals are important sources of 

infection in urban areas. Molecular epidemiology studies have shown that some genotypes 

are closely related to the infection in humans and dogs (Smith et al., 2007). 

4. Microsporidiosis 

Microsporidia are small eukaryotic intracellular parasites considered to be true, due to the 

presence of nuclear envelope coated core of intracytoplasmic membrane systems and 

separation of chromosomes by mitotic spindle. However, these protozoa also possess 

characteristics of prokaryotes, as a small ribosomal RNA (rRNA) and the absence of 

mitocrôndrias, peroxisomes and Golgi cisterns. These indicators point to the fact that they 

are phylogenetically very primitive protozoa, however, molecular studies reveal a close 

proximity of this group of parasites with the fungi, a fact that still causes doubts in the 

classification of these parasites. Diarrhea is the most frequent health problem caused, mainly 

in immunocomopromised people. The transmission routes indicated are via airborne, 

person-to-person, zoonotic, and waterborne means (Didier, 2005; Theiller, Breton, 2008). 


The life cycle of microsporidia includes three distinct phases: the spores responsible for 

transmission of infection, the proliferation of vegetative forms of intracellular schizogony or 

merogony calls, and finally the formation of spores or sporogony (Mathis, 2000). 

The spores are very resistant structures to environmental conditions in its interior is an 

extrusion apparatus (polar tubule), whose function is to inject the infective material 

(sporoplasm) into the host cell. In the presence of favorable conditions, the extruded polar 

tubule and enters the host cell to inoculate sporoplasm in the cytoplasm of the same. The 

sporoplasm injected into the host cell starts proliferation merogony stage. His rapid 

multiplication occurs by binary or multiple fission. Then sporogony begins when meront 

acquire a dense amorphous layer around the cell, being called sporonts. These again can 

grow and multiply by binary or multiple fission sporoblasts to form, which, in turn, develop 

distinct cytoplasmic organelles and a thick wall, making the spores mature. The spores 

spread through the tissues of the host by infecting new cells and continuing the cycle 

(Mathis, 2000; Weiser, 2005). 

So far, no evidence of intermediate hosts or vectors of microsporidial infection in man. For 

some genera that infect mosquitoes, has been reported a complex sequence of development, 

involving alternation between different invertebrate hosts (Weber et al., 2002). 

The development of some species of microsporidia can be confined to cells of a single organ. 

However, other species can cause systemic infection. The clinical manifestations of infection 

are dependent on the species infected and the competence of the host immune response. The 

imbalance in the parasite-host relationship results in the proliferation and spread, causing 

cell destruction (Didier, 2005). 

So far have been described about 1000 species of microsporidia belonging to 100 genera, 

however, this number will increase as new hosts are being researched. In humans, the 

infection was first described by Matsubayashi et al. in 1959 and since then an increasing 

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number of cases have been reported particularly in immunocompromised individuals. The 

clinical manifestations include ocular lesions (conjunctivitis, chorioretinitis), muscles 

(myositis), kidney (nephritis), neurological (encephalitis), liver (hepatitis), peritoneal 

(peritonitis) and others (Abreu-Costa, 2005; Lallo et al., 2002; Lallo & Bondan, 2005; Weber et 

al., 2002). 

Some species of microsporidia infecting invertebrates and can cause serious economic 

losses. Two well-known examples are the Bombyx mori, which affects the creations of 

silkworm and Nosema apis, which infects bees and honey production decreases. On the other 

hand, some species are used in biological control of some pests, for example, Nosema locustae 

used to control locusts (Mathis, 2000; Méténier & Vivarès, 2001). 

More than 60 species, 11 genera of microsporidia have been described in fish. The most 

important genera are: Glugea, Pleistophora and Sprague. In freshwater fish and saltwater, the 

infection is highly contagious and can be fatal, the parasite is found in the intestine, bile 

ducts, liver, mesenteric lymph nodes, nerve ganglia, in the subcutaneous tissue, testes and 

ovaries (Rodriguez-Tovar et al.,2011). 

We found cysts of Giardia were found in faecal samples from 2 prehensile-tailed porcupines 

(Coendou villosus) and Cryptoporidium oocysts in 3 rodents - montane akodont (Akodon 

montensis), ebony akodont (Thaptomyces nigrita) and guainan squirrel (Sciurus aestuans). 

Microporidia spores were seen in the stools of small rodents, including 3 montane akodonts, 

1 prehensile-tailed porcupine and 2 pigmy rice rats (Oligoryzomys sp.), as well as of 3 

marsupials, including 1 gray slender mouse opossum (Marmosops incanus) and 1 big eared 

opossums (Didelphis aurita), and of 3 hairy-legged vampire bats (Diphylla ecaudata). This was 

the first description of microsporidiosis in wildlife animals in Brazil. The study emphasizes 

the importance of the animals, particularly small wild mammals, as potential sources of 

parasite infection to other animal populations, including man, in areas of deforestation 

(Lallo et al. 2009; Pereira et al. 2009). 

Few cases of human microsporidiosis had been reported until the advent of AIDS. However, 

it is now considered emerging and cosmopolitan. Among the species that cause human 

microsporidiosis, E. bieneusi is responsible for most infections occurring in approximately 

40% of AIDS patients who have chronic diarrhea. It is not clear, however, what the risk 

factors that may be related to the prevalence of infection. Most reports occur in male 

patients, HIV-positive and CD4+ lymphocyte count at or below 100 cells/mm3, with few 

cases observed in women and children (Malčekova, 2010). 


There is much controversy about the mechanisms of transmission of microsporidia. It is 

believed that the ingestion of spores is an important route for the species that infect the 

gastrointestinal tract of man and that environmental contamination occurs by the spread of 

the spores contained in faeces, urine and other excretions. In foxes, domestic dogs and 

squirrels, it was observed that transplacental transmission is an important mechanism of 

spread of the disease (Bern, 2005; Weiss, 2001). 

Although microsporidia spores are resistant to the environment, can be inactivated when 

exposed for 30 minutes at 70% alcohol, 1% formaldehyde or hydrogen peroxide 1% as well 

as when they are autoclaved for 10 minutes at 120ºC (Méténier & Vivarès, 2001). 

The phenotypic and genotypic differences among strains of Encephalitozoon cuniculi can be 

used to indicate the main sources of infection for man. The lack of a specific host, coupled 

with the fact that the primate is susceptible to encephalitozoonosis suggests that man can 

become infected when exposed to an infected animal (Bern, 2005).


The possibility that microsporidiosis is a zoonotic disease is still obscure. However, one 

must consider that these primitive protozoa have great capacity to adapt, since they are 

distributed among different groups of invertebrates and vertebrates (Didier, 2005). 

Transmission of invertebrate microsporidia to humans has been considered impossible. This 

hypothesis is based on the temperature differences between the two classes of animals. 

However, Trammer et al. (1997) obtained infection of athymic mice inoculated with Nosema 

algerae, one of Microsporidian of the culicids. These results demonstrate for the first time it is 

possible the development of invertebrate microsporidia in mammalian hosts. Therefore, one 

should consider the possibility that invertebrates may be sources of infection for human 

microsporidiosis. 

Diarrhea is the most frequent health problem caused by microsporidia, mainly in 

immunocompromised people. Waterborne transmission of microsporidia spores has not yet 

been appropriately addressed in epidemiological studies, due to the small size of spores. 

Their presence have been associated with waterborne outbreaks and also with recreational 

and river water (Fournier et al., 2000; Izquierdo et al., 2011). 

Several drugs have been used to treat microsporidiosis. Fumagillin was the first drug to 

have effective results in the control of Nosema apis and was subsequently described as a 

broad-spectrum drugs to combat various species of microsporidia that parasitise insects. 

This drug has managed to control the multiplication of E. cuniculi in vitro and has been 

effective in the treatment of ocular infection by E. hellen in HIV-positive (Mathis, 2000). 

Other active ingredients have been used to combat the microsporidia of invertebrates and 

small vertebrates. However, there are few drugs licensed for human use. Albendazol seems to 

be the most appropriate drug to combat infection by microsporidia (Bern, 2005; Weiss, 2001). 

Measures aimed at preventing infection by microsporidia are not specific, since the mode of 

transmission and sources of infection for the disease remain uncertain. However, as the 

transmission of this parasite appears to be based on the ingestion of spores from feces and 

urine, preventive measures aimed at controlling the intake of them. In hospital settings 

precautions with body fluids, and good personal hygiene of infected individuals. These 

measures are particularly important for the prevention of eye infection, which results from 

hands and fingers contaminated by spores of respiratory fluids or urine (Didier, 2005). 

The infective potential of the spores can only be evaluated in vitro for species that grow in 

cell culture. Experimental studies show that microsporidia spores can survive for months or 

years, depending on humidity and temperature, due to their chitin wall. Disinfectants usual, 

the simple boiling and autoclaving can kill the spores (Weiser,2005). 

5. Relationship between deforestation and the perpetuation of the parasites 

and enviromental 

Deforestation and ensuing changes in landuse, human settlement, commercial development, 

construction of roads, water control systems (dams, canals, irrigation systems, reservoirs), 

and climate singly and in combination have been accompanied by global increases in 

morbidity and mortality from a number of emergent parasitic diseases. 

The nature and extent of change in the incidence of parasitic disease are affected by changes in 

landuse and settlement, the time interval from one landuse to another/others, changes in type 

of soil and its degree of water absorption, changes in vegetation characteristics, changes in the 

types and amounts of bodies of water, their size, shape, temperature, pH, flow, movement, 

sedimentation and proximity to vegetation and, changes in climate (Table 6). 

45

46 


Deforestation envariomental changes 

Grazing land (cattle): 

create supportive habitat for parasites (specially Cryptosporidium genotype cattle) 

Small-scale agricultural plots: 

promove food contamination by water 

sugar cane or rice may be surrounded by a network of irrigation ditches and artificial 

bodies of farms and wells 

Climate changes: 

warmer temperatures cause increased precipitation intensity and more rainfall events, 

favoring the survival of cysts, oocysts and spores of the parasites in the environment 

Open areas: 

cleared lands have neutral pH and steep inclines and streams make large pools, both 

changes promote the creation of a microclimate favorable to the persistence of parasites in 

the environment 

Humans settlements: 

migrants become a new reservoir for transmission of parasitic disease (migrants, foreign 

or non-immune resident settlers, indigenous) 

animals and humans are exposed to new contacts in new environments 

roads facilitate acceleration of crop farming, ranching, logging, mining, commercial 

development, tourism and, building of dams and hydroelectric plants and new 

settlements, increase erosion and blocking the flow of streams 

Table 6. Environmental changes caused by deforestation and its relationship with the 

emerging parasites. 

6. Conclusion 

Cryptosporidium, Giardia and microsporidia are single-celled, microscopic organisms and are 

disease-causing parasites that may infect people through contaminated drinking water and 

recreational waters. Symptoms resulting from infection include mild to severe diarrhea, 

abdominal cramps, weight loss, bloating, and vomiting. Chlorine, a commonly used 

disinfectant in water supplies, can not eliminate Giardia, Cryptosporidium or microsporidia 

from water sources. Rates of deforestation have grown explosively since the beginning of 

the twentieth century. Driven by local to global demand for agricultural and forest products 

and expanding human population centers, large swaths of species-rich tropical and 

temperate forest, and prairies, grasslands, and wetlands, have been converted to specie-poor 

agriculatural and ranching areas. The global rate of tropical deforestation is continuing at 

staggering levels well into this decade, with more than 2.3% of humid tropical forests 

cleared between 2000 and 2005 alone. Parallel to this habitat destruction is an exponential 

growth in human-wildlife interaction and conflict, which has resulted in exposure to new 

pathogens for humans, livestock, and wildelife. This way, we can conclude that 

deforestation can contribute to transmission of waterborne diseases. 

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Impact of Deforestation 

on the Sustainability of Biodiversity 

in the Mesoamerican Biological Corridor 

Vani Starry Manoharan 1, John Mecikalski 2, 

Ronald Welch 2 and Aaron Song 2 

1 Environmental Sciences Division, Argonne National Laboratory, Argonne 

2 Department of Atmospheric Sciences, University of Alabama in Huntsville, 

USA 

Tropical rain forests play an essential role in housing global biodiversity, and they 

accommodate more than 50% of all species in the world while occupying only ~10% of the 

surface land of the Earth [Myers, 1992; Pimm, 2001]. However, during the last 10,000 years 

humans have significantly influenced land surface characteristics by altering the vegetation 

to include plant species more suitable for their consumption, a process that includes 

converting forests to agricultural lands, livestock grazing, and building settlements [DeFries 

et al., 2004]. Current rates of deforestation are extremely high and are known to have 

significant impact on regional and global atmospheric and climate changes [Laurence et al., 

2004] in addition to the direct local effects of deforestation. The scale and speed of global 

habitat loss and fragmentation is alarming, with only about half of the pre-industrial forest 

areas remaining as forests. These forest fragments are becoming the only refuge for most of 

the global tropical wildlife, but as these habitats fragment into smaller and smaller pieces 

and become more isolated, local extinction rates accelerate [Bennett, 1999]. 

Central America or Mesoamerica, which occupies only 0.5% of the global land area, provides 

habitat for more than 7% of the world’s species [Mittermeier et al., 2000]. This region has a 

population growth rate of > 2% per year with high levels of poverty, unsustainable 

exploitation of natural resources, soil erosion and one of the world's highest rates of 

deforestation, losing 2.1% of forests/year [FAO, 1999]. This scale and speed of habitat loss and 

forest fragmentation in one of the earth's biologically richest regions has led conservationists to 

propose the Mesoamerican Biological Corridor (MBC) project, an integrated regional initiative 

intended to conserve biological and ecosystem diversity in a manner that also provides 

sustainable economic development [Carr et al., 1994; Miller et al., 2001]. 

The MBC is an ambitious effort intended to connect large existing isolated parks, forest 

fragments and reserves with new protected areas through an extensive network of 

biological corridors within the five southern states of Mexico and the Central American 

countries (Guatemala, Belize, El Salvador, Honduras, Nicaragua, Costa Rica and Panama). 

The intent is to establish an environment that provides better prospects for the long-term 

survival of the native species, provides migratory pathways for the others, and addresses 

4

50 


the region’s socioeconomic needs. This would stem and reverse the erosion of biodiversity 

in the existing forest fragments in Mesoamerica. Ideally these proposed connecting corridors 

would contain the biological communities that were originally present. However, most of 

the current and proposed connecting corridors do not contain their original forest, but are 

instead occupied by agricultural landscapes containing croplands, grassland and various 

forms of degraded woodlands. Additionally, these regions have a varied topography of 

altitudes ranging between sea level and 3000 m. The topography and land characteristics of 

the corridors vary widely, from lowlands to high mountain peaks and dense-forest regions 

to croplands and woodlands respectively. The establishment of fully functional corridors 

will depend upon the regrowth of forests in many areas. However, the extent of 

deforestation within Central America may already have had climatic consequences that 

affect the stability and sustainability of currently protected areas and the proposed corridor 

regions [Laurence et al., 2004]. 

Fig. 1. The map of the Mesoamerican Biological Corridor. 

According to the Intergovernmental Panel for Climate Change (IPCC) 3 rd Assessment report 

[IPCC, 2001], there are high chances of adverse impacts on the existing natural global 

ecosystems, biodiversity and food supply due to the projected climate changes in the future.

Impact of Deforestation on the 

Sustainability of Biodiversity in the Mesoamerican Biological Corridor 

Observations show that there have been decreases in frog population and other small 

mammals that may be related to climate change [IPCC, 2001]. 

Previous studies show that changes in land use impact regional climate which in turn may 

enhance and sustain these changes [Hansen et al., 2001; Hansen and Rotella, 2002; DeFries et al., 

2002]. Deforestation changes the surface energy budgets and generally decreases latent heat 

(LH) fluxes from surface to the atmospheric boundary layer and increases the sensible heat 

(SH) fluxes [Lawton et al., 2001; Nair et al., 2003; Ray et al., 2006]. The net result is hotter and 

drier air over the deforested and forested regions leading to increased dryness. However 

due to the continuous deforestation throughout Central America, there is concern about the 

long-term stability and even the sustainability of the MBC. This chapter is built upon studies 

to understand the environmental stability of forests in the corridors and their surroundings. 

The map of the proposed MBC is shown in Figure 1. The regions in red color in the map are 

the proposed corridor regions. National parks and protected areas are shown in green color. 

The extractive reserves shown in brown color are proposed for conservation. Some hunting 

and logging are permitted in these reserves. However, loggings in the extractive reserves are 

followed by reforestation. The guiding rules for the extractive reserves vary from country to 

country, but the basic requirement of reforestation is the same. 

There are 600 protected areas as a part of the MBC network [Herrera, 2003]. The number and 

percent of territory covered by these protected areas are given in Table 1. 

Country 

Number of protected 

areas 

% of territory covered by the 

protected areas 

Southern Mexico 33 18.8% 

Belize 59 44.8% 

Guatemala 104 26.3% 

El Salvador 3 1.6% 

Honduras 106 19.0% 

Nicaragua 76 21.7% 

Costa Rica 151 24.6% 

Panama 69 29.5% 

Table 1. The number of protected areas in each territory and the percentage of area covered 

by them. 

According to Central American Protected Areas System (SICAP), 29 percent of the protected 

areas which had been legally designated by 1998 cover less than 1,000 hectares and 67 

percent of protected areas cover less than 10,000 hectares. Only 22 are larger than 100,000 

hectares and only four of these cover more than 500,000 hectares. 

2. Impact of deforestation on regional hydrometeorology 

The clearing of forest results in an immediate reduction of intercepted water storage 

capacity followed by a decrease in interception and transpirational loss of water to the 

atmosphere [Swift et al., 1975; Eshleman, 2004]. This is in turn followed by increase in runoff 

and/or overall decrease in water yield capacity [Whitehead and Robinson, 1993]. Further, 

deforestation leads to decrease in the water storage capacity of the soil [Shukla et al., 1990] as 

well as the fraction of available soil moisture [Ray et al., 2003; Manoharan et al., 2009]. 

Additionally, the deforestation reduces the overall roughness of the surface [Gash and Nobre, 

51

52 


1997] and increases the albedo above the region [Costa et al., 2007]. These factors have high 

impact on the aridity of these places. Moreover, deforestation leads to warming in the 

Tropics and cooling in the temperate regions [Bonan, 2004]. The changes in temperature can 

range from a couple of degrees to tens of degrees Celsius [Gash and Nobre, 1997; Ray et al., 

2003; Manoharan et al., 2009]. Thus, deforestation changes land cover. Thus influencing the 

albedo, surface temperature, soil fertility, surface roughness, soil moisture and changes in 

the magnitude of thermal energy (LH and SH) fluxes. The above changes, in turn, influence 

the regional boundary layer depth, cloudiness, cloud optical properties and local rainfall 

[Nair et al., 2003; Ray et al., 2003; Pielke, 2001]. In a stable atmosphere the clouds tend to form 

earlier over the moist surfaces, whereas in a less stable environment, the clouds tend to form 

earlier over the drier surfaces [Wetzel et al., 1996]. In a forested region the boundary layer air 

tends to be moister than in a deforested region. An average difference of 1g kg -1 difference 

in specific humidity has been observed between the forested and deforested forests in the 

Amazon [Bastable et al., 1993]. Pielke [2001] provides a detailed review of the influence of 

vegetation and soil characteristics on cumulus cloud formation and precipitation, and notes 

in particular that the alteration in heat fluxes as a result of deforestation modifies the 

convective activity by modifying the environment for thunderstorms, which are an effective 

conduit of heat, moisture, and momentum to higher latitudes and exerts major impacts on 

global weather and climate. 

MBCs are regions of contrasting vegetation types such as forests adjacent to deforested 

regions. There are several observational and modeling studies [Segal et al., 1988; Avissar and 

Liu, 1996; Weaver and Avissar, 2001] that have shown that such contrasting vegetation types 

lead to differential heating which in turn results in sea-breeze-like mesoscale circulations 

that increase cloudiness. Avissar and Liu [1996] noted that the updrafts created by surface 

heterogeneity are much stronger than those created as a result of mechanical turbulence. 

Climatic feedbacks from deforestation can also alter rainfall patterns, initially increasing 

[Avissar et al., 2002] then followed by drastic decreases in precipitation [Avissar et al., 2004]. 

For example, Lyons et al. [1993] showed that in southwest Australia a substantial clearing of 

native vegetation led to a 20% decrease in local winter rainfall. Also Ray et al. [2003] in their 

study over western Australia along the bunny fence (a rabbit proof fence running 750 km 

separating the native vegetation from the farmland) area observed a higher frequency of low 

level cumulus cloud cover over the native vegetation side than over the farmland. The land 

use changes led to differences in soil moisture availability and hence the surface energy 

fluxes which in turn enhanced the cumulus cloudiness over the native vegetation than the 

agricultural land. However other studies by Otterman [1990], Sud et al. [1993], Pielke et al. 

[1998] show that deforestation leads to decreases in rainfall. Since the mid 1970s, tropical 

forest regions have experienced declines in precipitation at a rate of 1.0±0.8 % per decade 

with sharp declines in northern Africa (3 % to 4 %/decade), marginal declines over Asia, 

and no significant trend in Amazonia. 

Forest clearing tends to increase SH fluxes and reduce LH fluxes. This, in turn, causes the 

development of deeper turbulent convective boundary layers, with widespread cumulus 

clouds more likely to occur over the deforested regions than over the pristine forests (e.g., 

Avissar et al., [2002]). Tendencies of increased convective available potential energy (CAPE), 

increased updraft widths and increased cloud base heights also have been reported (e.g., Negri 

et al., [2004]). CAPE is a measure of the energy available for convection. It is directly related to 

the vertical speed of the updrafts and thus higher values of CAPE indicate greater potential for 

severe weather. In tropical soundings, parcel temperatures of 1 to 2 K excesses may occur at a



depth of 10 - 12 km. A typical value of CAPE is then 500 Jkg -1. However, for a mid-latitude 

thunderstorm environment, the value of CAPE often may exceed 1000 Jkg -1, and in severe 

weather cases it may exceed 5000 Jkg -1. This small value of CAPE in the tropical environment 

is the major reason that the updraft velocities in tropical cumulonimbus are observed to be 

much smaller than those in mid-latitude thunderstorms [Holton, 2004]. 

Convective activity is strongly influenced by surface characteristics, with changes in land 

cover producing changes in local surface temperatures and precipitation rates. From satellite 

observations Rabin et al. [1990] and Cutrim et al. [1995] reported increased cloudiness over 

deforested areas in Amazonia and attributed this to land surface heterogeneity. Using GOES 

(Geostationary Operational Environmental Satellite), TRMM (Tropical Rainfall Measuring 

Mission) and Special Sensor Microwave Imager (SSM/I) satellite data, Negri et al. [2004] 

found that enhanced dry season surface heating created a thermal circulation which 

increased shallow cumulus clouds, and the precipitation resulting from deep convection 

over deforested relative to those over forested regions in Amazonia. However, observations 

are not entirely consistent. Based upon ten years of 3-hourly infrared window channel 

observations taken during the International Satellite Cloud Climatology Project (ISCCP) 

over a 2.5ºx2.5 o grid, Durieux et al. [2003] reported no significant differences in dry season 

cloud cover between forested and deforested regions of Amazonia. 

Modeling studies likewise are inconsistent. Eltahir [1996] reported that large-scale 

deforestation could weaken large-scale circulation patterns which would lead to reduced 

rainfall. At the mesoscale, Eltahir and Bras [1994] reported that deforestation will lead to a 

reduction in precipitation, but would have no effect on large-scale circulations. However, at 

small scales on the order of 10-100 km, Wang et al. [2000] reported that the organization of 

rainfall reflects land cover patterns and that there is enhanced cumulus cloud cover and 

enhanced deep convection over deforested patches. As expected they found no relationship 

between shallow clouds and land cover patterns in the wet season. During the period between 

the dry and wet seasons, they suggested that deforestation may enhance afternoon cloudiness 

while contributing nothing to precipitation. However, during the dry season they found that 

the organization of rainfall does reflect land cover patterns, with enhanced shallow cumulus 

over deforested patches leading to enhanced deep convection. In contrast recent modeling 

studies by Costa et al. [2007] and Sampaio et al. [2007] came to divergent results. In these studies 

Amazonian forest was replaced by pasture and soybeans. Costa et al. [2007] used the 

Community Climate Model coupled to the Integrated Biosphere Simulator (CCM3-IBIS) 

climate model on a 2.81 o grid and Sampaio et al. [2007] used the Centro de Previsao do Tempoe 

Estudos Climaticos do Instituto Nacional de Pesquisas Espaciais (CPTEC-INPE) global model 

at 2 o spatial resolution. Both results showed that replacement of forest by pasture leads to 

higher values of albedo, SH flux, and surface temperature with decreased values of roughness, 

turbulence, Leaf Area Index (LAI), root depth, LH flux, evapotranspiration, cloud cover and 

precipitation. Intriguingly, precipitation was decreased even further as pasture was replaced 

by soybeans. This was attributed to the albedo of soybean plantation being higher than that of 

the pasture. Sampaio et al. [2007] found that cloud cover was significantly decreased by about 

12% over deforested areas converted to pastures and by about 16% for soybean croplands, 

while precipitation decreased by 18% over pastures and 25% over soybeans. However, these 

studies utilized synoptic-scale grids that may not be representative of smaller-scale processes. 

Indeed, Sampaio et al. [2007] suggested that fragmented forest patches may create local 

circulations which in turn may enhance precipitation over the deforested regions. Table 2 

summarizes the above discussed literature survey. 

53

54 

S.N. Reference 

1 

2 

3 

4 

5 

6 

Otterman, 

1990 

Rabin et al., 

1990 

Lyons et al., 

1993 

Eltahir and 

Bras, 1994 

Cutrim et al., 

1995 

Avissar and 

Liu, 1996 

Data & Study 

Area 

Landsat, Israel 

AVHRR from 

NOAA-7, 

Oklahoma 

Field 

experiments, 

AVHRR, 

Southwestern 

Australia 

Amazonia 

GOES, 

Amazonia 

RAMS 

7 Eltahir, 1996 Amazonia 

8 

9 

10 

11 

Rabin and 

Martin, 1996 

Pielke et al., 

1998 

Wang et al., 

2000 

Weaver and 

Avissar 2001 

GOES, Central 

United States 

RAMS, South 

Florida 

MM5V2, 

Rondônia, 

Amazonia 

RAMS, ARM- 

CART, US 

Central Plains 


Key conclusions/Remarks 

Afforestation leads to reduced surface 

albedo and reduced soil heat flux and hence 

increases in precipitation. 

Clouds form earliest over regions of high SH 

and high albedo and are suppressed over 

regions of high LH fluxes. Convection 

enhancement at mesoscale results due to 

land surface heterogeneity. 

Substantial clearing of native vegetation lead 

to a 20% decrease in local winter rainfall. 

Cumulus cloud frequency is thus high over 

areas with high LH fluxes and high CAPE. 

Small scale deforestation (~250 km) may 

lead to reduction in precipitation, but would 

have no effect on large-scale circulations. 

Enhanced dry season cumulus clouds 

frequency over forest cleared regions. 

Contrasting vegetation types lead to 

differential heating which in turn results in 

sea-breeze-like mesoscale circulations that 

increase cloudiness. Updrafts created by 

surface heterogeneity are much stronger 

than those created as a result of turbulence. 

Large-scale deforestation (~2500 km) could 

weaken large-scale circulation patterns 

which would lead to reduced rainfall. 

Slightest change in elevation can modulate 

the cumulus cloud frequency. And cumulus 

frequency is inversely associated with plant 

cover and available soil moisture. 

During the past 100 years 11% decrease in 

summer deep cumulus rainfall due to land 

cover change and this climate change is 

irreversible due to permanent land cover 

change. 

At scales of 10 km observed enhanced cloud 

cover and enhanced and enhanced deep 

convection over deforested patches during 

dry season. 

Enhancement in mesoscale convection 

arising due to land cover heterogeneity.



S.N. Reference 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

Avissar et al., 

2002 

Ray et al., 

2003 

Durieux et al., 

2003 

Nair et al., 

2003 

Malhi and 

Phillips, 2004 

Negri et al., 

2004 

Ray et al., 

2006 

Costa et al., 

2007 

Sampaio et al., 

2007 

Nepstad et al., 

2002 

Data & Study 

Area 

Satellite 

observations 

and Model 

simulations, 

Amazonia 

ASTER, 

MODIS, GMS5, 

Southwestern 

Australia 

ISCCP, GPCP, 

TRFIC, 

Amazonia 

GOES 8, 

Landsat MSS, 

RAMS, Costa 

Rica 

Tropics 

TRMM & 

SSMI, 

Southwest 

Brazil 

MODIS, GOES, 

RAMS, 

Guatemala 

CCM3-IBIS 

climate model 

(2.8o), Amazonia 

CPTEC-INPE 

(2o), Amazonia 

Brazil’s 

Tapajo´s 

National 

Forest, in eastcentral 

Amazonia 

Key conclusions/Remarks 

Climatic feedbacks from deforestation can 

also alter the rainfall patterns, initially 

increasing followed by drastic decrease in 

precipitation. 

High frequency of cumulus cloud cover over 

regions of high LH heat flux and high 

available energy. 

More wet season rainfall in deforested 

regions and less dry season rainfall than the 

forested regions. 

Deforestation leads to warmer, drier air 

upwind of the mountain and increasing the 

cloud base heights. 

Since mid 1970s, significant decline in 

precipitation at a rate of 1.0±0.8 % per 

decade with sharp decline in northern Africa 

(3 to 4 %/decade), marginal declines over 

Asia, and no significant trend in Amazonia. 

During dry season there is enhanced 

shallow cumulus cloudiness and deep 

convection over deforested areas than dense 

forests. 

High frequency of cumulus cloud cover, 

high rainfall rate over drier deforested areas 

than pristine forests. 

Forests to pasture lead to reduced cloud 

cover and rainfall; forest to soybean lead to 

increase in albedo and further decrease in 

cloud cover and precipitation. 

Observed similar to Costa et al. [2007]. 

The forest leaves were quite tolerant to the 

soil moisture reduction provoked by 

throughfall exclusion. Instead of a pulse of 

leaf shedding, the exclusion treatment have 

inhibited the formation of new leaves, 

leading to a decline in fine litter production 

and, eventually, a thinning of the leaf 

canopy. 

Table 2. Literature survey on satellite remote sensing and modeling studies of impact on 

land cover change on regional cumulus cloud cover and precipitation. 

55

56 


Other significant and indirect impacts of land use changes include loss of spiritual and 

cultural benefits from ecosystems, both for the indigenous people and others enjoying 

recreational opportunities [Ramakrishnan, 2001]. Deforestation could also result in disease 

emergence [Patz et al., 2000; Patz et al., 2004] such as dengue and malaria [Tauil, 2001], 

diarrhea [De Souza et al., 2001] and other respiratory diseases [D’Amato et al., 2001]. 

3. Impact of deforestation on flora and fauna due to hydrometeorological 

disturbances 

Temperature, precipitation, wind/storms, solar radiation, long-wave radiation, atmospheric 

concentration of carbon dioxide and ozone influence biogeochemical cycles, greenhouse gas 

fluxes and surface energy balance, which in turn impact the biodiversity of ecosystems. 

Species tend to be attracted to their optimum climate. Therefore, if temperature and 

precipitation changes, species would be expected to either expand or contract their range 

depending on favored conditions [Peters and Darling, 1985; Ford, 1982]. 

Forests that are undisturbed tend to be dark under the canopy, humid, with stable 

temperature, and light wind [Laurence et al., 2002]. Deforestation creates forest edges with 

increased temperatures, reduced humidity and increased sunlight. This “edge effect” can 

penetrate 40 to 60 m deep in the forest [Kapos, 1989; Didham and Lawton, 1999]. These changes 

in the hydrometeorology parameters impact the flora and fauna found in the forest 

fragments. Many tropical animals require large areas of native vegetation for their survival, 

and isolation of forest fragments impacts their survival due to lack of water and food. 

Studies by Dale et al. [1994] over the tropical forests show that some species in small and 

isolated patches of forests do not cross even relatively small deforested areas. The survival 

of species in isolated forest patches strongly depends on suitable habitats of sufficient spatial 

extent to support their population. Decreases in the movement of animals across an 

ecosystem can limit/reduce the nutrient exchange between forest patches [Saunders, 1991]. 

For plants, hydrometeorological changes are impacted by decreased evapotranspiration, 

and soil moisture depletion. Fragmented forests are more vulnerable to lateral shear force 

exerted by increased wind speed, turbulence and vorticity [Bergen, 1985; Miller et al., 2001]. 

This increases the mortality rate of trees and damage within 100 to 300 m of the edges of 

forest fragments [Ferreira and Laurence, 1997; Laurence et al., 1998]. Laurence et al. [2000] found 

that in Amazonia trees die three times faster near the edges than those at the interior. Some 

trees simply drop leaves and die at the forest edges due to sudden changes in temperature, 

moisture and sunlight [Lovejoy et al., 1986; Sizer and Tanner, 1999]. Tree mortality impacts 

canopy dynamics [Ferreira and Laurance, 1997], which in turn alters forest structure, 

composition and diversity. New trees growing near the edges adapt to the new 

environment, but these are dissimilar to the forest-interior trees [Viana et al., 1997]. Thus, 

rare and localized species whose range becomes unsuitable tend to be threatened to 

extinction unless dispersion and colonization is possible. 

4. Outline of this study 

Although there are many studies as discussed above [Table 2], from both observations and 

models [Negri et al., 2004; Wang et al., 2000; Costa et al., 2007; Sampaio et al., 2007] to quantify 

the impact of deforestation on regional weather and climate, many of these studies 

exclusively focus on large homogeneous, pristine forests of Amazonia that were replaced by



farmlands and pastures. The results presented in the literature are not only inconsistent, but 

in many cases are opposing in their conclusions. Furthermore, it is unknown to what degree 

results reached for large, relatively homogeneous regions such as Amazonia are applicable 

to small regions with strong oceanic contributions to weather and climate. 

Ray et al. [2006] utilized satellite observations and regional atmospheric model simulations 

in Guatemala and adjacent areas and reported that deforestation locally intensifies the dry 

season, increasing the risk of fire along the long corridor regions connecting the protected 

areas. They showed that the deforested habitats in dry season have higher day-time 

temperatures, less cloud cover, less soil moisture and low values of Normalized Difference 

Vegetation Index (NDVI) than the forests in the same life zone. 

A detailed analysis of the climate parameters, such as change in land cover, surface 

temperature, NDVI, soil moisture, albedo, cloud formation, and precipitation over the 

protected reserves and the corridors connecting these reserves for different seasons, is 

required to determine the regions in the MBC that are potentially “stable” and “unstable.” 

This research work examines these issues in detail over samples of forested and deforested 

regions in Guatemala and the stability and sustainability of the MBCs in the region. analyzes 

whether relatively small-scale forested regions adjacent to deforested regions in Guatemala 

create meaningful differences in convective initiation and precipitation, which would be of 

importance to the sustenance of the proposed MBC. 

5. Study area 

Figure 1 shows the parks, reserves and protected regions within the study area along with 

the narrow corridors proposed to connect patches of similar habitat. The largest protected 

region is the Maya Biosphere Reserve in the northern Petén region which contains more 

than a million hectares of tropical forest. Cochrane [2003] showed that long corridors with 

long perimeters relative to their area may be particularly threatened by fire because forest 

edges usually are drier and more vulnerable. Ray et al. [2006] suggested that deforestation 

may be lengthening and locally intensifying the dry season which would adversely impact 

second-growth forest regeneration. 

Guatemala is composed of the mountainous highlands, the Pacific coast region south of the 

highlands and the hot, humid tropical and relatively flat Petén lowlands to the north. 

Approximately one-third of Guatemala is forested with about half of that total is composed 

of primary forests. The choice of this site is dictated in part because Ray et al. [2006] showed 

that at the height of the dry season (March), many of the forests in the Petén region have 

estimated rainfall deficits of up to 25mm which would have potentially serious 

consequences for the corridor regions. The Ray et al. [2006] results were generated from 

cloud cover rainfall regression statistics, and a more extensive analysis of the region is 

necessary to estimate the potential vulnerability of the proposed MBC corridors. 

6. Model 

The GEMRAMS atmosphere-vegetation coupled model [Eastman et al., 2001; Beltrán 2005; 

Beltrán et al., 2008] was used for the modeling experiments. It is comprised of the Colorado 

State University Regional Atmospheric Modeling System (RAMS) 4.3 [Pielke et al., 1992; 

Cotton et al., 2003] and the General Energy and Mass Transport Model [GEMTM, Chen and 

Coughenour, 1994]. RAMS is a general-purpose, atmospheric simulation model that includes 

57

58 


the equations of motion, heat, moisture and continuity in a terrain-following coordinate 

system. It is a fully three-dimensional and non-hydrostatic model. RAMS also includes a 

soil-vegetation-atmosphere transfer scheme, the Land Ecosystem-Atmosphere Feedback 

model, version 2 (LEAF-2) [Walko et al., 2000] that represents the storage and exchange of 

heat and moisture associated with the vegetation and canopy air and soil. 

GEMTM is an ecophysiological process-based model that can be used to simulate the 

dynamic interactions between the atmosphere and the growing canopy [Chen and 

Coughenour, 1994]. Several of the GEMTM components were coupled to RAMS: canopy 

radiation transfer, plant and root growth, soil water dynamics, biomass production and soil 

respiration submodels. These components require an additional set of parameters, mostly 

vegetation dependent, to characterize these biological processes. 

In GEMRAMS the near-surface atmosphere and biosphere are allowed to dynamically interact 

through the surface and canopy energy balance. Precipitation, canopy air and soil 

temperature, humidity, winds and surface LH and SH fluxes are computed by RAMS. 

Photosynthesis at the leaf-level is calculated for sunlit and shaded leaves as a function of 

photosynthetic active radiation (PAR) and temperature. Water stress effect on the assimilation 

rate also is considered. At the canopy level, photosynthesis and conductance are calculated by 

scaling-up from the corresponding sunlit and shaded leaves using sunlit and shaded LAI 

using light extinction coefficients from a multi-level canopy radiation model [Goudriaan, 1977]. 

The available photosynthate is allocated to leaves, stems, roots and reproductive organs with 

variable partition coefficients which are functions of soil water conditions. As water stress 

increases, the fraction allocated to root growth increases. The root profile is updated daily 

through the processes of branching, extension and death [Chen and Lieth, 1993]. 

Cumulus parameterization in the model is accomplished using the Kain-Fritsch scheme 

[Kain and Fritsch, 1993]. GEMRAMS is a state-of-the-art soil-vegetation-atmospheric 

simulation approach which offers much increased realism in modeling complex forestdeforestation 

circulation patterns. The original GEMRAMS model was built based on RAMS 

v4.3. The RAMS model has evolved to the latest version v6.0, and one of the major 

improvements includes the use of independent satellite observations of vegetation 

greenness represented by the Normalized Difference Vegetation Index (NDVI). 

6.1 Model configuration 

Two nested domains are used. The outer domain, with a horizontal grid of 40 km x 40 km 

and covering an area about 2000 km on a side, is centered at the point of 16.0N and -90.0W 

in Guatemala. This domain is used mainly to incorporate as much real-time weather as 

possible within the chosen regional scale domain. Real-time weather data are obtained from 

National Centers for Environmental Prediction Final gridded analysis datasets (NCEP FNL), 

which is global at 1 x 1 degree, 6-hourly. The inner domain in this study has a horizontal 

grid of 10 km x 10 km and covers an area of 620 km x 620 km, centered at the same point. 

The inner and outer domains are shown overlaid onto the map of vegetation types compiled 

from MODIS data by the University of Maryland in Figure 2a. Figure 2b shows the 

topography of the study region. The Petén study area is characterized by relatively low 

relief (< 300 m) and is bounded roughly from 16N to 18N and 89W to 91.5W. 

Thirty-five vertical levels are used in the model, with the lowest level 20 m from ground, 

increasing with a ratio 1.15 up to a maximum of 1200 m near the model top at 18.7 km. The 

fine vertical grid limited the model time step to 30 seconds for the outer and 10 seconds for



the inner domain. It is noted that the model was run with the “full” dynamic features 

(topography, various landscape features, etc.) over the two nested domains. 

Fig. 2. (a) The University of Maryland ecosystem map of Central America with outer and 

inner domains overlaid (red boxes), (b) The two nested domains used for model simulation 

shown over the topography map of Guatemala. The outer domain has a horizontal grid of 

40 km x 40 km and covering an area about 2000 km on a side and is centered at the point of 

16.0N and -90.0W in Guatemala. The inner domain has a horizontal grid of 10 km x 10 km 

and covers an area of 620 km x 620 km, centered at the same point. The major vegetation is 

the Evergreen Broadleaf Trees, which shows indeed a quite “smoothed” distribution. 

However, there are small-scale patchy, heterogeneous land covers which represent partial- 

or non-forested land covers. 

The northern half of the Petén region tends to be characterized by protected regions and 

tropical forests, while the southern and western regions are heavily deforested. LAI in these 

dense tropical rainforests are ~ 6 while values in the range of ~2 are associated with the 

small-scale, patchy, heterogeneous land surface conditions in the non-forested areas. 

Roughness heights are approximately 2 m in the forests and a few centimeters in the 

pastures. Albedo values of about 14% are taken for the forests and approximately 16.5% for 

the deforested regions. 

6.2 Soil initialization 

The model soil parameters are more difficult to initialize, due to the lack of satisfactory data 

in most regions. Reichle et al. [2004] discussed a three-way comparison regarding soil 

modeling (in-situ measurement, remote satellite observation, and numerical modeling) and 

pointed out that there is as yet no “consensus” among the three. In the current study, soil 

moisture is initialized using the NCEP FNL 2-layer soil data, in which vertical variation is 

incorporated into the model. Horizontally, the data over Guatemala is averaged. Soil 

temperature is initialized with “zero-offset” from the NCEP FNL lowest air temperature. 

Also, soil (3D) texture is initialized following the same procedure of LAI and vegetation 

fraction as provided by RAMS v6.0. 

59

60 

7. Model results 


The objective of this study is to determine environmental and climatic differences in 

forested and deforested regions in the lowland Petén region of Guatemala as well as 

circulation patterns both within these regions and along the borders. Large differences 

between the forested and deforested areas would support the hypothesis of Ray et al. [2006] 

who proposed that the proposed MBC corridors are potentially unstable. 

A 30-day simulation of the dry season is performed in the Petén region with the northern 

half forested and the southern half deforested. This is in rough agreement with the primarily 

forested conditions found in the northern Petén today and the largely deforested conditions 

found in the southern region. 

Figure 3 shows the model configuration of vegetation assumed in this study. The vertical 

north-south line (AB) through the center of the region at 90.25W shows the location at which 

most of the results are described. The horizontal east-west line through the center of the 

region at 17N divides the primarily evergreen broadleaf forested region to the north from 

the primarily deforested pasture region to the south. 

Fig. 3. Model Configuration: (a) Albedo of the Petén. (b) LAI field over the Petén on March 

8 th, 2003 (dry day) at 12 noon local time (1800 UTC). (c) and (d) Similar to (b) but for 

fractional vegetation cover and surface roughness factor respectively. 

Based upon satellite images of the Petén region, it is assumed that fractional vegetation 

cover is about 98% in the forested regions and about 70-75% in the deforested regions. As



seen in Figures 3 a, b, c and d the LAI is ~6 for the Evergreen Broadleaf Forests, surface 

roughness length is about 2m and surface albedo is about 14%. Note that once the 

vegetation type is chosen, the GEMRAMS model initializes the surface parameters. The 

value of 14% for the forests is consistent with satellite remote sensing estimates and the 

literature (e.g., Pielke, 1984, Table 11-8). Note that coniferous forests have much lower albedo 

on the order of ~10% and LAI values of 2-4, but these are not prevalent in the study region. 

In general, forests may have albedo values ranging from about 10% to 20%, depending upon 

forest type and season. Whereas, the LAI values for the deforested regions range from 1.5 - 

2.5 with very small roughness lengths (nearly zero) appropriate for pastures and with 

albedo ~ 16.5%.It is important to note that the GEMRAMS model is fully dynamic. 

Therefore, the surface parameters of albedo, LAI, fractional vegetation cover and roughness 

factor change over time in response to environmental conditions such as surface 

temperature, humidity and precipitation. However, for the short term study conducted here 

over a one month period, the surface parameters varied little. 

Within a typical dry season, there may be some days which are hot, dry and cloudless, others 

that have fair-weather cumulus and others that experience unstable atmospheric conditions, 

deep convection and heavy precipitation. Figure 4 shows daily precipitation averaged over the 

forested and deforested regions for March 2003. Note that there was no precipitation in first 

half of the month over both the regions. About a third of the days experienced small 

precipitation events such as showers, and about six of the days experienced unstable 

conditions with deep convection and heavy precipitation. The environmental conditions 

necessary for the formation of deep convection are destabilization of the air parcels and lifting 

the destabilized air to the level of free convection [Wallace and Hobbs, 2006]. The destabilization 

is associated with the lifting of the air parcel and the low level convergence influenced from 

large-scale forcings such as an extratropical cyclone, which are generally anticipated a day or 

more before. In this decade, 2003 was year of many extratropical cyclones. Central America 

was highly influenced by the troughs associated with the cold fronts coming from north [IPCC, 

2007].And the lifting of air parcels that initiates the deep convection is associated with 

localized, short-lived and less predictable forcings such as the sea-breeze or outflow of any 

pre-existing convective storms [Wallace and Hobbs, 2006]. 

Figure 5a shows SH fluxes peak values tend to occur at 1400LT, which is the time of 

maximum convection on days in which it occurs. SH fluxes range in value from about 400- 

500 Wm -2 for about the first two weeks of March and then again on the dry days of 21-22 

March. SH flux decreased to about 300 Wm -2 on 16 March the day convective rains. Then 

slowly increasedas the ground dried out. The day of 23 March had strongly unstable 

conditions and heavy precipitation and SH fluxes < 200 Wm -2 with similar low values at the 

end of the month. Note that SH fluxes are within 10% of each other in the forested and 

deforested regions throughout this dry season on dry and convective days. 

Figure 5b shows the corresponding LH fluxes during this month at the same time of the day. 

LH fluxes range from < 200 Wm -2 on the very dry days to > 600 Wm -2 on the very convective 

days during the later half of March. High LH release associated with warm surface 

temperatures acts to maintain the horizontal temperature gradient thereby increasing the 

supply of potential energy to build up the deep convection [Wallace and Hobbs, 2006]. Note in 

particular that the LH fluxes are up to twice as large in the forested regions during the “dry” 

days.Differences in LH fluxes between the forested and deforested regions are much smaller 

during the convective days. 

61

62 


Fig. 4. Diurnal (hourly) precipitations for March 2003 averaged over forested and deforested 

regions. 

Fig. 5. (a) Diurnal (hourly) SH flux for March 2003 averaged over forested and deforested 

regions, (b) same as (a) but for LH flux. 

It might be expected that the forests with their larger LH fluxes would produce greater 

cloud cover on the drier days, but this is not found to be the case during this study period. 

The following case studies examine representative dry, showery and convective days, 

comparing conditions in the forested and deforested regions in each case.



7.1 Dry day 

Table 3a,b shows temperature, LH, SH, PBL height, precipitation and cloud cover for the 

south (pasture) and north (forest) sides for both a representative dry day (March 8) and a 

representative convective day (March 23) as a function of time (0600, 1000, 1200, 1400 and 

1800 LT). These are values averaged over each of the two sides, forest to the north and 

pasture to the south. 

a) Dry day Convective day 

Time Temperature 

(K) 

LH (Wm -2) SH (Wm -2) Temperature 

(K) 

LH (Wm -2) SH (Wm -2) 

Pasture Forest Pasture Forest Pasture Forest Pasture Forest Pasture Forest Pasture Forest 

6am 296.18 293.69 12 8.23 12.47 15.31 299.11 297.57 100.95 31.87 14.44 49.56 

10am 305.92 303.56 163.96 54.02 309.58 439.61 302.12 299.42 618.114 719.05 87.52 47.08 

noon 310.49 306.44 37.61 149.31 471.17 485.66 302.73 300.89 670.557 476.62 122.63 82.4 

2pm 310.79 306.66 28.03 138.75 373.34 333.41 302.83 299.08 574.53 482.88 117.29 32.73 

6pm 303.73 300.45 0.5044 0.7795 7.12 -26.14 298.42 295.94 128.29 60.37 -5.57 -10.197 

b) Dry day Convective day 

Time Planetary 

Boundary layer 

(m) 

Precipitation 

(mm) 

Cloud Cover 

(%) 

Planetary Precipitation 

Boundary layer (mm) 

(m) 

Cloud Cover 

(%) 

Pasture Forest Pasture Forest Pasture Forest Pasture Forest Pasture Forest Pasture Forest 

6am 164.38 491.38 0.32 0.77 13.95 13.76 465.27 293.49 37.62 11.91 21.24 16.46 

10am 1104.23 1267.63 0.59 0.87 16.72 13.8 927.65 1199.39 44.97 15.58 34.17 22.68 

noon 1658.03 1811.46 0.59 0.87 13.85 13.76 1193.71 1456.6 50.73 18.12 33.08 22.49 

2pm 2017.77 2065.17 0.6 0.88 12.62 12.62 1272.75 1727.46 55.29 22.82 37.57 21.72 

6pm 1442.22 1088.76 0.6 0.88 10.88 12.14 834.66 994.18 61.39 25.52 20.67 18.85 

Table 3. Temperature (K), LH flux (Wm -2), SH flux (Wm -2), PBL height (m), precipitation 

(mm) and cloud cover (%) for the pasture (south) and forest (north) sides for dry day (March 

8 th, 2003) and convective day (March 23 rd, 2003) at 6 am, 10 am, 12 noon, 2 pm and 6 pm. 

Fig. 6. Spatial distribution of temperature at 2 m (K), SH flux and LH flux on the dry day 

(March 8 th, 2003) at 1400 LT 

63

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Fig. 7. Spatial distribution of temperature at 2 m (K), SH flux and LH flux at 1400 LT on the 

convective day (March 23 rd, 2003) 

For the dry day, Table 3a shows that temperature in the pasture is about 2K warmer than 

the forest in the early morning, increasing to about 4K warmer during mid-day. Indeed, the 

literature and the satellite observation studies discussed in Chapter 3 show that during dry 

season deforested regions tend to be hotter than forested ones in virtually all regions 

worldwide. Also we see that the values of LH and SH fluxes were very small in the early 

morning, with SH values approaching 500 Wm -2 in both pastures and forests by mid-day 

and then decreasing again by evening. Sensible heat values were relatively similar in both 

pastures and forests. 

Under these very dry conditions, values of LH from the pastures spiked to values of about 

160 Wm -2 in mid-morning (10am LT) due to “burnoff of early morning dew while forests 

had values only a third as large. By mid-day (noon LT) LH fluxes decreased to about 30 

Wm -2 in the deforested regions while values in forests increased to about 140 – 150 Wm -2. 

Note that during mid-day SH fluxes were about three times larger than the LH fluxes in 

both pastures and forests. 

Cloud cover ranged from about 12% to 14% over both regions during this dry day, and 

precipitation was minimal, averaging less than 1 mm in both regions. The PBL height was 



Under the relatively wet conditions of the convective day, a reversal in the behavior of the 

LH and SH fluxes is found, as compared to the dry day. On the convective day the SH fluxes 

are relatively small, in the range from 50 – 125 Wm -2 in both pastures and forests, while LH 

fluxes reached 600-700 Wm -2 in the late morning hours before decreasing somewhat during 

mid-day. Note that LH fluxes were significantly larger over pastures during mid-day than 

over forests and that SH fluxes were somewhat higher over pastures than forests. 

Cloud cover is significantly higher over pastures than over forests on the convective day, with 

values of about 23% over forests and up to 38% over pastures during the early afternoon. 

These differences in cloud cover also are reflected in differences in precipitation. The 

precipitation increased from about 12 mm in the early morning hours to about 25 mm by 

evening over the forests, an increase of about 13 mm during the day. However, precipitation 

increased from about 38 mm in the early morning over the pastures to about 61 mm by late 

afternoon, an increase of 23 mm during the day. Overall, precipitation was more than double 

over the pastures. On an average the rain rate of the forested region was 0.114 mm/hour and 

deforested region was 0.151 mm/hour. The results suggest that under sufficiently convective 

conditions, pastures generate significantly higher precipitation rates than do forests 

Fig. 8. Development of circulation patterns along the AB line shown in Figure 7 from early 

morning (6 am) to late afternoon (6 pm) on March 23rd, 2003 (convective day). The 

horizontal axis shows the latitude (in degrees) and the vertical axis is the height from the 

ground (in km). The red lines represent convective heating ( oC day-1) (solid line – heating 

and dashed line – cooling) and the green lines represent convective moistening (g kg 

(solid line – moistening and dashed line – drying). 

65 

-1day -1) 

The height of the PBL was higher over the pastures (~450 m) than over the forests (~300 m) 

in the early morning hours. By 1400 LT the PBL grew to about 1300 m over the pastures and 

to about 1700 m over the forests, a value only slightly smaller than during the dry

66 


conditions. The height of the PBL over forested regions is not highly sensitive to wet and 

dry conditions. However, note that the height of the PBL over pastures is very sensitive to 

wet and dry conditions, reaching only 1275 m during the convective day. 

Figures 8a, b, c show the spatial pattern of surface temperature, SH and LH fluxes, 

respectively, at 1400 LT over the study region. On convective day we don’t clearly see the 

difference between the forest and the deforested regions spatial plots as seen on a dry day 

(Figure 7). 

7.3 Circulation patterns 

Figure 8 shows the development of circulation patterns along the line AB in Figure 7 from 

early morning to late afternoon on 23 rd March, the convective day. In the morning hours, 

there is early convective activity over the pastures, with cloud tops reaching about 9 km. 

Note that convection is found only over pastures and not over the forests in the early 

morning. By 1000 LT strong updraft regions are developing in the convective regions and 

cloud tops reach about 12 km in height. At this time convective activity is initiated over the 

forests near the forest-pasture boundary. By local noon convective activity is found over the 

entire region, both over pastures and forests, with cloud tops reaching 13-14 km. Note the 

regions of strong updraft. By 1400LT the forest regions have ceased convection along this 

AB line, although very strong convection with very large updrafts is found over the 

pastures. By late afternoon, convective activity is ceasing, but with isolated cells over both 

pastures and forests. 

Note that the cloud top heights are approximately the same over both pastures and forests. 

To examine the realism of these results, GOES infrared imagery was obtained for 23 March 

2003. Cloud top heights were examined over both forested and deforested regions, and there 

were no significant differences in the results (not shown). Once generated the convective 

clouds in all situations modeled have sufficient CAPE to reach the tropopause level. 

8. Discussion and conclusions 

High surface temperatures together with the troughs associated with the cold fronts coming 

from the north tend to destabilize the air. This together with the local sea-breeze increases 

the potential energy required for increasing convection. And as we see from the results, 

convective activity with precipitation starts from the mid of March 2003. This together with 

the prevailing high surface temperature destabilizes the air leading to more convective 

storms during the latter days of March 2003. 

During dry conditions LH fluxes are very low over both pastures and forests, and SH fluxes 

are a factor of three to ten times larger. PBL heights reach 2000 m during the heat of the day 

and there is minimal cloud cover on the order of 13% and virtually no precipitation for these 

dry conditions. 

A very different scenario occurs during wet convective conditions. Under these conditions 

convection is initiated early in the morning hours over the pastures and not over the forests. 

By mid-day convection is found over both pastures and forests, and by late afternoon 

convection decreases over both regions, but much more so over the forested regions. LH 

fluxes become very large (~700 Wm -2) and are five to ten times larger than the SH fluxes. 

Cloud cover over the forests increases to about 22% during mid-day but up to about 38% 

over the pastures. Overall, substantial precipitation rates of 61 mm were found over the



pastures compared to less than half this amount (~25 mm) for the forests. PBL heights are 

much lower over pastures than over forests. 

These results are consistent with Negri et al. [2004] who utilized GOES data for their 

Amazonia study, a wet region. Higher cloud covers and precipitation rates are found over 

deforested regions. Manoharan et al. [2010] also used GOES data and found higher cloud cover 

over deforested regions in Guatemala. However, note that such conditions of higher cloud 

cover and precipitation are found only under wet, convective conditions and are not found 

under dry conditions. Furthermore, the results are consistent with Wang et al. [2000] who 

reported deforestation can create mesoscale circulations with rising motions that trigger dry 

season moist convection. The present results clearly demonstrate that much strong convective 

circulations are created over pastures (deforested) regions than over forests. 

In terms of the sustainability of the lowland corridor regions in the proposed MBC, the 

results strongly suggest that forested corridors will experience warmer conditions due to 

higher temperatures in surrounding deforested areas. Also from the observational study by 

Manoharan et al. [2010], we see severe dryness and drought prevailing in the region during 

2003. However, by far the most important factor is precipitation. During the first half of the 

month, there is little or no rainfall, whereas, during the latter days of the month (March 

2003) we see significant convective activity over the region. This is the result of the increase 

in energy that initiates convection by increased SH release during the initial days of March 

and associated local-sea breeze. Thus, the forested corridors will receive higher than normal 

precipitation rates due to the fact that surrounding warmer deforested regions generate 

higher convective activity. The above scenario implies a “climate tipping point” will not 

occur in the proposed corridor regions within the lowland regions of Guatemala in the 

study area which would threaten their stability and sustainability. 

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Dinaric Karst – An Example of Deforestation 

and Desertification of Limestone Terrain 


5 

Andrej Kranjc 

Slovenian Academy of Sciences and Arts 

Slovenia 

Dinaric Mountains are one of the main mountain systems of the Balkans. The name was 

given by of the imposing Dinara Mountain (1913 m) at the border between Herzegovina 

(Bosnia and Herzegovina) and Dalmatia (Croatia). Under the name of Dinaric Alps it 

appeared already in the 18 th century (Hacquet 1785). The part of Dinaric Mountains which is 

mostly built by carbonate rocks, limestone predominating, is called Dinaric Karst. The name 

Karst as well as the international term “karst” derived from the plateau Kras (Carso in 

Italian, Karst in German), the north westernmost plateau of the Dinaric Karst ridges (Kranjc, 

2011). Dinaric Mountains are a mountain chain approximately 650 km long and up to 150 

km large, covering an area of about 60 000 km 2, stretching between 42° and 46° of northern 

latitude (Fig. 1). 

Fig. 1. Delimitation of the Dinaric Karst after Roglić and Gams (Mihevc & Prelovšek, 2010).

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Geologically, Dinaric Mountains consist of two parts: Inner Dinarides at Northeast and 

External Dinarides at Southwest (Mihevc & Prelovšek, 2010; Zupan Hajna, 2010). While in 

the Internal Dinarides non-carbonate rocks prevail, in the External one the carbonate rocks 

are predominant – therefore there is karst. A. Penck’s student of Vienna “geomorphological 

school”, Jovan Cvijić was probably the most influential scholar to propagate karst and to 

substantiate the karst science. In his basic works of 1893 and 1895 (Cvijić, 2000) he stated: 

“All the forms on the bare limestone, made by water, we will call karst features”. Cvijić’s 

connotation of karst is “bare limestone landscape”. The travellers who travelled from 

Vienna to Austrian Adriatic port of Trieste were the most impressed by a sudden change of 

landscape. After Postojna, they entered a bare rock land, without surface water and 

especially without any greenery. In 1689, Valvasor in his topography wrote about the Kras 

(Karst) plateau: “Somewhere it is possible to see for some miles, but everything is only grey, 

nothing green, because all the country is covered by stones.” Illustration from the same 

work shows the cultivated land at the bottom of dolines only (Valvasor 1689). In many parts 

of the Dinaric Karst it is true for the actual situation (Fig. 2). On 18 th century military maps 

the entire Kras surface is shown as “”Steinigte Terrain” (rocky terrain) (Fig. 3). Description 

of individual settlements added as a comment to the maps often stated: “There are no 

forests or trees, just some bushes one hour away from the village” as shown by the example 

of the village Gabrovitza (actual Gabrovica pri Komnu) (Rajšp, 1997). 

Fig. 2. On Dinaric Karst cultivated land is mainly in the bottom of dolines only (photo A. 

Kranjc).

Dinaric Karst – An Example of Deforestation and Desertification of Limestone Terrain 

Fig. 3. Military map of Kras plateau from the second half of the 18 th century: great majority 

of the surface is “Steinigter Terrain” (Stony terrain) (Rajšp, 1997). 

Fig. 4. About 1850 the nowadays woody hill Sovič above Postojna was bare (Schmidl, 1854). 

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Impressions of travellers across karst terrain between Postojna and Trieste are all depressing 

(Fig. 4). Count Karl von Zinzendorf wrote in 1771:”The country is affreux. All these terrible 

rocks and in the midst of them some small cultivated parts of land encircled by stones...; ” 

and B. F. J. Hermann in 1780: “Anywhere you look, it is only desert...” (Panjek, 2006). To the 

end of the 19 th century and even later the impression of karst got from the published works 

of scholars as well as of laymen was one of bare rock and dry landscape. But it was not 

always such. On the Dinaric Karst, nowadays there are completely bare landscapes, mostly 

on the Mediterranean side, but there are also extensive forests covering slopes of high 

mountains and the tops of karst plateaus in the interior. Good examples of preserved forests 

are Rajhenau primeval forest (Kočevski Rog plateau above Kočevje in Slovenia) 

(Rajhenavski pragozd, 2011) and the forest Lom (Piceo-Abieti-Fagetum illyricum) on the 

Klekovača Mountain in Bosnia (Prašuma Lom, 2011). The first one occupies about 50 ha of 

Abieti-Fagetum dinaricum. The forest of Kočevje is a part of the biggest uninterrupted forest 

complex in the Western and Central Europe, stretching from the Kočevje region (Slovenia) 

to Gorski Kotar (Croatia). 

The aim of the case study of this chapter is to show that man is the main factor both at 

destroying his natural environment and at restoring it. The man is capable of both. In our 

case that means a complete deforestation, the changing of a heavy wooded landscape to a 

bare rocky desert and back again to a dense, although, to be true, not a “natural” or optimal, 

forest, as it shows the further text. Of course the time scale is different as well as the attitude 

towards these processes triggered in both cases by the human itself. The first dwellers 

millennia ago have not seen, they did not know and they could not imagine what a process 

they have started by cleaning land for pastures and fields. And the process of deforestation 

and finally desertification lasted thousands of years. If reforestation was premeditated by 

well planned actions the actors knew well their aim and purpose. Comparing the lasting of 

reforestation with deforestation this was a short but an intensive action. The karst terrain, 

Dinaric Karst especially, is such a terrain where the human activities leading towards 

desertification have shown their most disastrous consequences and where the opposite 

action, reforestation, demanded extremely great efforts and financial input. This case study 

is not meant to be just a history of a forest but also a warning what can happen, not only in 

the mist of history, but also nowadays. 

2. Deforestation 

Deforestation started in prehistoric times already, by the arrival of people with Neolithic 

culture, leading Neolithic way of life, the transition from gathering and hunting to 

stockbreeding and farming, the so called Neolithic revolution. The Balkan Peninsula is a sort 

of bridge between Near East, across Asia Minor towards Central and Western Europe and 

the Neolithic culture reached it between 6 500 and 6 000 BC. Neolithic farmers did not enter 

far into the Dinaric Mountains. Instead they advanced across the fertile plains along the 

Danube River on the North and along the Adriatic coastal strip in the South, so avoiding the 

mountainous regions (Velušček, 1999). So their impact on the forest of Dinaric karst had to 

be negligible, with some exceptions - the Butmir locality for example. Pollen analyses show 

that the intensive deforestation phase occurred due to grazing during prehistory on the 

plateau of Kras (Slapšak, 1995). To confirm the prehistoric deforestation Gams’ (1991) 

research on Rillenkarren is very illustrative; he found out that they started to be formed on


the plateau Kras 3 000 – 3 500 years BP, which is when the forest was destroyed and bare 

rock started to appear on the surface (Fig. 5). 

Fig. 5. Rillenkarren on a limestone rock: they start to form about 3 000 – 3 500 years ago 

while the smooth lower lying surface was covered by soil much longer (photo K. Kranjc). 

During the Bronze Age, the situation changed drammatically. The population increased and 

due to their economy (farming, stockbreeding, and ore mining) entered deeply into the 

Dinaric Karst where they had to clear and cut the timber, which was used for buildings and 

defence installations as well as for ore smelting. In Dinaric karst the settlements were 

concentrated mainly in two border zones: on the Adriatic coastal plains (Low Periadriatic 

Karst) and on the karst plains and hills along the Pannonian plain (Low Peripannonian 

Karst). The innermost parts of Dinaric Karst seem to remain quite untouched. Thanks to 

archaeological research we know that during this population expansion the climax Abieti- 

Fagetum forest was already being replaced by lower association of Quercus type in some 

parts of Dinaric Karst (Turk et al., 1993). 

During the Early Iron Age (Hallstatt culture) practically all the Dinaric Karst was settled and 

during the Late Iron Age (La Tène culture) Dinaric region entered into history: the native 

(Illyrian) tribes are known by names, in the southern Dinaric coast and islands Greek 

colonies were founded, from the North came a Celtic invasion and from the West the Roman 

one. From that period, the names of peoples living in the region are known: Illyrian tribes, 

Celtic tribes, Greeks and Romans. Regarding the use of timber and wood and the economy 

in general, the cause of deforestation, the choice of motives is very large: Illyrians deep in 

the interior of Dinaric Karst tended big flocks of sheep and goats using the transhumance 

system, and the farmers not abreast with the time used slash-and-burn system. To increase 

pasture areas shepherds also used the fire. On the Dinaric Karst, considering its climate, a 

forest fire does not only destroy trees and surface vegetation. Its consequence is much more 

important because it increases rain and wind erosion processes and rock aridity. In such 

cases woodlands can be really transformed into “rock deserts”. Metallurgy using so-called 

“bean ore” (Bohnerz) which is very frequent in karst soil and clay, had to consume big 

quantities of wood, as shown by modern experiments (Kranjc, 2002). Near Straža village by 

the town of Novo mesto (NW part of Dinaric Karst, Slovenian Low Peripannonian Karst), 24 

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smelting furnaces were burning simultaneously as is shown by the archaeological research 

(Dular & Božič, 1999). While the Romans (the army at the beginning and colonists later) and 

Greeks used wood for construction, industry (including metallurgy, charcoal and lime 

production, pottery), heating (thermae with big basins of hot water were necessity of 

townspeople everyday life) and many other needs connected with high developed culture. 

Along the Dinaric area - that is the Adriatic coast, shipbuilding was important too, for both 

the Illyrians as well as for Greek colonies. From historical sources it is known that the fleets 

consisted of a big number of smaller boats (lemba and liburna), but also big boats were 

constructed. Ancient authors, Polybius for example, often mentioned numbers of ships of 

Illyrian fleets:”Scerdilaedas … provided 40 lembas and Demetrios of Pharos 50”. To build 

the liburna, 33 m long and 5 m large ship with 36 oars, quite a lot of wood was needed. 

Polybius reported that Macedonian king Philippos the 5 th ordered to construct 100 liburnas 

during the winter 217/216 BC. For oars alone they needed 3 600 adequate straight young 

trees, not accounting for spare oars. That this was an important question proved the report 

of Andokidos who came to help to the army of Samos: “For the beginning, I prepared wood 

for oars for your army of Samos.” When Brazida conquered the town of Amphipolis this fact 

“… provoked great fear among the Athenians … because from there they got the wood for 

shipbuilding…”, both quotations taken from Thucydides (Cabanes, 2002). 

From the opposite part of the Europe, from Scandinavia it is reported that the Viking’s 

shipbuilding came to a serious deadlock because there were no more suitable trees in their 

homeland (Atkinson, 1979). In the Dinaric Karst such a problem is not known from historical 

times but aroused seriously in the 19 th century, as seen from the text below. The Ljubljana 

Moor lies at the foot of high karst plateaus of NW Dinaric Karst. Between the towns of 

Nauportus at the foot of the plateau with very important karst spring, and Emona, the Roman 

legion had to build a road across the Moor in the first years AD. For the base of the road 

structure they placed thinner round tree trunks on the marshy soil. Illyrians (and other native 

peoples) to defend their oppida used palisades while Romans used them to defend their legion 

camps and also to strengthen walls of stone and wood combined to protect towns. 

Of course there is little direct evidence of larger deforested surfaces existing during 

Prehistoric and first Historic times. In the Smederevo polje of Lika (Croatia), the position of 

skeletons in burial mounds shows that the landscape was open with a thin soil cover, that 

means there were no (more) forest at the time of a funeral (Horvat, 1957). Many parts of the 

Dinaric Karst that are nowadays forest-covered again were dramatically different during the 

Iron Age, when this was an open country with small fields and pastures, and fortified 

hilltop settlements. In Bosnia and Herzegovina the majority of actual barren landscape is 

found around the former Illyrian hill forts and settlements (Djikić, 1957). Nevertheless, at 

the beginning of the historical times there must still have been much of forest-covered land 

as proved by the topographical names. The Island of Korčula (Dalmatia) was called Korkyra 

Melaina by Greeks and Korkyra Nigra by Romans, both names meaning Black Korkyra 

because of its dense pine forest cover. On the plateau Kras cultivated land was in form of 

islands around the settlements and quite a lot of forest remained in between (Slapšak, 1995). 

Knowing people and their history it is sure that the wars and clashes of arms existed in 

Prehistoric times, too. From the beginning of history on there are records of them from all 

periods. These violent activities had and has (just to remember the Vietnam War) a very 

great impact on forest. “Plunder and burn” was the most common motto: to get the booty and 

to devastate enemy’s country, that means to ruin it economically. Burning a forest was not just


an economical measure; it was used as a war or raid tactics, too. For example, the Turkish 

regular army cut down and burned woods to make easier way for the cavalry and heavy 

artillery, and to destroy Hajduks (Balkan guerrillas) hidouts and dwelling places. The army 

was ordered to destroy forest on both sides of roads to get clear zones to thwart ambushes by 

the Hajduks. From the 17 th century there are picturesque descriptions of such activities in the 

diary of the Turkish traveller Evliya Çelebi (Evlija, 1957). Reciprocally, forest was sometimes 

burned too by the defending population in attempts to prevent the enemy’s attack. 

During the late Antiquity another reason joined “traditional” deforestation – invasions of 

Barbarian peoples. Between 3 rd – 8 th centuries they crossed and often settled the territories of 

the Roman Empire, in our case the Roman provinces of Illyricum and Dalmatia. Especially 

frequented was the direction of the Roman road Aquileia - Carnuntum, leading across Dinaric 

Karst on the section between Tergeste (Trieste) and Emona (Ljubljana). The consequence of 

their approaching and settling was the movement that is flight, of Romanised inhabitants 

towards the coast and especially on the normally overgrown islands which are stretching all 

along the Adriatic coast. Today these islands are effectively uninhabited but traces proved that 

they were relatively densely populated during the Late Antiquity. Maybe the population 

increase was not the worst. Such troubled times favoured stockbreeding over farming and 

overgrazing together with burning to increase pasture lands left many of the previous 

mentioned islands completely bare. Such conditions remained to modern times and now these 

islands are used as meagre pasture for sheep only, not to mention the summer tourism (Fig. 6). 

Fig. 6. Kornati islands, practically nothing but dry pastures for sheep (photo A. Kranjc). 

The next troubled times after the barbarian invasions regarding human pressure and impact 

on the forest was the Turkish occupation of the majority of the Balkans and Turkish raids 

into the neighbouring countries, which seriously began in the 14 th century. The interior of 

the Balkans was Turkish Empire, much of the Adriatic coast belonged to the Venetian and 

Dubrovnik (Ragusa) Republics, and small part of the NW part belonged to Austria, either in 

the frame of so-called Military Zone (nowadays Croatia) or the Duchy of Carniola. Before 

the 14 th century the forests which were not commune were divided into forests for hunting, 

oak forests and small forests, in the frame of the Austrian lands. Animal grazing was 

forbidden in them and for cutting or burning wood severe fines were foreseen. An Act from 

1550 allowed all the inhabitants of Trieste, mule drovers and butchers to cut wood and grass in 

all of the commune’s forests. In 1689 two revisers reported: “In the town, there is a shortage of 

fire-wood, it is impossible to make a stock, because all the forests are destroyed”. In 1719 the 

port of Trieste was proclaimed a “free port”. To the Austrian annexation of Bosnia in 1878 in 

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outline the situation remains the same. A significant part of the Christian population fled 

before the Turks and re-settled in Austrian, Hungarian, Venetian or Dubrovnik lands not far 

from the borders. These migrations included some long distance displacements. For instance, 

entire “tribal families” from the inner parts of Serbia moved to the Istria Peninsula in the most 

north westerly corner of the Adriatic Sea, and even to Carniola – nowadays Slovenia (Cvijić, 

1966). These groups moved with their flocks and commonly they continued primarily as 

owners and herders of grazing animals. Pressure on grazing land led to another increase in the 

rate of deforestation. The emigrants brought their slash-and-burn techniques, too. 

Transhumance together with burning, later also cutting of the forest, was preserved locally 

until the 20 th century, when they were observed by the first researchers, J. Cvijić among them. 

According to the eyewitness Gušić, the main reason of deforestation was clearing the land for 

new pastures or meadows and sowing of grain in “novine” (new fields), used once only (Gams 

& Gabrovec, 1999). That cutting and burning of a forest could locally trigger accelerated soil 

erosion is proved by the practice in the near past, when farmers in remote mountains have 

burned forest in order to create such “novina” (Kranjc, 1979). This process is not connected 

with the Dinaric Karst only but largely with the Mediterranean (Fig. 7). On Baleares Islands on 

the land not suitable for agriculture grazing was practised at least from the Catalan conquest 

(1229). Traditional economy was based on the repetitive burning of herbaceous brushwood of 

Ampelodesmos mauritanica. This released active soil removal as well as the progressive 

degradation of scrub formation. At the end of the chain the bare rock results (Ginés, 1999). For 

some of authors, the main reason of not only deforestation but of the degradation to the bare 

rock country was grazing, grazing by goats especially (Wessely, 1876). 

Fig. 7. In some parts of Dinaric Karst the burning of shrubs is still practised (photo K. Kranjc).


In parts of the Dinaric Karst under Austrian and Venetian influence, the agrarian pressure 

was not the only economic reason for deforestation. In the Venetian territory construction 

work and shipbuilding demanded large quantities of timber. It is reported that 1 200 000 

tree trunks were needed for use as piles to support the church of Santa Maria della Salute in 

Venice (Horvat, 1957). It is difficult to imagine the whole number of piles, using for the 

churches, palaces and other buildings in Venice. Without doubt a great part of them came 

from the Dinaric Karst. There is a popular saying that the Venice “stands on oaks from 

Karst”. In the time of the French Revolution (1792), the duty of the French consul in Albania 

was to take care of “cutting down construction timber for navy base at Toulon”. Marshal 

Marmont for example, the Governor of the Illyrian provinces under Napoleon, ordered to 

cut off the tops of all the trees in one of the still remaining oak forest in the vicinity of 

Trieste, called Frned, to use them as a timber for ships. As a consequence the forest decayed 

completely to 1820. In the Austrian part of the Dinaric Karst the farmers (villeins and serfs) 

did not have the right to cut timber for trade before the so-called “Land Release” issued by 

the Empress Maria Theresa in 1848. In NW part of the Dinaric Karst the Austrian Navy had 

forest reserves, mainly oak. In these forests it was forbidden to fell timber for other 

purposes. The Navy’s demand was great: the navy’s forester (by the way the inventor of the 

vessel screw, too) J. Ressel reckoned up that to construct and maintain during its 150-year 

life a wooden battle ship 120 000 tree trunks were needed. In that time Austrian merchant 

navy had 523 big ships. To maintain the number 6.244 m 3 of wood would be needed, while 

the production of the Istrian forests was 7.030 m 3 (Piškorić, 1993). Good husbandry would 

thus not cause the deforestation by itself. But In 1819 the marine forest reserve was cancelled 

and massive felling programme started. Timber was sold to Venice, France, and especially 

to England (Murko, 1957). It is not surprise that the emperor Maximillian when visiting 

Trieste in 1850, described the plateau Kras as a rock desert with a curse hanging over it 

(Anonym, 2001) (Fig. 8). 

Fig. 8. The view of the plateau Kras above Trieste in 1901 (Anonym, 2001). 

The consequence of the mentioned “Land-Release” was disastrous for the forests in the 

Austrian parts of the Dinaric Karst. According to this act farmers became owners of the land 

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which included right to cut down trees and to sell them. And they used new rights to a full 

extent, not thinking of replanting young trees. Parallel to this process, industrial development, 

especially mining and metallurgy contributed significantly to deforestation, even on remote 

karst plateaus in Slovenia and Bosnia and Herzegovina. During the second half of the 19 th 

century, special narrow-gauge railroads were laid down to facilitate exploitation of the Dinaric 

Karst forests. The changes or regression in some branches of industry, metallurgy especially, 

can show indirectly the changes in forest structure or deforestation even. An example is the 

decrease of iron industry of the well known industrial Ž. Zois of Kranjska (Carniola) at the end 

of the 18 th century. Some of his ironworks went short of fuel that is of charcoal. The so called 

“Slovene furnace” needed 50-60 % more charcoal than ore. Zois tried to use charcoal made of 

soft trees (spruce) instead of hardwood (beech). This is also one of the reasons of the change of 

the forest structure: for the shipbuilding the oak was over exploited and for the iron industry 

the beech (Kranjc, 2002) (Fig. 9). 

Fig. 9. “Cooking” of charcoal on the Dinaric Karst at the beginning of the 20 th century 

(Anonym, 2001). 

As indicated in the text above, there were different factors causing deforestation of the 

Dinaric Karst and there are regions affected by different steps of deforestation. In any case 

the factor was man, either through his economy as stockbreeding and transhumance, slashand-burn 

agriculture, fire wood gathering, construction and different branches of industry, 

shipbuilding and metallurgy emphasized or other, hostile activities, as “plunder and burn”, 

army movements, attacks and protection of them, and last but not least the pressure on 

agricultural land. Some parts, relatively small and rare, of the Dinaric karst are practically 

unaffected by the process of deforestation and still nowadays covered by a dense forest; 

some others have still forest cover but heavily changed, and the last stage is “šikara”, 

shrubberies and thickets. Big surfaces are pastures without any trees and some parts of the 

Dinaric Karst are bare rock landscape. Generally speaking the bareness of the Dinaric Karst 

is lesser in the central parts, and going towards the Adriatic coast, it increased reaching real 

rocky desert on Adriatic slopes and on the islands.


Fig. 10. Bare high ridge of the Velebit Mountain (photo A. Kranjc). 

Fig. 11. Stony pasture on the Pag Island, sea side slopes of the Velebit Mountain in the 

background (photo A. Kranjc). 

While the number of inhabitants increased, economic facilities did not follow the population 

growth. Data from the Karlovac district of Croatia, which has an extremely great proportion 

of karst landscape, can be shown as an example. In the middle of the 18 th century, there 

were 940 inhabitants per square mile, while hundred years later, in 1850, there were 1824. 

This means that the population nearly doubled in a hundred years (Wessely, 1876). It was 

not a specific of the Karlovac district, in many parts or even in majority of Dinaric Karst the 

greatest population pressure on karst land was during the 19 th century. Regarding the 

available data the example of Dinaric Karst in Slovenia can be taken into consideration. In 

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Slovenia as a whole there was a minimum of forest cover around 1875. Forest then covered 

37 % of the surface, while in the district of Postojna, which included a great part of the 

Dinaric Karst in Slovenia, the forest covered 26 % in the year 1880 (Azarov, 1994). According 

to Gams (1991) the plateau Kras had only 20 % of forest surface in 1900. In 1989 the share of 

forest increased to 51 % and nowadays the rate of forest still increases, its share being 

estimated to be over 60 % of Slovenia and over 50 % of the Kras plateau. On Kras, it is 

mostly the monoculture black pine tree forest. The course of reforestation is now going on 

by itself; pastures are becoming overgrown by shrubs and being slowly transformed into 

forests. The surface is slowly overgrown first by tall herbs, then with shrub species, and 

finally by trees and forest ground flora. The front of pine forest, if not interfered by human, 

progresses at a maximum of 17 metres per year; computer modelling forecasts that the 

whole region of Kras will be overgrown by shrubs and trees till 2013 (Pertot, 1989). 

On the karst of Croatia, forest cover decreased to an alarming state during the 19 th century, 

too. In this time there was hardly any forest on the coastal side of the Dinaric Mountains. 

But the records of “Trieste Commercial Commissariat” for 1756 showed millions of trunks 

(Wessely, 1876). In the second half of the 19 th century on the “Mountain Karst” (Fig. 10) of 

Croatia 39 % of surface was categorized as bare (non-productive) land, the “karst” in narrow 

sense of meaning, while on the “Sea Side Karst” such category includes 93 %, as shown by 

the same author (Fig. 11). It is difficult to imagine that nearly the entire country was 

“unproductive” bare rock landscape. In that time, the meaning of karst was just a “bare 

limestone landscape”. And what was the prospect for the future: “General-Domänen- 

Inspektor und Forstakademie-Direktor a.D.” reckoned that the surface of forest diminishes 

every year for 1 % in the frame of the Austrian Littoral and Dalmatia. 

3. Natural vegetation of the Dinaric Karst 

Available data suggest that originally about 90 % of Dinaric Karst were covered by a forest, 

in some areas even more. Many temperate taxa appear to have survived in the region 

during the last glacial in low but persistent population. A greater diversity of taxa existed in 

the mid to high altitude sites probably where the climate was more humid. At the 

lateglacial/Holocene transition many tree taxa expanded simultaneously. Changes in the 

composition of the early Holocene woodland included a change in the forest dominants 

between 8 000 and 7 000 BP, and the appearance and increase of Carpinus orient./Ostrya, 

Abies, Carpinus betulus and Fagus between 7 500 and 5 000 BP (Willis, 1994). In the northwestern 

part of Dinaric Karst mixed oak forest (Quercetum mixtum) prevailed during the first 

postglacial warm (Boreal) period. During the Atlantic period fir and beech (Abieti-fagetum) 

had already developed as a climax forest. Dinaric Mountains’ flora belongs to both the 

Mediterranean and the Euro-Siberian-North American phytoregions. In the Mediterranean 

region the main forest type includes Mediterranean (evergreen) oak (Quercus ilex) and Black 

Dalmatian pine (Pinus nigra dalmatica) while Dinaric fir and beech forest (Abieti-fagetum 

dinaricum) prevails in the interior. Black pine (Pinus nigra) is indigenous to some small karst 

areas of Slovenia (Culiberg et al., 1997; Šercelj, 1996) (Fig. 12). Development of the present 

day landscape started at approximately 4 500 BP with the onset of anthropogenic 

disturbance. Clearance resulted in increase of open ground herbaceous types with grasses 

(Willis, 1994).


Fig. 12. Pollen diagram, showing great change around the year 1 000 BC as well as around 1 

000 AD (Culiberg et al., 1997). 

4. Forest protection and reforestation of the Dinaric Karst 

As it was seen the main cause of the deforestation of the Dinaric Karst was man. But it does 

not mean that nobody cared for forests and did not see their importance. Officials, 

administrators, town councils realized at an early stage already, that deforestation could be 

a great threat and even an economic catastrophe, and a disaster for the everyday life of 

people. Many attempts of protecting and safeguarding forests by administrative, economic, 

penal and other measures are known from history, sometimes very strict. In spite of them 

deforestation reached alarming proportions. 

From the 12 th century on various Acts and documents are known, attempting to regulate 

tree cutting, protection of forest and reforestation. The town of Trieste, an important port at 

the foot of the plateau Kras introduced such an act in 1150 already. Similar to Trieste, who 

has no (more) forest in his hinterland, other towns of the Adriatic coast and islands, such as 

Korčula, Trogir, Dubrovnik, Skradin, Hvar, and Poljica, regulated the exploitation and 

protection of forest by town statutes enacted between 1214 and 1444. Venice edited a forest 

act in 1452 (Fig. 13) while for Istria Peninsula, Friuli and Karst the “Waldordnung” (the 

Forest Act) from 1541 is well known (Gašperšič & Winkler, 1986). 

Despite the concern for forests these acts show that at that time the deforestation by cutting, 

clearing and burning had already seriously started. However in some presently barren 

places, forests still existed (Horvat, 1957). That the matter was taken seriously shows the 

example of Trieste, where an armed guard had to be organised by the town to protect the 

local forest in 1583 (Guttenberg, 1881) (Fig. 14). The said Acts commonly included a ban on 

goats, or a complete ban on grazing in the forest. In 1764 the edict was issued banning the 

free pasture of goats in the Military Croatia. Thousands of goats have been sold or 

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slaughtered but at the end of the century there were again about 64 000 of them in Karlovac 

district only. In 1771 the Court office in Vienna issued a ban on the practice of transhumance 

across the karst of Carniola, which involved winter sheep grazing in Istria Peninsula 

lowland along the Adriatic coast, and summer grazing on the Kras and on higher karst 

plateaus (Nanos, Snežnik). Despite the interdiction a long distance transhumance, from 

Bosnian karst plateaus along the Adriatic coast to the Slovenian part of the Dinaric Karst 

and back to Bosnia along the Sava River valley, was practised from time to time until the 

second half of the last century. The last time it happened the police put the flock and the 

shepherds on the train and they were send back to Bosnia. The administration repeatedly 

issued acts on ban of goats: 1844 in Istria, 1870 in Gorizia, and 1874 in Trieste. The Republic 

of Slovenia (in the frame of Yugoslavia) banned goats by a decree in 1952, and finally 

Yugoslavia banned goat breeding, except in stables, in 1954 (Wessely, 1976; Papež, 1991). 

Fig. 13. Venice “Waldordnung” (Forest Act) of 1452 (Gašperšič & Winkler, 1986).


Fig. 14. The first black pine plantations of Kras plateau needed an armed guard (Anonym, 

2001). 

Even the Turkish threat sometimes had results that were favourable for forest conservation. 

To maintain the protection zone of dense forest against the advance and passage of irregular 

Turkish raiding bands, it was forbidden to touch any tree or bush in the forest within a few 

hours ride of the south-eastern border of Carniola. 

But all the administrative measures were of little help and there was less and less forest. 

Early already some specialists suggested the reforestation. In his book “Hydrographical 

letters from Carniola” (Kranjska, the Austrian hereditary land) which is in fact a description 

of Carniolian karst, T. Gruber proposed reforestation as the most effective measure against 

the wind “burja”, which caused quite an important damage and transport obstacle, 

especially in winter (Gruber, 1781). On the Istria Peninsula and in other parts of Dinaric 

coast under the Venetian Republic, all the oak forests and all oak trees everywhere were 

reserved for its shipyards, the Arsenal. The owner of the forest has the right to use only 

those forest products, trees, which the Arsenal did not need. Sentences were severe, 

including capital punishment. Of special value were naturally curved trunks of Quercus 

pubescens. In 1815, after the Vienna Congress, former Venetian territories of Istria and 

northern Adriatic Sea belonged to Austria. Soon after, in 1819, Austria had cancelled the 

navy oak reserve. The main forest keeper and navy manager of forest in Carniola, J. Ressel, 

realised that soon there would not be enough timber for navy needs. He proposed another 

type of navy forest reserve, different from the Venetian one, based upon constraint and 

punishment. He named his system “buying reserve” – the owner would get paid in 

advance, for each 10-years addition through growth, before cutting down a tree. Later, in 

1842 Ressel proposed “Die Wiederbewalderung der Gemainde Gründe Istriens” (The new 

reforestation of the commons of Istria) project. He tried to achieve reforestation by planting 

acorns, stating that the oak to be the best, but it proved unsuccessful (Piškorić, 1993). Later, 

in 1852 he proposed a similar plan for the part of Kras belonging to the towns of Trieste and 

Gorizia (Fig. 15). 

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Fig. 15. Josef Ressel (1793-1857) and the first page of his “History of E.&K. Navy Forests” of 

1855 (Gašperšič & Winkler, 1986). 

Meanwhile, the Ministry of Agriculture began to stimulate and finance reforestation, in 

response to complaints from karst communities and professionals, from the forestry bodies 

and J. Ressel especially, about the rigours of life on a barren rock landscape. A detailed 

study of such a life was made by Wessely (1876). In 1857, the first railway which crossed the 

Karst was completed, that is the connection of Vienna to Trieste. There were great problems 

protecting the railroad against the strong northeast wind called Burja in Slovene, Bura in 

Croatian, and Bora in Italian language, probably meaning or coming from “Borealis”. The 

major obstacle were snowdrifts formed by this wind. The Southern Railways Company had 

to build high drystone walls along the route of the railway; but they found it cheaper and 

better to plant trees along it; so they support and even join the reforestation programme. In 

1859 the first successful reforestation occurred, using young black pine (Pinus nigra var. 

austriaca) seedlings on the plateau of Kras at Bazovica in the vicinity of Trieste, by J. Koller. 

The first successes boosted confidence and the activity spread to other parts of the karst 

within and outside of the Austrian lands (Fig. 16). 

In 1885 finally the “Reforestation Act for Carniola (=Kranjska)” was issued, regulating the 

entire process, which included major work and investment as tree nurseries, wall 

construction, land preparation, and seedlings planting. On the karst terrains there were 

entire villages where reforestation provided the main or perhaps the only income. 

Everybody, men, women and children, was involved in these works: men were digging 

holes; women were planting seedlings, while children were bringing water and watering 

seedlings (Goll, 1898) (Fig. 17). Publications showing their success were published on 

different occasions (Goll, 1898). From all over Europe specialists came to Kras to see this 

successful reforestation, the senator Marquis de Campo and forestry engineer don Carlos de 

Mazeredo from Spain as an example (Rubbia, 1912).


Fig. 16. The first black pine plantations along the railway towards Trieste (Goll, 1898). 

Fig. 17. Managing of black pine plantation above the Trieste in 1905 (Anonym, 2001). 

Different activities were taking place in other parts of the Dinaric Karst too, the difference 

being due to local political and administrative particularities. There are also differences in 

the accessibility of documents and of publications. For some regions, in the frame of Austria 

for example, there is quite a lot of published works, technical documentation even, while for 

the parts which were in the frame of the Ottoman Empire, documentation is maybe more 

scarce and in any case more difficult to find and more difficult to understand, if written in 

Turkish language and in Arabic script. Under Hungarian administration Croatia was 

divided into Military Zone or Military Croatia (along the border with Turkish Empire) and 

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Provincial (Civil) Croatia. Due to Napoleon’s ultimatum, Doge Ludovico Manin 

surrendered unconditionally on May 12, 1797 and abdicated, while the Major Council 

declared the end of the Venetian Republic. The Illyrian Provinces were created by the Treaty 

of Schönbrunn in 1809, and embraced a big part of the Dinaric Karst, beside Carniola the 

former Venetian territories along the Adriatic coast. Bosnia and Herzegovina remained in 

the Ottoman Empire until 1878 when it was annexed by Austria. In 1918, when the Kingdom 

of Serbs, Croats and Slovenes, later the Kingdom of Yugoslavia, was founded, Bosnia and 

Herzegovina as well as the great majority of the Dinaric Karst were included in it. 

Fig. 18. The title page of the Venetian Forest Act for Istria of 1777 (Gašperšič & Winkler, 1986). 

In 1871 when the military organisation of Croatia ended, the Austro-Hungarian Emperor 

wrote: “… the income of the sale of wood from the country’s state’s forests has to be used 

for investing, especially for the reforestation of karst.” (Wessely, 1876). This was followed by 

the 1864 “Waldordnung” (The Forest Act) of the Military Croatia aimed at planting the 

barren land with a beech and a fir. Interesting illustration is the example of so-called 

“Laudonov gaj” (General Laudon’s Wood) at Krbavsko polje in the Lika region. The western 

part of the polje was covered by moving sands. The later famous Austrian 

“Generalfeldwachtmeister” (major-general) G. E. Laudon served there, at Bunić in Karlovac 

region in 1740s. After becoming the major-general, he ordered to plant the forest there to fix 

the sands and to prevent damage to agriculture. Under the Military administration about


700 ha was planted with black pine and oak, in the form of a military formation as said. In 

1965, it was proclaimed a forest reserve (Jaić, 2011). 

Reforestation of the karst pastures in the hinterland of the port of Rijeka began in 1857 

(Horvat, 1957). In Dalmatia, they tried to start reforestation under the Venetian regime 

already, by the so-called Grimani Act of 1756, but due to bad administration and corrupt 

officials the work did not even start (Fig. 18). During the time of the French Illyrian 

Provinces (1809-1813) each commune had to plant a “sacred wood” (bois sacré), but the 

provinces’ period was too short to achieve the desired results. Successful reforestation began 

in the 1880s following the example of Trieste. Yet in the countries of the Ottoman Empire, 

there are no Acts and no activities for protection of the forest or for reforestation known. 

From 1918 practically all of the Dinaric Karst was within the borders of the Kingdom of 

Yugoslavia. Not earlier than in 1929 the State’s Act on forest was issued with the essential 

prescription: “…all deforested lands especially on karst has to be set apart in the period of 

10 years with the aim to be afforested as soon as possible. Reforestation has to be realized in 

50 years…”. Even the state did not last 50 years and such a decree was impossible to 

implement, so it survived on paper only (Djikić, 1957). Systematic reforestation slowly 

spread over the entire of the Dinaric Karst and continued into the 1950s. In the socialist 

Yugoslavia, immediately after the end of the 2 nd World War, massive actions of reforestation 

in the form of “Mladinske delovne brigade” (Youth work brigade), a form of a voluntary 

youth work were organized in the form of summer camps. On the karst of Slovenia one of 

the last actions was the reforestation work in 1950s on the Vremščica Mountain between 

Postojna and Trieste. From these times on, reforestation is mainly the duty of the forestry 

organization and of the owners of the forests. 

Fig. 19. On a karst plateau, a pasture started to be overgrown by trees (photo K. Kranjc). 

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Defforestation, degradation and in some cases desertification even of Dinaric Karst started 

early in prehistoric times. They reached the peak in the second half of the 18 th and in the first 

half of the 19 th century. In any case, the reason was human factor, the economy with no 

attitude to sustainability at all. During the last thousand years it is possible to see the 

attempts to prevent the forest or even to meliorate, to reforest degraded lands. By the 

middle of the 19 th century such attempts were mainly unsuccessful, but from that time on 

the situation started to change rapidly. At the beginning, reforestation was a sort of massactivity 

while nowadays other factors join it. The general perception of the importance of a 

forest and of the sustainability helped a lot, but also the change of economy and activity of 

the population of the Dinaric Karst, the decline of the agriculture emphasized. Maybe the 

Dinaric Karst is turning to the other extreme – to be overgrown (Fig. 19). In Slovenia, on the 

Kras particularly specialists as well as laymen started to ask: how to prevent the Kras from 

becoming overgrown? “How to reasonably stop the overgrowing of Kras” is the title of a 

round table organized by the review “Kras” at Nova Gorica in 1997. The discussion also 

showed that foresters suggested replacing slowly black pine with oak (Mlinšek 1993). 

The foresters also suggested that Kras should be a field experimental laboratory of 

international importance to study the revitalization of a completely degraded landscape. 

Especially important should be the study of the revitalization of thermophile associations, 

which are the most affected and at the same time the most suppressed and neglected by the 

World’s public (Mlinšek, 1993). 

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Azarov, E. (1994). Črni bor na Krasu. Kras, No. 4, (1994), pp. 18-21, ISSN 1318-3257 

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Cvijić, J. (1966). Balkansko poluostrvo i južnoslovenske zemlje, Zavod za izdavanje učbenika SR 

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Cvijić, J. (2000). Karst, geografska monografija. In: J. Cvijić Sabrana dela. Knjiga I, Stevanović, 

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254-198-9, Postojna


Landslides Caused Deforestation 

6 

Diandong Ren 1, Lance M. Leslie 2 and Qingyun Duan 3 

1 Australian Sustainability Institute, Curtin University, Perth, 

2 School of Meteorology, University of Oklahoma, Norman, 

3 Beijing Normal University 

1 Australia 

2 USA 

3 People's Republic of China 

This study investigates landslide caused disturbances to ecosystem-vegetation burial and 

overturning of soil horizons which can accelerate vegetation loss, or even lead to complete 

deforestation of the landscape. A summary is presented of the historical development and 

recent advances in understanding and prediction of landslides/debris flows, which are 

major global natural hazards that have caused great loss of life and damage to property and 

infrastructure. The focus here is on rainfall triggered landslides/debris flows, and their 

deforestation effects. Debris flows have large destructive power because the solid and fluid 

forces interact within the sliding mass (Iverson 1997): the embedded boulders can exert 

great impulsive loads on objects they encounter; while the high fluid contents make debris 

flows travel long distances even in channels with modest slopes and therefore can inundate 

vast areas. Thus the whole spectrum of debris flows has deforestation effects (even small 

scale debris flows can denude vegetation). The first two sections of this chapter are 

concerned, respectively, with understanding ecosystems in terms of their soil and vegetation 

types, especially in regions that have sloping terrain. The emphasis is on the impact of 

landslides on deforestation, and this aspect has not yet been studied extensively. The third 

section focuses on the dynamics of landslides and provides a comprehensive description of 

their lifecycles, and the fourth section discusses the development and application of 

empirical and descriptive landslide models. Such models have contributed to knowledge of 

storm-triggered shallow landslides/debris flows. However, the deficiencies of empirical 

models are well-known and an alternative modelling technique, pioneered by the first 

author, is described in detail. This modeling approach is to solve the fully three 

dimensional, Navier-Stokes, and to that end a multi-rheological scalable, extensible geofluid 

model modeling system, known as SEGMENT, was developed. When coupled with a 

landslide model the system is called SEGMENT-landslide. The SEGMENT-landslide model 

has been used extensively in a predictive mode for both relatively short lead decadal times, 

and for century long predictions of landslides, in several very different locations. Here, the 

performance of SEGMENT-landslide is assessed for the Yangjiashan creeping slope in 

China, in predictive mode for the decade 2010-2109. Investigation of the impact of possible 

future precipitation morphological changes over this region utilizes 21st century simulations

96 


from 17 Climate General Circulation Models. SEGMENT-landslide performed encouraging 

well in both hindcast and forecast mode, providing prior and future vulnerability of the 

Yangjiashan creeping slope to landslides and rainfall rate thresholds for sliding to occur. 

SEGMENT-landslide also has been used to estimate landslide potential in other vulnerable 

regions, including southern California. Future work will focus on the influence of a future 

warming on the occurrence of landslides. Knowledge of upcoming changes in precipitation 

morphology is critical for predicting storm-triggered landslides and desertification. 

Advanced dynamical models, such as SEGMENT-landslide, which have a physical basis, are 

needed to supplement documentation of landslide occurrence. As there are biogeochemical 

submodels coupled in the SEGMENT system, it can be used to investigate significant 

environmental consequences of landslides, notably deforestation. 

2. Ecosystem of sloping terrain, soil and vegetation 

A unique feature of terrestrial ecosystems is that vegetation acquires its resources from two 

very different environments; air (for CO2) and soil (for inorganic minerals of nutrients). 

Landslides cause disturbances to ecosystem productivity by displacing the soil mantle. In 

addition to be the medium in which most decomposer organisms and many animals live, 

the physical soil matrix provides a source of water and nutrients to plants and microbes and 

is the physical support system in which terrestrial vegetation is rooted. For these reasons, 

landslide displacement of the soil mantle has severe ecosystem consequences. 

Sloping terrain creates unique patterns of microclimates through surface energy budgets, 

hydrology and availability of nutrients. For example, slopes facing the equator receiving 

more solar radiation than opposing slopes and hence usually have warmer and drier 

conditions. In colder or moister climates, the warmer microclimate of the equator facing 

slopes provides conditions that enhance productivity, decomposition and other ecosystem 

processes (including the formation of soils), whereas in dry climates, the low moisture levels 

on these slopes limits such production. Further, microclimatic variations associated with 

slope and aspect allows stands of an ecosystem type to exist hundreds of kilometres beyond 

its major zone of distribution. These outlier populations are important sources of colonizing 

individuals during times of rapid climate change and are therefore important in 

understanding species migration and the long-term dynamics of ecosystems. Topography 

also influences climate through drainage of cold, dense air in the form of katabatic winds, 

forming strong near surface air temperature inversions. Inversions tend to be strongest at 

night and during winter, when there is less warming of the surface, either in the form of 

shortwave solar radiation or longwave thermal from clouds, and hence insufficient 

convective mixing to remove inversions. Inversions are climatologically important because 

they increase the seasonal and diurnal temperature extremes experienced by ecosystems in 

low-lying areas. In cool climates, inversions greatly reduce the length of the frost-free 

growing season. The third aspect of the impact of sloping surfaces on ecosystems is surface 

hydrology. The surface runoffs on sloped surfaces are much larger than over flat terrain. For 

dry climates, this places severe water-limitations on production. Before addressing the slope 

effects on nutrients, we will review the soil formation process. 

Under a given climatic regime, soil properties are the major control over ecosystem 

processes. Soils are the locality where geological and biological processes intersect. Soils 

mediate many of the key reactions in the giant global reduction-oxidation cycles of carbon, 

nitrogen and sulphur, and provide essential resources to biological processes that drive these


cycles. As the intersection of the bio-, geo- and chemistry in biogeochemistry, soils play such 

an integral role in ecosystem processes that it is impossible to separate the study of soils from 

that of ecosystem processes. Soils are formed from weathered metamorphic rocks. The 

presence of living organisms accelerates the soil formation processes. Water is a pathway for 

nutrients entering an ecosystem and also is crucial in determining whether the products of 

weathering accumulate or are lost from a soil, especially the soluble minerals. Top soils 

generally are more fertile because weathering rates generally are larger at the surface. Also, 

leaching processes tend to transfer soluble ions (e.g., chelated complexes of organic 

compounds, iron or aluminium ions from precipitation or released in the weathering of upper 

layers soil) downwards. During the downward movement, they can react with ions 

encountered at depth under new chemical environments (e.g., increased pH value), or may 

precipitate out the system when dehydration occurs (e.g., water is evaporated in semi-arid or 

arid climate zones). As a consequence the levels of silica and base cations in the secondary 

minerals usually increase with depth and result in a nutrient poor deeper soil horizon. As iron 

and aluminium ions soluted in the soil water move downwards, slight changes in ionic 

content and the microbial breakdown of the organic matter both can cause the metal ions to 

precipitate as oxides. The deeper soil horizon containing iron-rich minerals usually are 

hardened irreversibly. These layers can impede water drainage and root growth. This is the 

case for tropical iron-rich soils, and similar processes exist for calcium (or magnesium) soils of 

arid and semiarid temperate climate zones. The hard calcic horizon at depth is formed when 

calcium carbonate precipitation occurs under conditions of increased pH, or under saturation 

concentrations of carbonate with evaporation of soil moisture. If debris flows remove the 

fertile top soils, the deeper horizon is exposed and this layer has poor water retention ability 

and is nutrient poor. Moreover, roots cannot develop within it and thus cannot support regrowth. 

Even for the depositional alluvial fans, the exposed deep soil and deep buried top soil 

forms a nutrient sink for the ecosystem, as the majority of the roots can use nutrients only in 

the upper one or two meters. 

3. Landslides are double-edged swords 

The surface of the earth, both land and beneath the oceans, is continually being modified by 

mass movements operating in response to gravitational forces. In this sense, landslides 

reduce the hill slopes to stable angles. They can assume the form of rockfalls, slumps and 

slides, and debris flows (Cruden 1991). For this study, the term landslide includes 

downslope movement over a variety of scales and velocities and also includes those related 

to vegetation cover. Rainstorms always are the prime cause. 

Topography influences soil through its effects on climate and the differential transport of 

fine soil particles (Amundson and Jenny 1997). Characteristics such as soil depth, texture, 

and mineral content vary with hillslope position. Erosion processes, such as landslides, 

preferentially move fine-grained materials downslope and deposit them at lower locations. 

Depositional areas at the base of slopes and in valley bottoms therefore tend to have deep 

fine-textured soils with a high soil organic content and high water-holding capacity. These 

areas also supply more soil resources (water and nutrients) to plant roots and microbes and 

provide greater physical stability than do higher slope positions. For these reasons alluvial 

fans at valley bottoms typically exhibit higher rates of most ecosystem processes than do 

ridges or shoulders of slopes. In brief, soils in lower slope positions have greater soil 

moisture, more soil organic matter content, and higher rates of nitrogen mineralization and 

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98 


gaseous losses than upslope soils (Matson et al. 1991). Therefore, on geological timescales, 

landslides help produce stable land suitable for agricultural and habitation (for tropical 

islands, also tourism) and provide materials that form fertile plains and valleys, beaches, 

and barrier islands. However, on a scale typical of a human life span, the benefits accruing 

from landslides are overshadowed by their destructive characteristics; they are hazards that 

should be understood and mitigated as much as possible. 

While it is reasonable to generalize that landslide activity is important, landslide impact on 

deforestation, on ecosystems in general, have not been well addressed. The following 

comments are not an exhaustive review but are intended to indicate the relevance of 

landslides to deforestation. 

Fig. 1. Global distribution of Net Primary Production (NPP, color shading, in g/m 2/yr) and 

occurrence (red crosses) of storm-triggered landslides (2003-2007). 

The main geological hazards of volcanic activity, earthquakes and landslides are commonly, 

but not entirely, associated with processes occurring in areas near subduction zones. It is 

there that elevations are high enough to generate instability and sources of hazardous 

agents are abundant. This explains why the landslides belt coincides with the Earth’s major 

earthquake belts. The Himalayas, for example is a hot spot (Figure 1). Figure 1 shows the 

primary landslides that are deeper than 2.5 m thick and contain >10 5 m 3 of solid material, 

enough to cause vegetation damages. Plotted are the cases for years 2003-2007. 

Landslides are one type of geological hazard. Geological hazards become so only where 

population, services or structures are at risk. In this sense, although northwestern China is a 

hot spot for landslides, the low population, by itself, reduces the hazard to a vanishingly 

small likelihood. In contrast, India and Indonesia both have large populations under the 

threat of storm-triggered landslides. It is reported (BNBP, 2009) that during the period 1998-


2007, 569 landslide events took place in Indonesia which caused 1326 fatilities and around 

1500 people missing, around 170,000 people evacuated. In the humid tropics, soil loss after 

landslides are also very high as soil will be broken and exposed to rainfall wash-resulting in 

increased sediment load in the streams and causing flooding in low lying areas. As the 

population of vulnerable regions increases, previously unoccupied alluvial fans are used for 

habitation (e.g., the Zhouqu county in China) and landslide events therefore have a great 

potential to impact human settlements or activities. 

The state of California in the USA is a locality where the combined effects of earthquakes 

and landslides have been documented extensively. The precipitation of this region, being 

regularly affected by ENSO and potentially also sensitive to climate change, has a large 

uncertainty in the future occurrence of storm-triggered landslides (Ren et al. 2011b). 

From Figure 1, there are fewer landslide hazards for regions with NPP

100 


After the soil is moistened, the cohesion between soil and the root surface is reduced greatly 

(to negligible strengths 50% granular material concentration, especially when boulders are 

entrained. Panels (b), (c) and (d) illustrate a full life cycle of the debris flow: (b) initiation, (c) 

sliding, and (d) cessation. Precipitation generated surface runoff washes the fine-grained 

material downslope. Because the steepness of the slope is graded (steep at top and gentler at 

the toe), granular material (saturated soils) are thicker toward the toe of the slope. As the 

sliding material run downslope, it entrains the pebbles and small stones (granular material) at 

the bottom. This will significantly change its rheological properties as it becomes drier and 

more viscous. The sliding material eventually ceases at places with gentle slopes, or where the 

slope angle reverses. In the lowest inset panel, the cracked rice paddy is indicative of effects of 

roots on holding the soil particles together: almost all rice bundles are located at the centers of 

the cracked cells. Major cracks rarely run directly across a bundle of rice sprouts.


Water typically leaves a landscape by one of several pathways: groundwater flow, shallow 

subsurface flow, or overland flow (when precipitation rate exceeds infiltration rate). The 

relative importance of these pathways is strongly influenced by topography, vegetation, and 

material properties such as the hydraulic conductivity of soils. Drainage (ground water) and 

shallow subsurface flow dissolve and remove ions and small particles that cement large soil 

particles when being dry. Overland flow (runoff) causes erosion primarily by surface sheet 

wash, rills and rain splash. Runoff is strong for bared ground (e.g., arid soil-mantled 

landscapes) or disturbed ground (by soil animals or human construction). Runoff of 3 mm/s 

suffices to suspend clay and silt particles and move them downhill (Selby 1993). As water 

collects in gullies, its velocity, and therefore erosion potential, increases. For clear (not 

1/2 1/3 

0.27 

dense) debris flow, erosion potential can be approximated as a C1gh D/ D0 

, 

where C1 is a coefficient depending on vegetation condition, α is slope, D is particle size and 

D0 is a reference particle size, h is runoff water depth, and g is gravity acceleration. 

Vegetation and litter layers level/spread out the peak of runoff and increase infiltration and 

drainage through reducing velocity with which raindrop hit the soil, thereby preventing 

surface compaction, and through the inter-connected channels webbed by roots and soil 

animals. For progressive bulking debris flows, these are apparent preventative features. To 

evaluate the ecosystem consequence of landslides, we also need to know the size of a 

landslide. It is apparent that the disturbance of landslides to ecosystem is through 

displacement of the fertile top soil layer. Whether or not the existing vegetation can be 

destroyed depends on the severity of the landslide. The severity of a landslide depends on 

its size, velocity and material composition. The first two points are obvious. The 

composition of the sliding material is important for landslide destructive potential primarily 

because the size of granular material. If the debris flow entrained big boulders during its 

downslope movement, the destructive potential to trees and buildings will be much greater 

than similar debris flows containing only sands and silts. The drag of debris flows to 

U 

obstructions can be expressed as eff Z 

 

, where U is flow speed, eff is effective dynamic 

 

viscosity (Pa•s). For a fast flow debris flows (3 m/s) and 50% solid material sludge of about 

2 meter depth, the stress exerted on obstacles is on the order of 105 Pa. When a large boulder 

encounters an obstacle, the energy is transformed primarily in the form of longitudinal 

waves (e.g., similar to acoustic waves in the air). The stress (pressure) impact on the obstacle 

is 0.5 bUV , with V the sound wave speed in solids (obstacle medium, at standard pressure 

of 1 atmosphere and temperature 25 °C, about 4500 m/s for bridges and concrete-steel 

buildings), and b 

is the boulder’s density. In contrast to turbid sludges, a boulder of about 2 

m 2 cross-sectional area (assuming this also is the area of simultaneous contact with an 

obstacle), of bulk density of 2.7×10 3 kg/m 3, can exert 10 8 pa pulse pressure, which is three 

orders of magnitude larger than the sludge. 

Figure 2 is a conceptual sketch of storm-triggered landslides/debris flows. Clearly there is 

usually a large (one order of magnitude larger than the channelled, streaming, concentrated 

flows of the dense mud) collection region, providing solid material for the downstream area. 

Along the flow, the sliding material is denser and viscosity is larger. At the spread region, 

because of the gained kinetic energy, it will not stop even the slope angle is less than the 

repose angle. In actuality, it continues to spread until the speed is reduced to zero, usually at 

bed slopes much smaller than the stable repose angle. In Figure 2, panel (a) is a plane view 

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102 


of the mass collecting region, panels (b) to (d) illustrate the life cycle of a debris flow. We see 

that the solid material is collected by surface runoff from a much larger area than seen with 

naked eye as mudslide. The portion with direct ecosystem damage primarily is the region 

with concentrated stream flows, which typically constitutes only about 2% of the entire 

collecting area. 

The August 8, 2010 Zhouqu landslide is a characteristic ‘progressive bulking’ type of debris 

flow. On August 7, Zhouqu’s Beishan slope received over 80 cm of rain within two hours, 

leading to widespread shallow landslides and debris flow generation. The town of Zhouqu 

is built on the sloping surfaces of previous debris fans formed at the base of steep rocky 

hillslopes. 

The debris fan at the mouth of the rocky creek, Sanyanyu, is usually stable because of the 

elaborated root system laced through the stony, loose soil. Prior to the intense cloudbursts, 

there was a long period of drought in the region and the ground surface was cracked, 

especially the mid-slope (1200-2500 m elevation range). In addition to causing rock falls 

(providing more solid sliding material), the Wenchuan earthquake, barely two years earlier, 

deepened the bedrock crevasses. Consequently, the drought stressed vegetation cover had 

little ability to intercept the rainfall and dampen the peak runoff. Much of the runoff water was 

channelled directly into the crevasses and deepened the shear zone. As the runoff water 

flowed down slope, it progressively increased the solid material contents (as a result of 

entrainment) and also its ability to further entrain. This positive feedback continues and at 

elevation of 2500 m, large boulders of 1 meter diameter can be picked up by the turbid mud. 

As water filled the crevasses, patches of soil layers up to 4 meters were made unstable and 

were scoured out and descended along rock creeks. The scoured was generally still dry 

(actually only the surface several centimetres are saturated). As it ran down the steep canyon, 

it further picked up debris as it travelled at ~5 m/s. By the time it reached lower, gentler slopes 

(~1200 m elevation), its mass had increased by one order of magnitude, but the overall water 

content was still low. Some parts (the finer granular components) simply came to a halt. The 

coarse granular material and boulders continued their motion and smashed into constructions 

at the mouth of the rocky creek. Subsequently, material disturbed by the slide, including wood 

and constructions, was washed by following slides and moved down the Sanyanyu creek, 

depositing debris all the way to the Bailongjiang River. This tragedy, and others in the 

previous rainy seasons following the Wenchuan earthquake, led to the awareness of the need 

to develop warning system, compile hazard maps, and adopt new legislation concerning forest 

practice and soil reservation, for regions on active faults. 

5. A recently developed landslide dynamics model: SEGMENT-landslide 

Because of their frequent occurrence, storm-triggered shallow landslides/debris flows have 

been actively studied. Empirical and descriptive landslide models have contributed much to 

the public awareness of landslide hazards and have led to valuable accumulated experience 

in identifying the key causal factors (Caine 1980; Cannon and Ellen 1985; Sirangelo and 

Versace 1996; Godt et al. 2006). Caine (1980) proposed the seminal rainfall intensity-duration 

threshold line, above which shallow landslides may occur (ID method hence forth), based 

on 73 landslides worldwide. 

In intensity-duration thresholds, a dataset consisting of rainfall intensity (I, mm/day) and 

rainfall duration (D, hr) of landslide events is first made/prepared. A scatter graph is then 

generated with rainfall duration as x-axis and rainfall intensity as y-axis. The equation of


rainfall threshold is a power-law curve that fit the points in the scatter plot (actually a lower 

envelope in that is a point lies to the right upper of the curve, landslides may occur), usually 

b 

take the form I aD 

, where a and b are positive constants that vary with soil, vegetation 

and land use. 

Godt et al. (2006) suggested that landslide-triggering rainfall must be considered in terms of 

its relationship with antecedent rainfall. For example, a heavy rainfall event within a dry 

period is not likely to trigger shallow landslides, while the opposite is true for lighter 

rainfall within a wet period. As it directly affects soil moisture conditions, Godt et al. (2006) 

correctly claim that antecedent rainfall must be included in an empirical model’s assessing 

of a rainfall’s potential in causing landslide. Godt et al. (2006) therefore is a significant 

improvement over Caine’s (1980) seminal rainfall intensity-duration threshold line 

approach. The antecedent rainfall index is usually defined as a red noise of the accumulative 

rainfall amount 3 days (for tropics) and 7 days (temperate climate zone) prior the landslide 

event. Recent empirical methods also compile many soil hydrological parameters by using 

water-balance models with little physical basis but are convenient for estimating soil 

moisture conditions. For example, Godt et al. (2006) uses a detailed assessment of rainfall 

triggering conditions, hill slope hydrologic properties, soil mechanical properties, and slope 

stability analyses. The accumulation of sliding material is a slow process (either rockfalls or 

aeolian processes or damaging erosion processes from weathering) compared with the 

sliding. Previous sliding will reshape the sliding material profile and may even completely 

remove the sliding layer. These will increase the stability of the slope and a similar rainfall 

amount may cause sliding on a reduced scale, or not at all. Thus, the empirical parameters (a 

and b) in the ID approach vary not only spatially but also temporally. In this sense, all 

previous ID approaches still lack the important time varying features. 

A synthetic consideration of preparatory and triggering factors, however, demands a more 

comprehensive modeling of the physical processes involved in landslides (Costa, 1984; 

Iverson, 1997). The overview by Iverson (1997) suggested several criteria for dynamic 

landslide models, including that a model should be capable of simulating the full startmovement-spread-cessation 

cycle of the detached material, and should cover a wide 

spectrum of debris flows. With continued growth and expansion of human population, raintriggered 

shallow landslides increasingly result in loss of life and significant economic cost. 

From an ecological viewpoint, landslides are an important factor in desertification over 

mountainous regions because they are very effective in transferring biomass from live to 

dead respiring pools (Ren et al., 2009). 

Along these lines of walking, there are physically-based slope stability models to simulate 

the transient dynamical response of pore pressure to spatiotemporal variability of rainfall 

(e.g., Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Analysis— 

TRIGRS, Baum et al. 2008); commercially available numerical modeling codes for 

geotechnical analysis of soil, rock and structural support in three dimensions (e.g., FLAC- 

3D, www.itascacg.com/flac3d), and fully three dimensional, full Navier-Stokes and multirheological 

modeling systems such as the scalable, extensible geo-fluid model, known as 

SEGMENT (Ren et al., 2008; Ren et al., 2009, Ren et al. 2010; Ren et al. 2011a,b). 

Slope stability models are based on the following reasoning: On a sloping surface, the 

gravitational force can be partitioned into a component normal to the slope (Fn), 

contributing to friction that resists sliding erosion, and a component parallel to the slope (Fp) 

that promotes sliding. A stability parameter, S, is defined as SF / F , where is the 

n p 

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104 


internal friction coefficient. This is the general form of S, but there are many specific forms, 

based on the fact that, landslides, as the movement of a mass of rock, debris or earth 

downslope (Cruden 1991; Dai et al. 2002), occurs when shear stress (Fp) is higher than shear 

strength (i.e., when S


 

0.5 

as I d/( S/ 

) , where d is particle diameter and s is the particle density. Soil 

e s 

moisture enhancement factor on viscosity is assumed varying according a sigmoid curve 

formally as Eq. (9) of Sidle (1992) but with the time decay term replaced by relative 

saturation. 

As derivatived from Eq. (1), the prognostic equation for surface elevation, h(x,y), is 

h 

VH hw 

top 

top 

t 

0 

Where X indicates evaluation at the free surface elevation. In the case with slope 

top 

movements, Eq. (4) is solved regularly to update the sliding material geometry. The w terms 

may include sedimentation rate and entrainment rate. Once the material is entrained inside 

the sliding material, it changes the rheological property of the medium and is advected 

within the sliding material. 

The viscous term in Eq. (2) implies an energy conversion from kinetic energy to heat. To 

make a full closure of energy, we need the following thermal equation: 

T 2 2 

c ( V) T kT eff 

(5) 

t 

 

Where c is heat capacity (J/kg/K), T is temperature (K), κ is thermal conductivity (W/K/m), 

and σeff is effective stress (Pa). The last term is ‘strain heating’, which is the converting of 

work done by gravity into heat affecting the sliding material by changing viscosity or 

causing a phase change. Above is the landslide component of SEGMENT-landslides. Other 

components are shown in Fig. 3. 

To describe the full start-slide-stop cycle, we boundle internal stress tensor as 

E 

105 

C S 

(6) 

, where gh 

is the gravitational potential, C is effective cohesion, and E the pressure 

perturbation caused by earthquake or human-induced disturbances at that location. µ< 

µ1=tgф, with ф granular repose angle. For conditions with ground water, hydrostatic 

pressure is usually included in S for convenience. The extreme values of the middle two 

terms on the right hand side are the yielding strength (shear strength) of the sliding 

material f C Sf(with 

subscript ‘f’ means failure). Note that, in addition to be soil 

moisture and soil chemical components dependent, C and are functions of shear stress 

(e.g., Schofield 2006). For unfractured bedrock, C is the dominant term, usually three orders 

of magnitudes larger than the remaining three terms combined together. For most of the soil 

(except pure sandy soil), cohesion and internal friction are both important in maintaining 

stability slopes. For fractured rocks and sandy soils, the internal friction becomes dominant 

term (not necessarily larger than gravitational potential, though. But, it is the horizontal 

gradient of the gravitational potential that caused motion, not the bulk term). When 

vegetation roots are involved, the mechanical effects are included in cohesion. Note that it is 

the interaction of distributed roots and the surrounding soil particles, not merely the 

(4)

106 


additive of root tensile strength and soil cohesion. In a certain way, it is like the iron web 

reinforcement inside concrete, except that soil moisture does not play a role in the concrete 

case for shear strength. 

The system described by Eqs. (1-6) is most suitable for study either deep-seated rotational 

landslides or shallow or storm-triggered debris flows. It is also convenient to investigate the 

positive feedback between deforestation, land use changes, undercutting of slope for road 

construction and expansion of settlement areas and landslides. For example, the effects of 

vegetation can be fully considered in this modelling system. The weight loading is set as 

upper stress boundary. The hydrological effects manifest in the parameterization of 

viscosity (dissolve of certain chemical bonds in clay soils, effects on cohesion and internal 

friction for sandy soils), the pore pressure adjustments of the spherical part of the stress 

tensor, and (minor) changes to the loading corresponding to the soil water weight (Smith 

and Petley 2008). The mechanical properties of the roots are implemented in the effective 

cohesion. The water distribution effects of vegetation roots are parameterized in a land 

surface sub-model. This sub-mode provides the soil moisture conditions for the sliding submodel. 

These explain how antecedent rainfall influence the saturation of soil and ground 

water level for a vegetated slope and its instability (Crosta 1998; van Asch et al. 1999) and 

provide a solid basis for discussing deforestation effect on landslide and the positive 

feedback that lead to further deforestation. Interestingly, ID empirical approach and the 

slope stability models are various forms of reduced form of the above equation set. For 

example, if the time dependence is neglected and three dimensional topography reduced to 

only including x-z plane, the governing equations can be written as (in component form): 

xx 

xz 

 

0 

x z 

 

xz zz 

g0 x z 

Further simplify the slope geometry to be of constant bedrock slope (α) and uniform sliding 

material thickness (thus, surface slope also is α), the volume integration of equations (7) and 

(8) yield 

GCeff Lsin / Sutgsin / Sv/ cos tgsin / Ssin 

 

eff eff v 

 

C L G C Lsin / S u tg sin / S / cos tg sin / S u tg / S 

cos E u0 h 

where u is the hydrostatic pressure from ground water, v is the net vertical support at top 

and toe (determined by boundary conditions), h is the horizontal support evaluated at top 

and toe, and E is the horizontal force exerted during earthquake. Eq. (9) indicates that 

slope stability is influenced by various factors, such as slope gradient ( ), soil properties 

(implicit in G, Ceff and ), ground water table and geomorphology (u and the boundary 

conditions). As for triggering mechanism, it not only include the storm trigger (excessive 

rainfall), but also the earthquake and volcanic activity ( E ). 

In the case without ground water and no effects of earthquake, taken the form 

(7) 

(8) 

(9)


SGCeff Lsin / cos tgsin / Stg 

eff eff sin / cos sin 

C L S G C L S tg tg 

, as discussed in the review of slope stability model in the previous subsection. 

Similarly, rainfall intensity-duration methods (IDs) based on the soil moisture sensitivity of 

the resistive terms (the middle two terms on the right hand side of Eq. (6)). Sliding material 

are a structured mixture, with particular spacing patterns and arrangements, of solid 

particles, pore water and, in some cases, cementitious material accumulated at particleparticle 

contacts. In addition to factors such as the electrical charge of the particles, and the 

chemistry of pore water, the chemical bonds are the key to the soil yield strength. As an 

example, there are the cementation-particles connected through a solid substance, such as 

recrystallized calcium carbonate formed in dry climate when seeping water experience new 

environments of high pH value and (or) the solvent get evaporated, as mentioned in Section 

1. Soil water, especially of acidic pH value (

108 


5000 landslides and ~3600 rock falls. The massive amount of potential energy was 

transferred into heat and enhanced local convection. The following storms incurred ~358 

debris flows, resulted in a direct economic loss of near $US60 billion. SEGMENT thus 

simultaneously solves the thermal equation, the dynamic equations and the surface 

kinematic (continuity) equations. 

Fig. 3. Conceptual framework of SEGMENT-landslide for the projection of storm-induced 

landslides. The numerical techniques, model physics, input and output parameters are 

described in Ren et al. (2010b). The model has wide applications in many other geophysical 

flows including glaciers and ice-sheets, snow and mud avalanches, soil and coastal erosion, sea 

level change following ocean bottom tsunami, and pyroclastic flows such as magma from 

volcanic eruptions. The land surface model component considers hydrological processes of 

soils and vegetation. The mechanical properties of roots and the biomass loading also are 

implemented in the landslide model component.Adapted from Ren et al. 2011a. 

SEGMENT, because it synthetically simulates landslides over a continuous regional area, 

can, in principle, minimize the false-alarm tendency of most empirical procedures. Against 

the 2007 wild fire-burn background, using the observed precipitation, SEGMENT simulated 

the landslide cases during year 2008 for a region with documented landslides (Ren et al. 

2011b). Inserting the landslide model component into a scalable and extensible system (Fig. 

3) also makes implementation of newly identified physical processes more convenient. This


advantage is apparent in a recent simulation of Zhouqu landslides, where SEGMENT 

satisfactorily demonstrated why rainfall intensity is a critical factor affecting slope stability 

for cracked slopes. 

For investigating environmental issues, the landslide model is inserted into the generalized 

scalable, extensible modelling system (SEGMENT). To illustrate the viability of SEGMENTlandslide, 

a case study is presented of a landslide in Yanjiashan of Hubei province, China. 

6. Case study for the Yangjiashan creeping slope 

SEGMENT-landslide uses the data typically required by a sophisticated land surface 

scheme. In addition to a surface elevation map and bedrock topography, the model also 

requires as input various geo-mechanical parameters such as cohesion, angle of repose 

(dry), density, porosity, field capacity, and saturated hydraulic conductivity. SEGMENTlandslide 

also requires a vegetation weight mask and a root distribution profile. 

Detailed geological surveys are conducted only for areas with important infrastructure in 

danger of destruction by natural hazards. Fortunately, we have access to one such geological 

dataset for a small region along the Qingjiang River, a tributary of the Changjiang River. The 

Yangjiashan creeping slope (YC) in China, is located at 29°50’ N and 109°14’ E (see Fig. 4). In 

addition to a recently detailed geological map, we also performed an engineering study of the 

soil and rock profiles from bore-hole drilling, extracting soil samples for laboratory testing 

(Table 1), and continuous displacement measurements within the creeping zone (Fig. 5). The 

shear strengths of the various materials present in the region also have been obtained. Both 

drained and non-drained shearing tests are carried out on the soil specimens (Table 1). In 

addition to recovering creeping zone rock and fluid for laboratory analyses, intensive downhole 

geophysical measurements and long-term monitoring provided the following 

information: the composition and geomechanical properties of active creeping zone rocks; the 

nature of the stresses responsible for sliding; and the role of pressurized water in controlling 

landslides recurrence, for field conditions with a wide dynamic range. Displacement surveys 

have been carried out continuously since July 2007, using the RST-IC3500 digital inclinometer. 

Displacement data from five boreholes (see Fig. 4, BH6-9) are analyzed in this study. The 

boreholes are labeled BH6, BH7, BH8, BH8-1, and BH9 (see Table 1). 

The YC region has a sub-tropical moist climate, with a typical monsoonal precipitation 

pattern. The annual mean precipitation ranges between 1100 and 1900 mm. This section of 

the Yangjiashan Mountains, because of its proximity to a dam, is well-instrumented and 

thus is an ideal region to verify the numerical model, which calculates the roles of pore 

pressure, biomass loading and root distribution, and the intrinsic friction of the stress 

distribution. In this region the major slides are preceded by creeping movements. Since the 

1960s, the YC slope has experienced repeated failures, as a result of exceptionally heavy 

rainfall periods in 1960, 1980 and 1997. During a storm in July 1997, very heavy rainfall fell 

over 2-3 days and caused substantial erosion in one small canyon. Field studies indicate that 

after a major rainstorm toe-slope failure occurs first, reducing the stability of the upper 

slope, and the failure then moves gradually to the upper slopes. The scenario therefore is 

that towards the end of a heavy rain storm, a block of material was undercut by the stream 

and moved into the canyon, its downhill movement left an unsupported upslope block 

which followed the movement. This, in turn, was followed by a third block as the movement 

retrogressed up the slope 

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Fig. 4. Topographic map of Yangjiashan Creeping (YC) slope (the upper map shows its 

location in China). Contours are surface elevation (m). Borehole locations are labeled with a 

red circle (e.g., BH06-9). Color shading indicates surface maximum attainable creeping 

speeds larger than 1mm/s, after a 50-year recurrence storm event. The primary sliding 

direction (red arrow) is determined according to subsiding and swelling belts. The X- and Yaxis 

are distances from the SE corner (29.9N, 109.1 E). 

The Yangjiashan community is situated on hills composed of inter-bedded siltstones and 

sandstones, occasionally interspersed with altered clay layers. The rocks range from highly to 

completely weathered at the ground surface. The weathered rocks date from the Paleozoic and 

Mesozoic eras, the 200-900 m thick yellowish interbedded sandstone and siltstone dates from


the Silurian period, and the grey siltstone is from the Triassic period. The infiltration of rainfall 

through macro pores, which are well-developed in the soil and rock mass, plays a critical role 

in slope stability. The hills intersect with canyons in which increased erosion takes place 

during the spring and fall rainy seasons. Although many of the drainage patterns in this 

region have been altered by human activity, thereby increasing the slope stability, some 

remain unchanged, even in the inhabited areas (eastern part of the slope). 

Young’s 

modulus 

E(GPa) 

Poisson’s 

raioμ 

density 

ρ(kg/m 3) 

cohesion 

C(MPa) 

Insitu rock and soils 0.16 0.24 2150 0.38 20 

Slip-surface material 0.032 0.32 1950 0.04 16 

Middle/lightly 

weathered mudstone 

1.6 0.2 2600 1.6 28 

internal 

friction 

angle φ(°) 

Table 1. Geomechanical observations and parameters obtained by field surveys and laboratory 

tests of saturated specimens. Rock strength and deformation properties are obtained from triaxial 

compression tests using INSTRON-1346. Four saturated specimens of each layer were 

tested. During tests, confining pressure was applied step-wise in 3 MPa increment (i.e., 3, 6, 9, 

and 12 MPa), and vertical load are applied at displacement rate at 0.1 mm/s. 

6.1 A prediction for the Yangjiashan slope for the 2010-2019 decade 

The Triassic siltstone of YC is especially sensitive to pore pressure changes. Because of the 

complicated stratification, the model is initialized with five borehole soil/rock profiles (18- 

50 m depths within the granular soil mantle), with a horizontal resolution of 10 m in 

delineating the 3000 m by 5500 m simulation domain. In order to reduce spurious numerical 

boundary effects, the simulation domain encompasses the entire region shown in Fig. 4. The 

sliding material forms a characteristic alluvial fan, that is with the down-slope section 

thicker than the upslope section. A thin plate splines (Burrough and McDonnell, 2004) 

interpolator is used to obtain a sliding mass depth distribution over the grid. 

In the YC site, there are three model sub-layers to delineate the sliding mass, and one layer 

to represent the montmorillonite, within which sub-layers 10-12 are assumed to define the 

slip surface. This is further supported by the creep monitoring data (Fig. 5). This layer has 

the same chemical composition as the overlying layer but is physically fractured. We use 3 

sub-layers to delineate the regolith layer because, although only ~1 m deep, it is the critical 

layer controlling water infiltration into the creeping slide mass. As it represents granular 

material under high confining pressure, the viscosity of this thin bed of finer-grained 

materials is smaller than that of the adjacent layers. The deep underlying rock layer is 

divided into 7 sub-layers, with mechanical properties specified from laboratory test results. 

As a link between strain and stress, viscosity Eq. (3) is the most important parameter in 

continuum modeling of slope movement. Under the same pressure gradient force, the 

smaller the viscosity, the faster the creeping rate. Non-fractured rocks have viscosity as high 

as 10 19 Pa•s. The creeping rate thus is minimal for non-fractured rocks. Fractured rocks 

(granular material), can have viscosities 10 orders of magnitude smaller, depending on 

confining pressure and lubrication condition (usually water content under natural 

conditions). A creeping curve indicates that the vertical flow shear is very large within the 

granular layer. Above and below the granular layer, flow shear is much smaller. For a non- 

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slip lower boundary condition, the creeping rate usually is imperceptible (below 53 m in 

Fig.5) before reaching the granular layer. 

Fig. 5. The observed (markers) and modeled (lines with same style) displacement-time 

curves for BH8. 

The absence of vegetative root strength binding soils increases susceptibility to sliding in 

loose soil on steep slopes during intense rainstorms (Dietrich and Perron 2006). Extra 

biomass loading also may contribute to slope instability during wet seasons. Ground surface 

biomass loading information for YC, because of its limited extent of only ~20 km 2, was 

collected by a geological survey team following a request by the first author. 

Using mean annual soil moisture conditions (obtained from NCEP/NCAR reanalyses, 

http://www.cdc.noaa.gov/data/gridded/data.ncep.reanalysis.surfacefluxes.html), a 

creeping rate is simulated using the SEGMENT model near BH8 of 17, 17.2, 16.2, 8.5, 5.2 and 

3.1 mm/yr respectively at 1.5, 3, 10, 30, 40 and 45 m depths, agreeing well with the observed 

measurements. The root mean squared error, when compared with the 108 measurement 

data grids, is overestimated by only 0.42 mm/year, well within instrumental error range. 

The depth of the sliding surface (49 m at this location) is accurately delineated. Sliding will 

eventually accelerate along this plane of weakness, which is composed of highly fractured 

sandy shale. Interestingly, under natural conditions, the creeping speed curve is not a 

simple dilatant profile: there are local minima/maxima. The faster creeping locations near


the surface in the curves are partially due to the low viscosity of this layer and partially due 

to the uneven surface loading in the down-slope direction of the borehole. At the time of this 

study, measurements are available only up to March 27, 2008. 

The displacement curve is assessed for January 1, 2010 based on the climatological 

precipitation over the region (see Fig. 5). The basal sliding attributed to the Wenchuan 

earthquake of 2008 also was taken into account. Compared with the initial measurements, 

the shallow level displacements are as large as 18 mm. Based on our analysis of the effects of 

the Wenchuan earthquake on the YC creeping slope, the creep soon will accelerate. The 

Wenchuan earthquake reduced the natural creeping period by at least five years. For a 

crevasse near BH06, the surface crack enlarged from 10 to 18 cm. If the crack geometry is 

assumed to be constant, the crack depth almost doubles. Estimated depth changes were 

made of other cracks. The changes in its natural sliding cycle are obtained by comparing its 

creeping speed under current conditions with the pre-quake conditions. For example, after a 

major slope adjustment, say in 1998, it is assumed that there are no cracks due to strain. In 

2008, before the earthquake, the cracks are already monitored. They all are located at model 

locations with large strain rates. The natural cycle is not difficult to estimate; 1mm/day is 

the critical value for next major sliding event. Even if there is little change in the 

precipitation morphology, over the next ten years there likely will be significant slope 

movements. However, if there is an intense rainstorm with over 150 mm/day at any time in 

the future period, then sliding becomes imminent. 

Three historical landslides (1960, 1980 and 1997) reported in the YC study area are separated 

by ~20 years (Fig. 6). In 1960 and 1997, high annual precipitation values of 1819 and 1771 

mm, respectively, were recorded. However, 1980 was relatively dry with 1200 mm total 

precipitation compared to 1600 and 1360 mm respectively for 1979 and 1981. Examination of 

the daily precipitation series from 1979 and 1980 indicates that in 1979 over 90% of the 

annual precipitation occurred in the latter half of the year, with no significant precipitation 

before June. Although the total precipitation for 1980 is small, the heavy precipitation in 

January 1980 immediately following the previous year’s precipitation events formed an 

extended wet period. There was a significant precipitation event in January, reaching a rate 

of 58.3 mm/day that lasted 4.7 hours on January 15. As a result, the deep soil moisture (0.23 

volume per total volume) remained relatively high for the remaining several months. What 

triggered the landslide was a very heavy precipitation event of a 100-year recurrence 

frequency, with a single day precipitation of 230 mm on June 11th. 

For storm-triggered landslides, those precipitation events separated by less than two dry 

days can be considered as one single ‘super’ rain event. Thus, unlike many previous studies 

(e.g., O’Gorman and Schneider 2009), which count daily precipitation one day at a time 

(traditional precipitation analysis), we count those extended super-rain-events, defined as a 

somewhat continuous rainfall period nowhere separated by more than two consecutive dry 

days (rain-event analysis, Ren et al. 2011a). Figure 6 compares two methods of rainfall 

analyses. Using a traditional analysis, the 1997 slide does not correspond any significant 

daily rainfall event (>100 mm/day). However, following Ren et al. (2011), all major 

landslides (red arrows in Fig. 6) are triggered by rain events with higher rainfall totals. For 

the YC, heavy precipitation is both an enhancement factor, and a determining factor, in 

triggering landslides. However, current observational data are less than 2 years long, which 

is far too short to unambiguously resolve this hypothesis. SEGMENT-landslide simulations 

indicate that, under long term mean soil moisture conditions, the creeping will achieve a 

near-surface movement rate of 0.1 mm/day near the head of the slope, sometime within the 

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next 40 years. Ironically, this creeping rate may never have been realized during the 

previous half-century. It has been interrupted by heavy rain storms and, consequently, 

slides have occurred before this movement rate ever was reached. 

Fig. 6. (a) Daily precipitation and (b) rain event analyses, with the three major historical 

landslides indicated by red arrows. In panel (a) the daily rainfall time series reveals that that 

the 1997 event was not an intense rainfall event but was composed of two consecutive 

rainfall events over several days. In panel (b), rainfall totals are plotted for each rain event, 

by calculating the cumulative rainfall for each rain event and placing the total at the center 

of the start and end times. In panel (b), it is clear that both the 1980 and the 1997 events 

correspond to large total rainfall events. However, large rainfall totals alone do not 

necessarily trigger slope movements but, as discussed in the text, result from the combined 

effects of a number of factors. There is a lack of observed daily precipitation prior to 1970 to 

perform similar analyses. The inset of panel (a) is the NCEP/NCAR reanalysis monthly 

precipitation data. For example, the year 1960 not only had intense August precipitation, it 

also followed immediately after 1959, which was a very wet year. 

For a storm with a 50-year recurrence frequency (~170 mm/day) more than 80% of the total 

water mass is channeled to the sliding surface through macro-pores, so movement rates can 

become dramatic toward the end of such a storm. The areas of significant deformation (i.e. 

maximum attainable surface sliding speed greater than 1 mm/s) after a 50-year storm are 

shown in Fig. 4 (with color shades). Thus far, the geological survey team has identified at


least 10 landslide scars and slide debris deposits, all within the color shaded areas in Fig. 4. 

For example, there are obvious landslide platforms near BH8 and another major one near 

the position labeled point ‘A’. A shear surface exists at point ‘A’ with a depth of about 70 m. 

When water drains down to this surface, the material strength at the shear surface is 

reduced to its residual value. The artesian pressures along the failure surface add to the 

instability of the sliding mass. 

Fig. 7. Viscosity changes between August 30 2007 and August 30 2008, at borehole location 

BH8. 

During heavy rainfall periods, water penetration reduces the strength of materials. In 

addition, hydrodynamic pressure along the slip surface further reduces stability of the 

sliding mass. The model simulations indicate that point ‘C’ is highly unstable under an 

extreme precipitation event. Failure will occur around this point first, triggering a failure of 

the upper portion. The sliding mass spreads about 50 m downslope and was brought to rest 

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by the lateral stress from the walls of the V-shaped gully which parallels the 750 m elevation 

contour. The sliding material can become up to 20 m thick in the lower elevations. The 

accumulated material also can block the gullies and enhance infiltration of rainfall into deep 

layers and cause pore water pressure increases, which is a lubricating effect in the model 

parameterization. This is especially important for the land segment lying between the 700 m 

and 600 m elevation, which is where the primary human residential areas are located. To 

obtain additional information for verifying the model credibility for slope stability at Point 

‘C’, several more bore-holes are required in slopes adjacent to the gully on the west bank. 

In the above scenario, a volume of 6.3×10 7 m 3 of soil and rock is estimated to be creeping. In 

retrospect, it appears landslides have been occurring in this region at intervals through 

history; but only part of the total creeping mass is involved in any particular landslide. 

Specifically, one portion may slide, causing a reduction in the stability of an adjoining 

portion; then, years, decades or even centuries later, a subsequent landslide will occur. As a 

consequence, the topography of the area is hilly and highly uneven. What determines the 

creeping rate of a slope is the material viscosity. We examined the modeled viscosity change 

near BH8 (Fig. 7). Because the rocks are heavily weathered, the viscosity is on the order of 

10 16 Pa s, which is two orders of magnitude smaller than for the same material in an 

undisturbed state. So, when dealing with fractured rocks as a whole, they must be viewed as 

granular material. The viscosity of granular material changes as strain accumulates. In this 

case, the viscosity is reduced substantially with time at all levels (e.g., it is reduced by >40% 

from year 2007 to 2008). This explains why the creeping tends to accelerate with time. 

The SEGMENT-landslide system is valuable for monitoring creeping because it can provide 

a dynamical representation of changes in the strain distribution inside the sliding material. 

Figure 8 shows the modeled creeping velocities for January 2012. A vertical cross-section is 

provided along the direction of the primary sliding direction, located near the demarcation 

line in Fig. 4, for the current geometry and a climatological mean soil moisture conditions. 

Because the sliding material depth is only one-tenth of the slope dimension, for a more 

effective display the flow field is transformed into terrain-following sigma coordinates. The 

surface corresponds to σ=0 and the bottom of the sliding mass corresponds to σ=1. For 

clarity, the portion with flow speeds less than 0.3 m/yr is filtered out. The formation of the 

local maximum speed ‘core’ (the band along the σ=0.8 level) near the bottom (Fig. 8b) is 

attributed to movement within the fractured layer. Varying the soil moisture conditions 

indicates that the movement of this layer is most sensitive to changes in soil moisture 

conditions. Any factors preventing surface water entering the ground will help reduce the 

acceleration of the sliding mass and delay future landslides. The landslides are sensitive to 

soil moisture conditions, but a qualitatively persistent feature is that the maximum strain 

area is located upslope, as shown by the warm color shading near ~750 m elevation in Fig. 

8a. At present there are crevasses with openings wider than ~5 cm of horizontal 

displacement. That the maximum speed cores are relatively isolated indicates that the 

sliding surface is not fully connected. In the upcoming 10 years, the causal mechanism for 

major slides remains the same, namely, the storm charging of the artesian aquifer and the 

lubrication of the granular layer by drainage water. 

To investigate the impact of possible future precipitation morphological changes over this 

region, 21 st century simulations are analyzed from 17 Climate General Circulation Models 

(CGCMS) (see Ren and Karoly 2006) under the SRES A1B (moderate) emission scenario 

(Nakicenovic and Swart 2000), which assumes a balanced energy source in a future of rapid 

economic growth. Future precipitation rates under the SREA A1B scenario are expected to


intensify in the upcoming decades, which is in accord with the consensus of the wider 

climate research community (e.g., Groisman et al. 2004, Karl and Trenberth 2003). In 

actuality, the total rainfall amount also increases, indicating that the primary mechanism 

may be the increase in atmospheric vapor concentration, as described by O’Gorman and 

Schneider (2009). 

Fig. 8. Creeping speed for January 2012, within a vertical cross-section (along the red arrow 

in Fig. 4) under climate mean soil moisture conditions. The top panel is displayed in 

physical space (vertical axis is elevation; horizontal axis is distance from the origin). The 

bottom panel is displayed in the σ terrain-following coordinate system. So σ = 0 corresponds 

to the surface and σ = 1 corresponds to the bottom of the sliding mass. The color shading is 

the magnitude of the full 3-D velocity. In panel (b) maximum cores (roughly at the σ = 0.8 

level) corresponding to the “creamy” basal sliding layer. 

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6.2 Discussion 

A measure of our understanding of slope sliding processes is our ability to predict the future 

behaviour of slopes under a range of conditions. In this study, the SEGMENT-landslide 

model (Ren et al., 2009; Ren et al. 2010; Ren et al., 2011a, b) successfully reproduced three 

historical storm-triggered landslides that occurred during the past half-century, for the 

Yangjiashan creeping slope (YC) in China. SEGMENT also was used to make one further, 

long term prediction, for the forthcoming decade, 2010-2019, which quantified the stability 

of the YC region and showed that slope movements will occur during the next decade and 

that, even more significantly, a major landslide is imminent if an intense rainstorm with 

over 150 mm of rainfall occurs at any time in that period, even in the near or immediate 

future. 

The SEGMENT landslide modeling system has demonstrated, from numerical experiments 

carried out over the YC region, that it can anticipate how strain accumulates. For example, it 

shows how load increases with precipitation and that there is an accompanying decrease in 

yield strength. SEGMENT can predict when sliding, or rapid slope failure, is probable, given 

the available meteorological parameters, soil properties and land cover conditions. 

For the YC, its particular geological constituents are the main cause of its landslide 

susceptibility to triggering by storm events. Heavy storms are enhancement as well as 

triggering factors. Our study points out an aspect that requires close monitoring because it 

likely is responsible for the upslope cascading of storm-triggered landslides. Model 

sensitivity experiments established a stability feature not investigated by the survey team. 

The model demonstrated that rainstorm generated instability at a given location forms the 

first sliding block in a sequence, by acting as a trigger for a domino-like slide that moves up 

the slope. 

We find that increased infiltration of groundwater into the sub-surface from storms 

increases creeping rates dramatically for weathered slopes. For slopes experiencing repeated 

failure-restore cycles, increased precipitation amount and intensity under a future warming 

climate are the two most important factors determining long-term increases in landslide 

frequency. 

Quantitative predictions of storm triggered landslides require a numerical modeling system 

like SEGMENT-landslide. However, some of the requirements of SEGMENT-landslide, 

especially the input and verification data, generally are not available even in current 

geological maps. These parameters include vegetation loading and root distributions in soils 

and weathered rocks. The application of the SEGMENT-landslide model to other regions is 

limited primarily by a lack of these high resolution input datasets. The landslide features 

implemented in SEGMENT-landslide, if adopted by the relevant community, hopefully will 

encourage the collection of such vital information in future surveys. 

7. Landslides in a future climate 

There is a considerable and expanding body of opinion which suggests that earth may suffer 

marked temperature increases over the next 50-100 years (Rind 1984) due to heat retention 

by the atmosphere caused by increased levels of the greenhouse gases (GHG) such as CO2, 

CH4 and oxides of nitrogen. The levels in the atmosphere have increased quite dramatically 

in the last 70 years and are expected to continue to rise. One consequence is an intensified 

hydrological cycle. Estimates of the effects vary widely but all predict some increase in 

storm-triggered landslides.


The global hydrological cycle also is assumed to be intensified. However, different regions 

may respond very differently (Ren et al. 2011a). For example, both monsoonal regions (e.g., 

the Yanjiashan creeping slope located in the Asian monsoonal region) and Mediterranean 

regions (e.g., California) show significant increases in extreme precipitation, but the average 

annual precipitation changes are different. Precipitation over the Asian monsoon region 

increases significantly, consistent with increasing tropospheric specific humidity, as pointed 

out by Allen and Ingram (2002). In fact, the increase in annual mean precipitation over this 

region is due mainly to the shift toward heavier precipitation events. The CCSM simulated 

precipitation trend over southern California under the SRES A1B scenario, counterintuitively 

indicates that total precipitation decreases by more than 0.1 mm/day on an 

annual basis. Storms become more intense but farther apart in time, favoring a droughtflood 

bipolar temporal pattern as suggested by Trenberth (1999). Importantly, current 

climate models show strong inter-model consistency for this finding. 

In landslide terms, the phenomenon has been postulated to be a potential eventuality in two 

different ways. One proposes that the debris flows will occur more frequently on smaller 

scales. The other suggestion, considered more likely by some concerns the less frequent but 

more disastrous outburst of storm-triggered landslides. One thing for sure is that many of 

regions with minor risks could be magnified if the earth’s climate undergoes significant 

changes over the next 50-100 years as a consequence of continued burning of large amount 

of fossil fuels. 

8. Summary 

Natural hazards are an ever-present threat to human lives and infrastructure. The need for 

greater predictive capability has been identified as one of 10 Grand Challenges in Earth 

Sciences (NRC, 2008). As an effort toward the goal of a reliable landslide mapping and 

warning system, we present a modeling system (SEGMENT) that systematically estimates 

the potential for landslides over a regional area, rather than for a single slope. The 

promising performance of the model is attributable to the use of a new, fully threedimensional 

modeling framework based on a newly proposed granular rheology, and to the 

use of a land surface scheme that explicitly parameterizes the hydrological characteristics of 

macro-pores. Some requirements of the model, such as vegetation loading and root 

distribution in soils and weathered rocks, are not available even in present geological maps. 

Applications of SEGMENT to other regions are limited primarily by a lack of high resolution 

input data sets. However, the new concepts implemented in the model, if adopted by the 

community, may encourage the collection of such information in future surveys. 

The anticipated future climate warming has influence on the occurrence of landslides 

caused by elevated water content in the ground. Changes in precipitation morphology are 

highly relevant for storm-triggered landslides and subsequent desertification, because the 

root system of vegetation has adapted to the current precipitation climatology and likely is 

not prepared for human-induced changes in climate. Under the current relatively stable 

astronomical boundary conditions there are natural “rhythms”, whereas human induced 

changes are likely to transition significantly in one direction, leading to a climate state not 

experienced before by the existing terrestrial ecosystem. Microclimatic variations associated 

with slopes allow stands of an ecosystem type to exist far beyond their major zones of 

distribution (Chapin et al. 2002). These outliers act as important colonizing individuals 

during times of rapid climate change. Destroying these outlier species (transitional belts on 

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Holdridge’s chart) by landslide burial, accompanying extreme precipitation, may slow 

down ecosystem migration in accord with climate change. 

Landslides are localized events. Advanced dynamical models with physical basis should be 

used as there is a need for prediction, rather than simply documenting the occurrence of 

landslides. SEGMENT-landslide is an effort in this direction. Because there are 

biogeochemical submodels coupled in the SEGMENT system, it also is an ideal tool for 

investigating the environmental consequences of landslides, including deforestation and an 

associated decrease in productivity. 

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Deforestation Dynamics: 

A Review and Evaluation of Theoretical 

Approaches and Evidence from Greece 

Serafeim Polyzos and Dionysios Minetos 

University of Thessaly, Department of Planning and Regional Development, 

Pedion Areos, Volos, 

Greece 

Amongst others, forest land use changes occur for multiple reasons and from interacting 

processes and mechanisms. Human-driven changes, at an array of scales, are affecting forest 

ecosystems accelerating changes such as global warming with adverse consequences on 

human well being. Research has demonstrated that, in the long term, there is to not a single 

factor or set of factors that can explain the emerging patterns of land uses and their 

associated changes (Chomitz and Gray 1996; Lambin, Turner et al. 2001; Aspinall 2004). Yet, 

the importance of forests on global environmental issues such as biodiversity loss and global 

warming is apparent. 

Deforestation processes have different characteristics across space and time (Verburg, 

Schulp et al. 2006). A particular combination of factors that may explain deforestation 

pattern somewhere, might not be applicable for justifying change in any other location or 

time period (Mahapatra and Kant 2005). Therefore, there is a need for conducting empirical 

investigations in order to analyse and understand the geographical and historical context of 

land use changes. In addition, forest fragmentation, conversion and modification have 

significant economic, social and environmental implications (Elands and Wiersum 2001; 

Walker 2001; Platt 2004; Verburg, Overmars et al. 2006) such as disruption in continuity of 

the natural landscape, forest and open-land constriction between agricultural and urban 

land uses, deterioration of vital habitats that sustain valuable biodiversity as well as broader 

issues such as air pollution. 

In this paper, we concentrate on reviewing and evaluating deforestation-related theoretical 

schemata. We introduce a representative collection of theories dealing, directly or indirectly 

with deforestation processes in order to provide guidance to an appreciation of the past and 

the future land use patterns. In addition, we give evidence of recent deforestation dynamics 

in Greece. 

2. Framework of review and evaluation 

Theories are presented in a timeline context. There is also a set of criteria used for the 

evaluation. The criteria are presented in table 1. 

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EVALUATION AXES SPECIAL CRITERIA OF EVALUATION 

A.1 Human decision level 

A. SCALE 

A.2 Spatial Level 

A.3 Temporal Level (time step) 

B.1 Raking of the phenomenon 

B. FOCUS OF THE THEORY 

B.2 Descriptive 

B.3 Causal 

B.4 Predictive 

C.1 Economic Mechanisms 

C. 

MAIN MECHANISM OF 

LAND USE CHANGE 

C.2 Social Mechanisms 

C.3 Administrative-Political Mechanisms 

C.4 Natural Resources 

D. INTERDISCIPLINARY 

D.1 Transectoral focus 

D.2 Sectoral focus 

E.1 Descriptive 

E. NATURE OF THEORY 

E.2 Geometrical 

E.3 Mathematical 

F. LEVEL OF DEVELOPMENT 

F.1 Developed countries 

F.2 Developing countries 

Table 1. Framework for evaluating of deforestation-related theoretical schemata. 

a. The scale in which the theory can be applied for describing and interpreting land use 

changes. 

The significance of scale is critical and concerns: 

The human decision-making process (single person, household etc). 

The geographical unit of analysis (spatial analysis) in which the theory can be effectively 

applied (city, region, country). 

The time unit of analysis, (temporal analysis) which is connected with the ability of the 

theory approaching short-term, medium-term or long-term changes. 

b. The central aim of the theory. 

Theories, mainly attempt to classify, describe, explain or even forecast spatial tranformation 

phenomena. Classification is a way of categorizing observed land use changes, so that any 

likely differences or similarities in the way that spatial phenomena transform space, are 

better understood. Descriptive theoretical approaches, in addition to classifying observed 

complex geographical formations, also attempt to determine concrete functional 

relationships and processes that, in turn, reveal likely associations between different uses of 

land. Explanatory theoretical perspectives concentrate on the determination of factors and 

dynamics that produce, wear out or eliminate certain land uses. Finally, predictive 

theoretical perspectives focus on the projection of spatial phenomena into the future aiming 

at forecasting any future composition of land use system. 

c. Main mechanism of land use change. 

The comprehension of underlying causes associated with land use change processes as well 

as The understanding of the major mechanisms of land use allocation is a critical matter. The 

existing theoretical pool in the field of land use change includes diverse approaches that 

either focus on economic processes and factors or on social and administrative processes, or 

support that the spatial distribution of different land uses is determined by the existing


A Review and Evaluation of Theoretical Approaches and Evidence from Greece 

distribution of natural resources. Therefore, it is extremely significant to understand the 

particular mechanism or set of mechanisms that each theoretical perspective puts forward in 

order to approach successfully complex spatial phenomena. 

d. Sectoral or multi-sectoral approach. 

The choice of how to approach a particular land use change process is of critical importance. 

Some theoretical perspectives follow a sectoral approach whereas some others employ an 

inter-sectoral logic. In the first case the theory focuses in only one category or subcategories 

of land use (rural use, urban use, forest use, or residence, industry, tourism, etc.). In the 

second case, the focus is shifted towards interpretation of intersectoral phenomena 

connected with observed land use transformations (e.g. urban sprawl etc). 

e. Nature of theory. 

The way that each theory has been stated or presented, relates to the level of formalism and 

scientific severity. Generally speaking, most theories adopt a verbal, geometrical, or 

mathematic approach or a combination of them, as a basic platform in putting forward their 

key statements. 

f. Level of development 

Remarkable differences in the levels of economic growth between countries have caused the 

emergence of at least two general categories of theories. There are theories suitable for 

satisfactorily interpreting spatial phenomena in developed societies, and also theories that 

mainly apply best in the case of less developed countries. 

Below, an attempt is made to critically review some of the most important theoretical 

approaches that directly or indirectly refer to processes in forest land use change. 

3. Review of theoretical approaches 

This section provides a brief review and evaluation of theoretical approaches on the 

phenomenon of forest land use change. The purpose of the review is to locate the major 

proximate and underlying causes of deforestation proposed by the literature. Recent 

attempts to theorise forest land use changes have yielded some noticeable contributions on 

the field of deforestation. 

One such contribution referred to as “forest transition theory” has been put forward by Mather, 

Grainger and Needle since the early ’90 (Grainger 1995; Reid, Tomich et al. 2006). According to 

this theoretical perspective, an overview of forest land use changes in the long run, provides 

firm evidence that while initially forest land areas retreat at a high speed, at same point, 

depletion starts slowing down. There is even a critical point over which the process of 

depletion reverses and forest land recovers by expanding into new areas. Prosperity level seems 

to have a key role in the whole process. The main land use change mechanism suggested by 

this theory is of economic nature and also has some common places with Kuznets’ 

Environmental Curve (Koop and Tole 1999; Ehrhardt-Martinez, Crenshaw et al. 2002) that 

links national or regional environmental quality with the state of economic development. The 

spatial level of analysis that the theory best applies is to nationwide or higher. 

Based on the aforementioned perspective, Mather (2006) and Grainger (1995) state that most 

of the developed countries have been in a state of forest transition as forest land has been 

expanding for several decades. Explanations concerning forest transition processes are 

sought both in development theory and modernization theory. These theoretical perspectives 

focus on the importance of economic and social changes that spring from the adjustment of 

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the economic, social and political structure to technological advances. In this respect, during 

the course of development increased pressure is put on forest land due to higher demand 

for land and forest related products. As a spatial unit moves to higher stages of 

development, pressure on forest land retreats because technological innovations allow for 

increased productivity in the primary sector, limiting the needs for expansion on forest land. 

At the same time, the increased rates of urbanization drive large waves of population away 

from the countryside into cities and towns lowering the pressure on forests by human 

activity in exurban remote areas. 

A sizable body of empirical research on forest transition theory has generated considerable 

evidence in favour of some of the theory’s reasoning (Koop and Tole 1999; Ehrhardt- 

Martinez, Crenshaw et al. 2002; Geist, McConnell et al. 2006; Reid, Tomich et al. 2006). 

Empirical evidence systematically suggests a negative relationship between the rate of 

deforestation and the rate of urbanization or the level of new technology adoption in the 

primary sector (Perz 2007). 

In a recent attempt of improving the context of the theory, Angelsen (2001) suggests that the 

stages of forest transition theory (low deforestation, intense deforestation, containment of 

deforestation, afforestation) can be better understood by focusing on the fundamental 

characteristics of the long-term relationship between agricultural land rent and forest land rent. 

Mathematically, this means that forest land use changes [DU]fr are a function of agricultural 

land rent [LR]agr and forest land rent [LR]frs of the type 

LR 

. 

 

 

agr 

DU fst f 

LR frs 

In this case, the most important part of the analysis is to identify the way and magnitude of 

the influence of applied policies on agricultural and forest land rent. However, Angelsen 

points out that diagnosis and measurement of the actual influence of policies on land rent is, 

at least difficult to achieve, since on top of the obvious and direct impacts, economic 

phenomena are usually involved in numerous feedbacks and interactions that produce new 

waves of influences on land rent. For instance, the adoption of a new technology in 

agriculture may initially make agricultural activities more profitable fueling the expansion 

of agricultural land on forest land. In the long run, however, balancing effect will emerge 

due to changes both in agricultural goods prices and wages in agriculture leading to the 

containment of the expansion or even a reverse process of forest advancement. 

The aforementioned analysis by Angelsen focuses on tropical regions, where one of the most 

important proximate causes of deforestation is believed to be agricultural activity. However, 

in cases where urban land uses control the process of deforestation, the relationship between 

forest and urban land uses in terms of land rent might constantly fuel urban expansion. An 

additional consideration regarding the Agelesen’s land rent approach rests on the property 

regime of forest resources. In several parts of the world, the great share of forest land 

belongs to the state and not to private owners. In those cases, where the state cannot assure 

its one rights on land there is more scope for forest land encroachment and exploitation 

through logging and cultivation. In these cases, forest land use change processes maybe 

better understood on the basis of Hardin’s theory of the tragedy of the common (1968). 

Individuals when act independently might have no motives for considering the need for a 

sustainable use of shared natural resources (Herschel 1997). The negative external



economies by the depletion of forest resources are spread to society whereas the economic 

profits from wood exploitation and agricultural production in formerly wooded land are 

capitalized on by the intruders. 

Approaches for unsustainable forest resource exploitation are not scarce. Several researchers 

(Roberts and Greimes 2002; Shandra, London et al. 2003), drawing from the “theory of 

dependence” by Baran, Frank and Amin as well as from “world systems theory” by Wallerstein, 

claim that the developed regions have establish a particular system of economic exchange 

that imposes certain land use patterns to the less developed regions. This particular 

economic exchange process between the developed and the less developed peripheries takes 

place on unequal terms resulting in the unsustainable use of natural resources in the less 

developed areas. Power, wealth and prosperity are, therefore, related to the depletion of 

forest resources in the developing regions and to the sustainable use and possible expansion 

in prosperous regions. 

Both the importance and the adaptive nature of strategies employed by agents in order to 

maintain their prosperity level were firmly established in the context of “multi-phasic 

response theory” proposed by Davis in 1963. It is now believed that applied more broadly, 

multi-phasic response theory can help to understand how agents decisions impact land use 

changes (Lambin, Geist et al. 2006). 

A similar body of approaches attempts to apply concepts from “game theory” in order to 

capture agents’ behaviour in forest land use change process. (Fredj, Martνn-Herrαn et al. 

2004). According to some of these perspectives, the major decisive force of the way forest 

resources are utilized is state policies. State policies are far from static paying particular 

importance to economic growth during periods of economic difficulties where 

environmental concerns are ranked low in social agenda. Therefore, regardless of the level 

of economic development, the changing conditions of economy over a period of time could 

result in the adoption of a sustainable or a less sustainable behaviour towards forest 

resource. However, in some cases the unsustainable behaviour adopted by agents may not 

lay on the difficulties brought about by unfavourable economic conditions. It might be, in 

fact, an act of land speculation based on either high tolerance shown by the political system 

or insufficient administrative and environmental monitoring mechanisms. Yet, in the 

context of game theory it is proposed that it is possible to arrive to sufficient and sustainable 

solutions to the issue of deforestation through cooperation and coordination of the involved 

parties and individuals (Fredj, Martνn-Herrαn et al. 2004; Stern 2006). 

On the other hand, there is a quite different view concerning the current social behaviour 

and state intervention towards forest resources. It is widely believed that current 

afforestation policies are not merely an opportunistic reaction to the undisputed acute 

depletion of forest resources and its associated impacts. Instead, they also revile a much 

deeper transformation of social attitudes and ethics towards the environment. Mather et. al 

(2006) argue that at least in the case of developed countries, a great part of society is driven 

by the principles of post-productivism philosophy. Profit maximisation is not the only as well 

as the central axis of individual behaviour formation. Economic growth is possible to coexist 

with protection and restoration of the environment. Several aspects of this new philosophy 

of post-productivism can especially be traced in the countryside (Shucksmith 1993) in the 

form of certain environmentally sensitive policies that are voluntarily embraced by farmers. 

Amongst other, afforestation policies, organic farming measures and codes of good 

agricultural practice aim to establish alternative agricultural land management as well as 

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sustainable ways of agricultural production. Current agricultural land uses are increasingly 

characterized by the aforementioned post-productivist features (Reid, Tomich et al. 2006) as 

the demand for environmental services has risen sharply in developed regions during the 

last decades. Therefore, land use changes in the countryside could possibly be better 

understood in the light of society’s priorities in the context set by post-productivism. 

In this respect, Maslow’s theory of the hierarchy of needs maintains considerable potential in 

explaining certain emerging land use conversions as well as land use qualitative 

modifications in exurban land use systems. The importance of the new post-consumption 

social motives and ethics stresses Inglehart (1990) pointing out that current economic, 

technological, and sociopolitical changes have resulted in an apparent transformation of the 

fundamental cultural characteristics of developed societies. This transformation is ceaseless 

and has a greater impact on new generations. As new generations replace the older ones in 

the system of political, social and economic organization, the characteristics of adopted 

economic development strategies change, as well incorporating more environmental 

considerations. The implied redirection in priorities, behaviours and ethics is mostly 

reflected on agricultural policies and thus on rural land uses. Nowadays farmers receive 

more subsidies and other benefits in order to sustain and improve the quality of soil than to 

increase agricultural goods production. 

Recently, the concept of post-productivism has been enriched with a spatial dimension 

grounded on the observation that some regions have developed stronger post-ponductivism 

structures than others (Agarwal, Green et al. 2002; Reid, Tomich et al. 2006). Postponductivism 

is thought of as a spatial phenomenon too, that is strongly connected to the 

rural patterns of land uses. Amongst others, Marsden (1998), Groot et. al. (2007) and van der 

Ploeg et. al. (2000) underline the fact that there exist considerable spatial differences within 

developed countries at the regional level in relation to the intensity one can observe 

evidence of post-productivism. Nevertheless, Mather argues that in spite of the observed 

spatial differences in the strength of post-productivism characteristics amongst developed 

countries and regions, the phenomenon is present and lies on deep social and cultural 

transformations in progress in the developed countries. Such transformations are capable of 

inducing constructive institutional interference which is both a cause and a consequence of 

new policy planning and application. 

The emergence of post-productivism concept as a mean of interpreting the observed rural land 

use changes has received considerable criticism. Among others, Evans believes (2002) that 

productivism and post-productivism carry a dualistic meaning just like fordism and postfordism, 

and therefore, cannot contribute to an in-depth understanding of complex spatial 

phenomena. Alternatively, more solid theoretical approaches should be considered such as the 

regulation theory. According to this perspective, new economic patterns and social structures 

are the result of capitalist economic crises as well as efforts to overcome these crises. 

Accordingly, the observed spatial inequalities and the associated land uses and characteristics 

of agricultural sector are analogous to the established production relationships, to spatial 

division of labour and to changing power allocation in the field of governance. 

Recently, ecological modernisation theory has been proposed as an alternative to post-productivism 

argument in terms of rural land use change theorisation. According to the advocates of this 

view (Andersen and Massa 2000; Buttel 2000; Marsden 2004), it is possible and therefore it 

should be pursued, economic growth and social prosperity to be in line with environmental 

protection. Technological advances are at the core of this perspective as they are thought of



being able of substantially contributing to the effective utilization of natural resource and to 

the decrease in the volume of both utilized raw material and produced waste. 

Another recent view on managing deforestation proposes the concept of “compensated 

reduction” (Santilli, Moutinho et al. 2005). According to this view, countries that are chosen 

to lower their national level of deforestation should receive post facto compensation if they 

commit to stabilize or even reduce deforestation in the future. In other words, designing and 

offering large scale incentives might be a strategy capable of managing high deforestation 

rates as it occurs in tropical forest regions. This proposal works similarly to the Certified 

Emissions Reductions (CERs) or the Clean Development Mechanism (CDM). It also relates 

to the notion of valuation of the “unpriced” services of forests in order to reduce 

deforestation through economic mechanisms. 

In several instances, there exist perspectives linking deforestation directly to the expansion 

of agricultural activities. Deforestation due to agriculture expansion is threatening several 

critical aspects of the environment such as biodiversity. Regarding the driving factors, it is 

thought that technological development and international prices are the basic drivers of crop 

expansion. Nevertheless, local actors can develop ways to apply sustainable changes in the 

economic activities and reduce negative impacts managing deforestation rates through 

conservation policies. It seems, that the powerful position of some actors in the predominant 

production and distribution chains of agricultural goods is influenced immensely where 

generated prosperity accumulates (van der Ploeg 2000; Allen, FitzSimmons et al. 2003). In 

several instances, the greatest share of added value deriving from the ongoing restructuring of 

agricultural sector is not yielded by local producers and as a result the future course of 

sustainable agricultural systems appears uncertain (Smith and Marsden 2004). It is suggested 

that a reasonable response to the aforementioned issue could be the placing of restrictions on 

the size of food distribution enterprises as well as localising their characteristics (Raynolds 

2000; Allen, FitzSimmons et al. 2003; Seyfang 2006; Feagan 2007). However, such arguments 

are directly opposite to the prevailing process of globalization in food production and 

distribution. It might be more realistic as well as effective to accent and emphasize the 

fundamental qualitative differences between globalization and localization approaches. 

Globalization of food production and distribution systems abstracts and alienates economic 

transactions from their social and environmental context (Raynolds 2000; Seyfang 2006). 

Heterogeneity of rural social and natural forms and processes is neglected. In globalisation 

era, it is almost impossible for a consumer of a particular agricultural commodity to identify 

the particular social relations and environmental circumstances under which the commodity 

was produced (Allen, FitzSimmons et al. 2003). Contrarily, the suggestions of green economy 

favour the identification and designation of the social and environmental framework of 

production allowing the emergence of economies of place (Seyfang 2006). 

The success of economies of place, however, presupposes reconnecting effectually 

consumers and producers and establishing trustfulness and reliance amongst all involved 

actors (Seyfang 2006). Hence, it might be worth focusing on raising social capital in agriculture 

as well as building sufficient stocks of trust, communication and cooperation (Rahman and 

Yamao 2007). Even though, some critical views (Hinrichs 2003; Winter 2003; Winter 2005) 

point out that economies of place form a defensive strategy towards the ongoing 

globalization-driven transformations in agricultural production and distribution. 

This strategy is even likely to result in certain spatial disparities in the near future. At the 

regional and national scale the shift of society towards quality local products may apply 

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high pressure on the developing regions and nations which primarily rely on exports of 

agricultural products. Sustainable consumption, therefore, has a worth investigating spatial 

dimension needing more thorough consideration. In this respect, the notion of global 

ecological citizenship suggests that duties and obligations should be perceived in a wider scale 

and that consumption of agricultural goods should be guided by global sustainable thinking 

(Dobson 2003; Smith 2005). 

The theoretical schema of desakota by McGee (2007; 2008), is a relatively recent perspective 

trying to put urban expansion and land use change in the broader context of globalization. 

The perspective attempts to integrate new economic developments, technological change 

and other higher level forces with lower level factors such as distance, availability of 

infrastructure and new business opportunities. Although the model has mostly been tested 

in Asian regions, it is believed to hold significant potential for western Europe as well (Xie, 

Batty et al. 2007). 

Finally, the theoretical schemata concerning the structure and evolution of urban space 

include several perspectives based on urban geography and political economy. Among 

others the theoretical steam of expanding city addresses the importance of current 

technological progress in the field of information technologies, the massive increase in the 

volume, flow speed and spatial extent of goods and services exchanged as well as the new 

social values and ways of living (Munoz 2003; Zhang and Sasaki 2005). According to Ingram 

(1998), the contemporary city is characterized by a strong tendency of sprawl for both 

people and employment (Thurston and Yezer 1994). 

Despite their diverse origins and spatiotemporal scales of employment, the theoretical 

perspectives presented share some common features. It seems that both distance and 

accessibility have a significant influence on deforestation and land use patterns. Moreover, 

technological changes in transportation play a key role in urban evolution. The new social 

ethics, behaviours, preferences and ways of living also influence considerably the structure of 

space. Population and demographics which are traditional forces of change need also be taken 

into account in the context of regional and urban development. 

Summing up the discussion of the above perspectives, it can be argued that the common 

ground between the aforementioned theoretical schemata lies on the ascertainment that 

exurban land use conversions and modifications are advancing at a high rate (McCarthy 2005; 

McCarthy 2008). To some theorists, those changes are circumstantial or adventitious reactions 

of invested capital on rural areas in order to protect and insure its reproduction (Evans, Morris 

et al. 2002). Therefore, the current trajectories of rural land use changes could rapidly shift to 

new directions due to changes in the global economic environment. On the other hand, a 

growing number of arguments suggest that emerging land use patterns rely on deeper and 

stable changes in fundamental characteristics of society in terms of the people’s attitude 

towards natural environment (Inglehart 1990; Buttel 2000). However, it is still evident that the 

rural land use systems is still a matter of concern and an issue for study and evaluation. In the 

following sections, we attempt to produce empirical evidences on some of the theoretical 

arguments described above concerning the case of recent forest land use changes in Greece. 

4. Synthesis 

Following the literature review on deforestation, It is evident that a variety of economic and 

social forces might work competitively or additively towards the configuration of land use 

patterns across the regions or the prefectures of a country (Verburg, Soepboer et al. 2002;



Verburg 2006). Following, we make an attempt to connect all the important aspects of 

deforestation in a coherent conceptual framework. Based on the conceptual framework, an 

empirical forest land use change model is proposed and the possible effects of all 

explanatory variables are discussed hypothesizing that the effect of a particular variable 

may differ between geographical areas. 

The underlying causes of forest land use change vary from locality to locality as well as 

amongst countries (Wood and Skole 1998; Lambin, Turner et al. 2001; GLP 2005). These 

economic, socio-cultural and political forces are capable of impacting negatively or 

positively the extent and distribution of forest land. Their influence on forests usually 

results in quantitative changes as well as qualitative changes. Both conversions and 

structural habitat modifications are of great importance to policy makers. Reality is even 

more complex because of the usual linkages and interactions between positive and negative 

factors of change (Mahapatra and Kant 2005). The likely aggregated outcome of the 

combination of negative and positive factors is difficult to understand and predict. 

However, it may be more feasible to identify some dynamic processes through which 

socioeconomic and political factors operate resulting in distinct patterns of land uses. In this 

respect we propose urban sprawl, location decisions of economic activities, agricultural 

expansion and agricultural abandonment as the main factors of forest land use change. 

In most cases, quantification of these forces is a difficult task, as is also difficult finding the 

appropriate methodology capable of giving reliable estimations of the magnitude and 

importance of the relationships involved. A wide variety of approaches and techniques have 

emerged for this reason, with the intention to rationalise decision-making about land use 

change issues (Upadhyay, Solberg et al. 2006). How and to what extent existing 

methodologies have satisfactorily reached this target is also a matter of research. Among 

applied methodologies, statistical techniques concerned with land use change dynamics are 

the most widely used. These models primarily focus on the causes of deforestation rather 

than its sources. Observed land use patterns are tightly connected to urban and regional 

development policies and to the enlargement of the regional economy. Their ceaseless 

transformation is fuelled by the need for serving the rapidly changing economic and social 

requirements as well as for fulfilling newly arising demands as a result of economic 

liberalization, privatization and transformation of lifestyle. 

The morphology and evolution of land use patterns have been extensively studied and 

theorised by scientists of different disciplines (Wood and Skole 1998; Irwin and Geoghegan 

2001; Walker 2001; Verburg, Schot et al. 2004; Walker 2004). Thus, a plethora of theoretical 

and modelling approaches have been developed so far in order to provide possible 

explanations of land allocation processes. Two general categories of land cover /use 

changes are described in the literature: conversions and modifications (Baulies and Szejwach 

1998; Briassoulis 2000; Lesschen, Verburg et al. 2005). Land cover conversion refers to a 

change from one cover type to another whereas land cover modification implies structural 

or functional transitions in cover without loss in initial determinative characteristics. 

Similarly, land use conversion refers to a complete change from one use type to another 

whereas land use modification implies structural or functional alterations in use without 

loss of initial attributive characteristics. Finally, the driving forces (causes) of LUC change 

can be divided into two categories: Proximate causes and underlying causes. Proximate 

causes of land use change are associated with coarse anthropogenic operations that directly 

influence spatial patterns as, for instance, urbanisation, agricultural expansion and forest 

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exploitation (Geist and Lambin 2001; Lesschen, Verburg et al. 2005). Underlying causes of 

land use change are associated with generative agents that weave proximate causes, such as 

economic, socio-demographic and technological factors (Geist and Lambin 2001; Lesschen, 

Verburg et al. 2005). 

Proximate Land use change forces at the Macroscopic level 

causes 

Functioning and expansion of Transportation and other kinds of 

urban land forms and activities infrastructure 

Functioning and expansion or Livestock breading systems and 

shrinkage of agricultural 

activities 

forest resources exploitation 

Underlying Economic Factors 

causes 

Sector arrangement of the 

regional economy 

Sectoral Employment 

Sectoral structure Investments and business location 

decisions 

Size and synthesis of imports and 

exports 

Consumption patterns 

Productivity Diffusion of technology and 

adoption of innovations 

Competitiveness of the economy Mean size of businesses 

Technological level of the 

Scale and agglomeration 

economy 

economies 

Taxation Investment incentives and 

development policies 

Income distribution 

Social factors 

Added value 

Population skills level Housing policy 

Education level Institutions 

Social infrastructure Population quality in the public 

sector 

Social security 

Demographic factors 

Life style 

Population changes Indirect population potential 

Urban and rural population Direct population potential 

Age of the population 

Environmental factors 

Population mobility 

Soil fertility Biodiversity 

Topography Ecosystem productivity 

Climatic conditions Water resources 

Stretch of the coastline Insular or mainland area 

Table 2. Proximate and underlying causes of deforestation 

Another useful distinction regarding land use change driving forces. could also be taken 

into account. They can be categorised into “endogenously changed or shifting or 

metamorphotic forces” that usually change very quickly over time (e.g. employment



patterns of the new economy, location and relocation decisions of certain types of firms, 

supply and demand of certain products and services) “slow-shifting forces” (e.g. population 

size and other demographical characteristics) and “conditioning forces” which usually 

exhibit a temporal stability (e.g. soil types, geomorphology). The last categorisation of 

driving forces, in a way, implies that a steady state of land use patterns should almost never 

be expected. This endogenous, ever-changing nature of certain forces has been pointed out 

by theoretical approaches such as game theory and has also certain modelling implication in 

land use change studies. As Arthur (2005) states, out-of-equilibrium situations or the 

emergence of equilibria and the general unfolding of patterns in the economy calls for an 

algorithmic approach. Land use patterns may represent temporarily fulfilled or unfulfilled 

complex expectations not necessarily rationally formed as in the case of El Farol Bar 

problem (Arthur 1994). Table 2 provides a summary of some deforestation driving forces 

under the aforementioned categorisation framework. 

Regarding economic factors, their main effect on land use changes depends mostly on the 

changes of the sectoral structure of economy. For instance, there might be labour transfer 

from an economic sector to another as in case of agricultural expansion or large-scale 

tourism development that may follow a decrease in industrial employment. The 

aforementioned structural changes are likely to influence decisively the land use system and 

also result in changes in the allocation of labour and land. 

Population movements between regions are also important influential factors of land system. 

Such movements may have a direction from rural areas to large urban concentrations or there 

may be a reverse process of rural rebound where people move away from cities towards rural 

regions. In the first case, there might be intensification of the use of land in peri-urban space. In 

the second case, land use change happen in the countryside. 

Finally, energy policy and taxation in resources such as natural gas and petrol are possible 

to result in an increase in the use of fuelwood and thus in deforestation or they may result in 

the development of alternative energy sources such as wind energy, solar energy etc. The 

likely results on land use system and on forests are complex and difficult to predict. They 

depend on the applied economic policy and also on the level of environmental awareness of 

people and authorities. 

The impacts of the ongoing economic crisis on forests and on land use system in general, are 

difficult to forecast as there is no information on the likely duration of economic crisis, and 

on the particular counties, regions and economic sectors that will be affected most. 

5. Evidence from Greece 

Land use changes involve several positive and negative impacts on economic, social and 

environmental aspects. These impacts could have limited territorial scope or wider 

territorial implications, causing changes in the use of land in a greater geographical scale. 

(Chhabra, Geist et al. 2006). Impacts can also, have short-term, medium-term or even longterm 

action, be additive, synergistic, reversible or irreversible. Overall implications depend 

on recipients’ degree of sensitivity, the ability to absorb or cope with pressure, as well as the 

type, intensity, extent and duration of pressure. Consequently, recognition, estimation and 

evaluation of likely economic, social and environmental impacts connected to forest land 

use changes, is a difficult process (Chhabra, Geist et al. 2006). Fig. 1, presents the 

geographical distribution and magnitude of some key phenomena associated with forest 

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land use changes in Greece. Based on the information of the Fig. 1, it is possible put forward 

some comments concerning land use change in Greece: 

High deforestation constitutes a significant process of land use change in several insular 

as well as mountainous regions of the country (Minetos and Polyzos 2010). A limited 

number of mainland coastal regions as well as some regions adjacent to large 

metropolitan areas, present high deforestation rates. Examining the information on the 

maps, it is obvious that deforestation, very often, coexists with the urban sprawl and 

illegal housing activity. Beyond the obvious impacts on the biodiversity of these 

regions, there also emerge several questions concerning erosion processes, flooding and 

loss of ground via rain water washings. Taking into account that edaphogenic processes 

follow the geological time scale, such changes should be considered as being 

irreversible. At the same time, the cost of protecting human activities from flooding 

events increases, the available fresh water resources lower and microclimate and living 

conditions at the local level change. In the long term, reduction of biodiversity is 

expected to affect negatively development opportunities and to also influence the 

regional level of prosperity (Minetos and Polyzos 2010). Finally, it is worth mentioning 

that deforestation processes at numerous localities accumulate affecting wider areas at 

the regional scale and also fuelling global environmental issues such as global warming 

and climate change. 

Increased conversion and modification of agricultural land present high rates in the 

case of regions with large urban concentrations, in regions adjacent to the 

aforementioned ones as well as in several insular regions (Minetos and Polyzos 2009). 

Processes that contribute to the configuration of this pattern are: (a) Urbanisation of 

agricultural land and, (b) abandonment of marginal agricultural land. Therefore, in a 

great number of the aforementioned regions, the loss of agricultural land is connected 

to pressures deriving from urban sprawl and illegal housing activity (Minetos and 

Polyzos 2009). In the rest of the regions, loss of agricultural land is connected to the low 

competitiveness of agricultural sector and the problematic environmental and 

demographic characteristics within which agricultural activity takes place. 

It is apparent that economic forces such as land-rent, lead to structural changes to the 

economic base of regional areas in question (Polyzos 2009). However, if we take into 

consideration the way in which this economic transformation (illegal housing, urban 

sprawl) is happening, then it is likely that several negative economic, social and 

environmental impacts will emerge having long lasting action. More specifically, the 

shrinkage of the economic base of the regional spatial units and, in certain cases, the 

observed orientation of local economic base to a single activity generates phenomena of 

"monoculture" (e.g. tourism) in the economy. 

Urban sprawl, concerns most regions of the country but it presents particular intensity in the 

western and southern areas as well as in most of the islands. High sprawl of urban activities 

in ex-urban location is observed in also relatively remote areas (Polyzos and Minetos 2009). 

They are also frequent commercial linear developments following the major interregional 

transportation routes as well as extensive low density areas of urban forms (residential 

units, tourism infrastructure, etc). A development pattern like this needs to be supported by 

a large amount of infrastructure (road axes, networks of water supply, networks and 

installations of waste water treatment) the size of which might be disproportionate to the 

size of served population.



Fig. 1. Matrix of regional spatial units in Greece and the magnitude of significant land use 

change phenomena (NSSG 1994; NSSG 1995; NSSG 1999; NSSG 2004; NSSG 2004; NSSG 2006). 

135

136 


Illegal housing seems to be high in almost all neighbouring regions to large urban 

concentrations, and also in several costal locations and remote areas (Polyzos and Minetos 

2009). While in the past, illegal housing activity as a phenomenon was concentrated in urban 

and suburban space, resulting in significant negative consequences to the formation and 

functionality of cities, nowadays it appears that illegal housing phenomenon influences 

wider spatial units. 

a b 

c d 

Fig. 2. Hot-spots of land use changes: a) Very significant land use changes b) significant land 

use changes c) moderate land use changes d) low land use changes 

The phenomenon of land use change happens for multiple reasons and also presents 

important spatial differentiations. In order to acquire an overall understanding of land use 

transformations and modifications, we attempt to locate "hot-spots" (Reid, Tomich et al. 

2006) or "regions of very high activity" across the country (Figures 1 and 2). In the first 

column of matrix in Fig. 1 we have coloured with red regions that present very high (VH) 

rates in at least two of the four spatial phenomena that mainly drive land use changes. They 

also present high (H) activity in at least one of the four aforementioned phenomena. These 

regions constitute the "hot spots” or "first level areas” of land use change. The spatial 

"distribution of "hot spots" is presented in the Fig. 2a. In these areas, land use changes are 

rapid and extensive and they perhaps jeopardize the fundamental regional characteristics as 

well as economic, social and environmental equilibrium of these areas.



We have also created a second category of hot spots (regions with orange shading in matrix of 

Fig. 1). These areas include regions with high or very high intensity in three out the four 

phenomena or regions with very high intensity in two out of four phenomena, excluded the 

prefectures of first category. These regions constitute constitute "second level hot spots. Their 

spatial distribution is presented in the Fig. 2b. They are regions in which land use changes are 

significant in magnitude and their future course may become particularly problematic. 

A third category of regions consist of areas with high or very high rate in two out of four 

phenomena. In these areas, land use changes are of a moderate magnitude either because 

these regions are in a kind of recession compared to their past size of activity (eg Attica, 

Thessalonica, Dodekanisa etc), or it is expected that they are going to accelerated in the near 

future (Trikalas, Ioanninas etc). In most of the rest regions land use change phenomena 

present low intensity. 

Summarising the above discussion, it could be supported that the applied spatial policy in 

Greece attracts a relatively low interest compared to other sectoral policies. Consequently, 

objectives regarding regulation of space are not always explicit and compatible while 

sustainable management of space, protection of environment and relaxation of regional 

inequalities still remain issues that need to be managed and placed into a proper policy 

context. 


This paper has dealt with theoretical perspectives concerning forest land use change in 

general, as well as the factors of land use changes in Greece. Making informed land policy 

decisions is central to achieving sustainability at a regional level. Prior to formulating certain 

sustainable policy objectives and targets, the baseline information needed is the 

identification kind of the driving forces that influence current forest land use patterns. 

Generally speaking, these driving forces are closely associated with the economic, social and 

environmental context within which the regions exist and function. The effects on forest 

land of the predictor variables that where employed by this study, while significant in most 

regions are still characterized by many uncertainties. Some theoretically interesting 

explanatory variables have indicated that the effects of certain processes on land use 

changes may be important but not always straightforward. 

A synthesis and evaluation of the results brings up some important issues relevant to the 

theoretical framework of the field. In particular, a noticeable argument relates to the course 

of development of deforestation rate in the long term, when the major competing uses to 

forests are urban and not agricultural land uses as it is assumed by Angelesen’s (2007) 

model in the case of tropical deforestation. It seems that when the antagonism involves 

urban and forest land uses and when also the types of forest ecosystems fall into the 

category of not-productive forests (as it is the case for most Mediterranean forests) then 

land-rent generated by forest uses is unlikely to compensate for the one coming from urban 

development of the land. Urban land uses through market mechanisms will tend to outrage 

forests. As long as the Greek law is strongly opposed to the conversion of forest, it seems 

that, at least in the sort term, the only way of confronting this market-induced process might 

be the restructuring of the law enforcement mechanisms. 

On the other hand, it seems that in most urban prosperous regions as well as in their 

vicinity, deforestation has slowed down, although additional data need to be analysed. 

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138 


Deforestation moves to more remote regions as accessibility improves. There are 

interactions amongst indirect population potential and illegal housing activity that influence 

the spatial distribution of deforestation rate. This complex relationship implies a 

geographical transfer and dispersion of illegal housing phenomenon from urbanized 

regions to remote, less-urbanized ones. It is likely that urban populations remain the major 

source of illegal housing activity; however they now tend to exercise this activity in longer 

distances due to accessibility improvements. If this is the case then deforestation controllers 

are still in urban areas even though the impacts of their acts are being systematically 

“exported” to other areas. The geographical transfer of deforestation relieves forests in the 

places of origin but at the same time, it escalates pressure on the host areas’ forests. 

An additional issue, relevant to the abovementioned argument, relates to the geographical 

characteristics of the areas being deforested. In insular prefectures, land surface is a scare 

resource and areas covered with forests are limited. If such spatial units were involved in a 

lengthy urban-forest land use antagonism, it would probably be difficult for all stages of forest 

transition theory to take place. Due to geographical remoteness, small-sized insular regions 

cannot easily base part of their development on exploiting the land of adjacent regions. Thus, it 

is difficult for islands to “export” deforestation processes. Bearing in mind the scarcity of 

developable land, high rents associated with urban land uses, the lack of forest cadastral maps 

and the insufficient forest law enforcement mechanisms, it is more likely for insular forests to 

move to the direction proposed by the theory of the tragedy of commons rather than the one 

proposed by forest transition theory. Even if the country as a whole managed to increase its 

forests, there would probably be winners and losers at the regional scale. 

Generally speaking, the spread of urban uses in the countryside has negatively affected forests 

either directly or indirectly. The improvement of transportation infrastructure, the expansion 

of urban plans, urban sprawl, illegal housing activity and legal building construction activity 

create a complex negative background for forest land uses. On the other hand, changes in 

Gross Domestic Product in agriculture and change in tourism infrastructure either have 

limited or zero adverse impacts on forests. It seems that the observed improvement in the 

performance of agricultural sector in the relevant regions has not occurred in the expense of 

forests through some expansion of cultivated land. In addition, new tourism accommodation 

infrastructure, possibly due to the mandatory environmental impact assessment introduced in 

the early ‘90s, had limited negative effects on forests. However, regions with high growth in 

their tourism accommodation in ex-urban areas show significant signs of deforestation. 

In the context of planning a sustainable forest policy, accessibility issues as well as the 

spatial patterns generated by urban phenomena such as urban sprawl and illegal housing 

are of crucial importance. At first glance, the improvement in the regional level of prosperity 

might be associated with lower deforestation rate. However, it is not certain whether this 

additional prosperity has been achieved in a sustainable manner. It is likely that 

improvement in prosperity at some location has been achieved in the expense of forests at 

some other location. These results could guide further research into improving the 

understanding of spatial processes such as forest land use changes and into rationalizing 

forest-related decision making. Strategic project monitoring and appraisal as well as 

evaluation of project impacts on land uses can help spatial planners and land use decisionmakers 

to introduce specific environmental protection objectives into land development and 

planning processes.



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Part 2 

Mapping Deforestation

Geospatial Analysis 

of Deforestation and Land Use Dynamics 

in a Region of Southwestern Nigeria 

Nathaniel O. Adeoye 1,*, Albert A. Abegunde 2 and Samson Adeyinka 2 

1 Department of Geography, Obafemi Awolowo University, Ile-Ife, 

2 Department of Urban & Regional Planning, Obafemi Awolowo University, Ile-Ife, 

Nigeria 


Deforestation is a complex phenomenon as there is little agreement about the components and 

the processes involved in it. FAO, (2001a) defined deforestation as ‘the conversion of forest to 

another land use or the long-term reduction of the tree canopy cover below the minimum 10 

percent threshold’. The world’s original forest area, estimated at about 6 billion hectares, has 

declined steadily and about one-third of the forests have been lost during the past few 

hundred years (Sharma et al., 1992). The forests and woodlands of North Africa and the 

Middle East, for example, declined by 60 percent; those of South Asia by 43 percent; of tropical 

Africa by 20 percent; and of Latin America by 19 percent (Houghton et al. in WRI, 1987). 

Although the world’s forest area has been declining for centuries, it is in the last half of 20th 

century that the process was accelerated to an alarming rate (Osemeobo, 1991; Federal 

Environmental Protection Agency, 1992; Jaiyeoba, 2002). Since the 1960s, there has been a 

major change in the rate at which the forests are cleared. FAO, (1997) reported annual rates 

of deforestation in developing countries at 15.5 million hectares for the period 1980-1990 and 

13.7 million hectares for the period 1990 - 1995. The total forest area lost during the 15-year 

period was more than 220 million hectares, much larger than the total land area of Mexico. 

Between 1950 and 1983, the area of Africa’s woodlands and forests declines by 23% from 901 

to 690 million hectares. Between 1981 and 1985 tropical African countries such as Nigeria, 

Cote d’lvoire, Sudan and Zaire were losing their forest at annual rates of 4.0%, 2.5%, 5.0% 

and 3.7%, respectively (IBRD/World Bank, 1992). In absolute terms, tropical forests in Africa 

are being lost at the rate of 3.7 million hectare a year with over half of the deforestation in 

West Africa alone (UNEP, 2002). The rate of deforestation in Nigeria in the 1980s was of the 

order of 400,000 ha yearly, while afforestation was a mere 3,200 ha. At such rates, Nigeria’s 

remaining forest area would disappear by 2020 (Jaiyeoba, 2002). 

Deforestation and forest degradations are now widely recognized as one of the most critical 

environmental problems facing the human society today with serious long term economic, 

social and ecological consequences. This issue has received much attention from policy makers 

to general public in recent years with vivid images of cleared forests and burning trees around 

*Corresponding Author 

8

146 


the world. One of the consequences of deforestation is that the carbon originally held in forests 

is released to the atmosphere, either immediately if the trees are burned, or more slowly as 

unburned organic matter decays (Moutinho and Schwartzman, 2005). As reported by Diaz, et 

al., (2002), tropical deforestation in the Amazon alone is responsible for 2/3 of the Brazilian 

greenhouse gas emissions and it is estimated that 200 million tons of carbon, not including 

emissions from forest fires, are released annually into the atmosphere. 

The effect of deforestation on biodiversity and climate change has been the subject of scientific 

studies and many documentaries of media. Achard et al., 2002; Houghton, 2003; Fearnside and 

Laurance, 2004, revealed in their studies that Global deforestation and forest degradation rates 

have a significant impact on the accumulation of greenhouse gases (GHGs) in the atmosphere. 

The Food and Agriculture Organization (FAO, 2001) estimated that during the 1990s 16.1 

million hectares per year were affected by deforestation, most of them in the tropics. The 

Intergovernmental Panel on Climate Change (IPCC) calculated that, for the same period, the 

contribution of land-use changes to GHG accumulation into the atmosphere was 1.6±0.8 Giga 

(1 G = 109) tonnes of carbon per year (Prentice et al., 2001), a quantity that corresponds to 25% 

of the total annual global emissions of GHGs. The United Nations Framework Convention on 

Climate Change (UNFCCC), in recognizing climate change as a serious threat, urged counties 

to take up measures to enhance and conserve ecosystems such as forests that act as reservoirs 

and sinks of GHGs. The Kyoto Protocol (KP), adopted in 1997, complements the UNFCCC by 

providing an enforceable agreement with quantitative targets for reducing GHG emissions. 

Besides, Aina and Salau, (1992) and Adesina, (1997) reported that forest loss leads to loss of 

wildlife habitats and extinction of plant and animal species that play important roles in 

maintaining a balance in the environment. 

From the foregoing, it becomes obvious that the world tropical forest including Nigeria is 

depleting fast because of human influence. This is a problem at the macro and micro-level; 

such depletion is the result of government activities such as road development, arable 

farming, and land clearing for pasture (Osemeobo, 1991; Taylor et al., 1994; and Olofin, 

2000). Statistical estimates have also shown that there is a negative correlation between 

exploitation of the forest and conservation in Nigeria that is, the annual rate of forest los is 

greater than the rate of conservation (Osemeobo, 1990). 

The state of forests in general and tropical forests in particular, has been drawing the 

increasing disturbing attention of the world community. For instance, in the tropics, where 

about 2.5 billion people depend on natural forest resources for many economic and 

environmental goods and services, the depletion of forests has been posing threat to their 

means of livelihood. 

Recently, the United Nations initiated a global awareness through its Global Environmental 

Outlook 2000 (GEO, 2000). In the developed countries of Europe and America, this 

awareness is high and it is the cause of several policies and strategies aimed at 

environmental preservation and conservation. In developing countries the awareness is just 

emerging. Presently 115 nations have Environmental Protection Agencies (EPA) and more 

than 215 international environmental treaties have been signed on issues bothering on 

global warming, biodiversity, ocean pollution, ozone layer depletion, and export of 

hazardous wastes (Ibah, 2001). 

In Nigeria, government is also taking steps to correct the nation’s degenerated 

environmental condition. One of such efforts is the establishment of Federal Environmental 

Protection Agencies (FEPA), (now a department under the Ministry of Environment), with

Geospatial Analysis of Deforestation 

and Land Use Dynamics in a Region of Southwestern Nigeria 

its branches in all the states to monitor environmental quality including the forest resources. 

Very recently, and considering the need for environmental preservation, the government 

has established the Federal Ministry of Environment to oversee the country’s environmental 

problems. Many Non – Governmental Organizations (NGOs) have also sprung up to discuss 

environmental degradation and proffer solutions (Ogunsanya, 2000). 

The waves of concern on the state of our forest resources and the condition of the 

environment, have translated into a number of researches. Among the academics, studies 

have clearly revealed that forests worldwide have been and are being threatened by 

uncontrolled degradation and conversion to other types of land uses, influenced by 

increasing human pressure due to uncontrolled increase in human population resulting in 

agricultural expansion; and environmentally harmful mismanagement, including, for 

example, lack of adequate forest-fire control and anti-poaching measures, unsustainable 

commercial logging, overgrazing and unregulated browsing, harmful effects of airborne 

pollutants, economic incentives and other measures taken by other sectors of the economy 

(Houghton et al. in WRI, 1987; Sharma et al., 1992; Olofin, 2000; IPCC, 2000; FAO, 2001a; 

WRI, 2001; Jaiyeoba, 2002). Scholarly writings have also tried to explain the dimension 

and severity on global environmental changes (Arokoyu, 1999; Ogunsanya, 2000 and 

Jaiyeoba, 2002). 

Studies have also explored the causal factors of deforestation. For example, there is a general 

agreement that deforestation is due to drought, forest fire, use of fuel wood, spread of 

extensive agriculture, and rapid urbanization, among others, (Areola, 1994; Olofin, 2000; 

Meyer and Turner, 1992; Taylor et al., 1994 and Smith, 1993). There are also substantial and 

growing works on resource management and conservation for the purpose of improving the 

environment (Areola, 1994; Smith, 1993; Mitchell, 1989; Munro et al., 1986; Olokesusi, 1992; 

Reed, 1996 and Rees, 1990). 

In 2001, FAO published the Global Forest Resources Assessment 2000 (FRA 2000), which 

was largely based on information provided by the countries themselves and a remote 

sensing survey of tropical countries. It was supplemented by special studies undertaken by 

FAO. Among the outputs were two new global forest cover maps, estimates of forest cover, 

deforestation rates and forest biomass for each country, and several specialized studies on 

such topics as forest management and forest fires (FAO, 2001). 

But Boroffice, (2006) argued that the often-quoted rates of deforestation for Nigeria were 

based on mere estimates or surrogate data rather than empirical studies. Most of the 

vegetation maps produced by international organizations, such as FAO, for the country are 

nothing more than broad generalizations which are not usually in tandem with local 

realities and are therefore, of little use to local authorities for planning purposes. Thus, the 

rate of forest loss at both local and national levels is not known with any accuracy. 

To further the frontiers of knowledge on the state of the forest resources, which is still an 

inconclusive issue and to establish the emergence land use pattern from the depleted forest 

area, this study therefore, examines and analyses the extent of forest loss and land use 

dynamics; measures the rates of change over the periods of 25 years (1978-2003) in a part 

of southwestern Nigeria using a set of remotely sensed images and geographic 

information system (GIS) technology. There is a consensus among scientists that satellite 

images and GIS provide a reliable means for adequate and regular monitoring of forest 

estate. According to Ayeni (2001), application of GIS in environmental monitoring in 

147

148 


developing world is still at its infancy yet; it has been extensively used in Europe and 

North America. Recognizing the importance of reliable tool for forest monitoring both for 

environmental sustainability as well as economic well being, there is, therefore, need to 

explore the tool in the developing world. 

The specific objectives of study are to: 

i. identify the categories of land use and land cover from remotely sensed images; 

ii. measure the areal extent of each of the land uses/covers 

iii. assess the rate(s) of change in land use and forest area and compare the rates of change 

over time; and 

iv. analyze the temporal patterns of land use and changes in forest coverage area over the 

period 1978-2003 

2. The study area 

Studies on deforestation can be carried out in any part of the world where growth in 

human population has taken place, which still has potential for further growth and 

development. The choice of the study area is however, guided by the primary objective of 

the study to examine the occurrence of deforestation, which is better perceived in area 

where there has been rapid urbanization as the case of the study area. The study area is a 

region located in Southwestern Nigeria, well known for its dense forest resources and 

fertile soils. However, as a result of rapid population growth, which led to the 

development of urban centres and increased farming activities, much of the forest areas 

have been converted to farmlands, perhaps to meet the needs of the teaming population. 

The emerging concern about the disappearance of forests in the study area therefore 

presents the reason for a study of this nature. Coincidentally there is a preponderance of 

various types of data from which the type of study envisaged here can be expected with 

minimum difficulty. Besides, the concern for sustainability provides the impetus for the 

choice of the area. 

The study area spans part of Osun and Ekiti States (Figure1) and lies within latitude 7° 30´ 

and 7° 45´ North of equator and longitude 4° 30´ and 5° 00´ East of the Greenwich (Figure 

2). It is about 866.25 sq kilometres rectangle in size. On the Nigeria topographical map, it 

is found in Ilesa sheet 243. The relief is rugged with undulating areas and granitic outcrops 

in several places. The notable ones among the hills are the Efon-Alaaye hill to the 

east of the study area, and domed hills in Ilesa area. The climate is of the lowland Tropical 

rain forest type with distinct wet and dry seasons. Many factors are responsible for the 

removal of forest resources in the area; among them are intensive agricultural practices, 

the establishment of more local government areas in recent years, the development of 

tertiary institutions and location of industrial plants as the case of Ilesa, which led to the 

influx of people to the area and subsequent development of housing estate and 

infrastructures. 

According to 1991census figure, the study area is a populated area. Because of the dense 

population, the area has witnessed great structural development and growth, which in effect 

brought negative impact to the natural resources. The area of major population 

concentration is Ilesa with population of 130,321 based on 1991 census figure. Other 

populated areas include Ijebu-Ijesa, Efon-Alaaye, to mention but a few.



or 

B ipe 

Ob ok un 

Iles ha We st 

EK ITI 

OS UN 

Iles ha Sou th 

Ori ad e 

A ta ku mo sa E as t 

A ta ku mo sa W 

es t 

E fo n 

E k 

iti W e st 

OSUN 

Boripe 

Obokun 

Ilesha West 

Ilesha South 

Oriade 

Atakumosa West 

Atakumosa East 

Efon 

Ekiti West 

EKITI 

50 0 50 100 Kilometers 

LEGEND 

Ekiti state 

Osun state 

Fig. 1. The LGAs of the study area 

415000 

410000 

405000 

400000 

395000 

390000 

385000 

775000 

Osu 

775000 

780000 

Ilase 

Ibodi 

780000 

785000 

Idom inasi 

ILESA 

785000 

LGAs of the study area 

790000 

790000 

795000 

795000 

800000 

800000 

805000 

Ilare Esa-Odo Esa-Oke 

Ipole 

Iloko 

Iw araja 

Ijebu-Ijesa 

Ijeda 

Erinmo 

Erin-Ijesa 

Erin-Oke 

Ipetu-Ijesa 

805000 

6.8 0 6.8 13.6 Kilometers 

Fig. 2. Major communities of the study area 

810000 

Aramoko-Ekiti 

810000 

Ikogosi 

Ogotun 

815000 

815000 

W 

415000 

410000 

405000 

400000 

395000 

390000 

385000 

N 

S 

E 

LEGEND 

W 

Main road 

Major Towns 

N 

S 

E 

149

150 

3. Conceptual clarification 


3.1 Deforestation 

Deforestation is a complex phenomenon as there is little agreement about the components 

and its process. FAO (2001a) defined it as ‘the conversion of forest to another land use or the 

long-term reduction of the tree canopy cover below the minimum 10 percent threshold’. 

Deforestation is also referred to as “complete destruction of forest canopy cover through 

clearing for agriculture, cattle ranching, plantations, or other non-forest purposes” (Poor, 

1976; and Mayaux and Malingreau, 1996). Other forms of land-use changes, such as, forest 

fragmentation (altering the spatial continuity and creating a mosaic of forest blocks and 

other land cover types), and degradation (selective logging of woody species for economic 

purposes that affects the forest canopy and the biodiversity) are often included in estimating 

deforestation. The characterization of forest into one of these categories depends on the 

temporal and spatial scale of observation. The subjective meaning of the term deforestation 

is thus not only linked to a value system but also to the nature of the measurement designed 

to assess it (Poor, 1976; and Mayaux and Malingreau, 1996). Adopting different perspectives 

of deforestation in data analysis have caused considerable variations in estimation of the 

area of forest cleared. 

3.2 Facts about deforestation 

The world’s original forest area, estimated at about 6 billion hectares, has been declining 

steadily. About one-third of the forests have been lost during the past few hundred years 

(Sharma, et al., 1992). Between 1850 and 1980 about 15 percent of the earth’s forests and 

woodlands disappeared as a result of human activities. While the forests and woodlands of 

North Africa and the Middle East, declined by 60 percent; those of South Asia by 43 percent 

that of tropical Africa declined by 20 percent; and of Latin America by 19 percent. 

Depletion of forests is of particular significance because in the tropics, about 2.5 billion of 

her people depend directly or indirectly on natural forest resources for many economic and 

environmental goods and services. Between 1980 and 1985 the estimated annual rate of 

tropical deforestation was 0.6 percent or 11.4 million hectares (FAO, 1988). Recent studies 

estimates deforestation in the tropics at a rate of 17 to 20 million hectares annually (Rowe et 

al, 1992). Although the world’s forest area has been declining for centuries, it is within the 

last half of the 20th century that the process became accelerated. Since the 1960s, there has 

been a major change in the rate at which the forests are cleared. A recent study by FAO 

(1997) puts the annual rates of deforestation in developing countries at 15.5 million hectares 

for the period 1980-1990 and 13.7 million hectares for the period 1990 - 1995. The total forest 

area lost during the 15-year period was more than 29.2 million hectares. 

In 1999, the FAO reported that 10.5 per cent of Africa’s forest had been lost between 1980 

and 1995, the highest rate in the developing world and in sharp contrast to the net 

afforestation seen in developed countries. Forest loss between 1990 and 2000 was more than 

50 million hectares, representing an average deforestation rate of nearly 0.8 per cent per year 

over this period (FAO, 2001a). Between 1990 and 2002, a total of 12 million hectares of 

forests were deforested, of which sub-regional West Africa accounted for 15% of the 

countries (UNEP, 2002). The rate of deforestation in Nigeria in the 1980s was of the order of 

400,000 ha yearly, while re-afforestation was a mere 3,200 ha. At such rates, Nigeria’s 

remaining forest area would disappear by 2020 (Jaiyeoba, 2002). On the global scale, the rate 

of tropical deforestation is not known with any accuracy (World Resources Institute, 2003).



This informs gap in our knowledge. However, certain factors have been advanced to be 

responsible for deforestation and forest degeneration. 

3.3 Causes of deforestation 

Deforestation occurs for many reasons but it is important to distinguish between the causes 

that are directly related to deforestation and those that are underlying. The direct causes are 

those activities (by individuals, corporations, government agencies, or development 

projects), which clear forests. Underlying causes are those behind the direct causes, which 

motivate the direct causes. (http://www.wrm.org.uy/deforestation/UN report.html). 

Direct causes include commercial timber production, clearing of land for agriculture and 

urban expansion, and harvesting of wood for fuel and charcoal. These activities also open 

up forests by the construction of access roads to logging sites, fragmenting the forests and 

facilitating further clearance, resource extraction, and grazing by locals and commercial 

organizations (Rowe et al., 1992; UNDP, UNEP, World Bank and WRI, 2000; State of the 

World's Forests, 2001). According to the United Nations Framework Convention on Climate 

Change, the overwhelming direct cause of deforestation is agriculture. Subsistence farming 

is responsible for 48% of deforestation; commercial agriculture is responsible for 32% of 

deforestation; logging is responsible for 14% of deforestation and fuel wood removals make 

up 5% of deforestation (UNFCCC, 2007). 

Indirect (underlying) causes of deforestation include population growth, policies, 

agreements, legislation, lack of stakeholder participation and market factors that encourage 

the use of forest products, leading to loss, fragmentation or degradation (Rodgers, Salehe 

and Olson, 2000). Other causes of forest loss are conflict, civil wars and lack of good 

governance (Verolme and Moussa, 1999). 

In Africa as in all parts of the world, deforestation were caused by a combination of natural 

and human factors, the chief of which has been the conversion of forest lands to agricultural 

land (Adesina, 1997; Rowe et al., 1992). As Williams (1990) reported, the introduction of new 

crops and new methods of exploitations around 1600 radically altered tropical forests. It was 

reiterated that forests were cleared to make way for cash crops such as, rubber in Malaysia 

and Indonesia, coffee in Brazil, tea in India and China, sugar cane in the Caribbean, tobacco 

and oil palm in Asia. 

Ola-Adams (1981) attributed the removal of forested areas to intensive agricultural 

practices. It was reported that approximately 2,000 hectares of western edge of Ogbesere 

forest reserve in Nigeria had been cut over and replaced by permanent agriculture. The 

study further revealed that in some other parts of the high forest, several areas of forest 

estate were being de-reserved for the establishment of agricultural crops. Besides, in the 

current efforts to diversify the country’s economy, large areas of high forest zones were 

being cleared and planted with food and tree crops; 45,845 ha. for food crops; 10,000 ha. for 

oil palm; 73,000 ha. for cocoa and about 140,000 ha. for rubber plantations. 

There are many causes of contemporary deforestation, among them are corruption of 

government institutions (Burgonio, 2008; WRM, 2003), the inequitable distribution of wealth 

and power (Global Deforestation, 2006), population growth (Marcoux, 2000), and 

overpopulation (Butler, 2009; Stock and Rochen, 2009), and urbanization (Karen, 2003). 

Globalization is often viewed as another root cause of deforestation (YaleGlobal, 2007; Butler, 

2009), though there are cases in which the impacts of globalization (new flows of labour, 

capital, commodities, and ideas) have promoted localized forest recovery (Hecht, et al, 2006). 

151

152 


Experts do not agree on whether industrial logging is an important contributor to global 

deforestation (Angelsen and Kaimowitz, 1999; Laurance, 1999). Some argue that poor people 

are more likely to clear forest because they have no alternatives, others that the poor lack the 

ability to pay for the materials and labour needed to clear forest (Angelsen and Kaimowitz, 

1999). One study found that population increases due to high fertility rates were a primary 

driver of tropical deforestation in only 8% of cases (Geist and Lambin, 2002). However, a 

shift in the drivers of deforestation over the past 30 years has been noted. Whereas 

deforestation was primarily driven by subsistence activities and government-sponsored 

development projects like transmigration in countries like Indonesia and colonization in 

Latin America, India, Java, and so on, during late 19th century and the earlier half of the 

20th century. By the 1990s the majority of deforestation was caused by industrial factors, 

including extractive industries, large-scale cattle ranching and extensive agriculture (Rudel, 

2005; Butler and Laurance, 2008). 

3.4 Effects of deforestation 

Deforestation causes extinction, changes to climatic conditions, desertification, and 

displacement of populations (Sahney, et al, 2010). It is a contributor to global warming 

(Fearnsidel and Laurance, 2004) and is often cited as one of the major causes of the enhanced 

greenhouse effect. According to the Intergovernmental Panel on Climate Change, 

deforestation mainly in tropical areas, could account for up to one-third of total anthropogenic 

carbon dioxide emissions (IPCC Fourth Assessment Report). But recent calculations suggest 

that carbon dioxide emissions from deforestation and forest degradation (excluding peatland 

emissions) contribute about 12% of total anthropogenic carbon dioxide emissions with a range 

from 6 to 17% (Van der Werf, et al, 2009). Scientists also state that, Tropical deforestation 

releases 1.5 billion tones of carbon each year into the atmosphere (Defries, 2007). 

Reducing emissions from the tropical deforestation and forest degradation (REDD) in 

developing countries has emerged as new potential to complement ongoing climate policies. 

The idea consists in providing financial compensations for the reduction of greenhouse gas 

(GHG) emissions from deforestation and forest degradation" (Wertz-Kanounnikoff and 

Alvarado, 2007). 

Deforestation results in declines in biodiversity. Estimate shows that rainforest is losing 137 

plant, animal and insect species every single day due to deforestation, which equates to 50,000 

species a year (Rainforest Facts, 2010). Leakey and Roger, (1996) state that tropical rainforest 

deforestation contributed to the ongoing Holocene mass extinction. But scientific 

understanding of the process of extinction is insufficient to accurately make predictions about 

the impact of deforestation on biodiversity (Pimm, et al, 1995). Most predictions of forestry 

related biodiversity loss are based on species-area models, with an underlying assumption that 

as the forest declines species diversity will decline similarly. However, many such models 

have been proven to be wrong and loss of habitat does not necessarily lead to large scale loss 

of species. Species-area models are known to over-predict the number of species known to be 

threatened in areas where actual deforestation is ongoing, and greatly over-predict the number 

of threatened species that are widespread (Pimm, et al, 1995). 

3.5 Control 

Major international organizations, including the United Nations and the World Bank, have 

begun to develop programs aimed at curbing deforestation. The blanket term Reducing



Emissions from Deforestation and Forest Degradation (REDD) describes these sorts of 

programs, which use direct monetary or other incentives to encourage developing countries 

to limit and/or roll back deforestation. Funding has been an issue, but at the UN 

Framework Convention on Climate Change (UNFCCC) Conference of the Parties-15 (COP- 

15) in Copenhagen in December 2009, an accord was reached with a collective commitment 

by developed countries for new and additional resources, including forestry and 

investments through international institutions, that will approach USD 30 billion for the 

period 2010 - 2012 (UNFCC, 2009). Significant work is underway on tools for use in 

monitoring developing country adherence to their agreed REDD targets. These tools, which 

rely on remote forest monitoring using satellite imagery and other data sources, include the 

Center for Global Development's FORMA (Forest Monitoring for Action) initiative 

(FORMA, 2009) and the Group on Earth Observations' Forest Carbon Tracking Portal, (GEO 

FCT, 2010). Methodological guidance for forest monitoring was also emphasized at COP-15 

(UNFCC, 2009). 

4. Methodology 

4.1 Data acquisition and image processing 

Remotely sensed data of different sources were used for this study (Figures 1, 2, 3, & 4). This is 

because of the constraint of the availability of field data in this part of the world. These are 

Landsat MSS, acquired on March 15, 1978 of 80m spatial resolution; SPOT XS, obtained on 

May 19, 1986 and SPOT XS acquired on November 28, 1994, of 20m spatial resolution 

respectively; NigeriaSat_1 acquired on September 23, 2003, of 32m spatial resolution (Table 1). 

S/no Data type Date/Year 

obtained 

1. Landsat MSS 

March 15, 

1978 

2. SPOT XS 

May 19, 

1986 

3. SPOT XS November 

28, 1994 

4. NigeriaSat-1 

September 

23, 2003 

Spatial 

Resolution 

80m 185km 

20m 60km 

20m 60km 

32m 600km 

Source: Author’s field survey 

Table 1. Attributes of the images used for the Study 

Swath Spectral bands Agency 

4 Bands [Blue, 

Green, Red & 

Near infrared] 

3 Bands [Green, 

Red & Infrared] 

3 Bands [Green, 

Red & Infrared] 

3 Bands [Nearinfrared, 

Red & 

Green] 

153 

FORMECU, 

Abuja, Nigeria 

RECTAS, Ile-Ife, 

Nigeria 

FORMECU, 


NASRDA, 


Ideally, studies such as this would be better conceived if images were acquired twice a year 

to allow for seasonal variation in foliage coverage. In southwestern Nigeria for instance, the 

rainy season spans the period of eight months that is, between March and October while the 

dry season starts from November and lasts until February. The images used are both 

acquired in rainy season (Landsat MSS, 1978, SPOT XS, 1986 and NigeriaSat-1, 2003) and dry 

season (SPOT XS, 1994). These differences in the date of acquisition may cause disparity in

154 


the results. However, they were used because of the spectral information of the study area 

they contain. 

The data were extracted as a sub-scene from the original dataset. For the purpose of 

temporal land use/cover change detection, a common window covering the same 

geographical coordinates of the study area was extracted from the scene of the images 

obtained. The sub-map operation of ILWIS 3.2 Academic allows the user to specify a 

rectangular part of a raster map to be used. To extract the study area from the whole scene 

of the images obtained, the numbers of rows and columns of the area were specified. While 

Landsat MSS 1978 contained 600 lines and 733 columns pixels, SPOT XS 1986 and 1994 

consist of 1373 lines and 2005 columns pixels, respectively, when NigeriaSat_1 2003 has 1083 

lines and 1150 columns of pixels. 

Fig. 3. Landsat Multispectral (MSS) 1978 

The false colour composite was used for all the image data to relate colours and patterns in 

the image data to the real world features. For Landsat MSS 1978, channel 1 was assigned to 

red plane, channel 2 to green plane, and channel 3 to blue plane. This makes the band Red, 

Blue, Green (RBG-123) false colour composite. For SPOT XS 1986 and 1994, channel 3 was 

assigned red plane, channel 2 to green and channel 1 to blue plane. The band combination 

then consisted of Blue, Green and Red (BGR-321) to produce false colour composite. For 

NigeriaSat_1 2003 false colour composite, channel 1 was assigned to red plane, channel 2 to 

green plane and channel 3 to blue plane. This puts the band combination as Red, Green and 

Blue (RGB-123).



Fig. 4. SPOT Satellite XS 1986 

With constraints such as spatial, spectral, temporal and radiometric resolution, relatively 

simple remote sensing devices cannot record adequately the complexity of the Earth’s land 

and water surfaces. Consequently, error creeps into the data acquisition process and can 

degrade the quality of the remotely sensed data collected. Therefore, it is necessary to 

preprocess the remotely sensed data before the actual analysis. Radiometric and geometric 

errors are the most common types of errors encountered in remotely sensed imagery. The 

commercial data provider has removed the radiometric and systematic errors of the data 

used. However, while the unsystematic geometric distortion remains in the image. The 

geometric errors were corrected using ground control points (GCP). 

The process of georeferencing in this study started with the identification of features on 

the image data, which can be clearly recognized on the topographical map of the study 

area and whose geographical locations were clearly defined. Stream intersections and the 

intersection of the highways were used as ground control points (GCP). The latitude and 

longitude of the GCPs of clearly seen features obtained in the base map were used to 

register the coordinates of the image data used for the study. All the images were 

georeferenced to Universal Transverse Mercator projection of WGS84 coordinate system, 

zone 31N with Clarke 1880 Spheroid. Nearest-neighbor re-sampling method was used to 

correct the data geometrically. A correlation threshold was used to accept or discard 

points. The correlation range was within limits that is, 1 pixel size. The x and y corrections 

were below 0.5 pixel. 

155

156 

Fig. 5. SPOT Satellite (XS) 1994 

Fig. 6. NigeriaSat-1 2003 

Deforestation Around the World



4.2 Classification 

In this study, the satellite images were classified using supervised classification method. The 

combine process of visual image interpretation of tones/colours, patterns, shape, size, and 

texture of the imageries and digital image processing were used to identify homogeneous 

groups of pixels, which represent various land use classes of interest. This process is 

commonly referred to as training sites because the spectral characteristics of those known 

areas are used to train the classification algorithm for eventual land use/cover mapping of 

the remainder of the images. 

To validate the tonal values recorded on the satellite images with the features obtained on the 

ground and also to know what type of land use/cover is actually present, the study engaged 

in ground truthing to five communities of the study area. These are Ilesa, Ijebu-Ijesa, Efon- 

Alaaye, Iloko-Ijesa and Erin-Oke (Figure 2). Before the ground truthing, map of the study area 

was printed and was used as guide to locate and identify features both on ground and on the 

image data. The geographical locations of the identified features on the ground were clearly 

defined. These were used as training samples for supervised classification of the remotely 

sensed images. Eight categories of land uses and land covers were clearly identified during 

ground truthing. These are forest/secondary re-growth, agro-forestry, arable farmlands, 

fallow/shrub, bare soils, water body, bare rocks and built-up areas/settlements. 

4.3 Accuracy assessment 

Determination of meaningful change categories was conducted by evaluating the 

classification accuracy. Every classified pixel has accuracy for a particular land use/cover 

type. The most common and typical method to assess classification accuracy Error Matrix 

(sometimes called a confusion matrix or contingency table) was used to assess the accuracy 

assessment for this study. Error matrix compares, on a category-by-category basis, the 

relationship between known referenced data and the corresponding results of an automated 

classification. Such matrices are square, with the number of rows and columns equal to the 

number of categories whose classification accuracy is being assessed (Jensen, 1996). 


5.1 Accuracy assessment of satellite images 

The accuracy assessment of four temporal data shows that most land use types were 

classified with acceptable level of accuracies. The low classification accuracies found in 

Arable farmlands, Agro-forestry and Fallow/Shrub classes was due to the similar spectral 

characteristics in them and the prevailing season, which posed constraint to the 

classification process. However, the overall accuracy of the land use categories makes the 

study reliable for planning. The average reliability of Landsat MSS 1978 was 57.24% while 

the overall accuracy was 76.20%; SPOT XS 1986 average reliability was 66.46% and the 

overall accuracy of 67.43%; SPOT XS 1994 average reliability was 65.04 % while the overall 

accuracy was 60.88%. NigeriaSat_1 2003 average reliability was 63.25 % and the overall 

accuracy was 72.05% (Tables 2, 3, 4 and 5). 

Agrofo- Arable Bare Exposed Forest Settlement Shrub/ Water ACCURACY 

restry farmlands soils rock 

Fallow body 

Agroforestry 216 4 0 12 82 0 190 0 0.43 

Arable 

farmlands 

13 1801 302 74 1309 0 2782 10 0.29 

157

158 


Agrofo- Arable Bare Exposed Forest Settlement Shrub/ Water ACCURACY 

restry farmlands soils rock 

Fallow body 

Bare soils 3 1460 2417 44 271 74 359 6 0.52 

Exposed rock 0 82 77 4580 443 0 1 73 0.87 

Forest 70 549 50 2274 45232 0 3011 48 0.88 

Settlement 0 0 52 0 0 2695 0 0 0.98 

Shrub/Fallow 653 882 54 6 3809 0 5809 0 0.52 

Water body 0 55 26 129 274 0 1 33 0.06 

RELIABILITY 0.23 0.37 0.81 0.64 0.88 0.97 0.48 0.19 

Average Accuracy = 56.92 % 

Average Reliability = 57.24 % 

Overall Accuracy = 76.20 % 

Table 2. Matrix of land use/land cover for 1978 

Agroforestry Arable Bare Exposed Forest Settlement Shrub/ Water ACCURACY 

farmland soils rock 

fallow body 

Agroforestry 2027 0 1 0 1 0 29 0 0.82 

Arable 

farmland 

0 1254 241 10 0 0 38 544 0.57 

Bare soils 113 5000 3227 238 78 68 1000 3002 0.21 

Exposed rock 0 173 367 4052 21 0 29 2303 0.50 

Forest 214 15 146 146 23942 0 4431 4 0.72 

Settlement 0 3 41 0 0 22488 0 0 0.85 

Shrub/fallow 226 559 959 32 511 0 11444 21 0.80 

Water body 0 195 85 107 0 0 3 1381 0.78 

RELIABILITY 0.79 0.17 0.64 0.88 0.98 1.00 0.67 0.19 





Agroforestry 

Arable 

farmlands 

Bare 

soils 

Exposed 

rock 

Forest Settlement Shrub/ 

fallow 

Water 

body 

ACCURACY 

Agro-forestry 3763 0 0 0 124 0 109 0 0.78 

Arable 

farmlands 

0 33 2 1 0 0 3 0 0.82 

Bare soils 5 7825 11370 1222 238 113 2717 274 0.41 

Exposed rock 0 139 493 15659 1890 0 940 19489 0.35 

Forest 711 4 0 678 22169 4 1071 141 0.87 

Settlement 0 0 54 0 0 25244 0 0 0.86 

Shrub/fallow 137 1031 466 120 861 3 8163 7 0.70 

Water body 0 5 34 64 8 0 33 1926 0.89 

RELIABILITY 0.82 0.00 0.92 0.88 0.88 1.00 0.63 0.09 




Table 4. Matrix of land use/land cover for 1994



Agroforestry 

Arable 

farmland 

Bare 

rock 

Forest Bare 

soils 

Settlement Shrub/ 

fallow 

159 

Water 

body ACCURACY 

Agroforestry 1333 0 0 54 0 0 44 0 0.93 

Arable 

farmland 

0 5485 363 0 464 0 1662 44 0.68 

Bare rock 0 928 13507 0 34 30 19 1530 0.84 

Forest 2244 0 10 11412 0 0 4558 26 0.63 

Bare soils 1 5174 41 4 10517 720 6118 124 0.46 

Settlement 0 174 22 0 1438 13267 7 632 0.85 

Shrub/fallow 1475 504 64 657 1351 0 24127 123 0.85 

Water body 0 86 207 0 4 58 0 263 0.43 

RELIABILITY 0.26 0.44 0.95 0.94 0.76 0.94 0.66 0.10 





5.2 Land use change between 1978 and 2003 

The overall results of the study indicate that the area of forestland has been continuously 

declined, while the area of shrub/fallow, built-up area (settlements) and waterbody was 

proportionally increased (Table 6). 

Arae in Percentage 

100% 

80% 

60% 

40% 

20% 

0% 

Source: Author’s Data Analysis 

Fig. 7. Land use/land cover change 1978-2003 

Forest 

Arable 

Agro-forestry 

Bare soils 

Fallow 

Bare rock 

Water body 

Settlement 

Land use type 

2003 

1994 

1986 

1978

160 

Table 6. Land use change between 1978 and 2003 


5.3 Forest status between 1978 and 2003 

The area of forest in the study area was becoming smaller with time (Table 6). In 1978, forest 

area was 72,310.93 hectares, which constituted 60.40% of the entire land use. By 1986, the 

areal extent had decreased from 72,310.93 to 30,587.80 hectares, a decline of 57.7% within the 

period of 9 years. In 1994, there seems to be an increase in the areal extent of the resources. 

This is because in 1986, it was 30,587.80 hectares but in 1994 it increased to 36,635.75 

hectares. This increase was not an expansion in the coverage but re-growth of the forest 

area. In 2003, the forest resources in the study area had almost disappeared when the areal 

extent was reduced to 18,841.70 hectares. This represented a decline of 48.57% within the 

period of 10 years. From the analysis, a total area of 53,469 hectares of the forest resources 

was removed. 

The decline in the forest area as discovered in the study confirms the report of FAO (2001) 

that tropical forest including Nigeria was on the decline. According to the United Nations 

Food and Agriculture Organization (FAO), 93,900 km 2 of forest were cleared per year during 

the 1990s, with annual rates of forest loss (positive for all continents with tropical forests): 

Africa 0.8%, Asia 0.1%, Oceania 0.2%, North and Central America 0.1%, and South America 

0.4% (FAO, 2001). The study also upholds the recognition of Cassel-Gintz, and Petschel-Hels 

(2001) that discovered deforestation in their recent study and concluded that it is one of the 

core problems of global environmental change. UNDP, UNEP, World Bank and WRI, (2000) 

associated extensive deforestation with loss of biodiversity, climate change, watershed 

degradation and these pose threat to cultural survival of indigenous population, economic loss 

among others. According to the estimates by the Food and Agricultural Organization (FAO 

1983), Nigeria through careless exploitation and husbandry, destroys about 600,000 ha of her 

forest every year, as against the reforestation efforts of about 25,000 ha a year, which 

replenishes only about 4 percent of the loss. At such rates, Nigeria’s remaining forest area 

would disappear by 2020 (Jaiyeoba 2002).The general effect of timber exploitation mostly from 

the high forests which cover only about 12.41 million ha of the country’s 91.1 million ha of 

land space, (i.e. about 13.5%), is that the forest areas are fast reducing to savanna vegetation. 

Consequently, the forest stability is disrupted and its ecosystem has been seriously disturbed.



The disturbances are not only in terms of its ability to regenerate through the natural process, 

but some species of trees and fauna are being endangered (Okafor 1988). 

In recent times, precisely June 1992, the Earth Summit in Rio de Janeiro, Brazil more 

correctly known as the United Nations Conference on Environment and Development 

(UNCED) was convened. There was an agreement on a set of "Principles for a global 

consensus on the management, conservation and sustainable development of all types of 

forests" and devoted a full chapter of its Agenda 21 to "Combating deforestation". Since this 

conference, there has been debate on the phenomenon and conscious efforts are being made 

to carefully use and protect natural resources mostly in the developed countries. But there 

seems to be limited enthusiasm from developing countries, including Nigeria, where 

ineptitude, particularly as regards the implementation of environmental laws, has been the 

greatest bane to a sustainable environmental practice (Lines et al.1997; Ibah. 2001). For 

instance, between 1995 and 1999 the military junta in Nigeria showed nonchalant attitude to 

the sustainability of the forest resources. This gave room to illegal lumbermen to penetrate 

the forest in the country, both reserved (protected) and the opened forest (unprotected), as 

the case of the study area, thus declined the area of forest nationwide. In poor and 

developing countries where often the administrative structures are either too weak and/or 

corrupt to enforce rules and regulations effectively and where tax and incentive systems do 

not work, forest clearing and environmental degradation has proceeded unabated. 

5.4 Arable farmland status from 1978 to 2003 

Arable farming is one of the prominent economic activities among the people of the study 

area as it is reflected in Table 6. The farmlands occupied 9,441.88 hectares, which 

represented 7.89% of the entire land use in 1978. In 1986, the land area of arable farmlands 

expanded from 9,441.88 to 11,830.08 hectares, making an increase of 2,388.20 hectares. But 

there seems to be a decrease in the cultivated area in 1994. This is due to the period at which 

the image was obtained that is, November, the beginning of dry season in southwestern 

Nigeria. At this time, most crops had been harvested except few such as, cassava, maize, 

among others which are left for proper maturation. Thus, majority of the farmlands are 

abandoned till the next planting season and they are sometimes overgrown with shrubs 

during this period. Between 1994 and 2003, arable farmlands increased from 6,830.37 to 

15,923.20 hectares, representing an increase of 133.13%. 

A close assessment of arable farmland areas between 1978 and 2003 shows that, 6,482.32 

hectares of the land area was gained. This is negligible if we are going to solve the problem 

of food insecurity. However, the expansion of farmlands and declining in the forest area 

affirms the claim of Ola-Adams, (1981) and Williams, (1990). In their studies it was 

discovered that forests were removed to pave way for food and tree crops. Besides, the 

United Nations Framework Convention on Climate Change (UNFCCC, 2007) asserted that 

the overwhelming direct cause of deforestation is agriculture. In their discovery, subsistence 

farming was responsible for 48% of deforestation; commercial agriculture 32%; logging 14% 

and fuel wood removals make up 5% of deforestation. This was the observation of the study 

during the ground truthing. Besides, arable farmlands were scattered all over the study area 

especially around the settlements and some newly deforested areas and there were no large 

scale mechanized farming in the area. Again, arable farmlands were common features 

around the mountainous terrain and crops such as, maize, yam, cassava, rice, among others 

were mainly grown in the study area. 

161

162 


5.5 Status of agro-forestry between 1978 and 2003 

Agro-forestry, otherwise known as tree crop plantation in the study only constituted 0.47% 

that is, 558.76 hectares of the entire land use in 1978 (Table 6). The improved spatial 

resolution of the image data (SPOT 1986 and 1994) however, brought a significant difference 

to what was recorded in Landsat MSS 1978. Also, variation in the period at which the image 

data were taken influenced the differences in the pixel statistics of the two images. It should 

be recalled that while SPOT XS 1986 was obtained in May, the beginning of rainy season in 

southwestern Nigeria, SPOT XS 1994 was acquired in November, the beginning of dry 

season in the area. In May, tree canopies are just springing up due to the long periods of 

dryness. On the other hand, the tree canopies are still fresh in November despite the 

inception of the dry period. This difference in the time period is capable of influencing the 

canopy characteristics of the farmlands, consequently affecting the result. The estimated 

area of agro-forestry in 2003 was 4,774.30 hectares, which represented an increase of 

342.03%. Although agro-forestry is not commonly found in the study area due to poor 

topography in some areas, the tree crop plantations are well grown in some areas around 

Ilesa, Erin-Oke, Aramoko-Ekiti (Figure 2). Because of the economic value of the crops, many 

deforested areas have been replaced with the cocoa plantations in particular, which 

confirms the findings of Ola-Adams, (1981) and Williams, (1990). 

Although research in agro-forestry especially in Africa, has paid a significant quantum of 

emphasis on this concept for about three decades, it again is adaptation of earlier system of 

slash and burn and perennial forest gardens, although on a more scientific basis. The 

benefits of incorporating trees, especially fast growing legume species, lies in increasing soil 

fertility, obtaining complimentarity in resource use, reducing environmental stress and 

protecting crops. 

5.6 Bare rock status from 1978 to 2003 

The pixel statistics in Table 6 shows that the underlying rocks were being exposed in the 

study area. For instance, the area occupied by the exposed rock was 7,340.62 hectares in 

1978, which was 6.13% of the total land use. The proportional decreased in 1986 was due to 

the prevailing season that is, wet season. During this period, the whole landscape was 

covered with vegetation making it difficult for rock to be seen as rock. This constraint in 

effect influenced the recorded areal extent of 2,246.29 hectares, which represented a decrease 

of 69.40%. In 1994, the area occupied by rock increased to 12,979.33 hectares, because of the 

prevailing season that is, dry season. At this time, the vegetation had dried up thus, making 

exposed rocks visible for classification. The year 2003 showed a decrease of 43.89% in the 

area of bare rocks. This could be because of the prevailing season at the time the image was 

acquired that is, September, the peak period of raining season in southwestern Nigeria. It is 

instructive to note that the rocks of the study area show great variation in grain size and in 

mineral composition, which enhanced the growth of dense vegetation whenever there is 

enough moisture. During the field survey, some quarry sites were sighted, which could in 

effect lead to the reduction in the size of the bare rocks in the study area. 

5.7 Status of bare soils from 1978 to 2003 

There are continuous exposure of soils to intense solar radiation and direct precipitation in 

the study area. This is shown by the expansion of bare soils discovered in the study. For 

example, in 1978, the area occupied by bare soils was 2,213.28 hectares representing 1.85% of



the entire land use area. This increased to 9,347.52 hectares in 1986, representing 322.30% 

increase over the period of 9 years (1978 - 1986). The bare soils decreased by 53.48% in 1994 

and expanded by 33.73% in the year 2003. Between 1978 and 2003, the estimated area of bare 

soils expanded from 2213.3 hectares to 5814.9 hectares, which put an increase at 3601.6 

hectares (Table 6). 

Although the prevailing season partly influenced the trend, it was obvious some land areas 

were void of vegetation in the study area. Continuous exposure of soils could pose serious 

problem to agricultural sector and consequently continuous food insecurity. This is because 

clearing of forest lands contribute to severe soil erosion, soil infertility and flooding. The 

multiplier effect on the economy could be severe if quick action is not taken. In the first place, 

soil erosion would reduce agricultural land area, which in effect could induce food shortages 

and out-migration of the able-body from the rural community to the urban environment. This 

claim is already manifesting in most rural communities in Nigeria including the study area. 

Arokoyu (1999) reported that “environmental devastation has led to the loss of the means of 

livelihood of people, fall in agricultural outputs, out-migration of able-bodied youths, and 

engendered social rifts and intensified confusion. Presently, a community in the study area 

(Efon-Alaaye) has already been devastated by soil erosion and government had sunk millions 

of Naira to redeem the community from being swept away by erosion. This situation is 

capable of discouraging the able-bodied youths from the community and induces migration to 

urban centres, thereby increasing urbanization process. 

5.8 Water body status from 1978 to 2003 

The drainage basin area in 1978 was 1.39 km 2. This increased to 57.83 km 2 in 1986, which 

was equivalent to 4060.43% increase. This increment was dictated by the improved spatial 

resolution of SPOT XS 1986, which is 20m as against 80m spatial resolution of Landsat MSS 

1978. In SPOT XS 1986, streams, rivers, ponds and dams were more clearly visible, which 

enhanced the area calculation. In 1994, the drainage basin area calculated reduced by 

20.15%, which consequently put the total area at 46.18 km 2. The reason for this can be traced 

to the prevailing season. At the time the image data was obtained, the rain had just ended in 

southwestern Nigeria and the tree canopy was still fresh and dense. Most streams and rivers 

were covered with riparian forest thus, making it difficult for rivers to be assigned to class 

ID for eventual classification. In 2003, the area of water body was 46.09 km 2, which was still 

a reduction in the coverage of water body. The reason for this is connected to the prevailing 

season that is, wet season and some noises in the image data, which in effect reduced its 

optimum visual systems. The pixel statistical result of water body in the study does not 

reflect the situation on ground and does not support the present claim of climate change. 

High spatial resolution images is therefore, recommended for study on hydrology. 

5.9 Status of fallow/shrub from 1978 to 2003 

Bush fallow is a periodic relocation of farmland for the purpose of allowing soil to regain its 

fertility. Table 6 shows that the area of fallow has been increasing over the years, the highest 

being recorded between 1994 and 2003. For example, between 1978 and 1986, the fallow area 

increased from 23,397.9 hectares to 35,330.3 hectares, which puts the percentage change 

within the period of 8 years at 51%. This therefore implies that per year, the land areas that 

were left to fallow in the study area were 1,492 hectares. The spatial coverage, however, 

decreased from 35,330.3 hectares in 1986 to 26,804.2 hectares in 1994, which put decline at 

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164 


24.13%. This indicated that 8,526.1 hectares of fallow lands were used up within the period 

of 9 years. But between 1994 and 2003, the trend changed as fallow area increased from 

26,804.2 hectares in 1994 to 59,436.8 hectares in 2003. This shifted the percentage change over 

the period of 9 years from 24.13 to 121.74. 

The increase in the areal extent of shrub/fallow and a decrease in the forest area suggested 

that part of the deforested areas were abandoned to secondary re-growth. According to 

Aweto (1990), in Nigeria, the area previously characterized by continuous forest cover has 

been converted into secondary re-growth vegetation, mainly as a result of shifting 

cultivation and lumbering. In study area, some deforested areas were left uncultivated 

thereby converted to secondary re-growth while some farmlands were left to fallow thus, 

created a large expanse of fallow lands in the area. 

5.10 Built-up area status from 1978 to 2003 

The development of settlements in the study area has been rather gradual. This is because 

the study area comprises of many rural settlements and few urban centres and why major 

development takes place in the urban settlements, little or no development takes place in the 

rural settlements. This accounted for the proportion of 943.39 hectares in 1978. Although 

there was an increase in the areal extent in 1986, it only amounted to an increase of 66.17% 

over the period of 8 years. Settlements also expanded in their areal extent between 1986 and 

1994, (i.e. 1567.6 to 1,947.9 hectares). This shows that within those periods, all the 

settlements expanded by 380.3 hectares, which constituted 24.26% increase. Between 1994 

and 2003, settlements expanded by 142 hectares, which makes the total area covered by 

settlements to increase from 1,947.9 hectares, to 2,089.6 hectares. This shows that annually, 

between 1994 and 2003, 15.78 hectares of lands were gained for settlements, which 

represented 7.29% growth between 1994 and 2003. 

The expansion of settlements, due to increased in human population and decrease in forest 

area shows that forests were been destroyed in the study area to pave way for human 

habitation. This confirms the findings of the Mather, (1990) and Harcourt, (1992) that 

reported an inverse relationship between population and forest cover. Geist and Lambin, 

(2002) also revealed that population increases due to high fertility rates were a primary 

driver of tropical deforestation in only 8% of cases. 

6. Conclusion 

The study, through the capability of GIS technology and remote sensing data revealed a 

steady decline in forest area and land use intensification with the expansion in farmlands, 

fallow ground and built up/residential areas. This indicates that forests were being 

converted to agricultural use and housing estate. However, the disappearance of forest 

resources could pose serious threat to biodiversity. For instance, there has been an underlying 

assumption that as the forest declines, species diversity will decline similarly. Many such 

models have been proven to be wrong and loss of habitat does not necessarily lead to large 

scale loss of species. Study that will give more insight on the process of biodiversity extinction 

resulted from deforestation will be needed since the scientific understanding of the process of 

extinction is insufficient to accurately make predictions about the impact of deforestation on 

biodiversity. Moreover, it should be borne in mind that forest resources preserve ecosystem, 

offers economic and social opportunity for people. Besides, forests foster medicinal



conservation. There is therefore need to protect our forest since unprotected forest will 

disappear faster than the protected one. Demarcating some forest zones as forest reserve areas 

could do this. Also, the indigenous participation and the involvement of the community 

people together with the forest guards in forest monitoring will go a long way to salvaging our 

forest from total depletion. The approach of GIS in this study was found useful as adequate 

tool for regular and up-to-date monitoring of forest and earth resources. 

7. Acknowledgements 

The impetus received from Prof. Bola Ayeni (Department of Geography, University of 

Ibadan, Nigeria) and Prof. F. A. Adesina (Department of Geography, Obafemi Awolowo 

University, Ile-Ife, Nigeria) in contributing a chapter to this book was appreciated. The 

authors valued the cooperation of the staff of RECTAS, Obafemi Awolowo University, Ile- 

Ife; NASRDA, Abuja and GIS unit of the Department of Geography where the satellite 

images used for this study were collected. Finally, the privilege offered the authors by the 

CEO of INTECH open access publisher to contribute a chapter to this book was treasured. 

8. Definition of terms 

Deforestation: 

Deforestation is the removal of a forest or stand of trees where the land is thereafter 

converted to a non-forest use. Examples of deforestation include conversion of forestland to 

farms, ranches, or urban use. Deforestation occurs for many reasons: trees or derived 

charcoal are used as, or sold, for fuel or as timber, while cleared land is used as pasture for 

livestock, plantations of commodities, and settlements. 

Land use: 

Land use is characterized by the arrangement, activities and inputs people undertake in a 

certain land cover type to produce change or maintain it (FAO, 2005). Land use is the 

specific activity a piece of land is put into. Various land use patterns emerge after the land 

has been subjected to use over time. In the rural area for instance, the type of land use 

include farming, plantation, grazing, etc. 

Land cover: 

The definition of land cover is fundamental, because in many existing classifications and 

legends, it is confused with land use. Land cover is the observed (bio) physical cover on the 

earth’s surface (FAO, 2005). 

Biodiversity: 

Biological diversity or biodiversity means the variety of plant and animal life at the 

ecosystem, community or species level and even at the generic level, Biodiversity is most 

commonly measured and reported at species level with characteristics such as species riches 

(number of species), species diversity (types of species) and endemism (uniqueness of 

species to a certain area) being the most useful elements for comparison (UNEP, 2002). 

Environmental degradation: 

Environmental degradation can be defined as any modification of the environment that 

implies a reduction or loss of its physical and biological quantities caused by natural 

165

166 


phenomena or human activities ultimately representing a decrease in the availability of 

ecosystem, good and services to human populations (Landa et al., 1997). 

Spatial & spectral resolution: 

The spatial resolution refers to the size of the area on the ground that is summarized by one 

data value in the imagery. This is the Instantaneous Field of View (IFOV). Spectral 

resolution refers to the number and width of the spectral bands that the satellite sensor 

detects (Eastman, 2001). 

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Unsupervised Classification 

of Aerial Images Based on the Otsu’s Method 


9 

Antonia Macedo-Cruz1, I. Villegas-Romero3, M. Santos-Peñas2 and G. Pajares-Martinsanz2 1Hidrociencias, Campus Montecillo, 

Colegio de Postgraduados, Montecillo, Estado de México, 

2Facultad de Informática, Universidad Complutense de Madrid, 

3Universidad Autónoma Chapingo, 

1,3México 2España Remote-sensing research focusing on image classification has long attracted the attention of 

the remote-sensing community because classification results are the basis for many 

environmental and socioeconomic applications. However, classifying remotely sensed data 

into a thematic map remains a challenge because many factors, such as the complexity of the 

landscape in a study area, selected remotely sensed data, and image-processing and 

classification approaches, may affect the success of a classification [1]. 

In forest management, a number of activities are oriented towards wood production or 

forest inventories with the aims of controlling parameters of interest such as diameter of 

trees, height, crown height, bark thickness, canopy, humidity, illumination, CO2 

transformation among others, always with the goal of environmental sustainability with 

high social impact. The unsupervised classification of aerial image offer solutions for 

monitoring production in forest trees while the same time costs are minimized. Also with 

Unmanned Aerial Vehicles (UAV) equipped with an appropriate image classification 

system, have become powerful tools for early fire forest detection and posterior monitoring. 

This technology has also been applied for crop monitoring under wireless sensor network 

architecture. 

Clustering is the task of categorizing objects having several attributes into different classes 

so that the objects belonging to the same class are similar, and those that are broken down 

into different classes are not. Clustering is the subject of active research in several fields such 

as statistics, pattern recognition, machine learning, data mining, information science, 

agriculture technology and spatial databases. A wide variety of clustering algorithms have 

been proposed for different applications [1], [2]. 

Classification and segmentation in agriculture and forest management is an interesting 

topic but not new. There are many classification approaches that are oriented toward the 

identification of textures in agricultural and forest images. Most of them can be grouped 

as follows.

172 


Currently, many of the agriculture, livestock and forestry are planned using spatial 

analysis tools, seeking different specific objectives [3]. In this sense, the images acquired 

by remote sensors provide the necessary spatial resolution to obtain information about 

objects, areas, or phenomena on the earth’s surface, at different scales. These sensors 

measure the intensity of the energy emitted or reflected by the objects by means of the 

electromagnetic spectrum [1]. 

There are many segmentation techniques reported in the literature [4, 5]. Most color 

image segmentation techniques are usually derived from methods designed for 

graylevel images. Processing each channel individually by directly applying graylevel 

where the channels are assumed independent and only their intra-spatial interactions 

are considered [6]. Another option is decomposing the image into luminance and 

chromatic channel: after transforming the image data into the desired (usually 

application dependent) color space, texture features are extracted from the luminance 

channel while chromatic features are extracted from the chromatic channels, each in a 

specific manner [7]. Reference [8] and [9] show combining spatial interaction within 

each channel and interaction between spectral channels and gray level texture analysis 

techniques are applied in each channel, while pixel interactions between different 

channels are also taken into account. 

Based on the presented considerations and in order to tackle the classification problem 

addressed in this paper, we have designed a new automatic strategy based on the 

thresholding Otsu´s method is proposed. The first step consists in the thresholding of each 

R, G and B channels into two parts based on within-class and between-class variances 

suggested by Otsu [10]. This allows to classify each pixel to one part of each channel, so that 

conveniently combined the a pixel should be classified as belonging to one of the eight 

possible classes. Although in this paper we only use eight classes, the method can be easily 

extended to more classes, as described in section 2.2.2, even we can achieve until twenty 

seven. This makes the main contribution of this paper. 

Additionally, one major advantage of this algorithm is that it does not need to know how 

many classes are required to be clustered in advance, as it is required for most supervised 

clustering processes. The termination criterion is established based on the within-class 

variance, according to the Otsu’s method. 

The proposed method is compared against the well-known Fuzzy c-means clustering [11], [12]. 

The prediction of the correct number of clusters is a fundamental problem in unsupervised 

classification problems. Many clustering algorithms require the definition of the number of 

clusters beforehand. To overcome this problem, various cluster validity indices have been 

proposed to assess the quality of a clustering partition [13]-[16]. Five cluster validation 

indices have been used in our tests, they are: Dunn’s [15]-[19], Davies-Bouldin [15], [19]-[21], 

Calinski-Harabasz [15], [21]-[24], Krzanowski and Lai [22], [25]; and Hartigan [21], [22], [26]. 

We have used five because there is not relevant conclusions in the literature about their 

performance, depending on the application their behavior could vary considerably. Based 

on the above indices we have verified the best performance of our approach against the MS 

method, particularly in the images where water bodies are present. 

The remainder of the paper is organized as follows. In Section 2, materials and methods; two 

issues will be addressed, unsupervised classification of color images and five of cluster 

validation indexes. Section 3, result and discussions; Conclusions are presented in Section 4.

Unsupervised Classification of Aerial Images Based on the Otsu’s Method 173 

2. Materials and methods 

2.1 Study area 

In the present investigation 16 color aerial photographs in digital format were used, owned 

by the Institute of Geography of the Autonomous University of Mexico, taken in October 

1997. The photographs correspond to the catchments of the river La Sabana, Guerrero, with 

spatial resolution of 1:19500, and three-band spectral resolution visible and radiometric 

resolution RGB from 0 to 255 levels. As an example, Fig. 1 displays a representative image of 

the set of images analyzed in this work. As we can see it contains several textures which 

must be classified as belonging to a cluster. 

Fig. 1. Land cover images in RGB color model 

2.2 Classifier based on the theory of the Otsu’s method 

2.2.1 Brief description of the Otsu’s method 

Otsu’s method [10], one of the most widely used thresholding techniques in image analysis, 

has showed great success in image enhancement and segmentation. As mentioned before, it 

is an automatic thresholding strategy; we exploit the automatic capability for designing the 

unsupervised classification strategy justifying its choice. 

This research sought the optimal threshold (single or multiple) for each of the spectral bands 

of the color image by applying the algorithm modified [27, 28]: 

The number of pixels with gray level i is denoted f i giving a probability of gray level i in an 

image of 

f 

i pi N 

Then, the probability distributions for each class is 

(1)

174 

w 

k 

pi 

iC k 


The k w is regarded as the zeroth-order cumulative moment of the kth class Ck. and the 

means for classes is 

i. pi 

uk 

(3) 

w 

iCk In the case of bi-level thresholding, Otsu defined the between-class variance of the 

thresholded image as: 

Where 

And 

k 

2* ( ) 2 ( ) 2 

B 

w 1 

u 

1 

u T 

w 2 

u 

2 

u 

T 

(4) 

M 

 

T 

 

k 

 

k 

k1 

M 

wk 1 

(6) 

k1 

Assuming that there are M-1 thresholds, {t1, t2, …, tM-1}, which divide the original image 

into M classes: C1 for [1,…, t1], C2 for [t1+1, …, t2], … , Ci for [t i-1+1, …, t i], …, and CM for 

2 

[tM-1+1, …, L], the optimal thresholds {t1*, t2*, …, tM-1*} are chosen by maximizing B as 

follows 

then 

{ t *, *,..., *} arg { 2 

1 

t 

2 

t 

M1 Max 

B 

( t 

1 

, t 

2 

,..., t 

M1 

)} 

1 t 

1 

... t M1 

L 

2 M 

( ) 

2 2 M 

2 

B 

wu 

k k 

u T 

wu 

(8) 

k k 

k1 k1 

A threshold value tOtsu developed by Otsu is the one that maximizes vart between-class, or 

equivalently minimizes vart within-class. 

Min1 t 

Var 

1 

t 

 

tOtsu t L withinclass arg ( ) 

t arg Max ( Varet ) 

Otsu t L between class 

where L is the number of gray levels in each band, in our images L is 256 because each pixel 

is represented with eight bits. 

(2) 

(5) 

(7) 

(9)


2.2.2 Unsupervised classification strategy by within-class and between-classes 

spectral variances 

There are three steps in the proposed classification strategy. First, the assignment process, 

that consists in assigning one of the possible classes to each pixel. Second, the codification of 

each cluster, which is identified by a label. Finally, a regrouping process so that very similar 

classes are merged into one. 

2.2.2.1 Assignment process 

Given a pixel i located at (x, y) in the original RGB image. Its three spectral components in 

this space are obtained, namely R(x, y) = ir, G(x, y) = ig and B(x, y) = ib. 

As already mentioned, the thresholding methods split the histogram into two regions. As 

there are three spectral components, six sub-regions are obtained. If necessary, successive 

thresholding can be applied to each spectral channel. The second thresholding produces 

three partitions per channel. If a third thresholding is applied, four regions per component 

are obtained and so on. Therefore, assuming that eventually the number of thresholds per 

channel is M, there will be tR1, tR2, … tRM , thresholds for channel R, and in the same way, tG1, 

tG2, …, tGM for component G, and tB1, tB2, …, tBM, for component B. Based on this, each pixel i 

can be coded as is according to its spectral components by Equation (10): 

0 if i t 

 

 

1 if t i t 

i 

 

s 2 if t i t 

 

M if is tsM 

s s1 

s1 s s2 

s2 s s3 

where s denotes the spectral component, i.e., s = R, G or B, and tsi are the consecutive 

thresholds. 

For example, it is known that in the RGB colour space values are in the range [0, 255]. So, 

considering the spectral component R with two thresholds, tR1 = 120 and tR2 = 199, a pixel 

will be coded as 0, 1, or 2, if its spectral value R is smaller than 120, between 120 and 199, or 

greater than 199, respectively. 

2.2.2.2 Cluster labelling 

Once the whole image has been coded, the next step is the labelling of the existing classes. If 

M thresholds haven been obtained, there are n = M + 1 histogram partitions per channel, 

and therefore the number of possible combinations is nd, where d is the number of spectral 

components, i.e., d = 3 in the RGB colour space. This number of combinations represents the 

number of classes. Each cluster is identified by its label. Every pixel is assigned its 

corresponding label according to Equation (11). So, given the pixel i ≡ (x, y) with codes , ı, and ı, its label will be given by P as follows: 

2 

i R G B 

(10) 

p ni ni i 

(11) 

2.2.2.3 Merging process 

Let Ck be the number of clusters obtained by the classification procedure, where k identifies 

a class between 1 and nd, each class containing Nk pixels of the original image. It could be

176 


said that each class is defined by a tri-dimensional vector (d = 3). The elements of that vector 

are the spectral components of the pixels according to the RGB colour model, i.e., i ≡ 

( , , ) for the pixel i ≡ (x, y), if the pixel and its spectral components belong to class Ck. 

For each class, the average value of the membership degrees to that class is calculated by 

Equation (12): 

k k k , , 

1 

k R G B ik 

(12) 

N 

k ikCk 

Based on the potential of Otsu’s method, it is possible to estimate the within-class and the 

between-classes spectral variances, denoted by k 

and kh respectively, according to 

Equations (1) and (16). Obviously, is only related to class Ck and, as expected, kh 

 

 

k 

involves the two classes Ck and Ch, k ≠ h: 

2 

1 

2 

2 

k k k k k k 

k iR R iG G iB B 

 

dN 

k ikC 

 

k 

 

 

 

1 k h 

2 

k h 

2 

k h 

2 

kh R R G G B B 

 

 

 

 

d 

(14) 

Based on those variances, some classes can be fused due to their spectral similarities. The 

similarity is a concept defined as follows. Given the clusters Ck and Ch, for k ≠ h, they are 

merged if k kh h kh . This is based on the hypothesis that if a good partition is already 

achieved, the classes obtained are properly separated, without overlapping, and then no 

further fusion is required. On the contrary, if classes overlap, the between-class variance kh is 

greater than the individual within-class variances, k and j . This re-clustering process is 

repeated until all the between-class variances are greater than their corresponding within-class 

variances. Without lost of generality, if two classes are merged, the resulting fused class will be 

re-labelled with the name of the class with the smaller variance value. This does not affect the 

classification process because only labels are modified. 

After the fusion process, it must be checked if more clusters are necessary. This is carried 

out on the basis that if after the combination process no class has been fused, it means that 

more clusters are needed. A new clustering process starts again with the number of 

thresholds increased by one. This is repeated until a fusion occurs. 

2.3 Fuzzy C-Means clustering 

Fuzzy c-means clustering (FCM) is a data clustering technique wherein each data point 

belongs to a cluster to some degree that is specified by a membership grade. This technique 

was originally introduced by Jim Bezdek [11], as an improvement on earlier clustering 

methods. 

The FCM algorithm attempts to partition a finite collection of elements X x1, x2,..., xn 

into a collection of c fuzzy clusters with respect to some given criterion. 

The FCM algorithm, processes n vectors in p-space as data input, and uses them, in 

conjunction with first order necessary conditions for minimizing the FCM objective 

functional, to obtain estimates for two sets of unknowns. 

12 

(13)


The unknowns in FCM clustering are: 

1. A fuzzy c-partition of the data, which is a c x n membership matrix U ik Vcnwith 

c rows and n columns. The values in row i give the membership of all n input data in 

cluster i for k=1 to n ; the k-th column of U gives the membership p of vector k (which 

represents some object k) in all c clusters for i=1 to c. Each of the entries in U lies in [0,1]; 

each row sum is greater than zero; and each column sum equals 1. 

2. The other set of unknowns in the FCM model is a set of c cluster centers or prototypes, 

arrayed as the c columns of a p x c matrix V. These prototypes are vectors (points) in the 

input space of p-tuples. Pairs (U,V) of coupled estimates are found by alternating 

optimization through the first order necessary conditions for U and V. The objective 

function minimized in the original version measured distances between data points and 

prototypes in any inner product norm, and memberships were weighted with an 

exponent m>1 

That is: 

As X x1, x2,..., xnand 

the set all Vcn real matrices of dimension c x n, with 2 c n. 

Can 

be obtained a matrix representing the partition follow U ik Vcn. 

The basic definition 

FCM for m > 1 is to minimize the following objective function: 

n c 

m 2 

m ik 

k i G 

k1i1 G is a matrix of dimension pxp symmetric positive definite 

Where 

min z ( U; v) x v 

(15) 

 

2 t 

k i G k i k i 

x v x v G x v 

(16) 

i 

n 

1 

m 

n ik m k1 

ik 

k1 

k 

v x i 1,..., 

c 

 

1 

 

 

2 m1 

 

 

2 

xk v 

i G 

ik 

c 

2 m1 

 

i 1,..., c; k 1,..., 

n 

1 

 

2 

j1 

xk vj 

 

G 

 

The exponent m is known as exponential weight and reduces the influence of noise when 

getting the centers of the clusters. The higher the m > 1, the greater this influence. More 

details on fuzzy c-means clustering [11, 12]. 

2.4 Methods for cluster validation 

Evaluation of clustering results (or cluster validation) is an important and necessary step in 

cluster analysis, but it is often time-consuming and complicated work [16]. 

(17) 

(18)

178 


The procedure of evaluating the results of a clustering algorithm is known under the term 

cluster validity. In reference [15] two kinds of validity indices are showed: external indices 

and internal indices. A third is added in reference [29], based on relative criteria. The first is 

based on external criteria. This implies that we evaluate the results of a clustering algorithm 

based on a pre-specified structure, which is imposed on a data set and reflects our intuition 

about the clustering structure of the data set. The second approach is based on internal 

criteria. We may evaluate the results of a clustering algorithm in terms of quantities that 

involve the vectors of the data set themselves. The third approach of clustering validity is 

based on relative criteria. Here the basic idea is the evaluation of a clustering structure by 

comparing it to other clustering schemes, resulting by the same algorithm but with different 

parameter values. 

To evaluate the proposed classification method, five cluster validation techniques are 

applied, based on internal criteria. These indices are used to measure the "goodness" of the 

result of the grouping; comparing the proposed classification method against the old pattern 

recognition procedure called Fuzzy c-means clustering. 

2.4.1 Dunn’s index 

This index identifies sets of clusters that are compact and well separated. For any partition 

U X:X 1 

...Xc where Xi represents the ith cluster of such partition, the Dunn‘s 

validation index, D, is defined as: 

 

 

( 

X , X ) 

i j 

DU ( ) min min 

1 i c1jcmax ( 

X 

k 

) 

ji 

1 k c 

 

 

where ( X 

i 

, X 

j 

) defines the distance between clusters Xi and Xj (intercluster distance); 

( X 

k 

) represents the intracluster distance of cluster Xk, and c is the number of clusters of 

partition U. The main goal of this measure is to maximize intercluster distances whilst 

minimizing intracluster distances. Thus large values of D correspond to good clusters. 

Therefore, the number of clusters that maximizes D is taken as the optimal number of 

clusters, c. 

2.4.2 Davies-Bouldin index 

As the Dunn’s index, the Davies-Bouldin index aims at identifying sets of clusters that are 

compact and well separated. The Davies-Bouldin validation index, DB, is defined as: 

( ) ( ) 

1 c X 

i 

X 

j 

DB( U) max 

 

ci1 ( X 

i 

, X 

j 

) 

 

where U, ( X 

i 

, X 

j 

) , Δ(X 

i 

),Δ(X 

j 

) and c are defined as in equation (20). Small values of 

DB correspond to clusters that are compact, and whose centers are far away from each other. 

Therefore, the cluster configuration that minimizes DB is taken as the optimal number of 

clusters, c. 

(19) 

(20)


2.4.3 Calinski and Harabasz index 

The index of Calinski and Harabasz is defined by: 

B /( 1) 

( ) k 

k 

CH k 

W 

k 

/( nk) where k denotes the number of clusters, and BK and Wk denote the between and within 

cluster sums of squares of the partition, respectively. Therefore an optimal number of 

clusters is then defined as a value of k that maximizes CH(k). 

2.4.4 Krzanowski and Lai index 

The index of Krzanowski and Lai is defined by: 

where; 

diff 

KL(k) k 

diff 

k1 

2/ p 2/ p 

diff 

k 

( K 1) W 

k 1 

k 

W 

(23) 

k 

and p denotes the number of features in the data set. Therefore a value of k is optimal if it 

maximizes KL(k). 

2.4.5 Hartigan index 

The index of Hartigan is defined by: 

(21) 

(22) 

( ) log B 

Han k k 

(24) 

W k 

where, BK and Wk denote the between and within cluster sums of squares of the partition, 

respectively. Therefore a value of k is optimal if it minimizes Han(k). 


In accordance with the objectives and methodology used in this research encouraging 

results were obtained regarding the proposal to adopt the criterion used in Otsu's method to 

the process of clustering and unsupervised classification of colour images, and the 

application of cluster validity methods by five cluster validation indices, compare the results 

with those generated by the old pattern recognition procedure, Fuzzy c-means clustering. 

In the present paper three issues are addressed. First, our proposed RGB unsupervised 

classification method. Secondly, to compare results we apply an old pattern recognition 

procedure, the Fuzzy c-means clustering. Third, five of cluster validation indices will be 

proposed to evaluate the quality of clusterings. To demonstrate the effectiveness of our 

proposed RGB unsupervised classification method, using 16 digital images from colour 

aerial photographs in which they can observe different land cover objects such as buildings,

180 


streets, roads, tree crop plots, temporary plots of crops, pastures, water bodies etc.. Due to 

limited space, only the results of one experiment are included. 

3.1 Unsupervised classification method by theory of the Otsu’s method (results) 

The proposed methodology is based on the method of Otsu. First a single thresholding of 

each of the bands of the RGB image, creating two classes per band as RGB in forming our 

startup account with eight classes. However, not all are representative, so that once 

segmented in this way the image is inserted through a sorting process exhaustive analysis of 

the variation between classes and within classes. 

According to the characteristics of land cover images, which must be considered objects 

with different heterogeneous properties in size, shape and spectral behavior, we make the 

classification of the image labeled and grouped by simple thresholding, using the 

comprehensive analysis of variances between classes and within classes, to group and 

classify objects in the image. 

Fig. 2. Image classified by the proposed classification Otsu method, where the optimal 

number of class is five. 

As a result of this process, the classifier automatically grouped the different objects of the 

landscape into five classes, generating the classified image showed in Fig. 2, and the number 

of pixels per class, you can see in Table 1. With size of 1 542 288 pixels per band, where 

53.1% of the surface of the image contains vegetation cover, the image represented by the 

blue color, and 14.8% contains bodies of water identified by the gray color. The 8.3% contain 

natural green grass, identified by the red color, 11.7% contains dry natural grass in the 

image identified by the yellow color, 12% contain areas without vegetation identified by the 

cyan color. Among the latter the accumulated deposits on the river bank, the effect of 

Hurricane Pauline, as well as streets and buildings are considered.


Classes Pixels Color 

Plant Coverage 818,893 Blue 

Water Bodies 228,830 Gray 

Natural green grass 128,199 Red 

Dry natural grass 180,696 Yellow 

Area without vegetation coverage 185,670 Cyan 

TOTAL 1,542,288 

Table 1. distribution of the classification of Fig. 2 

3.2 The Fuzzy c-means clustering (results) 

As a result of this process, the classifier automatically grouped the different objects of the 

landscape into three classes, generating the classified image showed in Fig. 3, and the 

number of pixels per class you can see in Table 2. With size of 1 542 288 pixels per band, 

where 23% of the surface of the image contains plant coverage, identified by the green 

colour; 36% of the surface of the image contains trees, identified by the blue colour; 28% of 

the surface of the image contains natural green grass, identified by the red colour; 9% of the 

surface of the image contains dry natural grass, identified by the yellow colour; and 4% of 

the surface of the image contains the accumulated deposits on the river bank, the effect of 

hurricane Pauline, the image represented by the cyan colour. 

Fig. 3. Image classified by Fuzzy c-means clustering; where the optimal number of class by 

DB is five.

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As showed in visual analysis (Fig. 2 and 3), our proposal clearly identifies the water bodies, 

whereas by Fuzzy c-means clustering classifier water bodies with grass coverage are 

confused. 

Classes Pixels Color 

Plant Coverage 358,176 green 

Trees 549,978 Blue 

Natural green grass 424,131 Red 

Dry natural grass 143,474 Yellow 

Area without vegetation coverage 66,529 Cyan 

TOTAL 1,542,288 

Table 2. Distribution of the classification of Fig. 3 

(a) (b) 

Fig. 4. Image classified by Fuzzy c-means clustering; where the optimal number of class is 

seven, by CH and Han, in (a); and two by Dunn and KL, in (b). 

3.3 Quantitative results to validate the optimal number of clusters 

Since clustering is an unsupervised method and there is no a priori indication for the actual 

number of clusters presented in a data set, there is a need of some kind of clustering result 

validation.


The results of the classification of colour images from aerial photographs by the proposed 

Otsu’s method, and the method known as Fuzzy c-means clustering are evaluated using five 

levels of validation. These indices are detailed in Section 2.3. Since the results of the Fuzzy cmeans 

clustering algorithm, requires as input, the number of clusters, so it runs for 2.3, ..., 6; 

Fig. 3 corresponds to the optimal number of groups according to DB, who is the same as the 

proposed method. 

No. Class 2 3 4 5 6 7 Optimo 

DB 0.660 0.597 0.576 0.535* 0.605 0.584 minimum 

CH 2.222 3.018 3.317 3.600 3.810 3.891* maximum 

Dunn 2.374* 1.417 1.199 1.154 1.020 0.926 maximum 

KL 57.660* 37.527 29.976 25.080 21.922 20.104 maximum 

Han 36.324 18.041 11.896 8.577 6.639 5.494* minimum 

* optimal 

Table 3. Results obtained from 5 internal indices to validate the optimal number of clusters 

generated by Fuzzy c-means clustering. 

Davies-Bouldin Index says: the cluster configuration that minimizes DB is taken as the optimal 

number of clusters, therefore for this methodology, the optimal number of clusters is 5. 

Calinski and Harabasz Index says: an optimal number of clusters is then defined as a value of 

k that maximizes CH(k), therefore for this methodology, the optimal number of clusters is 7. 

The Dunn’s Index says: the value that maximizes D is taken as the optimal number of 

clusters, therefore for this methodology, the optimal number of clusters is 2. 

Krzanowski and Lai index says: a value of k is optimal if it maximizes KL(k), therefore for 

this methodology, the optimal number of clusters is 2 

Hartigan index says: a value of k is optimal if it minimizes Han(k). therefore for this 

methodology, the optimal number of clusters is 7. 

With the executed validation indices for Fuzzy c-means clustering, we want to find a match 

on the number of clusters of the new classification proposed. This was achieved with the 

Davies Bouldin index. Therefore, it indicates that the number of classes generated by the 

new classifier is optimal. 


Otsu's method improved and changes implemented in this research can get the optimal 

threshold value as a basis for segmenting and classifying images in RGB color model, using 

a method of unsupervised classification. 

Under the principle of the concept of within-class and between-class variances as suggested 

by Otsu; the algorithm automatically regrouped and merged different values of the groups 

obtained from the image in RGB colour domain and once the within-class variance is less 

than the between-class variance for each clustered class, the algorithm is finished.

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Since this algorithm does not need to know how many classes are required to be clustered, 

five cluster validity indices have been proposed to validate if the number of clusters 

classified by Otsu is suitable. Davies Bouldin index indicates that the number of classes 

generated by the new classifier is optimal. 

Also the classification made by the proposed method is better than that Fuzzy c-means 

clustering, as showed in Fig. 2, our method properly classified water bodies, whereas Fuzzy 

c-means clustering confuses the water bodies with vegetation. 

This method unsupervised of image classification, can be widely used as support in 

decision-making in aspects of the environment by diagnosing areas of interest such as the 

loss of tree cover due to deforestation or fires, crop areas, Water Bodies, etc.. by virtue the 

proposed methodology to classify areas of different coverage density including areas where 

the soil surface has been exposed to erosion. 

5. Acknowledgment 

The authors would like to thank the World Bank from the Robert S. McNamara Fellowships 

Program (RSM) to support this research on: “Técnicas Inteligentes de Reconocimiento 

Aplicadas a la Clasificación del Uso del Suelo”. 

The Carolina Foundation and SRE_Mexico by co-financing for doctoral studies. 

The “Colegio de Postgraduados”, Mexico, for permission to join the Teachers' Training Sub- 

Investigators. 

The UNAM by providing the aerial images, which is essential material for this investigation. 

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10 

Deforestation and Waodani Lands in Ecuador: 

Mapping and Demarcation 

Amidst Shaky Politics 


Anthony Stocks1, Andrew Noss2, Malgorzata Bryja2 and Santiago Arce2 1Idaho State University, 

2Wildlife Conservation Society – WCS 

USA 

One of the major forces of deforestation around the tropics is the chipping away of forested 

areas for pastures by agricultural peasants who are difficult to control by remote central 

governments (Colchester 1998) and by loggers who enjoy the same advantages of working 

in isolated areas as colonists and who tend to bring roads into the forest. The major danger 

to many forests is fire, made more likely by the agricultural colonization that follows road 

construction (Nepstad et al. 2001). In the Ecuadorean Amazon fire is not a major threat, 

probably because of the year-round rainfall regime that maintains high levels of humidity, 

and road construction is driven first by oil exploration and exploitation activities that in turn 

facilitate access and settlement by colonists and loggers (Bromley 1972; Viña et al. 2004). 

Ecuador’s 1964 Law of Agrarian Reform and Colonization classified large portions of 

Amazon as unoccupied, allowing colonists to claim 50 ha plots along roads, directly 

promoting deforestation by requiring proof of improvements to establish legal land titles 

(Bilsborrow et al. 2004; Bremner & Lu 2006; Fuentes 1997; Kimerling 1991). 

In many parts of the world, however, the tropical forests have potential allies in the form of 

indigenous people who have inhabited the forest for millennia and are anguished about 

seeing it degraded and cut down. In fact it is estimated at present that 85% of the world’s 

areas designated for biodiversity conservation are inhabited by indigenous peoples, 

whereas outside of the parks and nature preserves, the world’s remaining pristine forested 

habitats are nearly all occupied by indigenous peoples (Alcorn 2000; Colchester 2001; 

Schmidt & Peterson 2009; Weber et al. 2000). This is true of the Ecuadorean Amazon in 

particular (GeoPlaDes 2010). In fact, conservation-minded outsiders have only a few choices 

it they want protection for these habitats. They can try to protect the forests while excluding 

the indigenous people – treating them essentially as fauna and making enemies of them – or 

they can assist them as allies (Colchester 2000, 2004; Schwartzman et al. 2000). The latter 

choice carries its own problems. Not all indigenous people want to save the forest, given 

their current assessment of costs and benefits of doing so; a certain amount of discrimination 

is necessary. Those who do usually want to either own the land (Colchester 2000), or, in the 

case of protected areas, to have signed legal agreements with governments giving them use

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and management (or co-management) rights which carries its own problems with 

coordination, bureaucracy and political will on the part of the state. 

Although recognition of ownership and/or control of large tracts of land by private 

individuals or groups is easy for conservationists in the developed world, it becomes much 

more problematic in remote forest frontiers where indigenous people may be less visible, 

forested areas are often not densely populated and conservationists may have closer 

relationships with governments than with indigenous peoples. Hence, efforts in countermapping 

have become common in the past 20 years in order to identify the areas occupied 

by and claimed by indigenous peoples (Chapin &Threlkeld 2001; Peluso 1995; Poole 1998; 

Stocks 2003; Stocks & Taber 2011; Wainwright & Bryan 2009). 

Are indigenous people, once they have control and appropriate resources, able to protect 

forested habitats? Many would say that it is a mistake to equate indigenous occupation with 

conservation (e.g., Redford & Stearman 1993; Redford & Sanderson 2000). While this may be 

true as a general statement, the specific local outcome depends on a number of factors that 

include indigenous levels of organization, the kinds of resources available, their sense of 

place connected with a history of occupation, their own economy and the political power of 

competitors– industrial or individual – that also seek to occupy the forest. Evidence from a 

few sources in the neo-tropics indicates that when indigenous people are protecting a 

historic homeland with some outside help, they tend to be more successful in maintaining 

forests than colonist populations faced with essentially the same economic realities 

(Colchester 2000; Lu et al. 2010; Nepstad et al. 2006; Ricketts et al. 2010; Soares et al. 2010; 

Stocks et al. 2007). Indeed, in the Brazilian Amazon, the inhibitory effect on deforestation of 

various kinds of protected areas (strict protection, sustainable use, indigenous lands and 

military areas) is greatest in the category of indigenous lands (Soares et al. 2010). In the 

Ecuadorean Amazon this feature of indigenous lands is particularly notable in the A’í 

(Kofán) case (Borman et al. 2007) and the protection by the A’í of their forests on titled 

(resguardo) lands is also true in Colombia. One recent study with a broad world-wide sample 

argues that forests owned and/or controlled by local communities, tend to have less 

deforestation. Livelihood benefits and carbon storage both increase when the historic 

dwellers own the land or can otherwise control land use (Chhartre & Agrawal 2009). 

This chapter is premised on the idea that the Waodani people of Ecuador have the essential 

prerequisites of ownership, sense of historic occupation, threats from colonists and outside 

assistance to halt the deforestation of their titled territory; and that physical demarcation of 

their land is a necessary step in the protection of the forest. However, the particular kind of 

political organization they currently exhibit, the nature of the colonists that are deployed along 

their frontiers, industries within their territory, and overlaps with national protected areas 

make demarcation a somewhat different exercise than one would think. The rest of this paper 

explores the history of demarcation and the consolidation of land under their control and 

process of providing technical assistance to them in the face of what we have called “shaky 

politics.” The dilemma of working with the Waodani is not uncommon in conservation. 

Conservationists of tropical forests typically encounter groups with essentially egalitarian 

political philosophies who find the establishment of hierarchical bureaucracies difficult to 

maintain and impossible to make function in the ways that western bureaucracies operate 

with top-down control. What makes the Waodani case an outlier relates to the recentness of 

their permanent contact with western civilization and the powerful forces arrayed against 

their own control and management of their titled territories. There is something to learn about 

working with indigenous people in conservation from this case.


Mapping and Demarcation Amidst Shaky Politics 

2. The Waodani people and their territory 

The Waodani (also known as Huaorani or Auca) were the last indigenous people in Ecuador 

to be “integrated” into the western world, with two clans—the Tagaeri and Taromenane— 

remaining in “voluntary isolation” to this day. Their language has no known relatives and at 

the time of contact only included two words borrowed from other languages (Trujillo León 

1996); their DNA confirms their extended isolation from neighboring peoples (Cardoso et al. 

2008; González-Andrade et al. 2009). A tiny population of no more than 500 people 

defended with their spears through random and dispersed raids a vast territory of over 

20,000 km 2 between the Napo and Curaray rivers from all outsiders—Kichwas, colonists, oil 

companies, the Ecuadorean military, and missionaries (Cabodevilla 1999; Holt et al. 2004; 

Kane 1993). They did not participate in historical trade networks and emphasized extreme 

closure from all outsiders whom they call “cowode” or cannibals (Rival 1999). Their political 

structure was egalitarian—centered on the “nanicabo” or family longhouse—with no 

classes, no chiefs or leaders. Gender roles were also flexible (Holt et al. 2004). A man could 

become a leader for a specific event, and when the event passed so did his leadership. The 

intensely independent and individualistic social system led to frequent divisions of 

nanicabos and/or moves to distance themselves from conflicts and to escape retaliation. The 

primary mechanism for social control was peer pressure, with the ultimate threat of death 

by spearing (Rival 1999; Yost 1991). 

Under increasing pressure from oil companies exploring their territory for oil reserves and 

from colonists in the early 1950s, the Waodani responded with spearing raids, at the same 

time intensifying an internal cycle of violence that produced the highest rate of death by 

homicide for any indigenous group anywhere (Beckerman et al. 2009), and induced several 

young women to escape to neighboring Kichwa farms where they became slaves (Yost 

1981). An attempt by Summer Institute of Linguistics (SIL) missionaries to make peaceful 

contact resulted in five missionaries being speared in 1956—an event that brought 

worldwide attention to the plight of the Waodani (Eliot 1958). Two SIL women, one sister 

and one wife of the murdered missionaries, subsequently worked with one of the escaped 

Waodani women to learn the language, and together the three made peaceful contact with a 

first Waodani clan in 1958 (Yost 1981). SIL intensified their efforts and brought several clans 

together in an area known as the “Protectorado”, in the west of the historically occupied 

Waodani territory, and made the first formal territorial claim to the Ecuadorean government 

in 1964 (Yost 1979). CONFENIAE (Confederación de las Nacionalidades Indígenas de la 

Amazonía Ecuatoriana) provided further backing for this territorial demand, resulting in the 

first Waodani land title in 1983 for 66,000 ha of the Protectorado which effectively cleared 

the way for oil activities and the opening of the vía Auca further east (Kane 1993, Kimerling 

1991). The second and most important land title was awarded to the Waodani in 1990 and 

also represented a favor to oil companies, converting a large portion of the Yasuní National 

Park (declared in 1979) to Waodani territory, but under the condition that the Waodani did 

not oppose oil activities on their new lands (Kimerling 1991; Rival 1992), and recognizing 

the Auca road and collateral colonization as a massive cut into Waodani territory. 

The legally recognized lands of what number today approximately 3500 Waodani 

indigenous people in Ecuador therefore amount to around 700,000 hectares in three separate 

land titles given at three points in history with formal ownership vested in somewhat 

different ways (Figure 1 and Table 1). Additionally, a number of Waodani communities are 

located in the Yasuni National Park, while two Waodani clans remain formally 

189

190 


“uncontacted” and range widely within the titled territory and the park. All in all, the 

Waodani inhabit over 1.5 million hectares of land in the rainy upper Amazon and Andean 

foothills, arguably the world’s most biodiverse forest (Bass et al. 2010). 

Fig. 1. Waodani territory titles (Lara et al. 2002b) 1 

Year By whom and to whom Extension (ha) 

1960 (1983) IERAC (Instituto Ecuatoriano de Reforma Agraria 

y Colonización) to Waodani Ethnic Group, 

specifying the “community organizations” 

Tiweno, Tzapino, Wamono, Kiwaro, Dayuno, 

Toñampade 

1990 / 1998 IERAC to the “Waorani Ethnicity”, specifying the 

community organizations identified as: 

Kewediono, Damointado, Nuevo Tiweno, 

Kenaweno, Nuevo Golondrina, Cononaco, 

Owanamo, Tagaeri, Tiwino and Yasuní 

2001 INDA (Instituto Nacional de Desarrollo Agrario) 

to the Organización de Nacionalidades Waodani 

de la Amazonía Ecuatoriana (ONHAE) 

Table 1. Waodani territory titles (Lara et al. 2002b) 

66,570 

612,560 / 613,750 

29,019 

1The size of the first title is inaccurately represented in this map. The first territory has never been 

accurately georeferenced.



The process of land titling has accompanied an equally ambiguous process of organizational 

development for the Waodani. The Protectorado experience, while it brought an end to the 

more overt internal violence, was traumatic as it combined the initial contact with the west 

(sedentary communities, change in diets and technologies, dependence on outsiders for 

subsistence and new material goods, change in health conditions) with intense social change 

because of the concentration of the population in a small area, a new generalized leadership 

by a woman who acted as cultural broker, controlling the flow goods and services from 

missionaries and other outsiders (replacing the traditional situational leadership by men), 

and new relationships with Kichwas through marriage that facilitated access by Waodani to 

external resources while at the same time permitting access by Kichwas to Waodani natural 

resources and land (Holt et al. 2004; Yost 1981; 1991). As early as 1972, however, the 

Waodani in the Protectorado began returning to areas they had previously occupied to the 

west, in some cases settling near oil operations in order to benefit from work and gifts 

provided by the companies. 

3. The development of Waodani political representation 

The first formal Waodani political organization, ONHAE (Organización de la 

Nacionalidad Huaorani del Ecuador), was not constituted until 1990, by the first young 

Waodani men who had received formal education in Spanish, and supported by the oil 

company Maxus which required a formal counterpart to secure its operations in Waodani 

territory. In 1993 Maxus signed a ground-breaking 20-year agreement with ONHAE 

(although numerous oil companies work in Waodani territory this remains the only 

agreement signed with the Waodani organization and benefitting the Waodani people as 

a whole; subsequent agreements have only benefitted the communities within the oil 

company’s area of operations), providing resources for the organization itself and its 

leaders, as well as health, education and community development resources (Rival 2000). 

This agreement has been maintained by the Spanish company, Repsol, which took over 

Maxus operations in 1996. At the same time, however, in line with indigenous rights 

agendas and indigenous political organizations including CONFENIAE and CONAIE 

(Confederación de Nacionalidades Indígenas del Ecuador), ONHAE founders also 

expressed the new organization’s mission as preventing oil exploitation and road 

construction in Waodani territory, emitting a series of declarations to that effect since 1991 

(Lara et al. 2002a; Rival 1992). It appears, therefore, that the Waorani might have chosen 

the card that had been dealt. Even Ziegler-Otero (2004: 6) agrees that ‘to preserve any 

semblance of cultural self-determination, indigenous people must be capable of 

negotiating.’ Negotiating and making a deal with Maxus offered the Waorani at least 

some tangible – though temporary - benefits in the context where rejecting those benefits 

would have left the Waorani empty-handed. Indeed, there are facts indicating that the 

Waorani were aware of the dilemma and they chose the pact with Maxus as ‘a lesser evil’. 

As Aviles (2008:42-43) states, ONHAE representatives first attracted the attention of the 

public by marching in Quito and denouncing both Maxus and Petroecuador (Ecuadorian 

oil company). The fact that the same people ended up signing the deal with Maxus shortly 

afterwards demonstrates the ultimate powerlessness of the Ecuadorian indigenous people 

in their dealings with the state and the external sources of revenues. This turn of events 

191

192 


also shows the reason why dealings with the Waorani have been so frustrating for many 

international environmentalists. They resent ONHAE’s perceived lack of principles and 

feel that the leadership has ‘signed agreements with the Ecuadorian state and the oil 

companies, in apparent contradiction of their organizational positions and public 

statements’ (Ziegler-Otero:1). 

ONHAE and its successor organization, NAWE (Nacionalidad Waodani del Ecuador), since 

2007 therefore represent a radically new form of political organization for the Waodani, 

structured as required by external “western” agencies including the government and the oil 

companies as well as NGOs. A president and other leaders (“dirigentes” for education, 

health, territory, tourism, etc.) are elected by representatives of the various communities at 

assemblies held irregularly every 3-18 months. The communities themselves, now 

numbering over 40, are new social and political structures, though in practice they have not 

greatly altered the traditional nanicabo social system, despite boasting presidents and other 

leaders. Thus new communities continue to form, often with only one or two families who 

have moved away from another community because of disagreements or to gain access to 

new resources including external assistance. Often the community presidents are not the de 

facto authority in the community, and a number of “big men” act as local leaders 

negotiating with ONHAE/NAWE, with other influential Waodani individuals and 

communities, and with external actors. To the degree that external actors (government 

representatives, oil companies, NGOs) make agreements with these “big men”, obviously 

the strength of ONHAE/NAWE is undermined, and the generation of a Waodani 

conscience and unity falters. 

External actors assume that ONHAE/NAWE intermediate on behalf of the Waodani 

people, as a form of representative democracy whereby the leaders make decisions that 

express the will of the people, and sign agreements including land titles with 

ONHAE/NAWE on behalf of the Waodani people as a whole (Ziegler-Otero 2004). In 

contrast, the Waodani people themselves consider ONHAE/NAWE’s primary role to be 

the negotiation with oil companies (primarily Maxus/Repsol because individuals and 

communities negotiate directly with the other oil companies whose concessions overlap 

with the particular community’s land) and the administration of the oil company-financed 

projects (distribution of benefits including health services, school lunches and other 

education services, and other goods and services). A traditional mark of Waorani 

leadership is the ability to get goods from outsiders and the Maxus/Repsol negotiation 

reaffirms the traditional orientation of their leadership (Ziegler-Otero 2004:129). The 

people in the communities do not expect ONHAE/NAWE to make other kinds of 

decisions on their behalf (High 2006). Thus, one primary challenge for today’s NAWE is to 

provide – in addition to the social services – technical services that add value to the 

organization and involve them in overall conservation and development planning for 

Waodani holdings. 

Whereas fire is not the danger in eastern Ecuador that it poses in Brazil, nonetheless 

colonization and road-building fuel deforestation. For the Waodani, the colonization has 

historically been Kichwa migrants settling along the Napo River on the north who gradually 

have deforested land towards the interior, and Kichwa and other migrants settling along the 

Curaray River on the south with the same effect (Trujillo León 1996). Oil production has 

driven colonization (Kichwa, Shuar, and mestizo colonists) by penetrating the very heart of



the Waodani titled territory with two roads from north to south: one, the Via Auca since 

1980 which has sparked uncontrolled settlement and the other, the Via Maxus since 1993 

with relatively tight control over settlement by the Maxus and Repsol oil companies in turn 

(Finer et al. 2009; Kane 1993; Rival 1992; Villaverde et al. 2005). On the west, the major 

impacts have come from the expansion of the national agricultural frontier toward the 

Amazon with numerous roads connecting the rivers that flow from the Andes to join 

eventually with the Amazon River. Urban settlement near Waodani borders is common. On 

the east the titled territory is bordered by the Yasuní National Park which, at present, 

remains relatively un-colonized and is, as indicated above, the location of numerous 

Waodani communities. The two clans out of contact range both in the park and in the 

eastern part of the titled territory (Figures 2 & 3). 

Fig. 2. The Waodani territory within the Yasuní Biosphere Reserve (MAE, MGDF, UNESCO, 

WCS 2011). 

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Fig. 3. The Waodani territory, Yasuní National Park, and neighbors (WCS elaboration). 

4. WCS and the IMIL project: Strategies for shoring up the political structure 

and supporting conservation 

The Wildlife Conservation Society (WCS) has been working with the Waodani organization 

NAWE since 2007, under the USAID-financed “Integrated Management of Indigenous 

Lands” (IMIL) Project. The project provides continuation to a previous phase of USAID 

funding through the Chemonics-administered CAIMAN (Conservación de Áreas Indígenas 

Manejadas) project (2002-2007). WCS and NAWE signed a formal memorandum of 

understanding, and subsequently a sub-grant agreement, in order to promote territorial 

consolidation, institution-strengthening, capacity-building, and alliances with other 

organizations to support the Waodani people, culture and territory. The principal territorial 

concern expressed by NAWE to WCS and in a strategic planning exercise (Vallejo & 

Burbano 2008) was to complete the physical demarcation of the territory begun years before 

with CONFENIAE assistance (Kimerling 1991) and also advanced under the CAIMAN 

project (Chemonics International 2007). The principal institutional priority expressed by 

NAWE was the incorporation of Waodani technical staff to the organization, and the 

training of this staff. WCS therefore discussed with NAWE ways to address both issues 

through the consolidation of a technical team of Waodani mapping technicians who are 

capable of collecting field information with a GPS (Global Positioning System) unit and 

generating basic maps in ArcView. Additionally the IMIL project has approached the 

organizational strengthening of NAWE through providing USAID funds that NAWE can



use to carry out its own contract negotiation with outside technical actors. The outcome so 

far indicates a marked improvement in NAWE planning and responsibility for projects, a 

small but significant step in consolidating the territory and gaining community confidence. 

For example, NAWE technicians trained under the IMIL project have assumed 

responsibility for developing and implementing agreements with the Ministry of 

Environment’s Socio Bosque program (described further below) as well as community 

management plans; one has been elected community president, and others were hired by 

the Ministry of Justice in its program to protect the Tagaeri Taromenane Intangible Zone 

(described further below). 

The demarcation process successfully completed the remaining 89 km of territorial 

boundary between Waodani and neighboring Kichwa communities. These boundaries 

followed more or less those established in the legal titles, with local adjustments made by 

consensus with the Kichwa neighbors according to history of use and prior verbal 

agreements. The Waodani did not consider it necessary to demarcate the boundary with the 

Yasuní National Park as their elders, at least, consider the park to be part of their territory. 

In addition, during the boundary demarcation process WCS discussed with NAWE and the 

technicians the role of community mapping as a tool for territorial management, finding that 

ONHAE/NAWE had already recognized its importance in their territorial management 

plan (Lara et al. 2002a) and in their strategic plan (Marchán 2006) developed previously but 

not implemented. An enormous challenge facing the Waodani is the imposition of external 

management systems and boundaries on their territory—with at least eight active oil 

exploration and exploitation concessions overlapping Waodani territory. One assessment 

describes the titled Waodani territory as under the administration of oil companies with the 

approval of the government (Lara et al. 2002a). 

Also overlapping Waodani territory are a series of protected areas which each restrict 

Waodani actions: the Yasuní National Park was created in 1979, with significant boundary 

adjustments in 1989 and again in 1992; the Yasuní Biosphere Reserve was declared in 1989 

(including the entire Waodani territory); and the Tagaeri-Taromenane intangible zone was 

declared in 1999 and formally demarcated in 2007 (Finer et al. 2009; Lara et al. 2002a; 

Villaverde et al. 2005). The Waodani communities currently located within the Yasuní 

National Park are not permitted under Ecuadorean law to obtain land titles (Ecolex 2003). 

While the Waodani at times express their ignorance of these boundaries including the 

Yasuní National Park, the Tagaeri-Taromenane Intangible Zone, and even the border with 

Peru (Randi Randi 2003), the borders in practice mean that other actors are managing 

significant portions of Waodani territory. In addition, the 2008 Ecuador Constitution ratifies 

the government’s rights to sub-surface resources including oil and minerals, but also forest 

resources and environmental services (Bremner & Lu 2006; Reed 2011). Thus the Waodani 

are legal owners but not actual administrators of their territory (CARE 2002). 

Community mapping therefore represents a tool whereby the Waodani can visualize and 

negotiate with others their own land and resource use plans, zoning, and vision for their 

territory (Alcorn 2000; Eghenter 2000; Peluso 1995). 

The first community that requested assistance from NAWE and WCS technicians in 

undertaking a community mapping effort was Kewediono (= Keweriono / Que’hueriono) 

motivated by the association which unites five neighboring communities and which has 

developed a world-class and internationally acclaimed eco-tourism project—the Huaorani 

Ecolodge - supported by Tropic Tours in Nature. These communities had previously 

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defined a strict conservation area surrounding the lodge itself, and wanted maps to 

illustrate and reinforce these management decisions. The subsistence hunting area is the 

bulk of the territory, and is not subject to logging or deforestation for cultivation, while less 

than 1% of the territory has been deforested for farms and settlements (Custodio et al. 2008). 

The second set of communities requesting support is led by Gadeno (Gareno) which also 

boasts a tourism project, though one operated privately under concession to the Gadeno big 

man. The distribution of zones is similar to that for Kewediono (Table 2), with the detail that 

Gadeno also identified a small sacred area where the first Waodani had settled in this region 

(Custodio et al. 2009) and the mapping effort revealed an unsuspected zone where no 

hunting was permitted according to the tradition of the community of Meñempade, located 

on the same map as Gadeno. 

Communities Total area 

(ha) 

Conservation % Hunting % Deforested % 

Kewediono, Kakatado, 

Wentado, Apaika, 

Nenkipade 

59,900 7 92



territory (Stocks & Espín 2010), and is intended by the communities to serve as a tool for 

negotiating a co-management agreement with the National Park for the areas utilized by the 

communities, as well as for joining the Socio Bosque program. Lacking a title, the comanagement 

agreement is a pre-requisite for Socio Bosque. The northern boundary 

recognized by the communities is the Tiputini river, a boundary agreed with neighboring 

Kichwa communities, though both Waodani and Kichwa venture across the boundary. The 

recognition of the Yasuní park boundaries is also conditional on whether or not individuals 

perceive that the park and Ministry of Environment are providing benefits. Guiyedo is also 

the first community mapping exercise to include locations where signs of the Tagaeri- 

Taromenane uncontacted groups have been found by the Waodani. 

Withal, the mapping work with communities has identified the hunting turfs probably 

associated with the first Nanicabo settlements in each of the areas. These turfs (loosely called 

‘community territories’) are well-known by community elders and each contains areas 

identified with at least some hunting restrictions. In a situation of “shaky politics” at the 

level of the ethnic group, the turfs or territories reassure communities that their own 

management will be respected. Additionally demarcation at the level of the titled territory 

assures local communities that their land claims are taken seriously by the central 

authorities and that they share a common border with other Nanicabo communities. The 

ambiguity with regard to their neighbors of other ethnicities is somewhat resolved by the 

series of inter-community border agreements. In terms of strategy, the training of a technical 

team fielded by the central organization, NAWE, has proved to be successful, both in terms 

of getting the work done competently in association with professionals contracted by 

NAWE and in terms of community perceptions of added value to NAWE itself. The 

community conservation areas, now firmly geo-referenced, are the basis for agreements 

with the Pre-REDD+ program, Sociobosque, and will undoubtedly play a significant role in 

later REDD+ programs. Certainly it cannot be argued that these lands are not under 

pressure from deforestation by Kichwas and colonists entering Waodani territory. The 

efforts of NAWE and the communities to develop and implement local boundary and 

territory monitoring programs will continue to be critical in protecting them. 


The central issue in this chapter has been an attempt to avoid deforestation in one of the 

world’s most productive and critically threatened habitats through consolidating the hold of 

indigenous people on the land and by working with an indigenous organization with a 

tenuous hold on legitimacy and a limited mandate from communities in order to improve 

resource management and conservation. This is a challenge that most serious 

conservationists encounter at some time in their career if they work around forest frontiers. 

There is no easy recipe for success or guarantee that the forest will not disappear in a 

determined number of years, but our experience is that the probability of encountering a 

relatively intact forest in, say, 50 years is greatest if indigenous people are in control of the 

outcome in areas they have historically inhabited, and greater still if they still maintain 

language and customs connected to their historic past. The work IMIL has done with the 

Waodani so far has, arguably, increased both the definition of the land controlled by them in 

general and specifically by community – land that includes large areas of no-commercialhunting 

zones – and their ability to relate to a central ethnic organization that can be trusted 

to provide valuable assistance in the defense of land. 

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Because of the economy of the Waodani, the land they control is 99% or more in forest, so 

from a carbon perspective any investment in stability has benefits beyond the forest patches 

actually counted as conservation land. Such situations should be obvious targets for 

financing through global carbon markets as REDD+ proposes to do. 

From the point of view of a conservationist, the key to working with the Waodani is the 

recognition of their sovereignty over land and resources. This recognition should not carry 

with it the assumption that the Waodani people are “cultural” ecologists. Actually, they are 

and their ecological knowledge runs deep, but the social expression of their own ecological 

adaptation involved levels of violence no longer tolerated in the Ecuadorian state. The modern 

adaptation to multiple cash sources and the heritage of decentralized political control has left 

them more vulnerable than most groups, so the importance of a central organization with 

some input into resource management is emphasized by the situation. The rub will come if the 

state tries to exercise its own constitutionally-granted rights to the forests in imposed ways 

that do not recognize Waodani sovereignty. One hopes that wise heads prevail in the 

government ministries that deal with the Waodani. If the Waodani are assisted to manage the 

forests in ways that strike them as culturally appropriate, everyone wins. 

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11 

Sustainable Forest Management Techniques 


K.P. Chethan, Jayaraman Srinivasan, Kumar Kriti and Kaki Sivaji 

TCS Innovation Labs Bangalore, Tata Consultancy Services, 

India 

Forests being an indispensable resource play an important role in maintaining the earth's 

ecological balance. The major contributors of deforestation are logging off of trees (legal or 

illegal), tree theft, forest fire etc. Large scale deforestation has negative impact on the 

atmosphere resulting in global warming, flash floods, landslides, drought etc. Due to these 

adverse effects, forest management department all over the countries have taken steps for 

monitoring the forest to prevent deforestation. Several surveillance techniques have been 

employed for monitoring and prevention; they are broadly classified as Ground-based 

sensing techniques and Remote sensing techniques. 

Surveillance plays an important role in forest management. It had been used in the past and 

is still being used for monitoring and information collection. Ground-based techniques 

generally include surveillance by on-site security staff and mobile patrols monitoring the 

property by water, land and air (Magrath et al., 2007). Some complementary systems such as 

Fixed Earth System are also used with observation towers located at strategic points with 

specialized personnel for observing and detecting the presence of fire. All these systems are 

expensive and time consuming, requiring a lot of resources. 

Nowadays, remote sensing technologies are also used like, aerial photographs, automatic 

video surveillance, wireless surveillance systems and satellite imagery. Satellite imagery is 

very costly for use in monitoring any illegal activity like trespassers, tree theft and 

deforestation (if they are able to detect at all). On the other hand, with the technological 

advancements in wireless communication, various low power, and low cost, small-sized 

sensors nodes are available which can be readily deployed to monitor environment over 

vast areas. Wireless Sensor Networks (WSNs) technology is being used widely for 

monitoring and controlling applications. Currently three main wireless standards are used 

namely: WiFi, Bluetooth and ZigBee. Amongst them, ZigBee is the most promising standard 

owing to its low power consumption and simple networking configuration. Wireless sensor 

network based surveillance systems for remote deployment and control are more cost 

effective and are easy to deploy at location of interest. They can even reach those areas 

where satellite signals are not available. Moreover, they can be configured to monitor large 

areas and they have secure mode of data transmission. 

Environmentally, WSNs finds immense application in land management, agriculture 

management, lake water quality management, forest fire detection, tree theft prevention and 

also in the prevention of deforestation. In addition, WSN system has also been used for 

strain monitoring in railway bridges. (Bischoff et al., 2009), developed an event based strain

204 


monitoring WSN system for railway bridge. They used low power MEMS acceleration sensors 

for detecting an approaching train. Whenever this event was detected, strain gauges were 

operated and measured data was used for cycle counting based fatigue assessment of steel 

bridges. This event based detection was developed to manage the power consumption and 

make the system more energy efficient. Moreover, solar rechargeable battery powered base 

station was used to increase the system lifetime. 

WSN system has also been used for landslide monitoring and prevention. (Rosi et al., 2011) 

discussed the implementation and deployment details of a WSN system for landslide 

monitoring in Northern Italy Apennines. Six Micaz nodes having a 2 axis accelerometer for 

sampling vibrations were used. These vibrations were a result of slope movements caused 

by landslides. The data measured by the sensor nodes were routed to the brides and finally 

sent to the base station following a predefined static routing table. These examples give a 

fairly good idea of the amount of work and research going on in WSN area making them the 

most promising technology to use for monitoring and control purpose in diversified fields. 

A lot of research has been done using WSN for forest monitoring either for fire prevention 

or for monitoring the illegal logging activity. Some researchers have proposed algorithms 

for detection and prevention and have simulation results verifying their control. On the 

other hand, some have come up with the design, implementation and deployment of the 

system. The work on forest monitoring is not limited to fire and deforestation detection and 

prevention but also includes preserving and conserving the flora and fauna of the forests. A 

brief summary of the work done in this domain is given below. 

(Awang & Suhaimi, 2007), developed a WSN based forest monitoring system called 

RIMBAMON. This system consisted of sensor nodes deployed in the forest at specific 

distances for capturing temperature, light intensity, acoustic, acceleration and magnetic 

readings. MICA2 Mote was used for implementation for its long range in ISM band. These 

sensors were either mounted on the trunk of the tree at the ground level or kept along the 

roadside. The sensors used helped in monitoring any illegal logging activity in addition to 

detection of fire in the forest. Temperature and light intensity sensors gave an indication of 

both logging activity as well as presence of fire. On the other hand, acoustic sensors gave 

more information on logging activity alone owing to the abnormal sound associated with 

the usage of machinery, tractor or chainsaw. The system was simulated and tested well to 

capture and transmit data to the base station. It displayed the acquired data in form of 

graphs, tables and maps to help in taking prompt action. However, the system lacked 

remote monitoring through the web, which could be useful in monitoring hostile areas. 

(Harvanova et. al., 2011) proposed a Zigbee based WSN system for detection of wood 

logging using real time analysis of sounds from surroundings. The WSN system 

periodically acquires sound samples, processes it and transmits it to the central server. Tools 

which are vastly used for deforestation are chainsaw. There is a characteristic sound 

associated with a logging activity. Whenever, the sound samples acquired from the sensors 

matches the sound samples of logging tools, a logging activity is detected and the 

responsible personnel is notified through an e-mail or a SMS alert. 

(Soisoonthorn & Rujipattanapong, 2007), also studied the unique acoustic characteristics of 

the chainsaw and used it for detecting the activity of chainsawing leading to deforestation. 

The algorithm was based on a limited energy sensor node and combined three techniques 

which included adaptive energy threshold, delta pitch detection and energy band ratio in 

high frequency range. Since the energy characteristic of chainsaw is quite constant, state


machine used was further simplified for detection purpose. They could achieve the 

detection accuracy of 90.8% with this method. 

(Figueiredo et. al., 2009), studied the communication performance of WSN for preserving 

and conserving the flora and fauna of rainforests. A set of experiments were carried out to 

assess how data communication is affected by environmental parameters like, forest density, 

humidity and extreme temperature variations. It was concluded that communication range 

of a WSN system deployed in a dense forest gets reduced by 78% as compared to 

deployment in any other environment. 

A lot of study has been done on early fire detection and a number of techniques and sensor 

combinations have been investigated. Techniques include remote sensing techniques as well 

as event detection for wireless sensor networks. (Bahrepour et. al., 2008) presented a survey 

on automatic fire detection from a wireless sensor network perspective. The survey included 

fire detection techniques for residential areas; for forests and contribution of wireless sensor 

networks in early fire detection. Since the sensors used for detection were prone to noise, 

multiple sensors were used to reduce the false alarms generated in case of single sensor 

usage. Usually temperature sensors are combined with gas concentration sensors for better 

fire detection. In this study, it was concluded that in residential areas, ION detectors are 

more beneficial for flaming fire detection. On the other hand, photo detectors are more 

beneficial for non-flaming fire detection. Fire Weather Index (FWI), which resulted from 

several years of forestry research, can be used as promising factor for forest fire detection. 

(Lozano & Rodriguez, 2010), designed a WSN based system which monitored temperature 

and humidity for early detection of forest fires. Weather conditions especially temperature, 

humidity and rainfall determines the degree and speed by which fire spreads in the forest. 

The correlation between the various weather elements and flammability of the waste of 

branches and trees helps in predicting the risk of fire at any given location. Mesh topology 

was used to configure the communication network and temperature and humidity sensors 

were used to gather the data from the remote location. Through simulation it was shown 

that the system was capable of detecting fire at an early stage thereby, protecting the nature 

reserves. 

(Zoltan Kovacs et. al., 2010), presented a case study of a simple, low cost WSN system 

implementation for forest fire monitoring. Smoke detectors and temperature sensors were 

used to detect forest fire. A simple star topology was used to cut down on the computation 

and power consumption. (Zhang et. al., 2009), proposed a Zigbee based WSN system for 

forest fire detection in real-time so that decision to prevent or extinguish fire can be taken in 

real-time. The sensor nodes comprised of humidity, temperature, wind speed, wind 

direction, smoke, pressure and other fire monitoring sensors. The data collected by them 

was sent to the cluster head which was responsible for data aggregation and transmission. 

Network co-ordinators were responsible for network building, access control and other 

network management functions. The data was transmitted to the routers which established 

local databases and sent the data to the host for monitoring purpose over the internet. Some 

important factors related to ad hoc network technology, forest-fire forecasting model and 

determination of effective communication distance was discussed. 

(Wang et al., 2010), proposed a new wireless network implementation for forest fire 

monitoring based on Zigbee and GPRS technology. This work is quite similar to the one 

adopted by us in monitoring illegal logging of trees in the forest. This system was capable of 

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transmitting the data collected by the wireless network to FTP server through GPRS so that 

real-time data can be made available to the experts to help in decision making. The hardware 

schemes and program flows of the system were given. (Fonte et. al., 2007), designed a low cost 

system-on-chip microwave radiometer on silicon for remote sensing of temperature to find 

application in fire prevention. A detailed system analysis was carried out by means of 

simulations to study its feasibility in civil and environment safeguard applications. 

(Gil et. al., 2010), came up with a fire monitoring device which provides visualization 

services after gathering GPS and sensor data from the micro-system (quad rotor). The 

sensors used were CO2, humidity, fume and temperature. The data was wirelessly 

transmitted and displayed on the map using open map API to give information where the 

fire broke out. (Hefeeda & Bagheri, 2007), proposed a WSN system for forest fire modelling 

and early detection. Forest fire was modelled according to Fire Weather Index (FWI) system 

which is considered as one of the most comprehensive forest danger rating systems in North 

America. A k-node coverage problem in WSN for forest fire detection was studied and a 

approximation algorithm was proposed which had better convergence, promised optimal 

number of sensor usage and doubled the network lifetime than other existing algorithms. 

Some problems on optimization related to sensor nodes deployment were also explored. 

(Al-Turjman et al., 2009), studied the various design factors important for WSN system 

deployment especially in harsh environment like coverage, connectivity and lifetime. They 

explored the problem of placement of the relay nodes in 3D forest space. They formulated 

an optimization problem which focuses on maximizing the network connectivity with a 

limit on the number of relay nodes used. They came up with a threshold on a minimum 

number of relay nodes used for desired connectivity in harsh environment. 

Apart from using WSN for this application, some researchers have explored other 

technology also. (Luming et al., 2008), studied and came up with a new technology which 

combined the advantages of video monitor and GIS systems for fire prevention. These two 

techniques complemented each other well and helped in increasing the accuracy of fire 

detection and hence prevention, reducing the false alarm. Also, synchronous tracking of 

video monitored areas in GIS of forest resources helped in getting more accurate 

information of the land form of the affected area. 

Monitoring deforestation is a very complicated process. It becomes even more complicated 

with the increasing need of resources. Our work addresses the issue of deforestation 

detection and prevention using an event based WSN system. The design and 

implementation details of the sensor nodes are given. Mesh routing algorithm is used here 

for routing data packets to the sink whenever an event is detected. 

Following the brief introduction to the problem being addressed in this chapter, the other 

sections are organized as follows: Section 2 discusses the design concept of a WSN setup for 

monitoring large space like forests. This includes the advantages and challenges 

encountered in deployment of WSN for such an application. Followed by this, Section 3 

gives a detailed description of the WSN prototype developed which finds application in the 

detection of tree theft, forest fire and deforestation. Section 4 discusses the challenges faced 

in the deployment of the proposed prototype. The power requirement of the sensor nodes is 

handled by the power management unit which has a provision of harvesting energy from 

the surrounding to increase the network deployment lifetime. The various 'energy 

harvesting' techniques which can be used for recharging the sensor nodes are discussed in


Section 5. Finally, we have Section 6 giving the summary and important conclusions of the 

work discussed. 

2. Design concept of WSN system for forest monitoring 

Designing, deploying, and evaluating a novel wireless systems at a large scale requires 

substantial effort. One of the major applications of wireless sensor networks is in event 

detection. Here, a sensor network is monitoring certain phenomenon and the respective 

communication node needs to get triggered on occurrence of a certain event. Subsequently 

the event needs to be reported to the sink node as quickly as possible. The communication 

nodes can be sleeping for most of the time to conserve power since most of the events are 

rare in nature. But there must be a mechanism to wake them up for quick event 

transmission through appropriate synchronization. Some of the prominent applications of 

this category are detection of fire, intrusion, earth quake, landslide, theft of assets and other 

surveillance applications. However, it is still a great challenge to design a wireless sensor 

network system for rare event detection; where network lifetime and robustness is a major 

concern. Some of the recent developments include campus-wide and community-wide 

wireless mesh networks (Bicket et al., 2005; UCSD Active Campus; Camp et al., 2006), and 

real-world sensor network deployments in environments as diverse as forests, active 

volcanoes, and bridges. WSN system design for forest monitoring involves: 

Sensors 

Design of low power wireless communication module 

Simulation and implementation of energy efficient protocol 

Deployment strategies 

Middleware 

2.1 Sensors 

One of the main goals of sensor network is to provide accurate information about a 

sensing field for an extended period of time. This requires collecting measurements from 

as many sensors as possible to have a better view of the sensor surroundings. However, 

due to energy limitations and to prolong the network lifetime, the number of active 

sensors should be kept to a minimum. To resolve this conflict of interest, sensor selection 

schemes are used. The sensor selection problem can be defined as follows: Given a set of 

sensors S = {S1. . . Sn}, we need to determine the “best subset” S_ of k sensors to satisfy the 

requirements of one or multiple missions. The “best subset” is one which achieves the 

required accuracy of information with respect to a task while meeting the energy 

constraints of the sensors. So, we have two conflicting goals: (1) to collect information of 

high accuracy and (2) to lower the cost of operation. This trade-off is usually modelled 

using the notions of utility and cost: 

Utility: accuracy of the gathered information and its usefulness to a mission. 

Cost: These consist mainly of energy expended activating and operating the sensors which is 

directly proportional to number of selected sensors k. Another cost factor that can be 

considered is the risk of detecting active sensors especially if wireless communication is 

used. Table 1 shows the selection of sensors for different hazards 

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Hazard Application Sensor Application 

Observation Acceleration 

Experiment Acceleration strain 

Earthquake/wind Structural control Acceleration 

Health monitoring Acceleration/strain/Displacement 

Damage detection Acceleration/strain/Displacement 

Fire detection Temperature/Smoke/Acoustic 

Fire 

Gas leak detection 

Alarm ,warning 

Olfactory 

Sounder 

Evacuation control Temperature/Smoke/Acoustic/Light 

Crime 

Surveillance 

Security alert 

Acceleration/Light/Acoustic/Camera 

Sounder 

Table 1. Different Sensors used for Sensing Different Hazards 

2.2 Design of low power wireless communication module 

Power Management is the major challenge in wireless sensor network design. Sensor nodes 

of the WSNs are battery powered due to their nature of application and deployment 

requirement. However, batteries life time is limited life which affects the performance of the 

WSN and it needs replacement from time to time. To overcome this issue, lifetime of the 

battery can be extended by adopting the following approaches: 

1. Design of low power sensor nodes 

2. Energy harvesting 

Many applications require periodic monitoring rather than continuous monitoring of 

elements of interest. For such applications, the system need not be in awake state (high 

power consumption) all the time; instead it can be in sleep state (low power consumption) 

till it is required to monitor the elements of interest. This can lead to considerable reduction 

in power consumption. Many low power chipsets are now available which can be 

configured for such an application. 

Additionally, harvesting energy from the surrounding can play a significant role in improving 

the self sustainability of the WSN system. Once WSN is deployed it is expected to work 

continuously and autonomously with minimum or no human intervention. Therefore there is 

a need for sensor nodes to be self sufficient in terms of energy consumption. Energy harvesting 

can be performed from sources like solar, vibration, RF etc for recharging the sensor nodes 

batteries, thereby increasing their lifetime. The different energy harvesting techniques which 

can be employed depends on the location of WSN deployment. Further, the different 

techniques and their implementation is discussed in Section 5. 

2.3 Protocol section and simulation 

In WSN, most sensor networks are application specific and have different requirements. On 

the other hand the sensor nodes have a limited transmission range, processing and storage 

capabilities, energy resources as well. The routing protocols for wireless sensor networks are 

responsible for maintaining the routes in the network and have to ensure reliable multi-hop 

communication under these conditions. In consequence, all or part of the above mentioned 

design objectives need to be considered in the design of sensor network protocol. (Singh et 

al., 2010) provided a survey on challenges involved in the design of protocols for WSN. 

Below is the list of requirements to be considered in order to design and develop a good 

quality application protocol for WSN.


Small node size 

Low node cost 

Low power consumption 

Scalability 

Reliability 

Self-configurability 

Adaptability 

Channel utilization 

Fault tolerance 

Security 

QoS support 

Sensor locations 

Limited hardware resources 

Massive and random node deployment 

Network characteristics and unreliable environment 

Data aggregation 

Diverse sensing application requirements 

2.4 Middle ware 

As WSN vision evolves, multiple paradigms co-exist as single, multiple and internet-scale 

sensor networks. A diversity of approaches has been proposed to deal with the multitude of 

WSN application requirements. Current systems do not address most of these requirements 

adequately; especially the aspects like support for security, trust, transparency, mobility, 

and heterogeneity. 

Middleware for sensor networks is an emerging and very promising research area. Most of 

the reported works on sensor middleware are at an early stage, focusing on developing 

algorithms and components for data aggregation, self organization, , network service 

discovery, routing, synchronization, optimization etc to build higher level of service 

structures. They often lack attention for integrating these algorithms and components into a 

generic middleware architecture, and for helping application developers to compose a 

system that exactly matches their requirements. There are still few widely accepted software 

standards for middleware. SensorML (Sensor Model Language) for service discovery and 

Global Sensor Networks (GSN) (Middleware for Sensor Networks) or integrating virtual 

sensors have the potential for adoption. We also see the relevance of context aware 

computing technologies (Ontologies and expert systems) in creating a semantic layer for 

WSN applications. However, in this chapter the implementation was performed in the 

hardware without any separate OS. 

Microsoft has a web based visualization service called Sense Map (SensorMap) which can be 

used to host and share sensor data. Google Maps, Google Earth, Virtual Earth, are also 

provided as interfaces to the final user. In addition to computers, PDA (Personal Digital 

Assistant) and mobile phones can be used to monitor the data and subscribe to alert 

services. Also it will be interesting to see how virtual reality environments can provide 

effective visualization environment for WSN applications. WSN characteristics require a 

specific approach for middleware development that goes beyond dealing with resource 

constraints. It involves an end-to-end approach that handles the WSN as a whole rather than 

a group of individual nodes. This implies considerable consequences for typical middleware 

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services such as mobility, coordination, service discovery, security, data aggregation, quality 

of service, handling hardware heterogeneity, handling communication errors, scalability, 

and network organization. Good architectures are needed to integrate new sensor services 

to integrate safely with legacy systems; enhanced programming models, event propagation 

models, and data models to accommodate the requirements of sensor applications and 

services; and inventive design. There is no single middleware existing till date which 

addresses all these requirements. 

2.5 Deployment 

In general, deployment establishes an association of sensor nodes with objects, creatures, or 

places in order to augment them with information-processing capabilities. Deployment can 

be as diverse as establishing one-to-one relationships by attaching sensor nodes to specific 

items to be monitored (Przydatek et al., 2003), covering an area with locomotive sensor 

nodes (Bulusu et al., 2004), or throwing nodes from an aircraft into an area of interest (Karlof 

& Wagner, 2003). Due to their large number, nodes have to operate unattended after 

deployment. Once a sufficient number of nodes have been deployed, the sensor network can 

be used to fulfil its task. This task can be issued by an external entity connected to the sensor 

network. 

3. Proof of Concept (PoC) for forest monitoring 

The main objective of our system is to monitor tree theft/fire in the forest and alert using an 

event based wireless sensor network. In forest monitoring application, events like tree theft, 

fire etc. occur rarely, so in our implementation, the communication nodes are kept in sleep 

mode (until any event is detected) to cut down on the power consumption. Whenever an 

event is detected by sensor, it triggers the communication nodes. Subsequently, the event is 

reported to the sink node as quickly as possible and an alert is generated. In addition to 

event based monitoring, our system incorporates energy harvesting technique to power the 

sensor nodes and for carrying out other power management related tasks. The detailed 

description of the design and implementation of the proposed system is given below. 

3.1 Communication node design 

This section describes the sensor node architecture which has been designed and developed 

for the low power application stated above. Fig. 1 shows the functional block diagram of the 

sensor node developed for forest monitoring. The sensor node architecture mainly consists 

of a SOC (system-on-chip) for data collection, processing, networking and controlling; 

sensors for event detection; power supply for meeting the power requirements of the sensor 

node and RF energy harvester system for harvesting energy from RF. The hardware details 

of the various components of the sensor node are described below. 

3.1.1 SOC (System-on-Chip) 

MC13213 system on chip (SOC) from Freescale Semiconductor has been used for this 

implementation. The interrupt which is generated by the Key Board Interrupt (KBI) has been 

used to wake up the controller from the sleep mode. Whenever SOC receives an interrupt to 

KBI from the tilt/temperature sensor, it sends low signal on the corresponding KBI and wakes 

up the controller which starts its service from the corresponding service routine.


Fig. 1. Functional Block Diagram of the Sensor Node developed for Forest Monitoring 

On the other hand, when the neighbor nodes want to send the data, it first sends the RF 

signal and then sends the data. Received RF signal will interrupt the node and energy 

storage will occur based on signal threshold. When RF signal is within the predefined 

thresholds, threshold circuit sends an active low signal to KBI. KBI wake up the controller 

and start the corresponding service routine to listen its neighbour. If the received RF energy 

is out of these two thresholds, it activates the energy harvest mode. 

Energy of RF signal =Vf 

if TH1

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using rechargeable alkaline battery (two 1.5V cells in series). Since our system is an event 

based system, the system spends most of the time in sleep mode (consuming uAmps of 

current), so the current consumption of this system is very low. Therefore, once the battery 

is fully charged the system runs for minimum six months. In addition to that, RF energy 

harvesting technique is implemented in this system to recharge battery, which further 

increases the sensor network lifetime. RF energy is extracted from RF transmitters/senders 

specially designed for this purpose. Typically, RF senders are required for every 50 nodes, 

whose job is to transmit the RF signal with high power so that the nodes which receive the 

signal with high power will recharge their battery. Such RF sender is required to run for one 

or two days once in six months. 

RF harvesting technology can be used for multiple frequencies and can generate standard or 

custom output voltages. Batteries or any other energy storage devices can be recharged 

easily either in close proximity or remotely. In addition, some low power devices can be 

directly driven from the received RF power. In our application, harvester system for RF 

consists of Power harvester P1100 module from Powercast (Power harvester P1100 Module 

Datasheet), which converts received RF energy into DC power with high efficiency. With the 

help of a threshold circuit, this chip is used for two purpose here, one for waking up the 

sensor node and other for recharging the battery. When any neighbour node wishes to 

transmit data, it first sends the RF energy for approx.10 seconds. This is received by 

surrounding nodes and goes through P1100 module to get converted into DC power. The 

converted DC power is fed to the threshold circuit which decides whether the RF signal 

received is to wake up the controller or to recharge the battery. The decision depends 

entirely on the power available at the output of P1100 module and the threshold limits 

adopted. Depending on the decision, a KBI interrupt is generated if it is required to wake up 

the neighbouring nodes for data transmission. 

3.2 Network design architecture 

One of the important aspects of the network design is the communication of data among the 

sensor nodes. Therefore, it is highly necessary to design efficient routing algorithm 

considering multiple constraints that is inevitable in wireless environment. There are two 

types of routing possible based on the functionality of the nodes in the network namely, flat 

routing and hierarchical routing. In hierarchical routing, the whole network is divided into 

multiple hierarchies. Each node has different functionality with respect to the level of 

hierarchy. Zigbee routing is one such hierarchical routing protocol where the nodes are 

organized in a hierarchical manner. However, in flat routing protocol, also known as mesh 

routing protocol, all the nodes in the network are organized in the same hierarchy i.e. all 

nodes in the network have the same hardware and functional properties. Directed diffusion 

is one example of this type of routing. In this study, we propose a mesh routing protocol 

system suitable for monitoring the environment, wherein the nodes in the network are in 

the same hierarchy. The proposed protocol for this study is an event based protocol, where 

the nodes generate data corresponding to the event occurred and communicates it to the 

sink. The protocol has two phases: Configuration Phase and Routing Phase. 

Configuration Phase: In this phase, the whole network is in wake up state. The sink node 

sends the CONFIG packet which traverses through the network in multiple hops. All the 

nodes receive the CONFIG packet and construct the routing table. Routing table is used to 

route the data packets towards the sink node. Once the network is configured, the sink 

broadcasts the sleep packet throughout the network and all the nodes go to sleep mode.


Routing Phase: In this phase, an event detected in the vicinity of the sensor, wakes up the 

sensor node and records the sensed data at the node. This node then transmits a beacon 

signal for a pre-defined period of time to wake up the sensor nodes within its range 

(neighbours). The node selects the upstream node (towards the sink) using routing table to 

forward the data packet. A similar procedure is carried out on the selected upstream nodes 

to route the data packets to the sink node. The nodes enter back into sleep mode after a 

specified period of time. 

3.3 Visualization and monitoring 

A tool for monitoring and visualization of the forest related events was developed. It 

supported major features required for middleware for many practical applications involving 

sensor data collection, visualization and monitoring. The developed framework had two 

flavours. The first one is a PC based standalone tool, named as Wi-SenseScape developed in 

Java. It has a graphical user interface (GUI) supporting commands for network visualization 

including topology, node parameters, and sensor data. The GUI supports the specification of 

background and foreground image files to be displayed to adapt to various application 

scenarios. Events can be defined by specifying a mathematical expression based on the 

sensor parameters. This expression will be evaluated in real-time whenever the dependent 

parameter changes. Once an event is detected, there is a facility in this tool to link an action 

to a specific event based on user's discretion. 

The second one is a web based visualization system, named as Web-SenseScape (shown in 

Fig. 2), which integrates Google maps and other map sources as geographical reference 

layers. Markers are used for identifying the sensor node deployment which displays current 

details on mouse click. The WSN is represented in XML in a hierarchical structure. The 

structure is Network -> Clusters-> Nodes-> Sensors. A text based structure view is 

incorporated to enable the expandable tree visualization. The desired Sensor-ID could be 

selected to view the sensor data in text format or as a time series plot. 

Fig. 2. Web based WSN Monitoring and Visualization 

Specific to forest monitoring, this can be loaded with pre-defined blue-print of the forest which 

is to be monitored. An alert window provides real-time display of events that got triggered 

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based on the events defined. Appropriate audio-visual messaging or alarms can be invoked on 

the occurrence of specific events. For example, whenever tree falls it shows which tree has 

fallen by highlighting the tree and proper audio message to take necessary action. This 

framework is being extended to incorporate visualization of multiple data types and sources 

including cameras, microphones, GPS (Global Positioning System), medical sensors etc. 

represented with SensorML. Network management functionality is also being incorporated to 

enable interactions with the sensor nodes deployed in the field. The middleware architecture 

has been developed keeping in view of the flexibility, scalability and portability required for 

supporting multiple networking standards, applications and platforms. The next section 

demonstrates the application in the deployment scenario. 

3.4 Deployment of WSN for forest monitoring 

The typical setup of the forest monitoring application is shown in Fig. 3. Few wireless nodes 

(say N1 to N10) which are responsible for sensing the desired physical entities and 

communicating this information are deployed in the forest. The information gathered by the 

nodes is transferred to their upstream routers. One Base Station (BS) is used which gathers 

the information from the routers. This BS is in turn connected to host system through 

wireless connection which finally processes the information received from the BS and takes 

appropriate decision. 

Fig. 3. Demonstration Scenario of detecting a Fallen Tree using WSN 

In our application, the wireless sensor nodes are mounted on each tree in order to protect the 

tree from critical events like theft, fire etc. Wireless T-mote is the name given to each node. Few 

T-motes were attached to the tree models. It formed a simple dynamic tree network topology 

to route packets to the host system. The fall detection approach adopted by us can be 

explained with the help of Fig. 3. As can been seen from the figure, fall event is detected on 

N10 which may be generated in case of tree theft. When the tree is falling the tilt sensor 

generates the event and wakes up the node. This node transmits a beacon signal for a predefined 

period of time to wake up the sensor nodes within its range. A dynamic routing path 

created in the routing phase is used to send this message to the base station which is connected 

to the host system. In this example, routing path created via Node 8, 6, 4, 2, 1 is used for alert 

sending. Host system running the Wi-Sensescape application, provides services like: sensor 

data visualization and analysis, map of the network topology, alarm services, network analysis


and filtration of sensor data. This helps in studying and analyzing the activities and behaviour 

of the WSN deployed for monitoring purpose. 

3.5 Results and discussions 

This section discusses the important results and observations of the proposed system. In this 

work we have considered three scenario to calculate the power consumption of the sensor 

node, they are: (a)when there is no event, (b)there is event from the sensor (tilt 

sensor/temperature ) and (c) there is event from the neighbor to forward the data. After the 

event occurs, it wakes up the SOC and starts the operation. The SOC‘s current consumption 

is typically around 50mA, it continues till the communication is completed (generally it 

takes 3 to 4 seconds to complete its operation) and then it goes to sleep mode. Therefore, 

once the event occurs, the battery life is reduced by ~6 hours from the overall life of the 

battery. Table 2 shows the power consumed by the mote when there is no event. 

Components Mode of Operation Average Current Drawn 

Threshold circuit Active 100 A 

SOC Sleep 10 A 

Leakage - 15 A 

Total current required by the sensor node 

125 A 

Power required = 3V * Total current 

375W Table 2. Power Calculation with No Event 

Battery Capacitor( BC) 2000mA 

Total hours 16000 

Battery Drain of Usage 125A 

Total hours( TH) 

16000 

Total days 2years 

Hours of Usage per day 24 

Routing Protocol: The simulation of the routing protocol has been performed in NS-2 ver 2.32 

platform. We choose the amount of delay to analyze the performance of the protocol. 

Average Delay measures the average one-way latency observed between transmitting 

information from the source and being received by the sink. We study this metric as the 

function of the sensor network size. We generated variety of sensor network scenarios with 

different network sizes to study the performance of the routing protocol as a function of 

network size. In every experiment we study the performance with 10 different sensor 

network scenarios with the network size ranging from 50 to 150 nodes with the increment of 

25 nodes. We perform the simulation by keeping the network density constant of about 100 

nodes/m 2 throughout the experiment. We chose the transmission range of the nodes to be 

10m. The other sensor network fields are generated randomly thereby keeping the node 

density constant. We used the default 802.15.4 MAC and physical layer stack provided by 

the NS-2. We carried out the experiment by making a single sink and single source node 

participate in the event generation. Finally, we averaged the results over 10 different 

generated sensor network scenarios. Fig. 4 shows the graph of average delay for the 

simulation scenarios discussed above. The simulation was carried out for two different cases 

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where in case 1, the network is in wake-up mode throughout the simulation period. In this 

case, it is not necessary to use the RF signal to wake up the node. In case 2, the network is in 

sleep mode, where an event wakes up the sensor node and eventually intermediate nodes 

are woken up by the RF signal. We simulated this case by transmitting the RF signal 

continuously for a period of 1sec to wake up the nodes. As observed in case 1, the data 

packet reaches the sink node within negligible period as there is no overhead of waking up 

the nodes. But in case 2, as the number of hops to reach the sink increases, it takes a longer 

duration for a data packet to reach to sink as it includes the delay of wakening the nodes. 

Therefore, we concluded that the average delay is purely proportional to the number of hops, 

which in turn is dependent on the distance between the sink and event generating source 

node. The major advantage of using this protocol in relaxed latency constrained application is 

the amount of energy saved since the network is in sleep mode till an event occurs. 

Fig. 4. Latency Time v/s Network Size 

4. Deployment challenges 

Wireless Sensor Network is a very promising technology for monitoring and controlling 

large, remote and hostile areas. They are finding extensive application in the fields of home, 

industry, healthcare, agriculture and environment. Some of the reasons of its popularity are 

discussed below. The sensor nodes can acquire and analyze the measured data collected 

over vast distributed areas using multi-hop communication at lower wiring cost. Moreover, 

they can be deployed very quickly and easily without requiring any pre-existing 

infrastructure. They can even be integrated with existing external instruments in hostile 

areas to help collect, analyze and transmit data to the base station for control action. Lot of 

research is also going on in making the sensor nodes more energy efficient thereby 

increasing the lifetime of network deployment. 

Although WSN is very useful technology for precise monitoring of large, remote and hostile 

areas, it suffers from some disadvantages. Researchers have now found the difference 

between the predicted and observed behaviour of the wireless sensor networks after 

deployment in the field. Some of the constraints and challenges involved in designing a 

WSN based system for any application are:


Optimizing the size, power, cost and their associated tradeoffs 

Selection of network protocols that account for key realities in wireless communication 

Selection of real-time routing protocol (e.g RAP protocol, SPEED protocol) 

Selection of sensor network with limited processor bandwidth and less memory 

Improvement of communication range (Wark et al., 2008) 

Design of sensor network with low power consumption for long term deployment. 

Much of the current research focuses on how to provide full or partial sensing coverage 

in the context of energy conservation. In such an approach, nodes are put into a 

dormant state as long as their neighbours can provide sensing coverage for them. In 

addition, attention has been drawn towards event based systems which account for 

power consumption. 

Selection of optimum OS for middleware is a critical aspect of WSN. 

Design for security: Sensor nodes are often deployed in accessible areas, presenting a 

risk of physical attacks. The key challenges are establishment, secrecy and 

authentication, privacy, robustness to denial-of-service attacks, secure routing, and 

node capture. 

5. Energy harvesting techniques 

WSN nodes being battery powered are designed to be energy efficient so as to maximize the 

lifetime of deployment. Apart from the hardware design, substantial research has been done 

on designing energy-efficient networking protocols to maximize the lifetime of WSNs (Seah 

et. al., 2009, 2010). One of the major problems faced by WSNs is the lack of a reliable energy 

source. Most of the WSNs which are deployed are (primary) battery powered and they need 

replacement from time to time. Battery replacement might not be practically possible in 

many situations especially where the sensor nodes are embedded in structures and need to 

be installed for long duration so, there is a need of using rechargeable batteries which can be 

charged from time-to-time thus, continuously delivering power to the sensor nodes. The 

batteries can be charged from the mains but that would incur additional cost of wiring and 

cabling for each sensor node which might not be practically feasible. Therefore, different 

energy harvesting techniques are adopted to charge the batteries and make the nodes self 

sustainable. 

Since the WSN nodes require low power, micro-scale energy harvesting techniques are 

used to extract power at low levels from the surroundings. Micro-scale energy harvesting 

systems are now coming up for producing self sustaining low power electronics which no 

longer depend on battery for their operation. These systems are capable of extracting 

milli-watts of power from sources like light energy, vibration energy, RF and thermal 

energy. Harnessing energy from these sources has always been a challenge as these 

sources tends to be intermittent and unregulated although being abundantly available. 

The energy which is extracted from these sources can be stored in a capacitor, super 

capacitor, or battery. (Kompis & Aliwell, 2008), gave a review of the different energy 

harvesting technologies that could be used for remote and wireless sensing along with the 

limitations associated with the energy sources. Table 3 summarizes the various sources 

used for micro-scale energy harvesting along with the power estimate which can be 

extracted from them (Raju, 2008). 

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218 

Energy Source Harvested Power Estimate 

Light (photovoltaic) 10uW-10mW/cm2 

Vibration/Motion 4uW-100uW/cm2 

Temperature difference 25uW-10mW/cm2 

RF 0.1uW-1uW/cm2 

Table 3. Energy Sources and their Harvested Power Estimate 


As seen from the table, light (photovoltaic), thermal and vibration/motion energy seem to 

be more promising sources for low power applications. On the other hand, energy harvested 

from RF is very small and is still under the development stage but it finds considerable 

application in wireless power transmission esp. for powering low power Wireless Sensor 

Networks (WSNs). We have used RF energy harvesting technique in our prototype. The 

choice of the energy source for a particular application largely depends on the location of 

deployment. This section describes two energy harvesting techniques which could be 

employed for powering WSN nodes, these are: solar energy harvesting and RF energy 

harvesting. 

5.1 Photovoltaic harvesting system 

In this method, ambient light energy is harnessed and converted into electrical energy with 

the help of solar cells. Solar cells are essentially semiconductor junctions. Solar cells work on 

the photovoltaic effect in which, the light incident on the solar cell generates electron-hole 

pairs on both sides of the junction, the generated electrons and hole then diffuse in the 

junction and are swept away by the electric field thereby, generating current. The 

conversion efficiency of the solar cells is quite low ~10-20%. Fig. 5 shows the general 

structure of a solar cell. The power output of the solar cell is DC and so can be directly used 

to power DC loads or can be used for battery charging applications. They can even be used 

to power AC loads with the help of an inverter. In addition to solar energy, with recent 

advancements, a large range of low power solar cells are now available which are capable of 

working in indoor environment i.e. under a fluorescent source. 

Load 

front 

contact 

Fig. 5. General Structure of a Solar cell 

Light (solar radiation) 

Anti-reflecting coating 

electron-hole pair 

rear 

contact 

n-type 

p-type 

Emitter 

Base 

Advantages: clean source, requires less maintenance, noise-less operation, flexible in 

configuration i.e. it can be easily connected in series or in parallel combination depending 

on the power requirement. 

Disadvantages: power extracted is expensive due to high initial investment and low 

conversion efficiency, toxic chemicals like, cadmium and arsenic are used in the production


of solar cells which adversely impacts the environment, intermittent nature of the source in 

terms of power output. 

Applications: low power solar cells find extensive application in calculators, portable lamps, 

watches, battery chargers, remote telemetry and communication. 

5.2 RF Energy harvesting system 

RF energy harvesting is the process by which energy is derived from external RF sources, 

(e.g., FM, TV Towers, Wi-Fi, Cell towers and Mobile phones etc.) captured and stored for 

different applications like in Wireless Sensor Networks(Seah et al., 2009, 2010) and Wearable 

electronics. In this method, RF energy harvesting receivers convert ambient energy (i.e. 

surrounding RF energy) into electricity. Receiver circuit consist of a rectenna i.e. a special 

type of antenna which directly converts RF energy into useful DC electricity (Mohmmed et 

al., 2010) as shown in Fig. 6. In this case also, the power output is DC so it can be used to 

power DC loads or for battery charging applications. 

Fig. 6. General Block Diagram of RF Energy Harvesting Receiver System 

Advantages: Power can be transferred to remote locations where wired power is not possible, 

controllable, and predictable. Power can even be transferred over long distances by proper 

system design. 

Disadvantages: The amount of ambient energy captured is very small and irregular, so it can 

be used for powering very low power devices. 

Applications: wireless sensor network, consumer electronics, industrial and transportation. 

5.3 Photovoltaic harvesting system and its application in WSN 

This section describes the solar photovoltaic harvesting system for low power application 

with the main focus being their application in WSNs. Nowadays, ultra low power solar cells 

are extensively finding applications in Wireless Sensor Networks (WSNs) deployed 

outdoors as well as indoors(Hande et al., 2007). The next sub-section describes the 

commercially available low power solar cells which can be used for such an application. 

5.3.1 Solar cells (photovoltaic) for WSN 

Solar photovoltaics are more popular in high power applications due to the fact that the 

high cost of the PV panel can be supported by such applications. However, with 

technological advancements, several low cost solar cells are now commercially available 

which can be used for WSN application esp. in industrial and hospital environment where 

indoor lights are operational continuously. Solar cells are available in crystalline silicon, thin 

219

220 


film and many other varieties with a trade off between cost and efficiency. Solar cells are 

available for illumination levels starting from 200 lux (under a fluorescent source) to 

1000W/m 2 (under 1.5 solar spectrum). Typical voltage and current at maximum power point 

range between 1.2V-16V and 4uA-85mA respectively under different illumination 

conditions. The voltage per cell is low, so a number of cells are connected in series (stack) 

depending on the voltage requirement of the sensor node. The current requirement is met 

by connecting several such stacks in parallel. The overall cell configuration, acting as a 

single energy source can then be directly connected to the electronic device or through a 

charge storage device (like super capacitor, NiCad, NiMH, or Li rechargeable batteries) with 

charge controller system which limits overcharging of the batteries. 

5.3.2 MPPT for maximum utilization of the source 

The solar cell output is significantly affected by changes in the irradiation and temperature 

levels. Fig. 7 shows the current-voltage and power-voltage characteristic of the solar cell at 

particular irradiation and temperature level (Kumar, 2010). Since the current-voltage 

characteristic of a PV cell is non-linear, for a particular irradiation and temperature, there is 

a unique point on the power-voltage characteristic at which the photovoltaic power is 

maximum. This point is termed as the Maximum Power Point (MPP). The power, voltage 

and current corresponding to this point are referred to as PMPP (power at maximum power 

point), VMPP (voltage at maximum power point) and IMPP (power at maximum power point) 

respectively. As the irradiation level changes, the power output of the PV system changes, 

which in turn, changes the MPP. Fig. 8 shows how the MPP points changes under different 

irradiation levels. 

It is desirable to make the solar cell operate at MPP so that the source is utilized efficiently at 

all the times. This is made possible interfacing the solar cell with power electronic converter 

working as a Maximum Power Point Tracker (MPPT) incorporating one of the MPPT 

schemes. Various MPPT schemes for solar photovoltaic systems have been reported in 

literature (Faranda & Leva, 2008; Hohm & Ropp, 2000) with respect to their tracking speed 

and accuracy. 

Power and Current 

Current curve 

IMPP 

PMPP 

Voltage 

Power curve 

VMPP 

MPP 

Fig. 7. Current-Voltage and Power-Voltage Characteristics of the Solar Cell


Current 

Irradiation 

level 

Voltage 

MPPs 

Fig. 8. Variation of MPP with the Variation in the Irradiation 

5.3.3 Solar photovoltaic system for WSN application 

Designing a power supply that involves, generation, storage and conversion is very 

challenging. A lot of problems are encountered both at the design level as well as at the 

implementation level and various solutions have been given by people to solve this problem 

(Mahlknecht & Roetzer, 2005). The solar photovoltaic harvester requirements change 

depending on the application at hand. A number of factors govern the power being 

delivered by the solar cell. Since they are employed to power WSNs deployed outdoors, the 

placement of sensors is one of the factors which play a major role. Best compromise of the 

placement position should be identified between the best illumination and location 

requirement. It should be ensured that the mounting place is not shadowed by the other 

objects and the place is sufficiently illuminated. Apart from the placement aspect, the 

radiation pattern of the location needs to be studied as it helps in the sizing of solar cell 

arrangement. 

Incident 

radiation 

Solar 

Cell 

Power 

Management 

Storage 

Device 

Micro-controller 

Sensor 1 Sensor n 

Sensor Node 

Radio 

Fig. 9. Basic Block Diagram of a Solar Powered Wireless Sensor Node 

221 

Communication 

Antenna 

The basic block diagram of solar powered wireless sensor node is given in Fig. 9. It typically 

consists of a power management unit, controller, sensors and radio transceiver. The power 

management unit comprises of the solar cell configuration (series/ parallel combination) 

and a storage element (capacitor/battery). The sizing of the solar cell configuration is done 

based on the power requirement of the wireless sensor node (RF Monolithics Application 

Note M1002, 2010). The storage element can be a super capacitor or battery or a combination 

of both with super capacitor providing the peak current and battery acting as the main backup 

reservoir. Super capacitors are large capacitors available in the range of 50-100 Farad

222 


with operating voltage of 2.6V. Several of these can be stacked up in series/parallel 

combination to supply the peak power demand. There are four types of rechargeable 

batteries which can be for this application, these are: Lead Acid, Nickel Cadmium, Nickel 

Metal Hydride and Lithium Ion. Of all these, Lithium Ion battery is most preferred to power 

WSNs because of its operating voltage of 3.6V. Lead Acid and Nickel Cadmium battery 

although being cheap and easily available are not popular due to increasing environment 

concerns about the lead and cadmium content. 

The next block is the controller unit which processes the data obtained from the sensors 

connected to it and transmits the information using the radio transceiver. Some 

configurations might include MPPT device to effectively utilize the PV source. MPPT is 

popular in high power applications of solar photovoltaic where huge amount of energy is 

extracted. However, in low power applications, MPPT might add to power consumption 

and fail to be useful. Therefore, for low power applications demanding a constant output, 

the solar cell is interfaced with a power electronic converter. Power electronic converter 

works as a regulator/charge controller thereby helping the solar cell in meeting the constant 

load demand/charging the batteries. To conserve energy, WSNs operate in sleep mode, 

waking up at regular intervals to acquire process and transmit the sensor data to its 

neighboring devices/master system for decision making. The amount of current consumed 

by them in the sleep mode and wake up mode is of the order of 5uA and 200mA 

respectively. The current consumption and the time duration in each mode help in sizing 

the solar cell configuration for powering the sensor nodes. In addition to that, choice of 

battery and the depth of discharge also have an important role in determining the power 

requirement of the sensor node. 

5.4 RF energy harvesting system and Its application in WSN 

This section describes the RF energy harvesting receiver system for low power application 

with the main focus being their application in low power WSN. However, another way of 

supplying power to sensor nodes is through wireless power transmission. Wireless power 

Transmission (WPT) is transmission of electrical power from one point to another through 

vaccum or an atmosphere without use of wire any other substance. This can be used for 

applications where either an instantaneous amount or a continuous delivery of energy is 

needed, but where conventional wires are unaffordable, inconvenient, expensive, 

hazardous, unwanted or impossible. 

Nicola Tesla who invented Radio, also known as “Father of Wireless” was the first who 

conceived the idea Wireless Power Transmission and demonstrated it in 1899. The idea 

behind this investigation was that, in recent years the use of wireless devices is growing in 

many applications like mobile phones, medical implants (Huang et al., 2008) or sensor 

networks. This increase in portable wireless applications has generated an increasing use of 

batteries. Many researchers are working on energy alternatives to reduce down their 

dependence on batteries and come up with low power counterparts so as to increase device 

lifetime. The charging of wired applications is still easy because the user can do it easily, like 

for mobile phones. But for other applications, like medical implants or wireless sensor nodes 

located in difficult access environments, the charging of the batteries remains a challenge. 

This requirement still increases when the number of devices is large and are distributed 

over a wide area or located in inaccessible places. Wireless Power Transmission (WPT) can 

be used as one solution to overcome the above mentioned limitations or challenges.


Different methods exist by which electrical energy can be transferred from the source to a 

load without the use of wire. These are: Electromagnetic Induction, Magnetic Resonance and 

Electromagnetic Radiation. Out of these, electromagnetic radiation is most popular for 

powering WSN nodes. The various RF sources and RF harvester design for the WSN nodes 

are discussed in the subsequent sections. 

5.4.1 RF energy sources 

RF energy harvest method can be used to remotely charge the battery operated WSN device. 

The different categories of available RF sources (Kumar) which can be used for conversion 

are listed in Table 4. Along with the frequency range, the transmitted power for the various 

RF sources (transmitters) is also mentioned which can be captured and used for different 

low power applications. 

RF Sources Frequency Range Tx Power 

FM Tower 88-108MHz 10KW 

TV Tower 180-220MHz 40KW 

AM Tower 540-1600KHz 100KW 

Wi-Fi 2.4-2.5GHz 10-100mW 

Cell Tower 800,900,1800MHz 20W 

Mobile Phones 

GSM-900 

GSM-1800 

2W 

1W 

Table 4. RF Energy Sources and their Harvested Power Estimate 

When more power or continuous energy is required than what is available from ambient RF 

sources, RF energy can be broad casted in unlicensed frequency bands such as 868MHz, 

915MHz, 2.4GHz and 5.8GHz. Each region has limitations on transmitted power like for e.g. 

its 4W in North America and 2W in Europe region. 

5.4.2 RF harvesting receiver system for WSN application 

The basic block diagram of RF powered wireless sensor node is shown in Fig. 10. It typically 

consists of a energy harvesting unit, controller, sensors and radio transceiver. Apart from 

the energy harvesting unit, rest of the blocks remain the same for designing any WSN node 

as described in section 5.3.3. In any energy harvesting unit, mainly three components are 

required, these are: energy conversion, harvesting and conditioning and energy storage. 

Here, the energy harvesting unit (Hagerty et al., 2005) comprises of the rectenna (antenna + 

rectifier) and a storage element (capacitor/battery). The design of the rectenna is done based 

on the power requirement of the wireless sensor node. 

Since the amount of useful energy obtained from RF is very less, reduction in energy 

consumption of system makes RF energy harvesting more practical. Researchers have come 

up with new solutions to improve the amount of energy being harvested from RF sources. 

In particular, circular polarized antennas are being implemented in the rectenna design 

because they avoid the directionality of other antenna designs (Strassner & Chang, 2003; Ali 

et al., 2005; Ren & Chang, 2006). An array of rectennas is now increasingly being used to 

improve the power output (Kim et al., 2006). Several new rectenna design schemes (Park et 

al., 2004; Chin et al., 2005) have been proposed by researchers. (Harrist, 2004), discussed 

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224 


wireless battery charging using RF energy harvesting. A charging time of 4mV/sec was 

observed when mobile phone batteries were charged by capturing RF energy at 915MHz. 

Antenna 

Rectifier 

Rectenna 

DC-DC 

Converter 

Storage 

Device 

Micro-controller Radio 

Sensor 1 Sensor n 

Sensor Node 

Fig. 10. Basic Block Diagram of RF Energy Harvested Sensor Node 

6. Conclusion 

Communication 

Antenna 

This chapter discussed the aspects of using wireless sensor networks for forest tree 

monitoring and alerting using rare event detection with ultra low power consumption. In 

this prototype, two sensors (mercury sensor & temperature sensor) which work well for the 

detection of fire and tree theft were selected and mesh protocol was used for alert routing 

and event detection. Network lifetime and latency estimation for the deployment scenario 

showed the implementation feasibility of such a monitoring system for deforestation 

application. However, as the sensor nodes are battery powered, issues related to battery life 

and ease of battery replacement are major concerns for WSN applications that involve long 

term monitoring of vast area especially hostile areas. It is therefore necessary to have some 

means of recharging the batteries of the sensor node to increase the network lifetime. For 

this, one of the most common ways is to extract the energy from the surrounding 

environment. Life time of network is increased by adopting RF energy harvesting technique 

for recharging the sensor nodes. In addition to RF technique various other energy 

harvesting techniques are available that can be used for this purpose. The various energy 

sources which can be used for this prototype implementation have been explored here. A 

detailed description of solar and RF energy harvesting is given which can be used to charge 

the batteries and hence increase the lifetime of the deployed WSN system. 

In future, the efforts can be taken to increase the robustness of the WSN setup in case of (a) 

self organization network, (b) failure of the sensor node (auto healing of sensor node) and 

(c) false alarms generated by sensor nodes. 

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May 2009.

12 

Bunjil Forest Watch 

a Community-Based Forest Monitoring Service 


Chris Goodman 

Object Consulting Pty Ltd, 

Australia 

Imagine the power of an Internet-enabled social network that tracked disturbances to the 

world's most precious forests. Independent observers could expose failings in forest 

management and help improve governance. 

This scenario is not far-fetched, although satellite-monitoring technology has to be made 

more accessible to non-technical, grass-roots organisations that are independent of official 

agencies. The good news is that new and organic forms of social organisation and activism 

are possible by merging the blogosphere with new public tools such as CrowdMap.com. 

(Ushahidi, 2011). One example is an interactive map developed to show land grabs linked to 

political elites in Sarawak (Malaysia Today, 2011). 

This essay proposes a free public online service that provides non-expert conservation 

groups in remote locations with alerts about recent forest disturbances in their area. It also 

explains how such a service might work. Many of the required technical components 

already exist in various forms. 

Local conservation groups living in remote forest areas should not need to understand all 

the technologies behind the service; nor have advanced computing resources or broadband 

at their disposal. They should be able to just sign-up to receive free, timely reports about 

recent disturbances in their area, in their own language, on their phone. 

Under the proposal, local groups would control which areas are monitored by subscribing 

to the service. On receiving reports about a disturbance, the groups would perform 

enforcement activities according to their judgment and circumstances. They would also 

provide feedback by responding to the reports. 

It is critical that the complex collection and processing of remotely sensed data be 

completely automated. 

The proposal puts the public at the centre by actively encouraging the participation of 

volunteer observers to perform the routine task of regularly checking new images obtained 

via satellites. The volunteer observers need not be experts nor have any other connection 

with the local group other than a common desire to preserve the forest. The service itself 

would provide all the training for volunteers to become competent observers. 

Removing barriers to participation allows the service to be widely deployed. It is envisaged 

that environmental NGOs would promote the free service to communities in forest regions, 

while soliciting volunteers from among their international support base. 

A system that can provide this service would need to combine:

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Automated detection, collection, processing and delivery of new satellite images; 

A public online volunteer observer registration system. 

Automated distribution of observation tasks to volunteers; 

An open registration system to add new protected areas; 

Automated delivery of reports to remote conservation groups; 

And a process for local groups to respond to the reports with on-the-ground information. 

These features must all be integrated by a task-based workflow system. The workflow issues 

messages to volunteers when new images are ready and to local groups when new reports 

are made. It promotes regular interaction by actively prompting users to complete tasks and 

by providing encouragement for completing tasks. It also prompts the local groups to 

provide feedback on the reports they receive. 

The proposed monitoring service would build on the accomplishments of existing 

deforestation monitoring systems, but differ in a number of ways: 

It is geared towards early detection and intervention in user-selected areas, rather than 

a complete regional analysis; 

It is completely online and cloud hosted, so there are no infrastructure requirements for 

users; 

It is non-institutional, relying instead on online relationships and reputations; 

It formally separates observation and response into separate roles; 

It is completely workflow based. New data triggers tasks to create reports, which create 

new tasks; 

It is tightly integrated with online training, wikis, blogs and discussion forums; 

It relies on continuous user feedback for quality control and ground data collection. 

Volunteers would be able to register with minimal barriers to entry and then be encouraged 

to develop their skills and knowledge with online training and networking with other 

volunteers. 

This design is not an argument against automated detection. On the contrary, algorithms 

that can highlight deforestation and degradation assist volunteers to identify disturbances 

and help them know when to raise an alert. 

Nor is this an argument against developing capacity among local groups to perform their 

own monitoring. Using volunteers as a resource has several benefits. First, they already 

have familiarity with and access to modern computers, monitors, broadband bandwidth 

and social networks. Second, they belong to different networks than the local groups. These 

may be crucial for exposing corruption and lobbying internationally. Third, there are likely 

to be many more volunteers in urban areas willing to spend time monitoring, and this 

allows the local group to spend scarce resources on activities such as verification, 

enforcement and reporting. Finally, the service may foster a greater awareness of and 

connection to deforestation issues among the volunteers, as well as develop invaluable 

cross-cultural relationships between and among the local groups and the volunteers. 

This alert service would not work without complementary conservation strategies. Strategies 

include protected areas, UN-REDD, sustainable development, land reform and anti-corruption 

programs. National monitoring programs would also be required for systematic coverage and 

to measure net verifiable national reductions as required for REDD (Nepstad, et al., 2009). 

REDD stands for Reduced Emissions from Deforestation and Degradation. This UNFCC 

program is based on the idea that developed countries wishing to reduce climate change can 

pay developing countries to reduce CO2 emissions from deforestation or forest degradation

Bunjil Forest Watch a Community-Based Forest Monitoring Service 

through the implementation of policies such as strengthened law enforcement, fire 

management or sustainable forest management. The framework requires measuring the 

existing carbon stored in the forest and estimating what would be emitted under a business 

as usual scenario. A project to avoid those emissions is proposed and at the end of a set time 

period, the actual emissions are measured and compared to what would have happened. 

The reduced emissions have a financial value that can be traded in carbon markets. Some of 

the value is hopefully transferred to the locals as income for preserving the forest. Redd- 

Monitor.org has a good introduction to REDD and its many controversies. REDD is 

important but not essential to this service. 

Just as REDD threatens to recentralise forest governance (Phelps et al., 2010), this service 

may help democratise forest monitoring away from national forest departments where the 

capability and governance is not yet in place, and towards grass-roots organisations. 

There are several challenges to achieve this. One critical ingredient is regular, low-cost 

access to recent satellite images - and automated processing of those images into a format 

volunteers could reliably decode. 

To eliminate costs to end users, the system should be based on open-source software, cloudhosted, 

and have free regular access to timely satellite data. The solution needs to focus on 

simplicity of use and hide as much complexity as possible behind a well-designed webapplication. 

2. Purpose 

"Never depend upon institutions of government to solve any problem. All social movements are 

founded by, guided by, motivated and seen through by the passion of individuals." 

Margaret Mead 

This tool could provide a complementary self-selecting targeted approach to monitoring 

areas of high conservation value wherever a local group wishes to protect their forests from 

external threats. 

The main purpose of the service is to provide local conservation groups with timely 

information about forest disturbances in their area and to provide them with increased 

opportunities to respond quickly to recent deforestation, particularly illegal logging and 

land clearing. A recent study of Sumatra and Kalimantan found that at least 6.5% of all 

forest cover loss had occurred in land where clearing was banned, and a further 13.6 % 

where it was legally restricted (Broich et al., 2011). 

A secondary purpose is to develop networks between people working to conserve remote 

areas and ‘environmentalists’ in populated, digitally-connected areas. 

3. Who are the users? 

“Enforcement against illegal deforestation is clearly a state function, but civil society can 

provide a formidable assist with timely, high-quality, user-friendly information.” 

Three Essential Strategies for Reducing Deforestation 

(Aliança da Terra et al., 2007) 

This proposal separates forest monitoring into two main roles, the volunteer observers, who 

regularly review the latest satellite images and the local groups, who rely on alerts when 

disturbances are detected. Other roles include the sponsors & NGOs, satellite data 

providers, and developers. 

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3.1 Local groups 

“Community Forest Management (CFM) establishes formal systems between communities and 

Forest Departments in which communities have the right to controlled amounts of forest 

products from a given parcel of forest and in return agree to protect the forest and manage it 

collectively. Mostly these parcels are relatively small, from 25 to 500 hectares, being managed by 

groups of 10 to 50 households. A number of countries have used CFM very effectively to reverse 

deforestation and degradation processes” 

GOFC-GOLD Sourcebook (GOFC-GOLD, 2010) 

The service needs to be promoted to community-based conservationists who may live in 

remote forest communities and who may be difficult to reach via conventional marketing 

channels. The service must be distributed to the networks used by local groups and use a 

language they share. 

Local groups could be environment advocacy groups, rangers protecting a park, indigenous 

people protecting their land or community based forest managers. The local groups might 

be participating in a REDD project, or other programs. 

Groups may be isolated both physically and politically. Over 1150 rural activists have been 

killed in conflicts related to land in Brazil alone, according to Catholic Land Pastoral 

(Dangle, 2011). Murder convictions are rare and even rarer for those that hire the gunmen. 

Fighting deforestation is also dangerous in Indonesia and Malaysia. In Papua New Guinea, 

where more than 800 languages are spoken in one of the most biodiverse regions on earth, 

deforestation is running at over 1.5%pa, most of it illegal. 

“States with rain forests are often unable to collect optimal revenue from the massive profit 

earned by timber companies that harvest state forests because this profit already has a hidden 

destination. Heads of state and their political supporters are siphoning off these moneys to 

become phenomenally wealthy.” 

David Brown, PhD Dissertation (Brown 2001) 

As much as possible, the solution must remove the barriers for local groups to have access to 

timely reports. The groups cannot be assumed to have expertise in remote sensing, but may 

be able to interpret maps, directions or coordinates. Computer literacy cannot be assumed, 

but access to a mobile phone is almost universal. Access to smart-phones, GPS and phone 

cameras is becoming increasingly common but is not yet universal. Some literacy in the 

predominant national language is required by at least one member of the group or a trusted 

partner. The capacity to visit, investigate and record deforestation events in their locality is 

important. The ability to prevent or discourage deforestation in some way is also important. 

Engaging local groups would be the first bottleneck to expanding the reach of the service. 

Enhancing the monitoring capability could expose enforcement bottlenecks in that region. 

Other capacity constraints such as computation, memory and bandwidth are easier to 

overcome. It is unlikely to be difficult to recruit sufficient volunteers as each volunteer could 

potentially review an image 180km on a side (Goodman, 2010). 

The ‘user-experience’ for local groups should be designed to be sensitive to local and 

regional cultural norms and languages. 

3.2 Volunteers 

The volunteer observers sign up to monitor satellite images on behalf of a local group. The 

volunteers may be distributed around the world with no direct connection to the local group 

other than through the monitoring service. They must have adequate time and Internet access.


The Internet, as a low cost medium with global reach, can facilitate the formation of global 

virtual communities - compensating for a lack of critical mass of activists in a given country 

(Ackland et al., 2006), (Chadwick & Howard, 2009). Creating a critical mass is an even 

greater challenge for remote communities in developing tropical countries. 

Community Based Forest Monitoring could rapidly engage environmental activists who are 

already active users of the Internet. It may be quicker to develop the observational capability 

among digitally connected volunteers, and develop collaborative networks, than building 

the capacity in remote communities. 

To design the volunteers’ “user-experience” it is necessary to understand their reasons for 

participating. Volunteers may be motivated and inspired to be active by ecological 

experiences and connections with nature; a sense of personal responsibility; a desire to 

change the world and feeling that they could make a difference; by fear and anxiety about 

ecological crisis and commitments to justice; or by influential people and social networks 

(La Rocca, 2004). It also has to be cool. Barriers to becoming active include time available: 

lack of skills or confidence; alienation or lack of opportunity. Challenges for keeping 

volunteers active include making the work enjoyable and meaningful; making a difference 

and responsibility to the local groups. 

Each volunteer’s participation is sustained by the regular tasks assigned to them and by 

feedback from the local group. 

Volunteers are not necessarily living in the same country as the local group, although this 

might become common in tropical countries with advanced urban populations such as 

Brazil, Malaysia and Indonesia. They may even come from among the local group. 

Volunteers should ideally share a common language with the local group they serve. The 

volunteer user-experience needs to at least cater for English, French, Spanish, Portuguese 

and Bahasa speakers to cover the main tropical forest regions. 

Volunteers need to recognise the limitations on the local groups’ ability to combat illegal 

loggers, especially the great danger, difficulty reaching sites, and limited law enforcement. 

3.3 Environmental NGOs 

Non-Government Organisations could promote the volunteering opportunity to their 

members. They also provide a narrative structure to the regular tasks and feedback. 

It is possible (but not necessary) that local groups and volunteers enter into agreements to 

preserve the forest. These could be through a NGO. The service forms a backbone of 

information exchange and monitoring that may support the terms of the agreement by 

building trust among the parties. 

NGOs may wish to rebrand the service as their own. Associating the service with their 

trusted reputation gives credibility to the service while at the same time the service extends 

their offering and builds their networks. 

Environmental NGOs with strong regional networks among local groups are important in 

promoting the service through the local groups’ networks. These NGOs may even partner 

with local groups who need assistance with subscriptions, communications or translations 

in some regions. 

International NGOs with large member and supporter networks are important for 

promoting the service to potential volunteers. NGOs constantly struggle to find new and 

meaningful ways to engage with their supporters, beyond asking for donations. By allowing 

volunteers to ‘adopt’ a threatened area, the NGO provides an opportunity for supporters to 

feel they are contributing in a direct and meaningful way. 

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3.4 Sponsors 

Sponsors may provide a financial incentive to the local group to preserve the forest. It is not 

essential for the financial agreement to be integrated into the monitoring service. Sponsors 

can add incentives for subscribing to the system. Project sponsors may be affiliated with 

volunteers, NGOs or local groups or in a combination. 

Sponsors or NGOs may target then reach out to local groups in areas identified as high risk 

(Sales et al., 2011). 

Sponsors are also critical to financing the development, operation and maintenance of the 

system. 

4. Existing deforestation monitoring systems 

Before describing the proposed service in detail a review of existing deforestation 

monitoring programs is presented. 

Detecting deforestation and forest degradation from space by observing changes in light 

reflected from the canopy is not a straightforward task. Nevertheless, detecting 

deforestation from space has developed over several decades and is now considered routine 

(Asner, 2009). Detection of forest degradation is harder but also possible. 

Brazil has the largest and most systematic use of remote sensing for environmental 

protection of any country. Some notable operational systems include DETER and 

PRODES by the Brazil Space Agency, INPE; SAD by Brazilian not-for-profit IMAZON; 

and CLASlite by the Carnegie Institution for Science which is also focused primarily on 

the Amazon. 

Between 2005 and 2008, PRODES indicated deforestation in the Amazon had slowed 

compared to what it would have been without the detection and enforcement. However, 

more recent data from INPE indicates deforestation rates have accelerated by 27% from 

August 2010 to April 2011 (BBC, 9 May 2011 & The Guardian, 12 June 2011). 

4.1 INPE: DETER & PRODES 

“If you are going to do prevention and enforcement, you need to be there as rapidly as possible.” 

Gilberto Câmara, Director of INPE quoted by Alexei May in NYT (May 2008) 

The Brazil Space Agency INPE runs DETER and PRODES. The newer DETER system can 

detect large scale illegal logging in near real time while PRODES has higher resolution but 

results are only updated annually. 

DETER provides an update every 15 days and sends alerts to Brazil’s Ministry of 

Environment enforcement agency IBAMA, and police. Loggers are fined and sometime have 

their property confiscated. DETER relies on a range of satellites including Advanced 

Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the China-Brazil 

Earth Resources Satellite CBERS-2. 

The Brazil Space Program for monitoring the Amazon commenced during the Cold War as a 

military operation, but was later re-purposed to facilitate economic development and 

expansion. Now the technology has further developed and applied to the detection of 

deforestation. IT systems tend to reflect the organisation that created it (Rajão & Hayes, 

2009). Originally designed by specialists for use by officials and agencies, it is now evolving 

into an open system. 

The technology and capability developed by INPE should have global applicability, 

although it is currently only deployed over the Amazon region. Expanding deforestation


detection systems beyond Brazil’s national borders is challenging. Especially to protect areas 

covered by reluctant, indifferent or corrupt government agencies; where the government 

has limited jurisdiction; and where the country has no capacity to access and interpret the 

results or to enforce protection. 

INPE committed in 2008 to making the data and technology publicly available, through the 

Data Democracy Initiative of the Committee on Earth Observation Satellites (CEOS). The 

commitment extends to governments of developing nations. The CBERS for Africa project 

will provide CBERS images to African countries as part of the Group on Earth Observation. 

INPE software has also been released as open source as the SPRING library and INPE has 

released code for applications TerraLib, TerraView and Marlin built on SPRING. However, 

a non-specialist would be unlikely to figure out how to extract meaningful data from the 

current systems. 

4.2 IMAZON’s deforestation alert system 

Instituto do Homem e Meio Ambiente da Amazônia (IMAZON) developed Systema De Alerta de 

Desmatamento (SAD) to monitor deforestation in the Amzon. SAD reports monthly. Like 

DETER it uses the low spatial resolution (250m) images from the Moderate Resolution 

Imaging Spectrometer (MODIS) aboard ASTER and publishes data on Amazon 

deforestation rates each month. Unlike DETER, SAD uses Normalised Difference Fraction 

Index (NDFI) to detect not only deforestation but also forest degradation. This picks up a lot 

more land that is degraded. Both SAD and DETER results have been challenged by 

powerful opponents and withstood rigorous analysis. 

ImazonGEO is an open-source open-data Spatial Data Infrastructure (SDI) from Imazon that 

integrates remote sensing with law enforcement (Souza et al., 2009). 

Neither SAD nor DETER are good at detecting deforestation less than 25ha (Escada et al., 

2011). PRODES is better at detecting small-scale disturbances, but has low temporal resolution. 

One technique to improve detection capacity is to combine data from higher spatial resolution 

sensors with high temporal resolution sensors. This involves using the older but higher 

resolution images to extract better information from the newer but lower resolution images. 

Cloud cover can affect temporal resolution by preventing the satellite from capturing a clear 

image. Cloud cover particularly affects the humid tropics. Access to a range of sensors on 

different satellites can improve the frequency of capturing cloud free images. 

4.3 FORMA 

Forest Monitoring for Action (FORMA) is a prototype system by the Centre for Global 

Development that achieves good resolution using Time Series or Trajectory Based Methods 

based on the MODIS Vegetation Continuous Field product (VCF) to look at long term trends 

in change in NDVI. FORMA can detect deforestation the size of a football field. It also 

detects fires in near real time. The 2009 prototype covers Sumatra and is updated monthly 

(Hammer et al., 2009). 

4.4 CLASlite 

Carnegie Landsat Analysis System Lite (CLASlite) [claslite.ciw.edu] is an automated satellite 

mapping approach that performs statistical analysis on raw satellite images to detect subpixel 

changes in forest cover. While broad-scale clear felling is easy to detect, CLASlite can 

also detect selective logging down to one or two trees. It is able to distinguish undisturbed 

forest from recent degradation and regrowth. 

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After calibration, pre-processing, atmospheric correction, and cloud masking steps, 

CLASlite will analyse the spectrum reflected in each pixel. Vegetation that photosynthesizes 

has a different spectral signal from dead trees, rocks or soil. A 'Monte Carlo' analysis then 

produces a range of possible combinations that converge on the most likely explanation for 

the data (Asner, 2009). By determining the fractional cover from canopy, dead wood and 

bare surfaces, CLASlite can provide maps of the forest’s composition, including where it has 

been disturbed. If a tiny red reflection is picked up indicating bare earth, and that signal 

forms a line over several pixels, the most likely explanation would be a road. 

Detecting new logging tracks early increases the opportunity to combat deforestation and 

degradation, as these are often the first indication of more extensive logging to come. 

For input, CLASlite can use a wide variety of satellite imagery including: Landsat 4 and 5 

Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), ASTER, Earth 

Observing-1 Advanced Land Imager (ALI), Satellite pour l'Observation de la Terre 4 and 5 

(SPOT), and MODIS. 

4.4.1 Applicability 

Originally designed for lowland tropical forest, the CLASlite detection method has been tested 

on imagery from Borneo, Madagascar, the Hawaiian Islands and Mozambique (Asner, 2009). 

To generically detect deforestation and disturbance, the method needs to identify changes in 

forest canopy cover without being overly sensitive to variation in forest type (Asner, 2009). 

Results show that very different forests can be directly assessed and compared anywhere in 

the world by the system (Asner, 2009). 

4.4.2 Licencing 

CLASlite was created by Greg Asner and the Department of Global Ecology, Carnegie 

Institution for Science. CLASlite is supported by the Gordon and Betty Moore Foundation, 

the John D. and Catherine T. MacArthur Foundation, and the endowment of the Carnegie 

Institution for Science. 

According to the end user license agreement, the Carnegie Institution intends to work with 

third parties in the dissemination and use of CLASlite for the purpose of conducting 

environmental studies and monitoring. The user agreement protects Carnegie’s proprietary 

information, restricts copying and requires attribution to Carnegie in all reports. Results 

obtained from the use of CLASlite, are to be used for non-profit purposes only. The technology 

is provided for free to governments and others in Latin America (Tollefson, 2009). 

The software could have additional utility if published as a modular library with an 

Application Programing Interface (API). This would allow it to be mashed-up into new 

applications. Porting the software to Google Earth Engine should demonstrate this. 

4.4.3 Deployment and outreach 

A 2008 grant from the Moore Foundation has allowed the CLASlite team to provide training 

and technology transfer in most tropical forest nations in the Andes-Amazon region, stretching 

from Venezuela and Guyana across to Peru, Ecuador, and Bolivia (Carnegie, 2008). 

The capacity building program provides one-day workshops and technical support to 

government, academic and NGOs in the region. The aim is for each country to build up its 

own remote-sensing team (Regalado, 2010). 

The CLASlite user website is intended as a space for collective knowledge building to 

improve forest monitoring and management in the Andes-Amazon region.


4.4.4 Constraints 

"While the principal advantage of CLASlite is that it opens options to users who are not 

necessarily specialists, it is necessary to have people who know exactly what can and cannot be 

done with CLASlite. I don't know if I would call this a difficulty, but it is a characteristic shared 

with other approaches to monitoring deforestation." 

Manuel Peralvo, Ecuador (CLASlite, 2009) 

Although CLASlite is presently only available to non-commercial institutions in the Andes- 

Amazon Region, it demonstrates that forest monitoring can become an everyday activity 

that no longer requires huge investments in computers or expertise (Knapp, 2008). 

CLASlite presently requires some user technical training to install and maintain. To what 

extent could the preparation of images for CLASlite and the creation of maps be automated? 

The steps for preparing CLASlite input are not trivial. They depend on the satellite sensor 

and rely on third party software such as ENVI or ERDAS to: 

Geo-reference the image to a UTM projection (WGS-84 ellipsoid); 

For LANDSAT, resample the thermal band to match the spectral image resolution; 

Reorder bands, if necessary; 

Save the image to GeoTIFF or ENVI format; 

These steps are largely repetitive for regularly monitoring new images of the same location, 

so an automated image workflow could be contemplated. How well would CLASlite or its 

successor perform unsupervised is unclear. 

A new version of CLASlite is being integrated with Google Earth Engine. 

4.5 Other monitoring technologies 

The state of the art in forest monitoring is advancing on many fronts. New satellites and 

sensors are increasing resolution and hyper-spectral bands. Forest monitoring systems must 

remain adaptable to operationalize new sensors and algorithms as they become available. 

The PALSAR (Phased Array L-Band Synthetic Aperture Radar) sensor abort the Japanese 

ALOS satellite also showed it is possible to detect forest disturbances even through cloud 

cover (Kellndorfer, 2008). Unfortunately the satellite failed in April 2011. This capability is 

useful against illegal loggers who use persistent cloud cover to hide their operations. 

Airborne LiDAR (Light Detection And Ranging) is opening up a new dimension 

particularly for the estimation of carbon in a forest. 

Much effort is now focussed on political, financial and technical systems for valuing and 

measuring, reporting and verifying carbon stored in forests for the UN-REDD program. 

Using LIDAR this is technically possible but still difficult as it introduces carbon inventories, 

which are sensitive to forest type. The required political and international financial 

institutions add further complexity. REDD projects work on longer-term financial cycles and 

may operate better at regional and national levels. The technology for REDD is significantly 

more complex than the detection of recent deforestation (easy) or forest degradation (harder 

but established). REDD should promote considerable innovation in forest carbon monitoring. 

There are now many tools and standards from which to build a forest monitoring system. 

TerraLib, TerraView and Marlin are based on INPE’s SPRING open-source library. 

OpenLayers and Geographic Resources Analysis Support System (GRASS GIS) from the 

Open Source Geospatial Foundation (OSGeo) are also useful open-source toolkits. 

The Kings College London, KCL Geodata portal contains a collection of useful tools for 

environmental monitoring. [sites.google.com/site/consmapping] 

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4.6 Google earth engine 

Google demonstrated a prototype of Google Earth Engine at the IPCC COP15 in Copenhagen 

in December 2009. Earth Engine is a project of Google.org, Google’s philanthropic arm. It is 

supported by technology partners Greg Asner, the developer of CLASlite, Carlos Souza Jr. 

developer of IMAZON’s SAD and others. Financial sponsors include Google itself as well as 

the Gordon & Betty Moore foundation, which also sponsored CLASlite. 

As well as simplifying access to images, the Earth Engine will include algorithms that can 

transform the raw images into deforestation maps. 

This engine will allow Google’s vast storage, computational and bandwidth resources to be 

harnessed to provide post-processed images in a user friendly format. The problem of 

creating and maintaining IT infrastructure for distributing, storing and viewing large data 

sets is solved by moving the application to Google’s cloud. Users need only have a web 

browser. The engine will “Facilitate transparency and security to their data and results. 

Because the data, analysis and results reside online, they can also be easily shared and 

independently verified.” (Google.org, 2010) 

Google Earth Engine is expected to include (GOFC-GOLD, 2010): 

Integrated access to many satellite data products; 

A means to request additional data from public databases; 

Tools for creating spatial and temporal mosaics of the data products; 

Built-in mapping and monitoring algorithms; 

Atmospheric correction, if desired (See Asner, 2009); 

CLASlite forest-view, forest-cover or forest-change maps; 

Imazon SAD functionality; 

An API to introduce new algorithms; 

A geoviewer such as Google Earth browser plug-in; 

Google Fusion Tables; 

Just-in-time computation. 

Both CLASlite [code.google.com/p/claslite] and Imazon’s open-source Spatial Data 

Infrastructure (SDI) [code.google.com/p/imazon-sad] are being ported to Google Earth 

Engine. 

The prototype catalogue currently includes access to incomplete archives of many Earth 

monitoring satellites including LANDSAT, Terra & Aqua and various products from 

MODIS. These include Surface Reflectance images at multiple frequency bands, and mosaics 

or composite products such as MODIS Enhanced Vegetation Index (EVI) and Burn Area 

Index (BAI). 

The beta site demonstrates the potential with a high-resolution forest map of Mexico created 

in record time. Despite the potential, a casual visitor to the prototype may be disappointed 

as it does yet not contain recent images. To detect recent deforestation, the catalogue must 

be updated continuously. The solution must include a means to add to the catalogue on 

demand, and to load other data products. 

There is a lack of documentation regarding the prototype. An API has been foreshadowed 

but no product roadmap has been announced. 

All data will be ortho-rectified on import. This makes it possible to mix images from other 

sensors. A multi-sensor approach can adapt to missing or poor quality data leading to 

improved continuity. Ortho-rectified maps can be published to Google Maps or other 

viewers. This also makes it easier to combine the output with other maps and data.


It is unclear what form of open access will be granted to Google Earth Engine. The company 

has announced it is giving away 10 million hours of CPU per year, but not said how this will 

be metered. Developer access to code and user access to applications has yet to be defined. 

Implementing applications SAD and CLASlite into an API promises to create a next 

generation Spatial Data Infrastructure with unprecedented storage and computation power, 

increased usability, and universal accessibility for civil society. 

5. Bunjil forest watch 

Bunjil Forest Watch is a web service proposed by the author for the rapid detection and 

reporting of deforestation. Existing technology would provide the building blocks from 

which such a service might be built. 

This application does not attempt to measure, report and verify, just to detect, and let people 

on the ground verify and report back. This is simpler than the technology required for 

REDD. It is designed as an alarm bell to disrupt deforestation as it occurs and to assist 

advocacy. It emphasises speed of detection and intervention over systematic regional cover. 

The system proposed here is likely to work best on smaller scales. Many conservation issues 

apply at landscape scales where changes of geology or hydrology lead to unique ecosystems 

and conservation hot-spots. Sites that may be too big to easily monitor on the ground, may 

not be so big that local groups are unable to do field inspections or enforcement. 

The architecture comprises modules to collect satellite data; to process the images into maps; 

to enlist volunteers; to create and distribute tasks; to facilitate observation tasks; to generate 

and distribute disturbance reports and to ensure the integrity of the processes. 

Before describing each architectural component of Bunjil Forest Watch, a story about how it 

could work will help tie the pieces together. Imagine the end user’s perspective: 

“As a conservationist, wishing to protect my land from illegal clearing, I want to know 

about any changes to my land as soon as possible so that I can respond to them. I don’t 

know much about satellites, and I can’t afford to pay someone to continually check for new 

data. If a service could just email me to tell me when something changes and where, it 

would help me protect my land.” 

The local group learns of the existence of the forest watch service through their networks. A 

member may either visit the web site and self-subscribe or have a partner in a regional 

environment organisation set up the subscription on their behalf. 

The main web site has a link in multiple languages to take the user through the online 

subscription process to add a site to the monitoring list. The subscription records the name 

and contact details for the group, information about the area they wish to monitor and why. 

The borders of the area of interest can be defined by outlining them on a map. It is essential 

that this process not intimidate users by asking questions they may be unable to answer. 

Also, it should provide a help button to ensure that potential subscribers get assistance to 

complete the process. 

Anyone can register to be a volunteer observer. Observers may be recruited via 

environmental NGOs. During registration the observer is asked to commit to promptly 

completing any observation tasks sent to them, and to complete the online training. A 

volunteer may be assigned to one or more parts of a conservation area. 

New subscriptions may need to be processed by an expert group who can choose data 

products and processing appropriate for the biogeography of the Area Of Interest (AOI). 

Automated mapping algorithms may require tuning with locally-relevant training data and 

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forest definitions in order to produce maps that reflect different definitions of forests, 

deforestation and degradation. 

These experts may be drawn from among the volunteers, developers, environmental NGOs 

academics or remote sensing professionals. Selecting the appropriate satellite sensors, 

bands, algorithms and layers is complex. However, this need only be done once to set up the 

initial settings for a periodic monitoring service. When new images become available, the 

system must be able to process them automatically, adjusting for clouds, rainfall, haze, and 

seasons, while still presenting useful images to the observer. The goal should be to reduce 

the amount of expert input as much as possible through automation. The expert group may 

also need to vet subscriptions. 

Once a subscription is established the system must discover any new imagery that covers the 

conservation area of interest as soon as it is published. This calls for an automated ‘satellite 

spider’ to troll the databases of earth monitoring providers round the clock and to check the 

metadata describing new images to determine if they are relevant to an area of interest. 

Satellite images are large, and require processing before they are useful for detecting changes 

to forest canopy. The spider does not download the images, just the metadata necessary to 

check if they are relevant. When a new relevant image is found, the spider signals the 

processing engine. 

The processing engine fetches the new image, and performs the necessary adjustments, such 

as atmospheric correction; cloud detection; ortho-rectification and forest-cover spectral 

analysis. The actual steps depend on the satellite, sensor, and band and whether these steps 

have already been completed by the data provider. 

Once the image has been processed into a map showing forest cover, it is necessary to access 

older images or maps of the same place so that a comparison can be made. This is used to 

detect recent changes, either manually by an observer or using a forest-change algorithm. 

Finally the processing engine signals to the task manager. 

The Task Manager creates a new observation task and emails or tweets the volunteer using 

the following template: 

Dear , A new image is available for . The image was created 

on . You task is to look for recent changes to forest cover and file a report by . Click on this link to start your task before or click here if you are unable to 

start this task. 

The link opens a web application showing both the new and old images covering the area of 

interest. The area to be inspected is clearly outlined. Any areas where the forest-change 

algorithm has detected recent change are also highlighted. The observer is repeatedly tasked 

with reviewing the same area on a regular basis, and only needs to identify whether the change 

indicates a disturbance in that area. Basic training in image classification can be provided as an 

online course with completion being part of the registration process for observers. 

The volunteer can mark or outline any disturbance she sees using a small set of mark-up tools. 

She then adds a description to the place-mark, such as ‘new road’ ‘fire’, ‘crops’ or ‘clearing’. 

These place-marked descriptions are automatically collated into a disturbance report. When 

finished, she reviews the report and clicks send. If no disturbances are observed she files an 

empty report to complete the task. If unsure about a change, she may mark the location with a 

question for a more experienced observer to review and complete the task. 

On completing her task she immediately receives encouraging feedback and a summary of 

her cumulative activity and credits, as well as a list of other tasks that may be attempted.


Other tasks can include online training, or assisting other volunteers. These interactions 

reinforce the volunteers’ motivation to remain active. 

If any disturbance is reported, the system sends an email or text message alerting the local 

group. The direct contact details of local groups and volunteers are shielded in the system. 

The report includes the description and coordinates of each disturbance. If the group has 

Internet access, they may also review the raw images and maps in a web application, or a 

low-resolution version if bandwidth is limited. 

The local group acts on the report according to their judgement and resources. The local 

group may also forward the report to an enforcement agency, or send a ranger to investigate 

on the ground. They should not be pressured into actions by volunteers from the relative 

safety of a foreign country. 

The service asks the local group to respond to each report. The response can be via return 

email or text, or online. Each response is stored with the report to ensure transparency and 

to assist with accuracy and other issues. While a no comment response is allowed in some 

circumstances, the groups are encouraged to include in the responses the accuracy and 

utility of the report, what investigations were performed and describe any steps taken to 

deter future disturbances. This feedback helps the service improve. The local group may 

also update the integrated wiki of their area at any time. 

6. Architecture 

This section describes the main components of the proposed system. 

Because the system aims to provide a free service to local groups, while keeping volunteers 

engaged, usability is a primary concern. Difficult to follow instructions and slow or erratic 

responses must be avoided. All interactions must be self-explanatory and support multiple 

languages. 

6.1 Subscription manager 

This module handles requests from a local group to protect a conservation area and 

manages the steps in the subscription process. It also manages the local group's secure 

online account. Each subscription must include at least the following data before reports can 

be generated: 

Coordinates of the area of interest; 

Contact details to send reports; 

Report media supported (Fax, SMS, MMS, Email, Hi Def Web, Smart-phone); 

A name for the Local Group; 

Preferred language and other languages spoken; 

A unique name of the area of interest, such as a park or local name; 

Aims of the monitoring project; 

A commitment to use the reports to prevent deforestation. 

The subscription manager may also capture: 

Sponsoring NGO & contact details, if applicable; 

The local group's access to technology such as GPS, broadband or smart-phones; 

A means to hide the base location and identity of the local group behind an intermediary, 

such as an NGO should be available when requested. 

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The Subscription Manager also generates a geo-wiki for each subscription and encourages 

the local group to add further information to complete a profile of their land. To make 

subscriptions easier, this need not be completed immediately. This wiki template has 

sections for: 

Describing the area, e.g. geography, ecology, history, threats; 

Identifying any priority conservation areas within the area of interest, such as critical 

habitats; 

Uploading photos, stories, expert reports, and biographies of locals (if safe to do so); 

Visitor stories and photos. 

The site geo-wiki is moderated by the local group (or their NGO partner) so anyone can 

contribute. Each subscription also has a message forum dedicated to it, for sharing 

intelligence about the local area. This would be in the local group’s language. The moderator 

also has the option to request the boundary of the area of interest be nominated for inclusion 

in the World Database on Protected Areas [protectedplanet.net]. Conversely, if the protected 

area is already recorded in the database, then the subscription manager should be able to 

import this definition. 

6.2 Satellite data service 

There are many high resolution satellites from multiple providers. Satellite sensors such as 

Landsat TM and ETM+ (USA), Terra ASTER (USA-Japan), CBERS-2 (China-Brazil), SPOT 

MSS (France), and IRS-2 (India) provide data required for high resolution mapping of 

deforestation, logging, and other tropical forest disturbances (Defries et al., 2009). Each 

sensor has its own characteristics, making it more difficult to compare scenes. 

The coordinates of subscribed areas may be submitted to the satellite operator’s mission 

planners to increase the probability or frequency that the site will be scanned. 

The application does not access the satellites directly, but relies on public data providers 

such as USGS, ESA, JAXA and INPE. 

For rapid detection, the properties to look for in a satellite data service are: 

Covers the area of interest; 

Sensor has a suitable resolution and frequency bands; 

New images are continuously acquired at reasonable frequency; 

Data is (or can be) geometrically and radiometrically corrected; 

New data is processed and published as soon as it is captured; 

A notification service is available, containing meta-data describing the images, so that a 

spider can be configured to discover images; 

Meta-data is published using open standards; 

Free access to the public or at least copy-controlled access for not-for-profits at no cost. 

If free public access is not available, it may still be possible to negotiate restricted access that 

allows the application to create and display deforestation maps without sharing the raw 

data files. 

6.3 Satellite spider 

The satellite spider is a component of the application that continually looks for new 

images covering each subscribed area of interest. The spider maintains the coordinates of 

all active subscription areas. It trawls the image meta-data on online databases. It requires 

a separate ‘plug-in’ for each satellite data service supported. For example, USGS supports


an RSS service that allows the spider to read an XML data feed describing the latest 

LANDSAT data [landsat.usgs.gov/Landsat7.rss]. The spider must convert from latitude 

and longitude of the area of interest to the path and row of the satellite [glovis.gov]. Other 

providers may offer a Web Map Service (WMS). The spider runs 24/7 but does not 

actually download or process any images. Each time a new image is found that is relevant 

to monitoring an area of interest, the spider sends a request to the processing engine to 

download it and create a map. 

6.4 Image processing engine 

The image processing engine must download images found by the spider, process them 

automatically into forest-change maps and store them in a map server. 

The processing algorithms used are no different from those developed for existing detection 

systems, i.e. ortho-rectification, geo-registration, atmospheric correction, cloud removal, 

NDVI spectral analysis or ‘unmixing’. 

The processing engine has significant bandwidth and computation requirements and must 

be cloud hosted to avoid infrastructure maintenance. 

The Google Earth Engine API promises to provide much of this functionality. Alternative 

toolkits are available to do processing. Amazon.com could be used for cloud storage and 

computation. 

While a variety of sensors, data products and algorithms exist, the processing engine should 

aim to present a consistent display, independent of these factors. For example, a standard 

colour scheme and legend could be used to classify disturbance types. For a non-expert, the 

most easily understood imagery is high-resolution natural colour such as seen on Google 

Earth. However they are not updated regularly on Google Earth. Also, this is not the best 

format for detecting forest disturbance. Normalised Difference Vegetation Index (NDVI) 

images can complement visual images. Fire alerts should be integrated into the community 

based monitoring service. Existing fire detection systems based on MODIS, such as Indofire 

(Landgate, 2009) could be interrogated daily and send alerts to the volunteer and local 

group if a fire is detected near the area of interest. Fires are highly correlated with 

deforestation in many countries, depending on agricultural practices. 

Once the processing engine has created the map it signals the workflow system to create an 

observation task. 

6.5 Map server 

The Processing Engine stores new maps in a map server. The map server must keep a timeseries 

archive of forest-change maps and images for each area of interest, as well as 

disturbance reports, place-marks and corresponding responses from local groups. The map 

server does not need to store raw satellite data that is available elsewhere. 

When a volunteer is conducting an observation task, the Observation Portal requests maps 

from the map server to be displayed using an established API. Responsiveness to these data 

requests is important to the volunteer's experience of the Observation Portal. 

6.6 Volunteer manager 

This module handles requests from volunteers to register, and it manages their account and 

profile. Their real identity should be secured by the system. The manager records which 

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languages are spoken and the preferred countries or regions in which the user may wish to 

volunteer. 

The Volunteer Manager keeps track of tasks assigned and completed and any training the 

volunteer completes. It suggests available e-courses in which the volunteer can enroll. 

Volunteers may self-register and deregister, and edit most of their profile, but not their 

qualifications, ratings or feedback. 

Volunteers must pass some basic training and assessment, and agree to a code of conduct 

before qualifying. Observation tasks are only assigned to qualified volunteers. 

6.7 Observation portal 

The Observation Portal is a web application for comparing images and annotating 

disturbances. It could be built on existing geographic display tools such as OpenLayers or 

Google Maps. 

The Observation Portal must be configured to assist the volunteer complete the observation 

task. The link embedded in the volunteer’s email (or tweet) should open the portal at the 

correct coordinates and zoom, and display the correct layers to show the latest image and 

differences to previous images. Also displayed are any enhancements, such as fire detection, 

or outputs of automatic deforestation detection systems if available. The date each image 

was captured, the task id and due date are shown by default. The boundaries of the area of 

interest and relevant park boundaries should be displayed. 

It is crucial this is displayed automatically as soon as the user clicks the task-link in their 

email. Expecting volunteers to navigate to an online database, find, order and download 

images, then load them into the viewer would lead to a high failure rate. These defaults 

must either be setup automatically, or be preset by an expert when a subscription is 

created. 

Correct initial settings allow the novice volunteer to concentrate on observation rather than 

learning and adjusting the tools. Zooming, panning and selecting from a small set of layers 

are essential for the task. 

The user has options to display meta-data if desired, such as the sensor, band and 

processing steps. They may also call up earlier reports, images or maps that outline earlier 

disturbances, or predefined elements such as roads or management boundaries. Protected 

area boundaries may be accessed from online resources such as the World Database on 

Protected Areas [protectedplanet.net] via the Subscription Manager. 

The user experience is simplified down to just the graphic elements necessary to help the 

novice user to complete the current task, rather than present a rich and complex GIS 

interface. 

Why use human observers to compare the before and after images when automatic 

detection is possible? 

Firstly, because automatic detection can only create a map. It must still be interpreted by 

humans. Although the algorithm could send alerts automatically, the local group may need 

to analyse to check for false alarms. Secondly, volunteers may also become advocates. By 

exposing illegal deforestation to a globally connected audience, they increase awareness and 

engagement beyond national and bureaucratic hierarchies. New international networks 

among local groups and volunteers may help embolden local authorities and communities 

to better protect forests. 

One way to visually compare images or maps is to view them side-by-side. One service that 

demonstrates this technique is AnotherEarth.org (Firth, 2011). It displays two Google Earth


javascript windows side-by-side in a browser. Both new and old imagery is displayed from 

an identical viewpoint. Panning or zooming one window will pan or zoom the other to the 

same viewpoint. Because the images are geo-referenced, aligning old and new images can 

be automated and differences easily observed. Another technique is the before-after rollover 

developed by Andrew Kesper at the Australian Broadcasting Corporation to show the 

changes following natural disasters (Kesper, 2011). Other techniques include flicking or 

fading between images. Research on user preference and performance is required to 

determine the best methods. 

The Observation Portal also presents a reporting panel to the user. This contains a simple 

set of screen icons for drawing point, line or polygon place-marks. Each new place-mark 

or polygon includes a standard form for classifying disturbance types as well as free text 

annotations. The date, observer and task id is recorded automatically in each place-mark. 

The observer may create multiple place-marks for a single disturbance report, and may 

edit them until the task is sent. Once sent, the report can only be edited under version 

control. 

Completing the task is simply a matter of pressing ‘Send’, as the Workflow System 

automatically distributes the report, and ensures it is recorded and a response received. 

The reporting panel is implemented using a standard GIS toolkit. 

6.8 Report manager 

“A final important element is the portal for integration of ground-sampled data into this platform; 

including data from smartphones used in trials in community-based forest monitoring” 

REDD Sourcebook (GOFC-GOLD, 2010) 

This module manages the collection, storage and transmission of disturbance reports created 

by volunteers in the Observation Portal. It delivers the report to the local group, using the 

agreed contact details and method (email, text). The report manager also requests and 

manages feedback from the local group to rate the accuracy of each report and record any 

activities the local group made in response to the disturbance. 

The report manager archives all the reports relating to each protected area. Both the reports 

and associated feedback are stored in the geo-wiki for the area of interest, together with any 

maps created by the processing engine. They may be used for future research or tuning the 

system. They can be searched geographically, visually, temporally, by disturbance type or 

by volunteer. 

The Report Manager can translate standard fields in the report if there is a mismatch between 

the language of the report and the recipients. Automatic translation within major languages 

could be integrated. Other translations would need to be referred to a regional NGO. 

A disturbance report will contain: 

a reference to the originating task - so all task parameters are archived; 

the handle of the volunteer who completed the task, which links to their public profile; 

the time and date the report was completed; 

geo-referenced place-marks created by the volunteer; 

completed forms and annotations created by the volunteer for each place-mark; 

severity of the disturbance – from minor to serious; 

observer’s confidence of the accuracy of the disturbance. 

Once a local group responds, the report may also contain: 

veracity response: indicates whether the observation indicated a real change or not; 

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accuracy response: how accurate were the coordinates in the report. This may help 

detect alignment issues; 

conservation value of the information - whether it helped reduce impacts; 

comments or photos uploaded by the local group or NGO; 

actions taken, such as referrals to law enforcement. 

A response may be entered directly by the local group if they have web or smart-phone 

access, or by the NGO. The response may also add comments to the volunteer's report. 

Even when the local group has no GPS or other capability to record coordinates in their 

response, their text or photos can still be referenced back to the original disturbance report, 

and therefore indirectly geo-referenced. 

Local groups can also forward reports to enforcement agencies. Carlos Souza of Imazon 

describes a reporting system that will “allow users to … be able to identify cases of illegal 

deforestation that are being judged, send requests, as protests, to prioritize cases with the 

environmental agencies and courts, monitor the length of the proceedings, and receive alerts 

about the status of the process. We hope that this type of geo-wiki tool can engage civil 

society in order to accelerate the cases and bring positive pressure on the enforcement 

system to properly punish violators. This is important, because the application of 

enforcement law represents the major bottleneck to stopping illegal deforestation in the 

Brazilian Amazon” (Souza et al., 2009). 

6.9 Workflow system 

The workflow is the central control process that keeps the system alive by responding to 

external events and ensuring tasks are created, assigned and completed. The workflow 

ensures new data is processed, tasks are assigned and reports reach the people on the 

ground quickly and reliably. A good workflow system allows business rules and process 

logic to be encoded in a flexible but rigorous language, rather than buried in code. 

Yet Another Workflow Language (YAWL) is one open-source workflow language and 

implementation (Hofstede et al., 2008). 

Workflow systems are now commonplace in corporate enterprises to encode and automate 

business processes. Examples include defect management systems. Workflow systems can 

also facilitate virtual organisations, such as open-source software collaboration teams. A 

workflow typically describes an artefact as it is transferred from one stage to another. 

For this forest monitoring application, the flows would describe a raw or processed satellite 

image, an observation task, or a report. The workflow system assigns tasks to people or 

groups; manages the scheduling and email notification system; and supports resolution, 

escalation and exception processes. 

A new task is triggered when a new forest-change map is ready. The system assigns an 

observation task to the selected volunteer. The observation task may be reassigned to 

another volunteer if it remains in a queue for too long, or if the first volunteer requests a 

review. When a disturbance report is created, a new task ensures that it is sent to and 

acknowledged by the correct local group, and also ensures the local group sends a response 

within a reasonable time. 

Within this basic flow are many alternate possibilities, exceptions and error cases. A flexible 

workflow engine allows core behaviours to be reconfigured to suit ad-hoc organisations as 

they evolve. There is no reason to limit Bunjil Forest Watch to a single instance. Different 

groups may find reasons to build and deploy variations to meet unforeseen needs.


6.10 The social network 

A community-based forest monitoring system needs a wiki to encourage and strengthen 

community ties and share information. Social network software must be integrated into the 

solution to allow volunteers to interact with other volunteers, local groups, experts and the 

public, and to develop monitoring skills. 

The social network tools consist of a geo-wiki for each local group and subscribed area, a 

public profile page and optional blog for each volunteer, a general wiki for the application 

user guide, FAQ and support in multiple languages, and a discussion forum for posting 

questions and defects. 

Many of the interactions are automatically generated by workflow tasks. A user’s reputation 

is updated each time one of their reports is verified. Reports and photos uploaded by local 

groups may be automatically added to the site wiki and geo-located with the original 

disturbance report. Other interactions are initiated by users or prompted by the workflow, 

to keep the geo-wiki up-to-date. 

The social network could be implemented by customising an existing open-source Content 

Management System (CMS), such as Radiant or Wordpress. There are many mass-market 

social network sites; however a high level of integration and customisation is required to 

integrate with the workflow and keep the focus on the tasks. The social network must 

support volunteers and local groups to combat deforestation. 

The site geo-wiki could well be integrated into the UNEP’s ProtectedPlanet 

[protectedplanet.net] or Atlas of Our Changing Environment [na.unep.net/atlas/google.php]. 

These global Wikis already have much of the functionality required to define a communitybased 

protected area, add to a blog, and upload geo-located photos. Using existing 

infrastructure is easier than creating a new site, provided the workflow integration can be 

achieved. The social network could also link to content in relevant environment sites such as 

Mongobay.com or GloboAmazonia.com. 

Each volunteer has a profile page and optional blog for sharing stories about the observing 

experience. A volunteer’s real identity can be hidden, but their online reputation as a 

volunteer is tracked, including the punctuality, reliability and accuracy of their reports. 

6.11 Online learning 

Bunjil Forest Watch will rely on a Learning Management System (LMS) to manage online 

and collaborative training for volunteers. This includes a wiki, forum, course material, 

videos and exams. The main purpose of the online learning module is to improve 

volunteers’ observation skills and knowledge. The need for training users has been 

identified by both the CLASlite and IMAZON teams. 

The training describes a volunteer’s responsibilities and shows examples to help illustrate 

the kind of satellite images or maps the student will be likely to encounter and the sorts of 

changes to look out for. The training also shows how to create a report. 

To qualify as an observer, the volunteer must complete an online test where they review 

pre-analysed images and correctly identify threats. Further training is available to retain and 

increase competency and to broaden knowledge in subjects relevant to forest monitoring, 

for example forest ecology; remote sensing; sustainable development or cross cultural 

communication. Instituto de Pesquisa Ambiental da Amazônia – IPAM, have an online course 

on The Amazon Rainforest and Climate Change. [ipam.org.br/curso/login] 

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Volunteers must complete some online training to qualify as an observer. Only qualified 

observers receive real assignments and can send reports. This is to discourage uncommitted 

and unreliable users. 

Volunteers receive credits as they complete training modules. They also receive credits as 

they successfully perform observation tasks. Credits increase the volunteer’s grading. This 

allows volunteers to perform more critical tasks and assist or review others. 

The Learning Management System manages the syllabus of courses; enrolment in e-courses; 

and serves the training and examination material. The LMS can also be based on opensource 

software for example Chamilo [chamilo.org], Wikiversity [wikiversity.org] or Khan 

Academy [khanacademy.org]. 

Volunteers can also create course material for other users. 

Local groups can also access the training. Unlike volunteers, they are not required to 

complete training to receive reports. 

7. Discussion 

7.1 Mobiles and Smart-phones 

Local groups are not required to have knowledge of GIS, or remote sensing, but often will 

have access to a mobile phone and be within mobile range. Africa has already achieved 50% 

mobile phone penetration, rising at 20% pa. "Smart" phones with GPS and camera are now a 

mass-market technology in developed countries, but may remain too expensive for many 

local groups in developing countries for some years. However, a phone “app”, would 

greatly improve the utility of the service. It would incorporate the deforestation report, the 

original forest change map, and a GPS to direct the group directly to the disturbance. Using 

camera, messaging and GPS it would be simpler to file a response that is automatically and 

reliably cross-referenced and geo-located in time and space. 

Open Data Kit [opendatakit.org] by the University of Washington and Google.org is an opensource 

multilingual suite of mobile data collection tools for the Android platform. Woods Hole 

Research Centre has trialled the technology for collecting data from REDD forest plots. 

Smart- phones will greatly assist collecting reliable evidence for both scientific and enforcement 

purposes. The smart-phone app could be included as an enhancement, but making access to a 

smart-phone a requirement for participation risks limiting access to many groups. 

7.2 Accuracy 

"There has to be trust in the forest-monitoring data, and these nations have to see them as their 

own …There's this face-to-face collaboration that is really critical." 

Dan Nepstad, quoted in Nature (Tollefson, 2009) 

Volunteers, even with auto detection algorithms, still face a big challenge to correctly 

interpret the imagery, and distinguish a significant disturbance from artefacts such as data 

errors, seasonal variations or variations in viewing conditions. The generation of images is 

unsupervised and the viewer self-trained. This increases the likelihood of errors. 

Quality control is important to reduce incorrect reports. False positives create unnecessary 

work and travel for the local group while false negatives mean disturbances are not picked 

up. Opponents of the local group can use errors or inconsistencies to undermine the reports. 

Maps prepared automatically for alerting may not reach the standards required for longterm 

REDD Monitoring, Reporting & Verification (MRV). But they may not be required to 

since a more rigorously controlled analysis can still be created later to prosecute a case.


The usual way to measure the accuracy of a new system is to compare the results with 

known data. A pilot of the system can be chosen to overlap with an established regional 

monitoring program and the results can be compared. 

Additionally, as this system is repeatedly monitoring for change over fixed but relatively 

small areas, there is an opportunity to introduce self-correcting feedback. Ensuring the local 

users of the service report back to the observer on the quality of the reports gives a very 

good guide to the accuracy and performance of the system. 

This is why local groups must rate the quality of each report they receive. The responses are 

automatically collated and generate statistics on each volunteer as they become more 

experienced. The reputation of the observers and local groups accumulate with each 

transaction. This feedback helps tune the service to identify poorly performing volunteers, 

unresponsive local groups, common false alarms and system biases or failures. 

7.3 Public access 

While the LANDSAT archive and other data are publicly available online, a conservation 

monitoring service may also need access to higher resolution imagery. High-resolution visual 

images may be easier for non-experts to understand. However there are trade-offs in costs, 

temporal resolution (frequency) and bandwidth. Many deforestation detection systems 

combine multiple sensors to make a statistical estimate of where deforestation is occurring. 

However, the critical factor for alerts is timeliness. The main business model for most highresolution 

satellite providers is providing data to governments. Providing data for 

deforestation in remote areas, with suitable checks and balances, would not undermine this 

model and may enhance the reputation of the provider. For example, the GeoEye Foundation 

provides access to IKONOS (0.5m) resolution imagery to NGO’s for humanitarian purposes. 

After access to data, the next greatest issue is access to the technology to process the data. 

Fortunately, the owners and creators of the worlds most advanced forest monitoring 

systems share this aim. Increasingly, research is being published in open journals accessible 

to non-institutional scientists. 

Another hurdle is access to computing capacity to produce the images. It is significant, but 

not unachievable. The cloud computing paradigm for IT infrastructure is applicable to this 

application. There may be infrastructure providers prepared to offer free access during lowdemand 

for not-for-profit applications. Google’s aforementioned commitment to donate ten 

million hours of CPU time per year is promising. 

The interactive sessions must run as on-demand services, but these are data rather than 

computationally intensive. 

7.4 Costs 

The cost of computation continues to fall while speed continues to increase but removing all 

costs to local groups and volunteers is essential. 

The registration and subscription processes ensure that no image is downloaded or 

processed unless there is a local group prepared to protect the area and there is a volunteer 

prepared to observe the images on a regular basis. This avoids wasted computation and 

data transfer as areas outside the area of interest are masked out of the calculation. 

Obtaining regularly updated imagery at low cost is one of the key challenges. In 2008 the 

USGS made a decision that was a watershed for open access by providing free online 

LANDSAT data. Unfortunately, barriers to accessing the data remain, especially in Africa 

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where international bandwidth is limited (Roy et al., 2010). Barriers include limited tertiary 

education, especially in remote-sensing fields; conflicting national interests and priorities; 

inadequate awareness of potential uses; insufficient infrastructure and high data costs. 

The cost of implementing and maintaining the service must also be addressed. It is likely to 

be developed in a series of prototypes of increasing functionality. 

7.5 Displacement 

Although protected reserves reduce deforestation rates, they may not eliminate 

deforestation in the reserve completely (Clark et al., 2008). Displacement of deforestation 

occurs when an area is protected but its surroundings are not. If the area is reserved as a 

carbon bank then displacement is undesirable as emissions are merely moved rather than 

reduced. However displacement may be desirable if the purpose of the park is to protect a 

unique biodiverse area. For example, encouraging palm plantation corporations to shift 

expansions to areas already degraded by earlier logging, without destroying more primary 

forest, benefits both biodiversity and climate goals. 

8. Conclusion 

We can encode the motto think global act local into the DNA of our next generation of Earth 

observation infrastructure. This promises to open a new global front to combat illegal 

deforestation and degradation. 

The solution described here could be built from existing open-source components, hosted on 

cloud infrastructure. More and more satellite imagery is freely available on public 

databases, and methods to process the images are advancing. This paper has described one 

way that these available resources could be put to better use. 

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13 

Remnant Vegetation Analysis 

of Guanabara Bay Basin, Rio de Janeiro, 

Brazil, Using Geographical Information System 


Luzia Alice Ferreira de Moraes 

Federal University of the State of Rio de Janeiro UNIRIO 

Brasil 

The importance of tropical forests and the surrounding environment has been increasing, as 

specific threats and problems (e.g. deforestation, timber logging, infrastructure development, 

and mining) are generating increased atmospheric carbon dioxide concentration, with severe 

present and future consequences in climate (Schulze in Carreiras et al., 2006). 

Rio de Janeiro is a Brazilian state that presents the greatest diversity of ecosystems, 

including major portions of Atlantic Forest (Mata Atlântica in Portuguese), considered a 

hotspot for its importance and relevance in terms of natural resources and biodiversity. 

The high biodiversity in this biome is a function of the extreme variations in 

environmental conditions, and great differences in altitude, ranging from sea level to over 

1800 meters. 

The Atlantic Forest domain has the following delimitations established by the Brazilian 

Vegetation Map of IBGE (Veloso et al., 1991): ombrophilous dense Atlantic Forest; mixed 

ombrophilous forests; open ombrophilous forests; semidecidual stational forests; decidual 

stational forests; the countryside swamps, the northeastern forest enclaves (regionally called 

"brejos") and the associated ecosystems - mangroves and restingas. 

According to publication of SOS Atlantic Forest Foundation and the Brazilian National 

Institute for Space Research (2009) between 2005 and 2009, there was a loss of 1,039 hectares 

of Atlantic Forest in the state of Rio de Janeiro. The Forest is now fragmented in isolated 

remnants scattered throughout a landscape dominated by agricultural uses. According to 

Grimm et al. (2008), ecosystem responses to land changes are complex and integrated, 

occurring on all spatial and temporal scales as a consequence of connectivity of resources, 

energy, and information among social, physical, and biological systems. As terrestrial 

landscapes become increasingly fragmented, so do hydrologic connections between 

landscape elements (Pringle, 2001). 

The extensive deforestation at the Basin took place over a historic period. The colonization 

process of the area, specially of dense ombrophilous forest, was initially with sugar cane, 

coffee and orange plantations, followed by cattle raising and annual crops (CIBG, 2010), 

which favored the formation of erosive processes as well as silting of water bodies. The only 

area with significant forest remnants is found in high slopes, inappropriate for agriculture,

254 


especially within the limits of Três Picos State Park. According to Freitas et al (2010), roads 

and topography can determine patterns of land use and distribution of forest cover, 

particularly in tropical regions. 

Many factors have affected negatively and contribute to degradation of Baia de Guanabara 

Basin such as the growth of unplanned cities, mining areas, exotic monocultures planted 

without planning, industries, oil refinery, among others. According to Bidone & Lacerda 

(2003), there are 12 municipal districts, 7.8 million inhabitants and around 12,500 industries 

distributed unevenly over the drainage basin area (4,000 km2). 

With the increasing deforestation, there were created several protected areas to conserve 

natural resources and biodiversity existing within it. The National System of Conservation 

Units (SNUC) was created in Brazil by the Federal Law No. 9985/2000, establishing criteria 

and standards for the creation, deployment and management of protected areas. The 

Brazil’s conservation units such as parks, reserves and APAs (Environmental Protected 

Areas) are grouped into two categories: “sustainable use” and “integral protection.” 

Integral protection conservation units are protected areas which main purpose is the 

conservation of biodiversity. Only the indirect use of natural resources is permitted and 

natural processes shall take place without human interference. The following conservation 

units according to International Union for Conservation of Nature (IUCN) - management 

category I - III) fulfill this purpose: biological and ecological reserves/stations (I), national 

and state parks (II), natural monuments and wildlife refuges (III). 

Sustainable use conservation units were created with the idea of combining the conservation 

of biodiversity compatible with the rational use of the natural resources, while respecting the 

legislation that applies to such resources. The following conservation units (IUCN - 

management category IV- VI) fulfill this purpose: areas of relevant ecological interest (IV), 

environmental protection areas (V), extractive/fauna/sustainable development reserves (VI). 

Bidone et al. 1999 as cited in Bidone & Lacerda (2004), in accordance with land use criteria, 

classified the watersheds of the Guanabara Bay region into three types: (1) the pristine type, 

without anthropogenic activities, which generally belongs to legal environmental protection 

areas, with Mata Atlântica (i.e., a mountainous tropical rainforest type) and/or similar 

abundant vegetation on the slopes and natural coastal vegetation in the lowlands (grasses, 

savannas, ‘‘restingas’’); (2) the weakly impacted type with well-preserved Mata Atlântica 

and/or other remnant vegetation on the slopes, and lowland sectors with human activities 

(small-scale farming, tourist-urban activities); and (3) the highly impacted watersheds, 

densely populated and/or industrialized. 

Geographic Information System is a tool for evaluating and monitoring of environmental 

impacts (IBAMA, 2002) and has been widely used in watershed management, environmental 

zoning, support for studies of biogeography, monitoring animals, management of protected 

areas, evaluate deforestation, among others. GIS applications are tools that allow spatial 

analysis of landscape patterns and the consequences of human activities on these patterns 

(Tuominem et al. 2009). Though imagery resources can provide a reliable basis for measuring 

the amount and spatial configuration of forest clearing and exploitation. 

2. Objectives 

This chapter aims to provide information on land-cover in the Guanabara Bay Basin for 

monitoring its changing through time; to integrate remote sensing data aiming to provide a

Remnant Vegetation Analysis of Guanabara Bay Basin, 

Rio de Janeiro, Brazil, Using Geographical Information System 

diagnose of the distribution of vegetation remnants at Baia de Guanabara Basin among three 

periods ; to calculate the remaining areas of Atlantic Forest at the basin and correlate them 

with the altitude and slope; to use map algebra to combine raster maps of vegetation class 

and some other maps as the different conservation units at the basin to predict the remnant 

vegetation amount; to provide useful information for decision-making purposes. 

3. Study area 

The Guanabara Bay Basin is located in southeastern Brazil in the state of Rio de Janeiro, and 

its geographical coordinates are Latitude -22°20’S and - 22°59’S and Longitude - 42°32’W 

and - 43°34’W (Figure 1). This Basin is located in the tropical zone, with a typical hot and 

dry climate (Amador 1997). The annual average temperature reaches 24°C in the coastal 

plain and 20°C in the mountainous regions. The precipitation annual averaged 2,000mm in 

the Serra do Mar and oscillated between 1,000 and 1,500mm in the Baixada Fluminense 

(Amador op cit). 

Fig. 1. Study Area 

It covers an area of 4,198 km² and includes 16 municipalities that constitute part of the 

Metropolitan Region of Rio de Janeiro (IBG, 2010). Part of the Basin is located in the 

mountain range “Serra do Mar”, mainly mountainous region and of rough relief, with steep 

slopes and small valleys. The western part of the Guanabara Bay is called “Baixada 

Fluminense”, located in plain relief that belongs to the urban region of Rio de Janeiro. The 

Baixada encompasses especially the municipalities of Duque de Caxias, Nova Iguaçu, São 

João de Meriti, Nilópolis, Belford Roxo, Queimados and Mesquita. 

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256 

4. Methodology 


4.1 Data acquired 

4.1.1 Landsat images 

Image mosaics from Landsat 5 (Thematic Mapper) and Landsat 7 ETM+ (Enhanced 

Thematic Mapper plus) were obtained at INPE (Brazilian Institute of Spatial Research) and 

at USGS (United States Geological Survey’s Earth Resources Observation and Science - 

EROS) websites. 

The two images of three periods (1985, 2001 and 2010) were mosaiced to cover the area of 

Baia de Guanabara Basin, as below: 

1985- Landsat 7 ETM+ scenes 217/75 and 217/76 from July 04 and August 05, respectively, 

obtained at websites of INPE and USGS; 

2001- Landsat 7 ETM+ scenes 217/75 from September 04 and 217/76 from October 28, 

obtained at USGS website; 

2010-Landsat 5 TM scenes 217/75 from May 06 and 217/76 from February 15, obtained at 

INPE website. 

4.1.2 SRTM 

Images of the Shuttle Radar Topography Mission V. 4.1 (SRTM) in 1-degree digital elevation 

model (DEM) were obtained at the site of CGIAR- Consortium for Spatial Information 

(Jarvis et al., 2008), for the elaborations of maps of altitude and slope classes. 

4.1.3 Shapefile data 

Vector format at a scale of 1:50,000 relating to municipalities, hydrography and the 

conservation units, were obtained from government agencies like the Brazilian Institute of 

Geography and Statistics (IBGE); National Environment Institute (INEA), Guanabara Bay 

Remediation Program (PDBG), Mata Atlântica Biosphere Reserve (RBMA), Brazilian Institute 

of Environment and Renewable Natural Resources (IBAMA) and from municipalities. 

4.2 Images processing and classification 

This step is based on the application of techniques from digital image processing and visual 

interpretation of images to the acquisition of cartographic features. An image registration 

requires control points, a point whose coordinates reference is known. The three spectral 

bands of ETM+ and TM sensor with 30 meters spatial resolution (bands 3, 4 and 5) were 

registered through planimetrically correct maps. It was used the Universal Transverse 

Mercator (UTM) projection with longitude origin at 45 o00'00"W and datum SAD69. All 

image pre-processing procedures were done in SPRING 5.1.7 (Georeferenced Information 

Processing System), a state-of-the-art GIS developed by Brazil's National Institute for Space 

Research (Camara, 1996) and available for free on the web. It was also made a datum 

transformation for integrating the different data. 

The intent of the classification process is to categorize all pixels in a digital image into one of 

several land cover classes, or "themes". The software SPRING 5.1.7 was used to develop a 

statistical characterization of the reflectance for each information class for producing 

thematic maps of the land cover present in an image. It was made supervised classification 

(Atkinson, 2004; Foody, 2002; Richards, 1993), using Maximum Likelihood algorithm to 

extract the information and allow the mapping of land use and vegetation remaining 

(Waleed and Grealish, 2004). The area was classified into five major thematic categories as



following: 1)vegetation - tropical rainforest (ombrophilous forest), forested wetlands include 

mangrove swamps and pioneer formations and reforestation; 2- fields – including 

deforested areas, fields of altitude called “campos rupestres”; agriculture and pasture; 3) 

anthropogenic (Urban or Built-up Land)- including urban and industrial areas and 4) 

exposed areas (Transitional Areas) including nonforest, temporarily bare areas as 

construction is planned for such future uses as residences, shopping centers, industrial 

complexes. and 5)water –representing Guanabara Bay. 

4.3 Digital Elevation Model (DEM) from the shuttle radar topography mission 

It was used the digital elevation model (DEM) from the Shuttle Radar Topography Mission 

V. 4.1 (SRTM) to create raster maps of altimetry and slope. The altimetry data were sliced 

into five class intervals: 0-8m 8-50m, 50-500m, 500-1500m and >1500m for generation a raster 

map. The slope classes was defined into six intervals as followed: 0-3% (plain terrain), 3-8% 

(gently sloping), 8-20% (sloping), 20-45% (moderately steep to steep), 45-75% (very steep - 

mountain slope) and >75% (scarped). 

The vegetation classes were defined according to the altitude in: mangrove ( 0-7m), lowland 

(0-40m), lower montane forest (40-500m), montane forest ( 500-1500) and upper montane 

forest (1500-2200). 

4.4 Map algebra application 

Map algebra uses math expressions to combine raster layers using operators such as 

arithmetic, relational and boolean logic (Wang & Pulard, 2005). It was used the algebraic 

language as a tool to estimate the deforestation in the Guanabara Bay Basin using SPRING 

5.1.7 software through Spatial Language for Algebraic Geoprocessing (LEGAL). Map algebra 

creates new features and attribute relations by overlaying the features from two or more input 

map layers. Features from each input layer are combined to create new output features. The 

thematic maps of classified images and some other maps of altimetry, slope and conservation 

units had been manipulated using Boolean algebraic expressions describing the rules and 

conditions involved in the evaluation and evolution of the deforestation process. Some 

Conservation Units were cut at the limit of the Guanabara Basin, since the target of this work is 

to verify the remnant vegetation belonging to the Basin. The integral and sustainable 

conservation units were overlayed with the maps of land cover classification to create a new 

map of the remnants vegetation areas in the three study periods (1985, 2001, 2010). 

4.5 Maps elaboration 

Finally, thematic maps of land cover classification, vegetation remnants according to 

altimetry and slope classes, vegetation remnants in the conservation units and vegetation 

fragments were prepared using the softwares Spring (INPE) and ArcGis (ESRI) 

5. Results 

5.1 Supervised classification of the three Images (1985-2001-2010) 

The supervised Classification (Figures 2-4 and Table 1) shows a decrease of vegetated extent 

in 24.99 percent between 1985 and 2001. The removal of vegetation cover and riparian forest 

is directly linked to increase in pasture lands and agricultural lands over the three periods, 

as showed in land cover classification. 

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In the first time period (1985-2001) the vegetation clearance occurred in 321.989 square 

kilometers with an increase of agriculture and pasture lands. According to the image 

classification of 2010 period (Table 1) areas under or pasture use represent the major landcover 

type in Guanabara Basin, with 44.91 percent of land-cover classified. Although in the 

same period, some areas previously occupied by fields became urban and peri-urban areas. 

The increase of anthropogenic class was probably due to unsustainable land management 

and city expansion especially in informal settlements (“favelas”) with an increase of 

1,035.973 square kilometers of total occupation. As geographers and urban sociologists have 

long observed, topography is a key-element contributing to the heterogeneity of residential 

segregation (Medeiros, 2009). Rio de Janeiro offers a particularly interesting case, with 

favelas populating the hills and mountains right next to the high income areas (Medeiros, 

op.cit). According to Freitas et al (2010) roads and topography are not the current drivers of 

deforestation, but they act as attractors of land-use change and deforestation. In Guanabara 

Bay basin the observed linearity is due to the high rates of population growth and to 

unplanned occupation of watersheds, without the proper infrastructure to cope with their 

effluents (Bidone & Lacerda, 2004). According to Moraes (2009), escalating drought, 

deforestation, capitation, irresponsible land use, and pollution are direct consequences that 

demand an integrated management scheme. 

Fig. 2. Land Cover Classification map from 1985 period 

Figure 4 of the 2010 period also shows a large exposed area over 10 square kilometers in 

Guapi-Macacu Basin in the municipality of Itaboraí. The area was exposed due to 

excavation and earthmoving activities for the implementation of Petrobras Industrial 

Complex. According to the Environmental Impact Report (EIA), the basic petrochemical



unit of COMPERJ will process 150,000 bbl/day of domestic heavy oil to produce 

thermoplastic resins and fuels (Hernández, 2010). The establishment of petrochemical 

complex with the magnitude of COMPERJ can lead to an untenable situation due to the 

increase of the population rate in the Municipality of Itaboraí, which can cause serious 

damage to riparian vegetation and wetlands remaining in the eastern bay. Attention should 

be directed to potential social costs and impacts of large-scale projects in the Basin. 

According to Members of the Committee of the Guanabara Bay Basin (Hernández, 2010) 

water availability in Metropolitan Rio de Janeiro, considering the water imported from 

surrounded sub-basins, is no longer sufficient to meet the additional demand generated by 

the installation of the Petrochemical complex (Pedreira et al, 2009 as cited in Hernández, 

2010). Given the tendency toward continued population and Industrial growth, water 

availability will decline over time, though water availability per se tends to remain fairly 

constant (in terms of flow, but not in terms of quality) (Hespanhol, 2008). 


According to Hernández op cit. adequate water quality management is necessary for water 

resource management in a river basin, specifically having a sound water quality monitoring 

system to indicate the status of water body. Two other large-scale infrastructure projects are 

undergoing in the Basin: The Metropolitan Arch which will connect Itaguaí municipality to 

three other major highways: the BR-040 to Belo Horizonte (Minas Gerais) and Brasília 

(Federal District), the BR-116 to Bahia, and the BR-101 to Espírito Santo and the Gasduct 

Camboinhas Reduc III with 179 kilometers of extension. The Gasduct was been made in an 

area of Environmental Protection in Cachoeiras de Macacu Municipality. According to 

Hespanhol, op cit water conservation in the form of demand management should also be 

encouraged in industry, pressing for the adoption of modern industrial processes and 

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washing systems with lower water requirements, as well as water treatment stations for 

public supply through the adequate recuperation and reuse of water used to wash filters 

and decanters. The Niteroi Municipality Act (Law N o. 2.856/2011) establishes mechanisms 

to encourage the installation of collection system and wastewater reuse in public and private 

buildings. Under the Act, new public or private buildings, with an area over 500m 2 and 

water consumption greater than or equal to 20m 3 per day are obliged to encourage and 

promote gray water reuse. 


Land Cover Classses 1985 2001 2010 Variation (%) 

1985-2010 

Vegetation 1,590.738 1,268.749 1,193.087 -24.99 

Fields 1,791.635 1,985.152 1,833.160 +2.32 

Anthropogenic 661.874 829.596 1,035.973 +56.52 

Exposed 48.72 1.44 19.16 -60.67 

Table 1. Land Cover areas (km 2) in the three study periods. 

5.2 Conservation units in the Guanabara Basin 

The Figure 5, Table 2, shows the delimitation of the major conservation units in the 

Guanabara Bay Basin according to their uses as strict protection or sustainable use. The 

units are managed by Brazilian Institute of Environment and Renewable Natural 

Resources (IBAMA), Chico Mendes Institute for Biodiversity Conservation (ICMBio),



National Environment Institute (INEA) and municipalities, among which seventeen 

Environmental Protected Areas (APAS), five Parks, a Biological Reserve, two Ecological 

Stations, and an Ecological Reserve. The Atlantic Rainforest Central Mosaic which 

includes 22 conservation units and the Sambê Santa Fé Corridor which encompasses the 

mountains regions of Sambê, Santa Fé and Barbosão with well preserved forest stretches. 

Fig. 5. Main Conservation Units in the Guanabara Bay Basin 

The National System of Conservation Units (SNUC) was created in Brazil by the Federal 

Law No. 9985/2000, which includes the main categories of Protected Areas as follows: 

Area of Environmental Protection (APA): it is a rather large area characterized by a 

considerable population density and with abiotic, biotic, aesthetic, or cultural features 

of great importance, above all for the quality of life and human wellness. Protecting 

biological diversity, regulating the settlement processes, and ensuring the sustainable 

use of natural resources are among its main aims. 

Biological Reserve: it aims at strictly safeguarding the natural aspects within its borders, 

avoiding direct human interference or environmental changes, through measures to 

recover altered ecosystems and management actions necessary to recover or maintain 

the natural balance, biological diversity, and natural ecological processes. 

Ecological Station: it aims at safeguarding nature and carrying out scientific research 

activities. 

National Park: it aims at preserving natural ecosystems of great beauty and ecological 

importance, giving the opportunity to carry out scientific research activities or 

developing environmental education and interpretation activities, as well as promoting 

recreational activities at direct contact with nature and ecological tourism. 

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Area of Considerable Ecological Interest (ARIE): not very large area, with a scarce 

population density and extraordinary natural features of great importance at a regional 

and local level. 

Management 

Responsability 

ENVIRONMENTAL PROTECTED AREAS (APAS) 

Federal 1. Petrópolis; 2.Guapemirim 

State 3. Guapi-Macacú River Basin; 4. Gericinó- Mendanha 

Municipal 5. Estrela; 6. Suruí; 7. Guapi-Guapiaçu; 8. Tinguá; 9. Rio D´Ouro; 10. 

Tinguazinho; 11. São Bento; 12. Engenho Pequeno; 13. Pretos Forros; 

14. Retiro; 15. Pedra Branca; 16.São José; 17-Morro do Valqueire 

PARKS 

Federal 18. National Park of Serra dos Órgãos; 19. National Park of Tijuca 

Forest 

State 20. State Park of Três Picos; 21. State Park of Pedra Branca 

Municipal 22. Municipal Park of Barbosão 

BIOLOGICAL RESERVE 

Federal 23. Tinguá 

Federal 

ECOLOGICAL STATIONS 

24. Guanabara Ecological Station 

State 25. Paraíso Ecological Station 

ECOLOGICAL RESERVE 

Municipal 26. Darcy Ribeiro Reserve 

CORRIDOR 

State 27. Sambê Santa Fé 

ARIE 

Municipal 28. Guanabara Bay 

Table 2. Main Conservation Units in the Basin 

The Brazilian Forest Code (Law No. 4771/1965) defines the limits were set on the use of 

property, where existing vegetation must be respected and considered of common interest 

to all, except for the removal of vegetation for public service interests provided there are 

environmental licenses and compliance with established environmental compensation. 

According the Brazilian Forest Code and the Resolutions of CONAMA (National 

Environmental Council) numbers 302 and 303, Permanent Protection Areas (APPs): are 

protected areas, covered or not by native vegetation, for the purpose of preserving water 

resources, landscape, geological stability, biodiversity, the gene flow of wild fauna and flora, 

protecting the soil and ensuring the well being of the human population. The APPs include 

mangrove swamps, riparian vegetation, sand dune scrubs “restingas”, regions above 1,800 

meters of altitude and hillsides with slopes above 45 degrees. According to the Brazilian 

Code Legal Reserves (LRs) are areas located within a farm, with exception to permanent



preservation areas, necessary for sustainable uses of natural resources, conservation and 

rehabilitation of ecological processes, conservation of biodiversity, and the shelter and 

protection for native fauna and flora. Current Brazilian law provides that the Legal 

Preservation should be 80% in the Amazon, 35% in savanna regions in Amazonian states, 

and 20% in other regions in the country. The reforestation should be done with species 

native to the area. 

The new Forest Code in Brazil indicates some changes in regards to the Permanent 

Protection Areas (APP) and Legal Reserves (WWF, 2011). According to current legislation, at 

least 30 meters from banks and rivers, steep slopes, hilltops and wetlands should be 

protected. Thus, those who deforest need to restore vegetation. Under the new code, the 

minimum protection may be reduced to at least 15 meters, and meadows cease to be 

considered APP. In relation to Legal Reserves, properties of up to 4 taxed modules (varies 

among different municipalities) do not need to have a reservation, which will be mandatory 

only for properties that exceed four modules. The amendments to the Brazilian Forest Code 

may have an important negative effect on Brazil’s capacity to reduce emissions from 

deforestation and forest degradation. The proposed changes will effectively allow more land 

to be converted for agricultural purposes in Areas of Permanent Preservation, such as 

hillsides (inclusively forest land 45% in slope or over) and riversides. In addition, existing 

cultivation of some products including grapes, apples and coffee will continue to be allowed 

in areas designated as Permanent Protection Areas (APP). The bill provides an amnesty for 

some small landowners, and may encourage illicit practices. This new proposal will lead to 

serious consequences in decreasing of urban and peri-urban water supplies in the face of 

accelerating population and economic growth. In addition, deforestation and land clearing 

pose serious problems to the carbon cycling to McPherson (1998), urban forests can reduce 

atmospheric CO, in two ways. Trees directly sequester CO, as woody and foliar biomass 

while they grow. Also, trees around buildings can reduce the demand for heating and air 

conditioning, thereby reducing emissions associated with electric power production (Mc 

Pherson, op cit). 

5.3 Remnant vegetation in the conservation units of strict preservation uses and 

sustainable uses 

Figures 6 to 8 and Table 3 showed the remnant vegetation in Conservation Units of integral 

protection and of sustainable use in the Guanabara Bay Basin. The greatest loss of vegetated 

areas was observed in conservation units of sustainable use. In environmental protected 

areas "APAS", there was a loss of 20.23 percent in vegetation class between 1985 and 2010, 

the equivalent of 100.371 square kilometers. In the strict protection units as parks it was 

observed the vegetation loss of 11.27 percent which represents a decrease of 39.43 square 

kilometers in vegetated areas. The Tinguá Biological Reserve has been decreasing its 

vegetated in 5.23 percent along the study periods. 

As a Conservation Unit of Sustainable Use, the Environmental Protected Area of Guapi- 

Macacu River Basin, established in 2002, contributes to the supply of drinking water to nearly 

2.5 million inhabitants living in six municipalities in the State of Rio de Janeiro (Da costa 2007). 

This basin has suffered several interventions as the built of the Channel of Imunana with the 

purpose of draining the frequently flooded adjacent areas (Da Costa op cit). 

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Fig. 6. Remnants vegetation areas in conservation units of Guanabara Bay (1985). 

Fig. 7. Remnants vegetation areas in conservation units of Guanabara Bay (2001).



Fig. 8. Remnants vegetation areas in conservation units of Guanabara Bay (2010). 

It is evident in the Figures 7 and 8 that the higher forest cover rate is associated with the 

riparian vegetation along the rivers. Results show the disappearance of large part of riparian 

vegetation along the Guapi-Macacu river banks. According to CONAMA (National 

Environmental Council) Resolution No. 9 from 1996, riparian forests are considered 

corridors linking forest remnants, thus increasing landscape connectivity. In addition, forest 

fragmentation pattern of Guapi-Macacu river basin appeared to be associated with 

topography and slope. With the implementation of the Metropolitan Arch and the 

Petrochemical Complex it is expected a rapid anthropogenic increase moved by the process 

of building infrastructure networks in urban areas. 

The magnitude and extent of human impacts have altered biodiversity, hydrologic 

connectivity (Pringle,2001), conservation of aquatic ecosystems and also water supplies in the 

medium and long term. According to Pedreira et al., as cited in Hernández (2010), the main 

environmental pressures in water quality in the Guanabara Basin are: inappropriate land-use 

activities, specifically, removal of majority of the original vegetation cover, removal of riparian 

forest, unplanned urban sprawl, lack of sewage treatment and improper supervision of 

industrial activities; causing steep erosion and river siltation. Despite the promulgation of 

wide-reaching legislation, including Law 9,433/1997, which institute the National Water 

Resource Policy and defined the legal and administrative framework for the National Water 

Resource System and CONAMA Resolution 357/2005, which established the classification of 

water bodies and the conditions for effluents discharging, the water pollution is steadily 

increasing. In critical areas surrounding the Guanabara Bay (particularly along its NW coast), 

less than 60% of the population has access to adequate sewage treatment, only about 10% of 

the total sewage is treated before being released into the Bay, the rest being released untreated 

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into the Bay’s tributaries (Bidone & Lacerda, op cit). According to Hespanhol (2008) in terms of 

water resource management, it is therefore fundamental, especially in urban areas, that we 

abandon the outmoded orthodox principles and implement a new paradigm based on the keywords 

of water conservation and reuse, as only thus will it be possible to minimize the costs 

and environmental impacts associated with the new channeling projects. 

In the mountain regions, the most affected areas in terms of vegetated loss in the Guapi- 

Macacu Basin were observed in National Park of Serra dos Órgãos and also State Park of 

Três Picos. About eleven percent of the park´s vegetation has been lost between 1985 and 

2010, which represents an area of thirty three square kilometers. According to Goncalves et 

al. (2009) to reconcile conservation and land-use one of the alternatives is to establish buffer 

zones around protected areas, within which human activities are subjected to specific rules 

and restrictions. Brazil’s Conservation Units National System (SNUC) determines that 

protected areas should be surrounded by buffer zones where human activity is restrict, but 

the established size of the buffer seems arbitrary (Alexandre et al, 2010). In 1990, the 

National Environment Council (CONAMA) Resolution number 13 had already defined a 

buffer zone of 10 kilometers around protected areas, where any activity that may affect the 

biota should be licensed (CONAMA, 1990). In 2010 the resolution No. 13 was revoked by 

Resolution No. 428/2010, which reduced the buffer zone to 3 kilometers for licensing the 

enterprises of a significant environmental impact , located from the edge of Conservation 

Units, where the buffer zone is not established with the exception of private reserves 

(RPPN), the Environmental Protection Areas (APAs) and consolidated urban areas. 

Vegetation Remnant Areas (Square Kilometers) Variation (%) 

Conservation Units 1985 2001 2010 1985-2010 

Environmental 

Protection Areas 

496.193 427. 573 395.822 -20.23 

Parks 349.687 338.979 310.257 -11.27 

Ecological Reserve 4.141 3.685 4.120 -0.48 

Biological Reserve 151.083 149.767 143.172 -5.23 

Ecological Station 63.550 61.297 59.185 -6.87 

Sambê Santa Fé 

Corridor 

255.330 206.108 198.720 -22.17 

Table 3. Vegetation Remnant Areas (km 2) in the Conservation Units at Baia de Guanabara 

Basin 

5.4 Remnant vegetation according to altitude 

Guanabara Bay Basin is characterized by a large number of small ponds (over 40), usually 



According to Freitas et al (2010) the highest deforestation and fragmentation occurred in less 

declivous areas, where there are more roads, and more intensive land use. The results 

corroborate those previously reported by Freitas op cit, with a significant decrease in 62.01 

percent of vegetated areas in the lowlands, the equivalent of an area of 110.689 square 

kilometers. 

Fig. 9. Remnant vegetation according to altimetry (1985) 

The mangrove area has decreased from 86.308 km2 in 1985 to 67.397 km2 in 2010. The main 

causes of mangrove degradation in the Guanabara Bay Basin include population pressure, 

agriculture, as well as pollution. On a positive note, between 2001 and 2010 there was a 

small increase in mangrove area, probably due planting and replanting initiatives. However, 

the Landsat images do not allow more detailed assessments in relation to degradation stage 

of mangroves that continually receive different kinds of waste coming from urban, 

commercial and industrial activities. 

The major extension of vegetation remnants was observed in higher altitudes (“serras”). 

Areas with steep slopes are less used and is much more likely to remain forested (Silva, 

2007). Although montane forests and upper montane forests have been losing areas along 

the study periods, mainly due to intentional fires or those for clearing land for pastures. In 

September 2010, intentional fires in the State Park of Três Picos destroyed 80 hectares of 

pasture lands, natural forests and fields of altitude called “campos rupestres”. In August 

2011 a fire broke out 30 hectares of forest in the National Park of Serra dos Órgãos. The 

major cause of the vegetation loss in the Guanabara Basin was due to urban development 

without planning. 

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Area (Km 2) 1985 2001 2001 Variation (%) 

1985-2010 

Mangrove swamps and wetlands 

(0-7m) 

86.308 67.397 67.792 -21.31 

Lowland (0-40m) 178.497 90.401 67.808 -62.01 

Lower montane forest (40-500m) 884.356 691.698 672.229 -23.98 

Montane forest (500-1500m) 405.536 396.678 364.694 -10.07 

Upper montane forest (1500- 

2200m) 

Table 4. Remnant Vegetation in relation to altimetry 

17.90 15.05 10.1 -43.52 

5.5 Vegetation fragmentation in the Atlantic Rainforest Central Mosaic 

Landscape mosaics are described by the landscape components of patches, corridors, and 

the surrounding matrix ( Forman, 1995). Factors such as patch size and shape, corridor 

characteristics, and connectivity work together to determine the pattern and process of the 

landscape (Forman, op cit). Franklin et al. (2002) has proposed four requisites for building 

situational definitions of habitat fragmentation: (1) what is being fragmented, (2) what is the 

scale(s) of fragmentation, (3) what is the extent and pattern of fragmentation, and (4) what is 

the mechanism(s) causing fragmentation. According to Franklin op cit., fragmentation at the 

range-wide scale can affect dispersal between populations, fragmentation at the population 

scale can alter local population dynamics, and fragmentation at the home range scale can 

affect individual performance measures, such as survival and reproduction. The topography 

can also influence patterns of forest fragmentation and forest cover, as previously 

demonstrated in several regions, including the Brazilian Atlantic Forest region (Silva et al., 

2007; Freitas et al., 2010). Fahrig (2003) defined four effects which influence the 

fragmentation process on habitat pattern:(a) reduction in habitat amount; (b) increase in 

number of patches; (c) reduction in patch size; and (d) increase of isolation between patches. 

However, fragmentation measures vary widely; some include only effect (e.g., reduced 

habitat amount or reduced patch sizes) whereas others include two or three effects but not 

all four (Fahrig, op.cit). The large connected area (corridor) allows the exchange of genetic 

material with large populations. 

The study area is included in the Atlantic Rainforest Central Mosaic with a large and 

contiguous corridor observed in mountain region. The figures 12 to 14 show that the six 

classes of vegetation fragments, varied from very small fragments (

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Fig. 12. Vegetation Fragmentation in Guanabara Bay Basin (1985) 






In the same period, there was also a lack of continuity between forest fragments that were 

previously connected. Fragment size and connectivity are among the key landscape factors 

that affect species survival in fragmented landscapes (Carvalho et al., 2009). Metzger et al. 

(2009) suggested that fragment size is usually related to the amount and diversity of 

resources, which directly influence the size and number of resident populations. 

The study shows that the largest reduction in size of forest patches was observed on the 

plains. Although the vegetation patch in lowlands is extremely important to allow 

connection between highlands, it was observed that vegetation fragments become smaller 

and more widely spaced. Figure 12 shows a vegetation patch of 4,648 hectares in the central 

part of the Guapi-Macacu River Basin , which was reduced to some small patches 

disconnected in 2010. Also, the number of the bigger fragments declined while the smaller 

ones increased, which means that during that study period a strong fragmentation took 

place. According to Freitas et al. (2010) higher density of roads is a primary predictor of 

forest fragmentation and deforestation. As shown in Figure 14, there was a fragmentation in 

the south and southwest of the Sambê Santa Fé Corridor in some small patches of 

vegetation. According to the analysis in 1985 the Corridor of Sambe Santa Fé had a largecontinuous 

patch of 188.200 square kilometers. In 2001 this large patch was reduced to 

142.117 square kilometers and several fragments of different sizes, which will bring serious 

problems for the local biodiversity. The National Park of Serra dos Órgãos has lost 

approximately 20 percent of the vegetated area. 

5.6 Anthropogenic occupation in Guanabara Bay Basin in relation to slope classes 

The topography determines the expansion of roads and the land use activities, which will 

impact the forest cover (Freitas et al, 2010). The thematic maps overlay of anthropogenic 

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class and slope layers (Figure 15) showed that the human occupation occurs mainly in the 

slope classes between 0 and 3 percent, less than 10 meters of altimetry. The anthropogenic 

class occupied an area over 600 square kilometers in plan terrain, which is subject to risk of 

flooding, especially in rainy periods. In April 2010, heavy rain caused destruction and death 

in the State of Rio de Janeiro. The anthropogenic growth is also evident in slopes between 3 

to 8 percent, with 268 square kilometers occupancy in gentle sloping terrain. In steep slopes 

and near streams, where it is difficult to grow crops and accessibility is limited, forest is 

commonly found (Teixeira et al., 2009). In addition, in most cases the Atlantic Forest region 

is located in sites where access is difficult (Cabral et al., 2007; Silva et al.,2007). According to 

Freitas op cit, forests far from land use (buildings and agriculture) and major cities are more 

likely to be preserved and regenerated. The results corroborate those from authors cited 

above with low occurence of anthropogenic occupation on slopes between 20 to 45 percent 

and 45 to 75 percent with higher amount of remnant vegetation. This fact is explained by the 

difficulty of occupying the higher slopes and the lack of infraestructure which restrict the 

urban expansion. 

Fig. 15. Anthropogenic Occupation in relation to slope classes. 

Figure 16 shows that the anthropogenic occupation in plan terrain also occurs in buffer 

zones that must be preserved as the Permanent Protection Areas (APP), along the rivers, in 

the buffer zones of riparian vegetation. According to Naiman & Décamps (1997) riparian 

zones play essential roles in water and landscape planning, in restoration of aquatic 

systems, and in catalyzing institutional and societal cooperation for these efforts. Rivers and 

their adjoining riparian zones are considered to be the most important corridors for 

movements of animals in natural landscapes (Forman & Godron 1986). Furthermore, human



alteration of riverine ecosystems involves not only changes to flow regimes but also 

simultaneous changes in hydrologic connectivity (Nilsson et al. 2005). 

The human occupation is also observed in mountain regions, inside buffer zones of strict 

protection conservation units as Parks and Reserves. The Figure 16 also shows a drastic 

reduction of seven kilometers in buffer zones, which may allow the expansion and 

implementation of large-scale infrastructure projects and also a rapid urban occupation in 

the eastern part of Guanabara Bay Basin. There is a concern with the rapid urban growth in 

Itaboraí, Cachoeiras de Macacu and Guapemirim which could bring serious damage to 

surrounding pristine vegetation, riparian vegetation and also wetlands. 

Fig. 16. Anthropogenic occupation in buffer zones 

5.7 Critical areas for protection 

The Conservation Units in the Atlantic Rainforest Central Mosaic are becoming fragmented 

and have lost a great amount of vegetated areas along the study period. It was identified 

two critical areas that Conservation or restoration actions become more urgent: Sambê Santa 

Fé Corridor and Guapi-Macacu River Basin. The major rupture in the continuity of Sambê 

Santa Fé Corridor is observed in southwestern part of the corridor (Figure 17a,b,c) with 

highly fragmented landscapes into small and isolated fragments. In 1985 it was observed the 

close proximity of the patches and also the amount of vegetation areas was higher than in 

2010. Figure 17c shows that the patches were reduced in habitat amount, increase in 

isolation among patches and reduction in patches size. 

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274 

Fig. 17. Sambê Santa Fé Corridor 

(a) 

(b) 

(c) 




(c) 

Fig. 18. Guapi-Macacu River Basin (1985, 2001 and 2010), respectively. 

(a) 

(b) 

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The vegetation patches in Guapi-Macacu River Basin are being fragmented along the years. 

Figures 18a,b,c shows a decrease in amount of vegetated area among the study periods. In 

2010 it was observed that the vegetation fragments became smaller and isolated and the 

riparian vegetation is much less evident. Fig 18c shows that riparian vegetation had a severe 

decreasing in the central part of Guapi-Macacu. Human-centered attitudes toward water 

have deteriorated many riverine ecosystems, implying that the derived benefits have 

brought considerable environmental and social costs (Nilsson, 2007). Therefore, 

enhancements of river connectivity require thorough analysis and, ideally, should be carried 

out in concert with rehabilitation of flow dynamics (Nilsson op cit.). 

6. Conclusion 

Forests are cleared, degraded and fragmented in the Guanabara Basin. The riparian 

vegetation in the Guapi-Macacu river basin is disappearing over the years and it may soon 

affect the water supply in the Guanabara Basin. The most serious threat comes from the 

disorderly and irregular land occupation without urban planning. 

People yet ignore the importance of the riparian vegetation and this negligence will cause in 

a near future serious problems in the available ground water and consequently in the water 

supply. Degraded riparian vegetation leaves surrounding ecosystems vulnerable to some 

disturbances as flood and drought. The existing vegetation in the riparian zone needs to be 

kept intact or protected by law. There must be strongly enforced laws to limit urban 

occupation in the river bank and avoid activities which deplete the riparian vegetation. 

Despite its water reserves, Brazil now runs the risk of losing their most precious natural 

resources: water and forests, due to disorderly process of land occupation and irresponsible 

degradation of the environment. Everything that happens in one point of a river basin will 

influence the total of the basin. The irresponsible use of natural resources is bringing 

drought, which is bound to handicap the production of vital resources for sustaining human 

population, as is already happening in some parts of the world (Moraes, 2009). 

There is a need for the stakeholders to establish conservation initiatives and share 

experiences in order to safeguard the riparian vegetation, mangrove swamps and Atlantic 

Forest remnants of the Guanabara Bay Basin. The forests are essentials for maintaining a 

drinking-water provision. 

It is necessary urgent efforts to restore the remaining forests, with reforestation initiatives, 

especially in the headwaters of rivers. And to connect and expand the remnants of forests 

that are already fragmented which reduces the capacity of species to disperse through the 

landscape. 

The Landsat satellite becomes very useful for working with medium scale maps for 

distinguishing higher ranks of classification. Moderate resolution remote sensing is widely 

used in a variety of sectors including land use planning, agriculture, and forestry. However, 

this approach cannot be applied directly in large scale maps. Thus, it is necessary an 

integrated study combining high resolution satellite images with data from other sources 

(thematic maps and numerical data) for providing new possibilities for land-cover analysis 

of the Basin. The further studies using a large-scale vegetation mapping for verifying with 

detailed the different succession stages of Atlantic Forest and its biodiversity. According to 

National Environmental Council (CONAMA) number 6, 1994 and Law n. 11.428 (December,



2006) different types of land use and natural vegetation cover are categorized based on 

ecological succession. 

Water production for life also depends on protected areas. A connected administration 

(Federal, State and Municipal) of different protected areas is essential. Parks, reserves and 

environmental protected areas have to plan together and also work together against fires, 

environmental disasters, irregular occupations, trafficking and poaching of animals. Impacts 

in higher places affect lower places. So those the opposite. It is very important the 

interaction between public and private agencies and also non Governmental Organizations 

NGOs, universities, and research organizations aiming at define land use objectives and 

restrictions. 

The restoration of Guapi-Macacu River Basin is not easy and involves efforts by different 

sectors of society to the implementation of integrated alternatives such as: technical, 

ecological, and also environmental education programs and policies. It is also urgent a 

linking of remnant vegetation of lowlands with that of highlands aiming at guarantee the 

water supply in the basin and also the integrity of the ecosystem. 

Sambê Santa Fé Corridor restoration should be considered a high priority conservation 

action, since the corridor contributes to maintain the water quality and quantity of Guapi- 

Macacu watershed. 

In Brazil were created some Conservation Units and ecological corridors to preserve 

biodiversity and restore landscape connectivity. Many units have been established for over 

20 years and still do not have a management plan which difficult the environment zoning. 

Some NGO´s and public and private organs are currently involved with Atlantic Forest 

conservation efforts. Although, these efforts is not sufficient to preserve/conserve the 

Protection Areas and its remnant vegetation. This fact is mainly because the political and 

economic interests have a higher priority than environmental ones. 

Urban planning is essential, especially in the areas of influence of Rio de Janeiro 

Petrochemical Complex (COMPERJ) that are expanding rapidly. The urban expansion in the 

eastern part of the basin may cause serious damages to remnant vegetation , especially with 

the reduction of buffer zones, including strict protection in areas as parks and ecological 

stations. The application of public and private investments in water reuse is also urgent to 

meet additional population demand. 

With the increasing of world population it is necessary a change in way of life in order to 

seek what is really important for our survival by using different sources of sustainable 

energy as well as making use of non conventional materials. Humans can no longer 

continue exploiting and destroying the forests because in the end, we will be no water, no 

food and no prospect of survival in our planet. 


Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro 

(FAPERJ)- Auxílio APQ1, Brazillian Institute of Spatial Research (INPE); U.S. Geological 

Survey (USGS); Consortium for Spatial Information- CGIAR; Instituto Brasileiro do Meio 

Ambiente e dos Recursos Naturais Renováveis – IBAMA; Instituto Estadual do Ambiente 

(INEA); Reserva da Biosfera da Mata Atlântica (RBMA); Programa de Despoluição da Baía 

de Guanabara (PDBG); Instituto Brasileiro de Geografia e Estatística (IBGE). 

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Part 3 

Preventing Deforestation

14 

Preserving Biodiversity and Ecosystems: 

Catalyzing Conservation Contagion 

Robert H. Horwich1, Jonathan Lyon1,2, Arnab Bose1,3 and Clara B. Jones1 1Community Conservation, 

2Merrimack College, 

3Natures Foster 

1,2USA 3India 1. Introduction 

The natural world is in a chronic state of crisis and under constant threat of degradation, 

primarily by anthropogenic factors. In general, current conservation strategies have failed to 

effect long-range solutions to the rapid loss of biodiversity (Persha et al., 2011). 

Deforestation continues despite efforts by mainstream (top-down) conservation programs 

(Persha et al., 2011; Schmitt et al., 2009), and the effectiveness of large-scale protected areas 

has, at best, a mixed record of success (Brockington et al., 2008; Persha et al., 2011). Scientific 

disciplines, in particular, ecology and conservation biology, continue to emphasize threats to 

biodiversity (Schipper et al., 2008), to debate conservation priorities (Brooks et al., 2006), to 

advance unproven strategies (SSC, 2008), and to offer no more than hypothetical solutions to 

pressing problems (Milner-Gulland et al., 2010; Turner et al., 2007). The bulk of the scientific 

community remains tangential to the conservation needs of communities in habitat 

countries, with a critical lack of input and connectivity between the extensive scientific 

literature and ground-level practices (Milner-Gulland et al., 2010). 

Resurgence of the “fortress conservation”, “protectionist” narratives (commitment to 

conservation programs at the expense of indigenous and other local people) promoting a 

19 th century wilderness ideal free of humans remains a cornerstone of much conservation 

thought, policy, and planning. As pointed out by Brockington et al. (2008), commitment to 

community-based conservation “has been downplayed from being an approach to 

conservation to becoming a component to justify and legitimate interventions to create new 

protected areas or interventions to conserve specific species”. This “back to the barriers” 

movement (Hutton et al., 2005), supported by many conservation biologists (Kramer et al., 

1997; Oates, 1999; Terborgh, 1999), has been accompanied by an increase in conservation 

funding, with large conservation organizations reverting back to protectionist landscape 

conservation and away from community-based (ground-level or bottom-up) resource 

management (Hutton et al., 2005). 

In his discussion of the ongoing conflicts between indigenous peoples’ movements and 

conservation organizations, Dowie (2009) noted: “When, after setting aside a ‘protected’ 

land mass the size of Africa, global biodiversity continues to decline and the rate of species 

extinction approaches one-thousand times background levels, the message seems clear that

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there might be something terribly wrong with this plan… A better strategy might be simply 

to turn more human beings into true conservationists....” Community conservation projects, 

at the core, are based on the strategy of turning more human beings into conservationists 

(see Persha et al., 2011). The approach pursues this goal by working with people living in 

species-rich landscapes, assisting them to form networks with one another, with 

community-based organizations, with non-government organizations, and with government 

agencies for the protection of biodiversity and ecosystems (Brockington et al., 2008). When 

implemented according to field-tested procedures, community conservation provides an 

effective ground-level solution to environmental degradation and the loss of biodiversity 

(Horwich & Lyon, 2007; Horwich et al., 2011). Indeed, 60-85% of conserved areas are 

inhabited by people who are potential conservationists and who are necessary components 

of success (Brockington et al., 2008; Horwich & Lyon, 2007; Horwich et al., 2011; Persha et 

al., 2011). 

One flaw inherent to debates over the community conservation approach entails the type of 

questions being asked: Is community conservation successful? Who should be responsible 

for protecting natural resources? These are not, however, the truly relevant questions. 

Community conservation is one solution to environmental degradation, deforestation and the 

loss of global biodiversity. The truly relevant question is: Why aren’t all conservationists, 

scientists, in particular, conservation biologists, and non-government organizations actively 

incorporating successful community conservation models into their mission statements, 

policies, and programs (see Persha et al., 2011)? Community conservation projects are 

growing in number and in success (Borrini-Feyerbend et al., 2004; Dowie, 2009; Horwich & 

Lyon, 2007; Horwich et al., 2011; IUCN, 2003). Indigenous and other local groups are 

gaining political power and expertise, becoming conservation activists, and, in some 

instances, regaining management of homelands (Dowie, 2009). Indeed, many communities 

have initiated sustainable conservation projects (Pathak et al., 2004). There is also recent 

evidence that community-managed tropical forests show lower and less variable annual 

deforestation rates than do the traditional protected areas (Porter-Bolland et al., 2011 In 

Press) with potential for reducing carbon loss effecting global climate change (Soares-Filho, 

2010). However, the effects of community and indigenous managed projects in terms of 

their geographic scale, recognition by professional conservationists, including many nongovernmental 

organizations, as well as their economic and political influence, are not yet 

sufficient to mitigate the deleterious effects of increasing environmental degradation and 

escalating loss of biodiversity. This condition persists, in part, because regional and 

governmental entities, as well as non-governmental organizations, have failed to include 

indigenous and other community stakeholders as partners (Persha et al., 2011). There is a 

need for national and regional governments and non-governmental organizations to 

network with community-based organizations having the mission, goals, and objectives to 

initiate, facilitate, train, and empower communities in habitat countries to preserve and 

manage local resources (Horwich & Lyon, 2007; Horwich et al., 2010; Persha et al., 2011). 

Unsuccessful project outcomes have been minimized by the Community Conservation 

(www.communityconservation.org) model developed over time by trial-and-error but by 

now tested in the field and proven to be a valid and reliable procedure with utility for other 

community-based programs, as demonstrated by the cases discussed below. Table 1 gives a 

list of 23 projects that Community Conservation, Inc. has either initiated or contributed 

significantly to in its earlier stages. Three points are important to note from this table. Most 

important is that the highest level of community participation has occurred by encouraging

Preserving Biodiversity and Ecosystems: Catalyzing Conservation Contagion 

the creation of community-based organizations to manage or contribute to the project. 

This also implies a high level of community empowerment. In Assam, India, the Manas 

Biosphere Reserve is now being protected by a network of 14 community groups 

(Horwich et al, 2010, Horwich et al., 2011). In this regard it is notable that the 

communities played a significant role in having UNESCO recently remove the “World 

Heritage Site in danger” listing. In the cloud forests of Peru, the Yellow Tailed Woolly 

Monkey Project has been stimulating community groups to create community reserves 

under Peruvian law. In Papua New Guinea, the Tree Kangaroo Conservation Program has 

created a community group that is a federation of over 26 clans. The second point is that 

community groups have stimulated or contributed to the creation of new protected areas 

or act as complementary protectors of public and private lands. Thirdly, communities can 

play a major role in regional or landscape protection as is occurring in the Golden Langur 

Conservation Project in the Manas Biosphere Reserve in Assam, India (Horwich et al. 

2010), the Tree Kangaroo Conservation Program in Papua New Guinea (Ancrenaz et al, 

2007) and what is evolving in the Yellow Tailed Woolly Monkey Project in the cloud forest 

of Peru (Shanee et al., 2007). 

Table 1. Updated list of Community Conservation projects with selected information. 

Our community-based model, comprised of nine social stages, progresses as follows: (1) 

initial contacts with community leaders and elders to catalyze informal communication with 

village inhabitants, providing opportunities to openly and candidly discuss the significance 

of their resources and benefits to be gained from cooperative and participatory initiatives for 

conservation of their natural resources fostering (2) informal relationship-building in 

indigenous and other local communities leading to (3) participatory education providing (4) 

a window of opportunity for local conservation leaders to emerge (5) who invoke support 

from others, in our cases, the majority of villagers fostering (6) development of a formal 

infrastructure and plans within the possibilities of each community context. Eventually, (7) 

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as the cases presented in this article show, the lessons and activities implemented and 

learned in the initial target village diffuse through local networks of communication (e.g., 

hearsay; printed information from enlightened schoolchildren to their kin) broadening 

matrices of conservation activists through other modes of social transmission and problemsolving 

(e.g., observational learning; imitation; education; brainstorming sessions and focus 

groups; contacts by the target community to members of other villages inviting them to 

inspect their conservation efforts and to attend planning sessions, lectures, seminars, and 

public events; informal and formal visits from target community members to other villages). 

The first seven social stages of our community-based, bottom-up model have the potential 

to foster (8) diffusion from the target village to other communities and, beyond, to regional 

entities through a process that we term conservation contagion consolidating the horizontal 

network (e.g., community-based organizations and communities: Berkes, 2004). Finally, (9) 

educational initiatives, lobbying, and relationship-building with entities in vertical networks 

(e.g., regional, governmental, non-governmental, and international entities: Berkes, 2004) 

have the potential for linkage, creating multidimensional, multi-scale partnerships 

benefiting all stakeholders. Our formulations, catalysis and community contagion, are 

detailed below. 

The objective of this Chapter is to illustrate how community conservation, when carried out 

in the field, using tried and proven methods, is an effective solution to reducing 

deforestation and the loss of biodiversity and consequent climate change and reduced 

carbon emmissions. Section 2 defines the philosophies, concepts and practices in small 

projects that lead to successful results in contrast to the preponderance of large Integrated 

Conservation and Development Projects (ICDPs) whose contrasting philosophies, concepts 

and practices have resulted in mainly failures at high costs. Section 3 introduces the concept 

of conservation contagion and how, when it is stimulated, can lead to regional change. 

Section 4 complements the other sections with examples of successful projects from Belize, 

India and Namibia, illustrating successes that led to regional change with increased 

community protection resulting in increased reforestation and increases in focal species. 

Section 5 discusses lessons learned from the process of community conservation and its 

examples. Finally section 6 gives policy implications for future successful possibilities. 

2. What makes a successful community conservation project (CCP) 

Community conservation or community-based conservation projects under a number of 

names have been developed over the past two decades as important alternatives to the 

traditional protected areas that exclude humans. Community Conservation, Inc. (CC), and 

other non-governmental organizations are project identifiers used in the present chapter to 

designate ground-level initiatives developed over the past two decades as important 

alternatives to traditional conservation organizations, historically prioritizing protected 

areas independent of human interests and often excluding indigenous and other local 

groups from targeted areas (“fortress conservation”). These community conservation 

projects have been based on ethical, theoretical, and practical arguments of conservation 

practitioners and social scientists (Borrini-Feyerabend, 1996; Broad, 1994; Brosius et al., 1998; 

Davey, 1998; Gadgil & Guha, 1993; Ham et al., 1993; Johnson, 1992; Oates, 1999; Stolton & 

Dudley, 1999). However, in recent years there has been growing criticism of communitybased 

conservation programs and a call for renewal of protected areas that exclude local


communities from the programs and their management (Brandon et al., 1998; Inamdar et al., 

1999; Robinson, 1993; Terborgh, 1999). 

While many critics of community conservation projects are biologists (Oates, 1999; 

Terborgh, 1999), social scientists have also criticized these projects (Belsky, 1999; Brechin et 

al., 2002). Most of the criticism has been directed toward large integrated conservation and 

development projects (ICDPs) while the successes of small community-based projects have 

been overlooked (Horwich & Lyon, 2007). Even practitioners of ICDPs, however, have been 

disappointed by their limited achievements (Robinson & Redford, 2005) and are attempting 

to learn from their failures to maximize future success (McShane & Wells, 2004; Rhoades & 

Stalling, 2001). What seems clear at present is that ICDPs have been adhering to a faulty 

paradigm and have much to learn from community conservation and community-based 

forestry paradigms (Shepard, 2004). 

The philosophies of community conservation, community-based conservation and 

integrated conservation and development projects “originated from a shift in protected area 

management away from keeping people out by strict protection and toward more 

sympathetic treatment of local communities, including efforts to share benefits from the 

conservation of biodiversity“ (Wells et al., 2004). However, the philosophy and methods by 

which activities are carried out are extremely different between large ICDPs and smaller 

community conservation projects. These different postures have resulted in the lumping of 

unjustified criticism of our community conservation projects (Belsky, 1999, 2000) resulting in 

curtailed progress. Thus, it is important to differentiate the two organizational models. 

Community conservation projects are centered on conservation of natural resources and 

the role they play in the lives of indigenous and other local, rural peoples. Dealing with 

people on a one-to-one basis at a community scale has been a primary focus of Community 

Conservation’s projects. Optimally, the limited resources of community conservation 

projects, especially finances, are best distributed over a long time-period in order to use 

them efficiently and prudently. While natural resource conservation is central to 

Community Conservation’s priorities, it is followed closely by the needs of rural indigenous 

and other local populations, not only economic, but also social ones, emphasizing quality 

of life. 

There are some fundamental differences between how successful community conservation 

projects and most ICDPs approach a project (Table 2). Community conservation projects are 

holistic. Flexibility and the expectation of change are important to their success; thus, 

practitioners must be adaptable, learning from problems as well as successes. Regional 

planning can be accomplished by building on, and expanding from, small community 

projects, from the specific projects to the general goals, objectives and mission that fits the 

composite project and region. Community conservation projects focus more on doing than 

on planning. The primary role of community conservationists is as catalysts to educate, 

motivate, and reinforce residents and communities in habitat countries to protect and 

conserve their natural resources both for themselves and for the rest of the world. While 

projects should be long-term and on-going, the catalytic role generally ends once 

community members are prepared to assume responsibility for programs in their 

communities and regions. ICDPs, instead, generally have been pervasive in time and space, 

with no plans to exit their initiatives or to transfer leadership to indigenous and other local 

residents (Sayer & Wells, 2004). 

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Table 2. Major differences between community conservation projects (CCPs) and integrated 

conservation and development projects (ICDPs). 

2.1 Rural people are the solution not the problem 

Another difference between community conservation projects and ICDPs concerns 

significant philosophical differences resulting in very different approaches and practices. 

ICDPs conceptualize local communities as generators of habitat degradation (McShane & 

Newby, 2004; McShane & Wells, 2004) based on the premise that humans utilize natural 

resources and may abuse them when community and governmental regulating systems 

break down, characteristic of the “tragedy of the commons” (Feeney et al., 1990; Hardin, 

1968). However, if rural indigenous and other local people are seen as threats, they will have 

a greater probability of living up to that expectation. In contrast, when people depend on 

resources, they may be educated to understand that they must not over-exploit resources 

without losing them. Because they use them, they have knowledge of and appreciate them. 

Living on site, they can better protect the resources. But there are also many outside forces 

competing with rural residents for resources. Thus, by giving rural indigenous and other 

local people entitlement and responsibility over their resources, many will see the 

importance of biodiversity conservation. Indeed, in almost all cases, when we have asked 

local rural people for their help in protecting their natural resources, they have responded 

very favorably and effectively. 

2.2 Community scale at a personal level 

Another philosophical difference between community conservation projects and ICDPs is 

that ICDPs approach conservation on a large scale, possibly because donor agencies think


financially in those terms. However, major outlays of money and other resources for short 

time-periods have proven to be ineffective, inducing greed, waste, mismanagement and 

corruption. It may also highlight differences between the community base and corrupt 

western affluence (Gezon, 1997). More importantly, in order to work successfully with small 

rural communities, initiatives must cooperate at the lowest levels of community and 

regional organization, engaging personal, face-to face communication involving the 

thoughts, emotions, beliefs, attitudes and values of indigenous and other local stakeholders 

who are the voices of most decision-making, problem-solving and negotiations. The rural 

poor embody the same desires to assume responsibility for and to manage their own affairs 

as others, regardless of economic position. As a result, community involvement is an 

important ingredient for all conservation projects. Even when protecting large landscapes, 

community conservation projects can be effective at the regional scale by dividing initiatives 

into ground-level components for the retention of person-to-person interactions fostering 

trust and friendship. This process is exemplified by the case study of a community-based 

project in Assam, India (Horwich et al., 2010). 

2.3 Community conservation projects – below the “conservation radar” 

While ICDP enthusiasts have undergone some soul searching because of the backlash and 

criticism from biologists and sociologists, they have rightly begun to learn from ICDP 

failures (McShane & Wells, 2004). Unfortunately, small community conservation projects 

have been neglected and are “below the radar” of the mainstream conservation community 

(see Horwich & Lyon, 2007) not withstanding published accounts of successes as well as 

problems of such projects (Horwich, 1990a, 1998, 2005; Horwich & Lyon, 1988, 1995, 1998, 

1999; Horwich et al., 1993; Lyon & Horwich, 1996). Despite documented successes of 

community-based initiatives, funding for these projects has also been under the 

conservation radar, remaining at very low levels. 

Similarly, published articles have also ignored benefits to local communities from 

conservation of forests and their resources upon which indigenous and other local groups 

may depend (Shepard, 2004). Shepard (2004) notes “ conservation organizations themselves 

form their own international environment in which they talk to and argue with one another. 

Because they spend so much of their time in this company, there is too little exchange of 

ideas with those engaged in the forestry and poverty worlds.” She goes on to state that 

”ICDPs and the conservation bodies that manage them have been out of the mainstream of 

changing thought about forest management and rural livelihoods and now risk getting 

stuck with an old-fashioned paradigm. Though it will always be difficult to persuade people 

to abandon some of their old assumptions, it is now urgent that they consider doing so.” 

(Shepard, 2004). 

2.4 Community participation 

Level of participation - Communities are complex, heterogeneous groups of people with 

conflicting goals, aims, and desires. Complexities based on gender, politics, class, patronage, 

ethnicity, age, social standing and religion often have complex social histories that include 

exploitation, marginalization, and conflict (DuPuis & Vandergeest, 1996). For the last 

decade, the terms of community conservation, community participation, community-based 

conservation became buzz words as community conservation projects and ICDPs were in 

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vogue. However, what has never often been clearly differentiated is that community 

involvement can occur at many levels in a continuum from top-down management through 

informal and formal consultation to formal advisory committees and ultimately to 

community co-management and indigenous management (see Horwich & Lyon, 2007; 

Horwich et al., 2004) as discussed by Arnstein (1969), Berkes (1994), Barrow (1996) and 

Stevens (1997). Thus in any project, the level of community participation must be clearly 

identified. The main thrust in the projects that we sponsor is to facilitate, particularly by 

persuasion and education, creation of an empowered community-based group capable of 

continuing a project once we have left it (Table 1). The lowest levels of government 

involvement and the highest levels of community participation, as represented by 

indigenous and other local community co-management, allow for strong partners in 

decision-making and project control. Co-management allows for government checks and 

balances and support; as well, indigenous and other local management is currently working 

well at one of our projects, the Community Baboon Sanctuary in Belize, discussed below. 

Incentives for community participation - Top-down government management or private 

ownership are not the only ways to conserve and protect natural resources. Historically, 

many successful indigenous communal systems were working before Europeans came to 

dominate most natural landscapes. Singleton (1998) mentions institutions used by the Pacific 

Northwest Native Americans before the Europeans reached those shores. There have since 

been many struggles of poor people to regain those rights (Guha, 1989). Thus community 

conservation efforts have the potential to redefine and restructure managerial systems. This 

provides an enormous incentive for community participation and as has been shown by 

many forest projects (Poffenberger & Gean, 1996), and other Self Help development 

programs (Wilson, 2002), villagers have responded by being empowered to assume 

responsibility for their resources. On the other hand, efforts of ICDPs, offering a wealth of 

developmental possibilities to these same rural residents, often failed generally because of 

the limitations discussed below. 

Uphoff and Langholz (1998) present a model of three basic categories of incentives for 

people to conserve or exploit protected resources: 1) legal, 2) financial or 3) social/cultural. 

According to these authors, if initiatives incorporate all of these elements, they have a high 

probability of being adopted. Projects lacking the features have a low likelihood of being 

adopted. The authors noted that rewards from ICDPs’ conservation tactics “amounted to 

tacit bribes” for getting villagers to adopt new practices depending upon an infusion of 

outside resources. These “bribes” seemed to undermine community practices for the 

conservation of resources. An integrated balance of these three factors is needed to induce 

conservation,. Uphoff and Langholz (1998) make a strong case for the importance of social 

values leading to stewardship of natural resources, echoing what we have found in our 

experiences with community-based conservation. Although money was one motivator in 

our project at the Community Baboon Sanctuary in Belize, community members consistently 

demonstrated pride in their conservation efforts, especially their flagship “baboons” (the 

local term for black howler monkeys, Alouatta pigra). In Assam, the pride of achievement 

was integral to project success exemplified by local leaders reporting their accomplishments 

in developing Self Help Groups and forestry committees. These entities continue to provide 

important social functions for meeting and talking, for practicing thrift, and for receiving 

information on a short term basis (Wilson, 2002). More importantly, they have “a more


powerful purpose: to gain and share information, to take social action, and to link to 

government resources.”(Wilson, 2002). It is that power that has stimulated some of the local 

forestry groups in Assam to actively protect their forests for themselves and for the pride to 

convey their actions to project partners. There is also no surprise that rural villagers also 

appreciate natural areas for their beauty and greenness and enjoyed walking and sitting in 

the shade and seeing wildlife (Allendorf, 2007). 

Despite what we have found, the conservation community has focused on poverty and 

economic incentives as a prime motivator of poor rural communities. Indeed, a great deal of 

money has been spent on ICDPs and “despite this high level of investment and effort, we 

can only point to some individual, localized successes. Taken as a whole, we have had little 

impact on stemming or even slowing the rising tide of biodiversity loss.” (Kiss, 2004). In 

addition, little money from the large grants ever reached the beneficiaries on the ground 

level (Sayer & Wells, 2004). Indeed, when we look at the levels of funding in comparison 

with small successful community conservation project budgets, the failures in spending are 

extensive, almost obscenely wasteful. 

From 1990 to 2004, Kiss (2004) notes that the World Bank has supported 226 conservationrelated 

projects internationally with a total budget of $2.65 billion (from a variety of funding 

sources). In terms of prudent financing, $2.65 billion divided by 226 projects and by 14 years 

gives an average annual project budget of some $837,547. In comparison, two small 

successful community conservation projects annually averaged $12,035 (Community 

Baboon Sanctuary) for six years and $22,367 (Golden Langur Conservation Project in Assam, 

India) for seven years. ICDPs spent over 40 times that of the community conservation 

projects. While it is difficult to assess the total impact of all the World Bank projects relative 

to the two community conservation projects noted, it is clear that the scale of funding is 

dramatically different. 

Despite this financial information, many of the tenets that hold for successful community 

conservation are paradoxical. However, research has shown that rural people do not always 

act rationally and in their own interests (Ariely, 2010). Indian rural villagers were asked to 

do various tasks for three levels of pay. Those who could earn the equivalent of one day’s 

pay or two weeks pay did not differ. However, those who could earn the equivalent of 5 

months pay did the task significantly worse. Ariely (2010) noted that using money to 

motivate people could be counter-intuitive. For tasks requiring cognitive ability, low to 

moderate performance-based incentives can help. But if financial incentives are too high, the 

attention to the reward becomes distracting and creates stress that reduces the level of 

performance. 

2.5 Project implementation 

Planning versus implementation – ICDPs have invested a great deal of time and energy in 

project preparation, often executed by outside experts. These practices increase budgets and 

restrict program flexibility (Sayer & Wells, 2004). Community Conservation projects, in 

contrast, initiate ground-level efforts immediately, with some research, preparation and 

planning integrated into the early stages of a project. From the beginning, formal, expert 

knowledge and information have been united with local expertise throughout every stage of 

our programs as documented in the cases detailed in the present report. 

Flexibility and adaptability – While the ICDP paradigm attempts to reduce uncertainty 

through over-planning and preparation (Sayer & Wells, 2004), the community conservation 

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approach is resilient and capable of adapting to change. Although general planning is 

necessary, too much emphasis on planning and accompanying financial investments have 

plagued ICDPs and often left them with a legacy of inflexibility (Sayer & Wells, 2004). 

Further, it is likely that one disadvantage of the mainstream ICDP model is that it 

establishes unrealistic expectations and levels of resource-access for community members. 

With generalized goals from the initiation of our projects that are adaptable to the features 

of different local projects, we maximize flexibility and influence by relying primarily upon 

local resources, providing models that can be sustained by indigenous and other local 

groups over the long-term. In addition, since resource efficiency was necessary, particularly 

due to our limited funding, we developed contingency plans in the event that opportunistic 

responses were required. 

Funding and project length – Many mainstream ICDPs arguably wasted large amounts of 

money, often because of utilization of templates applied to all situations regardless of 

differences from locale to locale and project to project and also because, seemingly 

paradoxically, promiscuous infusion of resources unsuited to differing project scales has the 

effect of compromising the planning, efficiency, “goodness-of-fit”, and effectiveness of 

programs. Furthermore, resources often fail to impact the intended beneficiaries and their 

resources, directed, instead, into staff and consulting fees, centralized planning, and 

problem-solving and decision-making divorced from local community realities, 

requirements, criteria, and contingencies (Gezon, 1997; Sayer & Wells, 2004). In some cases, 

large amounts of resources were invested in small regions for short periods of time, draining 

critical resources for wider and more judicious efforts (Sayer & Wells, 2004). ICDPs 

generally forecast 3-5 years per project, a standardized time-frame often too brief, on the one 

hand, or unnecessarily extended, on the other, for successful implementation of their goals 

and objectives. Furthermore, ICDPs often fail to project and plan exit strategies, sometimes 

leading to abrupt termination of or unproductively extending involvement with community 

projects (Sayer & Wells, 2004). Our community-based conservation projects, instead, utilize 

modest funding consistent with local economies, maximizing project realism, “goodness-offit”, 

and successful long-term persistence. It is our position that both community 

conservation projects and ICDPs entail costs as well as benefits and that cooperation among 

these networks has the potential to minimize the disadvantages and maximize the 

advantages of all tactics and strategies. 

Project sustainability – While ICDPs were initiated with the hope of financial sustainability 

(Wells et al., 2004), our community conservation projects often have, as a goal, partial 

sustainability. In developing countries, entrance and user fees earned, for example, by 

ecotourism, classes on ethnobotany, or small businesses (e.g., restaurants serving and selling 

traditional foods) might promote village sustainability, pride in efforts supporting local 

conservation programs, expansion of the local economy, and imitation by other groups. 

Success of programs is not necessarily correlated with predictions from conservation 

biology or other scientific approaches, e.g, mathematical modeling, because factors critical to 

short- and long-term successes often arise as spontaneous, condition-dependent events. For 

example, the residents of the Community Baboon Sanctuary suggested a plan for 

sustainability based on mandating residents to take turns as local guides, denoting a more 

mature stage of project implementation as well as a potential template for other community 

conservation projects. With an increase to 6000 tourists, visiting the Community Baboon


Sanctuary, base-level financial sustainability became a reality, with additional programs 

requiring and stimulated by short-term grants (Horwich & Lyon, 1998). In reality, 

community or government protected areas in developing countries still need richer nations 

to infuse conservation efforts with financial and other resources (Wells et al., 2004; Balmford 

& Whitten, 2003); alternatively, other creative ideas such as trust funds or direct payments 

need to be considered (Kiss, 2004). Social sustainability of most Community Conservation, Inc. 

projects occurred because of sufficient, motivated, and prepared social capital combined 

with social and other (e.g., modest and targeted economic) incentives (Uphoff & Langholz, 

1998; Wilson, 2002). Although the Community Baboon Sanctuary has experienced 

significant challenges over time, it has persisted for over twenty-six years because 

community-based conservation became a guiding ethic in the minds and lives of local 

people. When the Community Baboon Santuary reached a turning-point in 1998, a local 

group of women formed the Woman’s Conservation Group to ensure its continuity and 

long-range stability (Horwich et al., 2011). 

Integrating conservation and development - While there is no question that, consistent 

with the mission of ICDPs, conservation and development should be integrated, 

community-based organizations have not developed explicit proposals for how the 

integration should be structured and implemented (Sayer & Wells, 2004). ICDPs maintained 

the false assumption that helping communities develop economically would lead to the 

conservation of natural resources (McShane & Newby, 2004). However, the opposite often 

happened as discussed above. A holistic approach of integrating conservation and 

development is, ceteris patibus, effective because economics is only one of the incentives 

communities respond to. A holistic plan may be a highly productive approach to integrating 

social incentives, development and conservation through facilitating the development of 

networks consisting of ground-level associations (individual and group) of communitybased 

organizations and non-governmental organizations based on trust. 

Financial donors - While donors provide the essential financing for successful projects and 

want their contributions recognized, it is to the benefit of community-based organizations if 

they provide a mentoring rather than a “hands-on” or otherwise controlling posture, 

particularly because community-based operations often require opportunistic responses to 

local conditions and processes that cannot easily be detailed in an application for funding 

and for short-range assessment by funding agencies. Donors to ICDPs often had controlling 

interests in these projects (Sayer & Wells, 2004). Community Conservation, Inc.’s project 

finances have been modest, falling under the “conservation community radar” as noted 

earlier. As a result, donor agencies generally did not attempt to maintain control of their 

investments; however, they rarely publicized successes of the community conservation 

projects, emphasizing, instead, large, “flagship” funding recipients likely to reinforce 

powerful and influential national and international networks. 

Non-governmental organization role – Non-governmental organizations can facilitate the 

empowerment of community-based organizations by providing seed money or by acting as 

a financial and tactical mentor. They can also provide management training for the 

community-based organizations (Horwich et al., 2004). Training would optimally be a 

combination of long-term mentoring combined with, for example, short-term seminars 

(Bernstein, 2005). Although others are skeptical that local communities are the best 

managers of their natural resources (McShane & Newby, 2004), Community Conservation 

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takes the position that indigenous and other local populations are, with training and other 

support (e.g., interactive networking), capable, even ideal, stewards of biodiversity in and 

surrounding their traditional lands because of the nature and degree of their historical 

connections with and investments in these habitat domains. 

A powerful and effective function of non-governmental organizations is to catalyze and 

facilitate communities to protect their natural environment (Horwich, 1990a; Horwich et al., 

2004) and to help them create new community-based organizations whose mission is 

environmental conservation (Agrawal & Gibson, 1999). Non-governmental organizations 

should retreat after the community-based organization develops capacities to function 

independently. However, non-governmental organizations should continuously monitor the 

progress of projects, providing additional advice and support when required and solicited. 

In 1989, subsequent to catalyzing a community conservation project around the Temash 

River in Belize, a biologically important mangrove habitat, the government was not 

supportive of our organization’s contingency plans. By 1997, three years after the zone was 

designated a protected area, our re-catalyzing the project through a meeting involving all 

stakeholders (Producciones de la Hamaca & Community Conservation Consultants, 1998) 

led to a newly formed indigenous Belizean non-governmental organization managed by 

Belize nationals in cooperation with local communities, creating a comprehensive project 

(Horwich, 2005; Caddy et al., 2000). 

2.6 Stimulating regional change from the bottom up 

In an effort to revise the role of ICDPs, it has been suggested that they are well situated to 

work at a landscape scale, integrating with networks at this level (Robinson & Redford, 

2004). However, small community conservation projects should not be excluded from these 

plans since they can be employed at a regional scale by initiating a number of community 

level projects within a region and having them collaborate, eventually unifying them into a 

larger project or federation that would include all of the participating community-based 

organizations, non-governmental organizations and government agencies (Horwich et al., 

2010). An ultimate goal should be integration of networks and clear delineation of functions 

(e.g., managerial, social, political, economic, etc.) at all levels of organization. 

Even though ICDPs often have functioned on the false assumption that national 

governments would embrace the idea of community involvement and pass laws and 

regulations to facilitate the community involvement in natural resource management 

(McShane & Newby, 2004), working with governments and their agencies to revise old and 

develop new policies remains an important objective. In Belize, successes of and publicity 

about the Community Baboon Sanctuary led other motivated communities to adopt that 

model, sometimes with modifications, creating their own conservation project, adapted to 

their own circumstances. Additionally, communities lobbied the Belize government to create 

new sanctuaries, using existing laws to create community-based sanctuaries and protected 

areas and the Belize government eventually adopted a community co-management policy 

(Meerman, 2005). Because the government of Belize recognized significant progress at 

community levels, forestry and fisheries agencies contracted with community-based 

organizations and non-governmental organizations to sign memoranda of understanding. 

Currently, approximately twenty village and town groups have signed or are in the process 

of negotiating such agreements (Young & Horwich, 2007) (Figures 1 & 2).


In Assam, India, the Golden Langur Conservation Project, initiated by Community 

Conservation, Natures Foster and Green Forest Conservation in 1998, targeted the full Indian 

range of the golden langur in western Assam. Through village meetings, seminars and 

general meetings the project brought civil authorities, non-governmental organizations and 

communities together. As communities participated in the project they formed communitybased 

groups leading to conservation contagion. Early on, the project focused on the Manas 

Biosphere, the largest main habitat of the golden langur in India. The project brought 

attention to the Manas Biosphere through a series of four celebrations throughout the 

Biosphere finally resulting in participation of 20,000 and then 35,000 attendees. A number of 

community groups formed and began carrying out forest protection patrols that were later 

funded by the Bodoland Territorial Council. Eventually, the project stimulated the 

organization of a network to meet and discuss protection of the entire Biosphere. 

Collectively, this led to regional change resulting in an increase of forest renewal and an 

increase of the targeted species throughout its Indian range from 1500 to now over 5600 

golden langurs (Horwich et al., 2010, 2011). There are also indications of an increase of both 

the elephant population (Ghosh, 2008b) and the tiger population (Anonymous, 2011). This 

community protection network played a major role in the UNESCO delisting Manas as a 

World Heritage Site in danger. 

3. Catalyzing conservation contagion 

Community conservation practitioners must view themselves as catalysts to stimulate and 

guide indigenous and local people, building their capacity and encouraging them to assume 

responsibility for creating solutions to the natural resource challenges in their communities. 

In the parlance of chemistry, catalysts (e.g., Community Conservation personnel) reduce the 

so-called energy of activation (i.e., ignorance of, passivity to, disinterest in, or resistance to 

conservation initiatives) to stimulate a reaction (e.g., progressive and sustainable 

conservation programs). In our experience, programmatic initiatives have the potential to 

spread to other communities and, often, throughout regions by a process of diffusion that 

we term conservation contagion. Sometimes conservation contagion diffuses more broadly, 

influencing protectionist policies at the country-wide or even international levels. Initially, 

these objectives may require non-governmental organizations to take a prominent, leading 

role. However, as a community becomes increasingly empowered and independent, the 

practitioner-organizers become less and less visible, and members of the community emerge 

as primary leaders. Although our 9-stage social model incorporates a number of catalytic 

factors facilitating the occurrence of conservation contagion processes (e.g., building trust 

with village members, infusing information via local networks), in reality, on the ground, 

they are intimately integrated with the social and cultural process, what Berkes (2004) terms 

“social-ecological systems”. Thus, successful development, management, and stabilization 

of horizontal networks are not automatic, linear processes but complex and dynamic ones 

over time and space. Our case studies from Belize, Namibia, and India exemplify 

individuals, community-based organizations, and non-governmental organizations 

catalyzing indigenous and other local people to participate in conservation initiatives 

followed by conservation contagion resulting from application of our 9-stage model. 

Catalyzing conservation contagion alone does not guarantee program success without 

application of all stages in our model that, although a dynamic process, significantly 

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tempers the programmatic, unstructured, and unpredictable state that Westley et al. (2006) 

call, “getting to maybe”. 

Conservation contagion may sound like a fuzzy or murky concept, but, when one sees the 

results, it is an important phenomenon that deserves study and the awareness of 

community conservation practitioners. The word contagion has both a negative meaning, as 

in the spreading of disease, and a more neutral meaning, as the rapid communication of an 

influence. In a sense, the phenomenon of trends that are communicated rapidly may be 

thought to spread like epidemics with three characteristics: 1) contagiousness, 2) small 

causes can have large effects and 3) change happens not gradually but at one dramatic 

moment (Gladwell, 2002). Given these characteristics, research on such phenomena would 

be difficult. However, studies on human networks (Christakis & Fowler, 2009) may give us 

some insight into conservation contagion. 

Since we have not carried out any research on the communications, social networks, or 

person-to-person interactions important in creating instances of conservation contagion we 

have observed, we can give only a start to understanding conservation contagion by listing 

and discussing some anecdotal observations that both seem to play a role in the 

phenomenon and show that contagion is occurring. We have identified eight facets: 1) 

copying of methods to initiate other projects, 2) person-to-person contacts, 3) presentations 

and responses to them, 4) formation of new community conservation organizations, 5) 

requests by communities or community groups to join an existing project, 6) knowledge of a 

project and requests for help by other communities, 7) large crowds at events and 8) creating 

project publicity within the country. Examples of some of these will be described and 

discussed in the following sections on Belize and Assam, India. 

4. Examples of successful community conservation projects 

4.1 Belize 

In the early 1980’s, cooperative organization and planning among Horwich, Lyon, and 

members of a community in Belize District, northwest of Belize City, led to the 

establishment of the Community Baboon Sanctuary dedicated to preservation of the 

endangered black howler monkey (Alouatta pigra) and its moist tropical forest habitat 

(Horwich & Lyon, 1988). This project led to development of a local ecotourism industry 

(Horwich et al., 1993). By 1990, the Community Baboon Sanctuary spawned creation of the 

country’s first rural museum, managed semi-independently under the umbrella of the Belize 

Audubon Society (BAS). At this time, Horwich and Lyon formed Community Conservation 

(Horwich 1990a, 2005; Horwich & Lyon, 1995, 1998, 1999; Young & Horwich, 2007), 

initiating what would become an international network of ground-level, bottom-up 

programs. 

In the Community Baboon Sanctuary project, Community Conservation and other nongovernmental 

organizations functioned as catalysts, first by initiating locally-based 

programs and, subsequently, by educating village leaders about the short-, mid-and longrange 

value of biodiversity preservation. At a later stage, community leaders employed their 

legitimate authority to influence village residents and to disseminate information, often 

through delegates. Once this stage successfully affected participation in conservation efforts 

and established community-based infrastructures devoted to conservation, committed 

villagers were recruited to serve as workers dedicated to managing and sustaining 

conservation programs (e.g., as office workers or guards to eject poachers from protected


lands). Outside organizers (e.g., Community Conservation personnel) were subsequently freed 

to devote attention to additional community-based conservation enterprises while 

continuing to maintain contact with and provide support to villages when required and 

solicited. In some cases, a small cadre of organizers maintained a physical presence in or 

near communities with viable conservation infrastructures, continuing to serve as 

facilitators and advisors, at least over the short term. In other instances in Belize, former 

organizers remained in villages as researchers or employees. 

In the 1990s, through a process of conservation contagion, formal publicity and informal 

dissemination of information about the Community Baboon Sanctuary stimulated and 

influenced many rural Belizean communities to cooperate, exerting pressure on central 

government for participation in their conservation initiatives. These events ultimately 

influenced the Belizean government to create a series of protected areas (Young & Horwich, 

2007) as well as a country-wide network of Special Development Areas including 

indigenous and local groups inhabiting the newly protected landscapes (McGill, 1994). In 

1994, the Belize government coordinated an ecotourism seminar (Vincent, 1994), published a 

community tourism booklet, and created a video on community-based tourism. In addition, 

the Belize Departments of Forestry and Fisheries began to negotiate informal and, later, 

formal co-management agreements with communities (Young & Horwich, 2007). 

Cooperative development of the Community Baboon Sanctuary catalyzed a series of 

reactions (conservation contagion from local level to central government: a “vertical 

network”) arising from our community-based conservation model. By 1991, communities 

led by St. Margaret’s Village, adjacent to Five Blues Lake, lobbied the government of Belize 

to create Five Blues Lake National Park and other protected areas in response to the 

country’s rapidly developing ecotourism industry (Young & Horwich, 2007). In 1992, the 

Minister of Tourism, Glenn Godfrey, embraced the Gales Point Project, subsequently 

leading government to include it in a new Special Development Area, a step preliminary to 

creation of a protected area in the region (McGill, 1994). 

In 1997, the Inuit Council of Canada visited Belize’s southern Toledo District, an area of 

Mayan concentration, to coordinate a seminar on co-management with the Kekchi Council 

of Belize (an indigenous Mayan organization), citing the Community Baboon Sanctuary as 

an example of community co-management. Eight years earlier in 1989, however, Community 

Conservation gathered signatures of local governments in three Toledo villages to initiate a 

cooperative plan for a Toledo Biosphere Reserve, composed of the Temash River, an 

important mangrove habitat, the Columbia Forest Reserve, and the Sapodilla Cayes 

(Horwich, 1990b). At that time, there was community support for the plan, but regional 

politicians showed no interest. 

Following the central government’s creation of the Sarstoon-Temash National Park in 1994, 

independent of input by communities impacted by the plan, Horwich traveled to Toledo in 

1997 to re-catalyze and revive a component of the Toledo Biosphere initiative. A strategy 

was devised whereby Horwich would work with Judy Lumb, a resident of Belize with ties 

to the Garifuna community in southern Belize, to organize a conference on community comanagement 

for the Sarstoon-Temash National Park stakeholders (see Producciones de la 

Hamaca & Community Conservation Consultants, 1998). From that event, the Sarstoon- 

Temash Institute of Indigenous Management (SATIIM), an indigenous-based nongovernmental 

organization created by Mayans and Garifuna, was developed to coordinate 

impacted communities and to manage the National Park using our model. 

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Based on the success and aftermath of the conference, Horwich and Lyon obtained a United 

Nations Development Project (UNDP) grant for the Belize-based Protected Areas 

Conservation Trust (PACT, 1998) to create a community co-management park system. In 

contrast to the Namibian case described below, PACT’s steering committee significantly 

modified project goals, leading to failure and abandonment of plans for the initiative 

(Catzim, 2002). However, a concrete result of our community-based conservation efforts in 

Belize was the central government’s eventual adoption of community co-management as 

national policy (Meerman, 2005) and signing of agreements with communities to co-manage 

at least a dozen protected areas distributed throughout Belize - both terrestrial and marine 

(Figure 1). Thus, implementation of the Community Conservation model generated the 

process of a conservation contagion that proved successful beyond anyone’s projections. 

Eventually, the Government of Belize supported enhancement of local natural resource 

management and production of systemic changes in conservation policy at the national 

level. However, it should also be noted that minimal government resources and political 

commitment (Catzim, 2002) have left many communities with insufficient support and no 

interconnected co-management system despite their interest in and will to proceed with 

conservation initiatives. Thus conservation contagion must be coupled with appropriate and 

sustainable support to achieve maximum impact. 

Fig. 1. Map of Belize showing distribution of the 12 community co-managed protected areas 

(dots) and the Community Baboon Sanctuary (arrow)


Figure 2 shows a synopsis of the catalyzing influences and social network of interactions 

that were occurring in Belize that led to the conservation contagion (for details, see Young & 

Horwich, 2007). The Community Baboon Sanctuary was initiated in 1985 in affiliation with 

the Belize Audubon Society, the latter having been commissioned by the Belizean 

government in 1984 to administer the existing park system. This partnership led to meetings 

between the Community Baboon Sanctuary Manager, the late Fallet Young, and the Director 

of the Cockscomb Basin Wildlife Sanctuary, Ernesto Saqui, an employee of the Belize 

Audubon Society. With the Community Baboon Sanctuary becoming widely publicized 

nationally and internationally, it had a conservation influence on rural communities 

country-wide (Government of Belize, 1998). In 1988, Young addressed the village of Monkey 

River, eventually catalyzing that community to move the government to create Payne’s 

Creek Wildlife Sanctuary, that had a significant population of howlers (Horwich et al., 1993). 

By the early 1990s, three other projects were developed using our model: Slate Creek and 

Siete Milas village project in 1991 in the Mayan Mountains (Bevis & Bevis, 1991), a sea turtle 

nesting project initiated on Ambergris Cay by Greg Smith which was moved to Gales Point 

in 1992 when we initiated the Gales Point Manatee project (Lyon & Horwich, 1996); and, the 

Community Hicatee (turtle) Sanctuary along the Sibun River in 1994. Siete Milas later 

worked with Itzamna, a community-based organization co-managing the Elio Panti 

National Park in the Mayan Mountains. 

Many rural communities in Belize, seeing the efforts of the Community Baboon Sanctuary 

villages, realized that they too could participate in the growing conservation/ecotourism 

movement and stimulated the government to create protected areas adjacent to their 

communities (Horwich & Lyon, 1999). St. Margaret's village initiated the establishment of 

Five Blues Lake National Park on Earth Day 1991 (Horwich & Lyon, 1999). We tried 

unsuccessfully to initiate a community-based project that included the Sarstoon-Temash 

mangrove forest area in 1989. Then, in 1994, it became a National Park without community 

input. The government held an ecotourism conference in 1994 and produced a movie and a 

booklet on community ecotourism (Horwich & Lyon, 1999). In 1995, the Association of 

Friends of 5 Blues Lake National Park signed a formal co-management agreement with the 

Government of Belize. In 1997, the Inuit Circumpolar Conference sponsored a comanagement 

workshop in Toledo featuring the Community Baboon Sanctuary as an 

example of co-management. Later that year, Community Conservation initiated the process for 

a stakeholders workshop for the Sarstoon-Temash National Park (Produciones de la 

Hamaca & Community Conservation Consultants, 1998). 

Stimulated by that conference, Community Conservation developed a proposal for a 

community co-managed park system that was to include Freshwater Creek Forest 

Reserve, Five Blues Lake National Park, Gales Point Manatee and Aguacaliente Wildlife 

Sanctuary (PACT, 1998). As a result of this project, when the Government of Belize 

created the Protected Areas System Plan in 2005 it had a section on community comanagement. 

They presented the plan to the conservation community in early 2006 

(Government of Belize, 2005). These actions were some that initiated the conservation 

contagion. Figure 2 gives a visual representation of a complex network that contributed to 

the conservation contagion that resulted throughout the small nation of Belize. Figure 2 

demonstrates the complexity of social connections that occurred over the years 

contributing to the conservation contagion. 

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Government – GoB - Government of Belize, For - Forestry Department, Fish - Fishery Department, 

PACT Coman - Protected Areas Conservation Trust co-management grant, Cmpol – Co-management 

policy, Eco Con - Ecotourism Conference, 

NGOs – CC – Community Conservation, BAS - Belize Audubon Society, Inuit Conf-Inuit Conference, Sat - 

SATIIM, Tast - Taste, Tid - TIDE, 

CBOs/PAs - Ag – Aguacaliente Wildlife Sanctuary, BB – Billy Barquedier, BC - Bacalar Chico, CB – 

Cockscomb Basin Wildlife Sanctuary, CBS – Community Baboon Sanctuary, Cc – Cay Caulker, CHS – 

Community Hickatee Sanctuary, FC – Freshwater Creek Forest Reserve, 5B – 5 Blues Lake National 

Park, GG – Gra Gra Lagoon, GP – Gales Point, GS – Gladen Spit, MB - Mayflower Bocwina National 

Park, MR –Monkey River, NK -- Noj Kaax Meen Eligio Panti National Park, PC - Paynes Creek, RB - Rio 

Blanco National Park, SaC - Sapadilla Cayes, SwC – Swallow Cay, SlC- Slate Creek, SpC - Spanish Creek 

Wildlife Sanctuary, ST- Sea Turtle Project, S-T – Sarstoon-Temash National Park. 

Fig. 2. Network connections of Community Conservation and their relationships with 

community-based organizations and Protected Areas, non-governmental organizations and 

government organizations. 

4.2 Namibia 

In the early 1980s in northern Namibia, wildlife was severely depleted by poaching (Hoole, 

2010). At the same time that the Community Baboon Sanctuary was developed in Belize, a 

non-governmental organization in Namibia, the Namibian Wildlife Trust, appointed Garth 

Owen-Smith, a former government game ranger, to respond to the poaching crisis in the 

northern area of the country by collaborating with village headmen who shared concern for 

the loss of wildlife (Hoole, 2010; Jones, 2001). Using village contacts from his prior career, 

Owen-Smith began to establish relationships with local headmen. Working with the 

government conservator, Chris Eyre and in cooperation with village leaders, Owen-Smith


instituted informal community game guard protection of wildlife, leading, over time, to 

increased population densities of large mammals threatened by poaching (Jones, 2001). In 

the mid-1980s, Owen-Smith and anthropologist Margaret Jacobsohn negotiated agreements 

with safari operators to pay the community a US$5 fee per tourist visiting the area to view 

game (Jones, 2001). In both Belize and Namibia, conservationists initiated their efforts by 

responding to environmental problems in discrete, manageable regions, engaging with 

stakeholders as allies having common interests and goals to preserve biodiversity. These 

associations also addressed resistance and other challenges arising within, between, and 

outside of village networks. 

By 1989-1991 a community game guard program was firmly established, and Owen-Smith 

and Jacobsohn moved on to initiate a second program in northeastern Namibia (Jones, 

2001). While projects in Belize had informal support from local politicians, there was no 

formal support from government because lands were privately owned by subsistence 

farmers. Their practices, especially “milpa” (slash-and-burn) or clear-cutting methods of 

land clearance, were often destructive to habitat and organisms inhabiting forests. In 

Namibia, before independence, the South African government viewed Owen-Smith’s liaison 

with black communities suspiciously, ultimately terminating support for his programs 

(Jones, 2001). By 1990, however, the Namibian initiative proved to be on the right side of 

history (Westley et al., 2006) when the country gained independence, extending rights over 

wildlife to the majority black government, thereby terminating private ownership of land by 

white farmers (Hoole, 2010). The post-independence government engaged the nongovernmental 

organization, Integrated Rural Development and Nature Conservation 

(IRDNC), formed by Owen-Smith and Jacobsohn, to develop a community-based natural 

resource management program (Hoole, 2010). This was similar to the process transpiring in 

Belize at approximately the same time. 

The pioneering projects in Belize and Namibia had similar trajectories, with conservationists 

and small non-governmental organizations acting as catalysts, initiating locally-based 

programs and educating villagers. In both cases, through disseminated information initial 

projects led to broad-based regional conservation efforts. This seemingly paradoxical role is 

the foundation of reliable and replicable tactics and strategies of community conservation, 

including features characteristic of Community Conservation’s 9-stage model. 

Namibian independence in 1990 removed the obstructionist South African regime, and the 

new Namibian government invited Owen-Smith and Jacobsohn to resume and to expand 

their work. Namibia’s community-based natural resource management program had been 

modeled after other African initiatives such as Zimbabwe’s Communal Areas Program for 

Indigenous Resources (CAMPFIRE) and Zambia’s Administrative Management Design for 

Game Management Areas program (ADMADE) (Hoole, 2010). Zimbabwe’s conservation 

efforts, however, were obstructed by resistance at lower levels of government, a condition 

opposite to that in Namibia (Murphree, 2005). With major financial support from World 

Wildlife Fund (WWF) and the United States Agency for International Development (USAID, 

2005) as well as backing from the central government, Namibia’s Communal Conservancy 

Program caught on rapidly, exhibiting the conservation contagion seen in Belize and Assam, 

India (Horwich et al., 2010). By 2009, Namibia was managing 59 registered conservancies 

cumulatively, covering 12.2 million hectares (Figure 3; 2009 map from www.nacso.org.na). 

Wildlife populations in the northeast conservancy regions increased markedly and money 

was generated from eco-projects (NACSO, 2009) with long-term potential for financial 

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sustainability. While the Namibian case demonstrates that our model for the 

implementation of reliable community conservation projects is not the only one capable of 

sustained success, the Community Conservation template is explicitly detailed by stages 

characteristic of all community-based conservation initiatives. We base this statement upon 

our documentation of the methods and outcomes of other programs compared and 

contrasted to our own model. 

Fig. 3. Map of Namibia’s 59 Community Conservancies in 2009 (redrawn from 2009 map on 

www.nacso.org.na). 

4.3 Assam, India 

The Golden Langur Conservation Project was initiated in 1998 to protect the Manas 

Biosphere Reserve and the golden langur (Trachypithecus geei), a folivorous monkey. Forests 

of the Manas Biosphere Reserve have been threatened by illegal logging since the early 

1990s, and, in the last 15 years, approximately one third to one half of the three reserve 

forests (~200,000 ha) making up the reserve (Ripu, Chirrang, and Manas), were deforested 

by clear-cutting (Bose & Horwich, unpublished data). Based on transect surveys, including 

interviews with residents in and near the reserves, these three reserve forests, a group of 

southern isolated reserve forests, and the Royal Manas Sanctuary on Bhutan’s northern 

border are presently the primary range of the golden langur. Understanding the potential of 

conservation contagion to create regional change, Community Conservation included local 

communities, non-governmental organizations, and agencies of the governing body of 

Assam in planning meetings and seminars to discuss the conservation project. While the 

Assam Forestry Department showed some interest in the effort, it was not until a new tribal 

government, the Bodoland Territorial Council (BTC), was formed in 2004, that community 

conservation was embraced, leading rapidly to the contagious spread of support for our 

proposals noted earlier, ultimately attracting crowds of 20,000 to 35,000 people (Figure 4) to 

our informational programs (Horwich et al., 2010).


Fig. 4. Crowd Attending the Manas Biosphere Celebration at Ultapani 

An important aspect of the Assam project is the manner in which a second tier of catalysis 

arose from activities of community-based organizations and non-governmental 

organizations. In 2006, Forest Protection Forces were created within the Manas Biosphere 

Reserve with support from the Bodoland Territorial Council. Subsequently, conservation 

contagion gained momentum, culminating in cooperative management by 14 community 

groups forming the Unified Forest Conservation Network of Bodoland, supported by local 

and governmental networks (Horwich and Bose, personal observation). A schematic 

representation of connections between the three forests making up the reserve is presented 

in Figure 5. The lower circular configuration representing the Unified Forest Conservation 

Network of Bodoland that protects the Biosphere mimics the military squad network 

topology of Christakis and Fowler (2009). This ring network topology experimentally was 

shown to facilitate problem solving (Christakis & Fowler, 2009). 

Similarly, contagion occurred around the Kakoijana Reserve Forest where community 

members inhabiting or proximal to the reserve forest, housing a small golden langur 

population, were attracted to the conservation project. Today, by contagious processes, 28 

communities surrounding Kakoijana Reserve Forest (Figure 6) have created two federations 

(Nature Guard, Green Conservation Federation) to protect the 17km 2 reserve (Bose & 

Horwich, personal observation). These are represented by the two upper, left spheres in 

Figure 5. Most importantly, these community protection efforts resulted in an increased 

Indian golden langur population from 1500 to almost 5600 langurs (Figure 7). The Kakoijana 

Reserve Forest increased its forest from 5% to 70-80% canopy (Figure 8) accompanied by an 

increase of golden langurs from less than 100 to over 500 langurs (Horwich et al., 2010; 

Horwich et al., 2011, Bose and Horwich unpublished data). 

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Government: As – Assam Forest Department, B – Bodoland Territorial Council 

NGOs: Aa – Aaranyak, C – Community Conservation, G – Green Heart Nature Club, N – Natures Foster, 

CBOs (black spheres): Biodiversity Conservation Society, Green Forest Conservation (larger black 

sphere), Manas Agrang Society, Manas Bhuyapara Conservation and Ecotourism Society, Manas 

Maozigendri Ecotourism Society, Manas Souci Khongar Ecotourism Society, New Horizon, Panbari 

Manas National Park Protection and Ecotourism Socierty, Raigajli Ecotourism and Social Welfare 

Society, Swarnkwr Mithinga Onsai Afut and four other unnamed community organizations. 

CBOs around Kakoijana Reserve Forests (black spheres upper left) Green Conservation Federation, 

Nature Guard 

Fig. 5. Network Connections Established by the Golden Langur Conservation Project in 

Assam, India Between Government, Non-Government and Community Organizations.


Fig. 6. Map of Kakoijana Reserve Forest surrounded by 28 villages composing the Green 

Conservation Federation and Nature Guard that protect the reserve forest 

Fig. 7. Graph of Indian Golden Langur Population estimates indicating population increase 

following initiation of the Golden Langur Conservation Project (data from Gee, 1964 for 

1960; Srivastava, et al. 2001 for 1997; Choudhury, 2002 for 2000; Ghosh, 2008a & b for 2008; 

Anonymous, 2009, Bose, 2007, 2008 (unpublished data), Ghosh, 2008a for 2009) 

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Fig. 8. Vegetation maps of Kakoijana Reserve Forest in 1996 and 2008 showing an increase in 

canopy cover from 5% to 70% as a result of community reforestation and protection.


In the Assam project, local villagers were trained as community organizers, community 

researchers, and community para-veterinarians (Figure 9). Through expansion of many 

programs and capacity-building, the project has influenced conservation efforts at broader 

regional scales, similar to the cases described for Belize and Namibia. 

Fig. 9. Veterinarian Sarma training para-veterinarians near Kakoijana Reserve Forest 

For example, one trained community organizer initiated self-help groups coordinating 

environmental awareness sessions around Manas National Park for the Pygmy Hog (Porcula 

salvania) Project (Bose and Horwich, personal observation). He is also organizing 

communities for the Assam Haathi Project whose goal is to mediate human-elephant 

conflict along the southern basin of the Brahmaputra River in Golpara District. Another 

local conservationist, trained in cutting-edge primate census methods by Community 

Conservation personnel, worked with the Tripura State Forest Department to census Phayre’s 

langurs (Trachypithecus phayrei), leaf-eating monkeys related to golden langurs. Members of 

communities in these areas gained capacity and experience and were able to enhance their 

employability as semi-professional conservationists, disseminating project goals and 

procedures throughout the region. They currently perform a role similar to trained parataxonomists 

in Papua New Guinea, in Guyana, and elsewhere (see Basset et al., 2000). In 

other locales, community members have been trained by a veterinarian as paraveterinarians 

(Figure 9), similar to bare-foot doctors supporting the Hen Can Change a Man 

Program organized to increase the income of villagers engaged in poultry husbandry 

around the Kakoijana Reserve Forest. These examples of effective educational initiatives 

reinforce our suggestion that all community-based conservation programs, ceteris paribus, 

are characterized by features similar to our 9-stage model because, due to psychological, 

economic, political, social and other comparable constraints, there are a restricted number of 

tactics and strategies likely to lead to sustained success. Training and capacity-building for 

both individuals and non-governmental organizations, then, have been integral and critical 

parts of expansion of community conservation programs on a range of fronts. Following the 

definition of catalysis presented above, energy inputs required to activate and effect initial 

stages of community-based conservation were decreased as a result of increased efficiency 

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resulting from skill- and evidence-based organization. The broad regional success in Assam 

and elsewhere is dependent upon community conservation contagion traceable back to a 

modest, small-scale starting point in local areas. Our model, then, schematizes the trajectory 

of successful bottom-up conservation programs, creating stable “horizontal networks” 

capable of developing productive associations and activities with components of “vertical 

networks” (Berkes, 2004). 

5. Lessons learned from practice 

There is a current dearth of articles comparing and contrasting community-based 

conservation, community conservation, collaborative management, ICDPs, etc. These terms 

have, effectively, become buzz-words with biologists and sociologists referring to "new 

conservation" models incorporating community-based conservation practices. With so many 

different approaches, it has become difficult to know what works best and what doesn’t. 

First hand practical experience with 23 small on-going community conservation projects 

over the past 26 years have given us insights about what factors are most critical for 

achieving program success (Table 1). 

Rural communities are a major resource for a new breed of conservationists. Since they live 

where the highest concentration of natural resources exists, can easily exploit them with 

traditional practices, have additional knowledge about their habitats and people, and are 

experts in their own right, mainstream conservationists must create incentives for 

indigenous and other local people in order that their talents, knowledge, expectations, 

desires, and persistence can be utilized for their own benefit and that of the forests and 

wildlife. Conservationists can use existing rural institutions or can help rural people create 

new institutions to deal with modern problems (Agrawal and Gibson, 1999). 

To be most effective, practitioners need to be catalysts of community conservation, helping 

community-based organizations to form in local communities, monitoring them, building 

capacity and re-catalyzing them when needed. When initiating a community conservation 

project, a well-crafted proposal may have major power to interest community members, 

government officials, donors, and other stakeholders to become involved. Enlisting a 

support coordinator and seed money are important to get a young project started and to 

help a fledgling community-based organization. 

Seed money and money for simple project maintenance is essential. Looking for a simple 

mechanism for partial financial sustainability is very difficult, but, when found, adds a great 

deal toward continuity and longevity, preventing community discouragement. Using a 

guiding, entrance, or membership fee for protected areas can create a minimal budget to 

keep the project going. Community ecotourism, while a double-edged sword, can provide 

potential in this direction. 

While financial sustainability is to be strived for, project continuity and longevity depend on 

creating social incentives. Properly equipped volunteers are very important to keep 

incentives going. Discontinuity of funds or other incentives discourages community 

members. Although projects may pass through ups and downs, if the original incentives 

were good, there is a recycling effect; a project is only a failure if it totally disappears. 

Establishing model projects is extremely important to encourage other projects and can have 

a regional effect. A replication of techniques from one project to another helps to propagate 

ideas and models.


Connection to land is important to rural residents and ownership contributes greatly to this. 

Thus, historical land tenure may be problematic or can be a source for positive conservation 

alternatives. While private landowners can make important contributions, encouraging 

communal and tribal or clan ownership, where in place, has a greater relevance for project 

longevity and long-term environmental protection, especially if there is a strong formal or 

informal institution in place. Since private landownership has its limitations, formal land 

protection mechanisms for private lands can create a long-lasting effect. There is a difference 

between land ownership and management and community groups can achieve some 

benefits from management alone. Land use planning provides an important vision for 

future conservation and protection. 

While governments are often slow to move, may be corrupt, or can hamper a project, there 

is a great need for balanced community-government communication. Too much 

government reduces community initiative. However, effective, strong laws can provide 

strong legal land protection. Additionally, once government sees the advantage to involving 

communities, their tactical and financial support can provide project sustainability. Comanagement 

of protected areas can thus be an important balanced conservation solution. 

Non-governmental organizations have an important role (Agrawal & Gibson, 1999) as 

intermediaries and community trainers and educators. They can provide an initial open 

communicatory link between communities and government. Often, they can provide 

expertise and motivation that governments cannot. Non-governmental organizations can 

provide networking to connect communities to the resources they need to develop a 

stronger community-based organization. In any effective system that involves communities, 

non-governmental organizations, and government, there is a need to strengthen all partners, 

especially the weaker ones. 

6. Policy implications for the future 

Comprehensive, multi-scale, and resilient policies are required to respond effectively to 

social, political, and economic networks facilitating successful catalytic incidents and the 

emergence of conservation contagion. In this chapter, we have documented a resilient 

community-based model whose successes have resulted from the expansion of existing 

ground-level networks into a broader horizontal matrix by capitalizing on catalytic events 

and subsequent contagious diffusion. These tactics and strategies operate along with 

indigenous and other local values, beliefs, folkways, resource use, and political structures as 

well as social, cultural, and economic activities in order to capitalize on opportunities to 

build complex horizontal networks (see Berkes, 2004, Christakis and Fowler, 2009). In these 

cases, we have observed that a critical mass of complexity (e.g., by increasing interindividual 

and multi-scale interactions) may lead to a threshold response at which a tipping 

point is reached, leading from one state of relative equilibrium to another (Gladwell, 2002). 

These non-linear events may have negative as well as positive outcomes from the 

perspective of conservation goals and objectives. Indeed, some studies suggest that 

increased complexity may lead some community members to resist or abandon active 

engagement with conservation initiatives (see Hoare and du Toit 1999 in Berkes, 2004), a 

dynamic state of affairs providing challenges to community-based organizations but capable 

of being addressed by components of our model applied to community-based organization 

programs by trained facilitators. 

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Community-based initiatives and their practitioners deliver specialized, reliable tactics and 

strategies to indigenous and other local communities for empowerment of community 

networks and, ultimately, transfer of power to these entities. Paramount to success is 

remembering that indigenous and other local communities are necessary components for 

solutions to the worldwide biodiversity crisis. We have demonstrated that application of our 

model and treating indigenous and other stakeholders with consistent and reliable respect 

and humility, valuing their folkways, habits, and cultures, their knowledge, leadership 

skills, and significant expertise without patronizing attitudes and behaviors, a majority of 

local stakeholders become willing and active participants in conservation programs (see 

Persha et al., 2011). If these factors are obtained, trusting, enduring relationships between 

horizontal and vertical networks can be built having the potential to establish multi-scale, 

multidimensional, complex, and dynamic associations, incorporating indigenous and other 

local people as co-conservationists (Haldane &May, 2011; Persha et al., 2011). In addition to 

consolidating horizontal and vertical networks, Community Conservation’s mission and the 

successful implementation of our 9-stage model that we extend to other community-based 

organizations, has promoted the success of our programs by providing incentives, fostering 

pride, advancing program self-sufficiency, and effecting sustainable project ownership. 

Our case studies demonstrate applications of the 9-stage paradigm that have influenced 

non-governmental organizations and/or central governments to adopt community-based 

conservation as policy, an outcome with the potential to impact conventional top-down 

environmental procedures linking horizontal with vertical networks. It is important to 

emphasize that successful implementation of our strategies depends upon careful, 

calculated planning resulting from the training and expertise of Community Conservation 

personnel. Equally important are the social and other skills, attendant traits, and motivation 

of indigenous leaders and other local individuals providing the initial commitment and 

impetus for successful implementation of our model and, later, cooperatively designed 

plans. Further, the flexibility and resilience of our 9-stage paradigm permits accommodation 

and adjustment to a range of local conditions, contingencies best evaluated stage by stage 

throughout the dynamic process of the multi-stage implementation of programs. 

An example of the flexibility and resilience of our 9-stage plan involves the different 

contexts and challenges encountered when community members of low socioeconomic rank 

prove, initially, to be the most committed to implementation of conservation projects. In our 

experience, when the principal innovators and drivers of change in a target community are 

of relatively low socioeconomic or other status compared to, for example, group leaders, a 

longer period of time is required for the broader community and region to absorb, integrate, 

and respond to new ideas and ultimate project success. The cases we describe emphasize the 

need to adopt a flexible, resilient, and holistic systems perspective including social, 

economic, and political components of values and practices of indigenous and other local 

agents’ relationships to their environment given the short-, mid- and long-range goals and 

objectives. 

Currently, indigenous or other local people with lower socioeconomic status, including 

some men, most women, and youth, are assuming key roles in Community Conservation’s 

community-based conservation projects. Large international non-governmental 

organizations may see the potential of using a catalyst method by adopting our 9-stage 

paradigm as a component of their conservation policy. Incorporating plans such as


Community Conservation’s tactics and strategies would permit non-governmental 

organizations, central governments, and other entities to modify their methods, developing 

paradigms representative of their own circumstances and complying with the goals, 

objectives, and philosophies of their particular organizations. For example, World Wildlife 

Fund began innovative community conservation projects in southern Madagascar and 

Namibia. Conservation International is supporting the Tree Kangaroo Conservation in 

Papua New Guinea (Ancrenaz et al., 2007). The Nature Conservancy has shown innovative 

community projects in Papua New Guinea and the Solomon Islands (Mayer and Brown, 

2007). Similarly, the Wildlife Conservation Society works with local communities to protect 

the critically endangered Cross River Gorilla (Gorilla gorilla diehli) limited to a restricted area 

along the Nigerian-Cameroon border (Nicholas et al., 2010). Successes can be documented; 

however, community conservation projects may not always be sufficiently comprehensive, 

ambitious, or efficient in time and energy to persuade non-governmental organizations with 

large budgets to invest in relatively small-scale conservation activities (Brockington et al., 

2008). Furthermore, in our experience, large-scale non-governmental organizations rarely 

advertise their small community conservation accomplishments, and, if this policy were 

reversed, community-based conservation would likely be poised to gain a significant degree 

of legitimacy with non-governmental organizations and other entities in vertical networks. 

Attempts to evaluate the outputs and successes of historical policies and practices of 

protective programs and the field of conservation biology suggest that these entities lag 

behind most other policy fields because of their resistance to and slow incorporation of 

participatory philosophy and models (Ferraro & Pattanayak, 2006; also see Milner-Gulland 

et al., 2010). Yet there is growing recent data that show the effects of community and 

indigenous projects in reducing deforestation. Recent studies by Porter-Bollard et al. (2011), 

comparing 40 tropical protected areas to 33 community managed forests, indicated that the 

community managed forests showed lower and less variable annual deforestation than the 

protected areas. Soares-Filho et al. (2010), focusing on Brazil’s recent push to reduce 

Amazonian deforestation by expanding the Amazon protected area network of 1.9 million 

km2, showed a generalized inhibitory effect on deforestation in protected areas which 

would greatly reduce carbon emissions which in turn would effect climate change. 

However, they used a broad definition of protected areas. Looking closer at these 595 

protected areas, only 90 or 15% were in the strictly protected category used by Porter- 

Bollard et al. (2011) while 494 or 83% were in the sustainable use (176) and indigenous lands 

(318) categories. Thus the positive results are mainly due to sustained use and indigenous 

lands, indicating the importance of community conservation. 

New conservation policies incorporate and embrace community conservation as one 

approach to biodiversity and ecosystem conservation, necessitating involvement and active 

participation by horizontal networks, including community and other stakeholder entities, 

community-based organizations, regional and national agencies, central governments, nongovernmental 

organizations, conservation biologists, and other conservation practitioners 

(e.g., forest guards and rangers; scientists conducting research in habitat countries and 

hotspots) and stakeholders. Connecting horizontal and vertical networks among 

conservation entities has the potential to increase network diversity, scale, and resilience, 

maximizing likelihoods of success of conservation programs. Such connectivity also has the 

potential to compensate for limitations of agents and entities in both horizontal and vertical 

networks since bottom-up entities have generally lacked the resources to invest in the 

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creation, management, and sustainability of global organizations or in sustaining these 

networks if and when established. Higher-order, protectionist networks with “fortress 

conservation” policies have traditionally been averse to incorporating the interests, values, 

and bodies of indigenous and other local agents and units (informal and formal) into the 

goals and objectives of their mission statements and programs. This state of affairs has been 

maintained in large part because the morality, ethics, beliefs, attitudes, and philosophy of 

local people’s relationships to their natural resources has conflicted with those of vertical 

networks’ historical prioritization of preservation over sustainable use of forests, 

waterways, etc. (Berkes, 2004). When multi-level networks cooperate and establish metanetworks 

designed to resolve differences of vision and to consolidate communication 

networks and integrated representation of all stakeholders, differences and potential 

conflicts can be minimized through a multi-scale and multidimensional system sensitive to 

the interests and objectives of all network units over time and space. 


We thank all of the community members and non-governmental organizations from the 

countries in which we worked, especially Belize and India. They are the heroes and doers. 

We are only the catalysts. We especially acknowledge the input and expertise of Dr. Colin, 

Jesse, and the late Fallett Young in Belize, whose support and encouragement were critical 

as we implemented our first international project. Our gratitude is also extended to Sadahib 

Senn, Raju Das and other members of Nature’s Foster, the late Rajen Islari, and others from 

Green Forest Conservation, Bablu Dey and others from Green Heart Nature Club, Mahesh 

Moshahary and others from New Horizon, Firoz Ahmed and Aaranyak, and the members 

of the Biodiversity Conservation Society, the Raigajli Ecotourism and Social Welfare Society, 

the Panbari Manas National Park Protection and Ecotourism Society, the Swarnkwr 

Mithinga Onsai Afut, the Manas Maozigendri Ecotourism Society, Manas Bhuyapara 

Conservation and Ecotourism Society, the Manas Souchi Khongkar Ecotourism Society, the 

Manas Agrang Society, the Green Conservation Federation, and the Nature Guard of 

Chiponshila for making the Golden Langur Conservation Project a major success. We also 

are grateful to Minister of the Environment Kampa Borgoyari and the Bodoland Territorial 

Council for their support. The Margot Marsh Biodiversity Foundation, Conservation 

International, Primate Conservation, Inc., World Wildlife Fund, and the US Fish & Wildlife 

Service provided financial support, and we appreciate the generosity of these organizations. 

Thanks, also, to Ashley Morga for critiquing the manuscript. 

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15 

Efficiency of the Strategies to Prevent 

and Mitigate the Deforestation in Costa Rica 


Óscar M. Chaves 

Instituto para la Conservación y el Desarrollo Sostenible (INCODESO), San José 

Costa Rica 

During the 2000-2010 period, worldwide deforestation, mainly due to conversion of forests 

to agricultural lands, was responsible for the loss of 5.2 million ha of forest per year or 140 

km 2 of forest per day (Food and Agriculture Organization of the United Nations [FAO], 

2010a). During this same period, Central America loss 248,000 ha of forest per year and, with 

the exception of Costa Rica, the forest cover continues decreasing in the area (Food and 

Agriculture Organization of the United Nations [FAO], 2010a). Tropical forest deforestation 

and the consequent habitat loss and forest fragmentation are among the main causes of the 

global decline in biodiversity (Brook et al., 2003; Sehgal, 2010). For this reason, studies 

evaluating the efficiency of the strategies contributing to prevent, minimize, and revert the 

forest covert loss are critical for the long-term survival of many species of plants and 

animals (including human beings). 

Costa Rica is often hailed as a model for how developing nations can balance the protection 

of the nature and the economic development. The country is recognized to devoted ca. 25% 

of its territory to forest conservation and for the income that the country generates from 

different sustainable activities (Buchsbaum, 2004; Food and Agriculture Organization of the 

United Nations [FAO], 2010a). 

However, the “conservationist” reputation of the country is relatively recent and largely 

contrasts from the unsustainable practices driving the economic model of Central America 

during the second half the XX century. Overall, the land speculation by cattle ranchers in 

combination with interest rate subsidies (i.e., financial incentives widely provided by 

government entities and public banks) is pointed as the main cause of deforestation in Costa 

Rica and in the rest of Latin America (Kull et al., 2007; Roebeling & Hendrix, 2010). Thus, 

since 1960 most government policies, national and international investment, and 

international cooperation programs have all promoted the deforestation, colonization, and 

“hamburgerization” (i.e., the pasture expansion for the beef industry proliferation for export 

to USA) of the Central American land (Harrison, 1989; Myers, 1981). As result of these 

“depredatory” politics, Costa Rican’s forest cover decreased from 2.71 million ha in 1950 to 

1.01 million ha in 1983, while the deforestation rates increased from 36,018 ha/year during 

the 1950-1961 period to 97,317 ha/year during 1977-1983 (reviewed by Harrison, 1989). 

However, the economic balance for the beef industry was extraordinary positive because the 

number of head cattle exported to USA explosively increased from 6903 in 1955 to 148,882 in 

1973. Even after 1983, the Costa Rican’s government across the Instituto de Desarrollo

320 


Agrario (IDA) continued promoting the deforestation and clearing of 500,000 ha of 

“underutilized lands” (i.e., forested or partially forested lands). Most of these lands were 

distributed among poor “campesinos”and/or small farmers to incentive the agriculture 

(Harrison, 1989). 

Although there is a complex network of environmental, social, economic, and politic factors 

influencing the changes in forest cover in the country (Calvo-Alvarado et al., 2009; Kull et al., 

2007; Sánchez-Azofeifa et al., 2001; Sánchez-Azofeifa & Van Laake, 2004), most studies 

highlight the importance of the ecotourism, the payments for environmental services, the 

private nature reserves, and the environmental education as strategies to prevent, minimize, 

and/or revert the deforestation in the country. These four factors are described below. 

1.1 Ecotourism 

Ecotourism is often perceived as an excellent tool for sustainable development in 

developing countries (Almeyda et al., 2010; Gossling, 1999). Among the main benefits of this 

“green” economic strategy frequently are mentioned: (1) the protection of forest resources 

and hence the prevention of deforestation in general, and (2) the economic benefits for local 

communities (Gossling, 1999; Horton, 2009). 

Since the boom of the ecotourism in Costa Rica, at the end of 1980s and into the 1990s, the 

activities related with ecotourism (e.g., proliferation of eco-lodges and/or private reserves, 

nature-tours, and agro-ecotourism), represent one of the main economic activities of the 

country (Koens et al., 2009; Weaver, 1999). Corresponding with this “boom”, the hotel sector 

in the country has grown over 400% from 1987 to 2000 (Instituto Centroamericano de 

Administración de Negocios [INCAE], 2000). As result, the agro-export economic model 

based in the extensive plantations of coffee and banana (i.e., the main economic activity 

since the nineteenth century), was displaced to a secondary place as source of economic 

resources (Iveniuk, 2006). Thus, annually ca. 1.2 million tourists visit the country, which 

result in an annual turnover range from US$ 1200 million in 2003 to US$ 1983 million in 2010 

(Instituto Costarricense de Turismo [ICT], 2011a). 

Although the above, some researchers question the contribution of the ecotourism to 

conservation and community development due to some potential negative impacts such as 

habitat destruction, waste generation, visitor impacts, and socio-cultural ills (Stem et al., 

2003; Wearing & Neil, 2009). Below I discuss this topic in the light of the evidence provided 

by different case studies in Costa Rica. 

1.2 Payments for Environmental Services (PES) 

The payment for environmental services is an economic incentive received to the landowners 

for the services and goods provided for their forested lands or forest patches (Sánchez- 

Azofeifa et al., 2007). In Costa Rica the current PES system was created with the 1996 Forestry 

Law 7575, which recognizes four main environmental services: mitigation of green house gas 

emissions, hydrological services, biodiversity conservation, and provision of scenic beauty for 

recreation and ecotourism (Kull et al., 2007; Sánchez-Azofeifa et al., 2007). 

The Forestry Law 7575 provides the legal and regulatory basis to contract with landowners 

for the environmental services provided by their lands, empowers the National Forestry 

Financing Fund (FONAFIFO) to issue such contracts, and establishes a financing mechanism 

for this purpose (Sánchez-Azofeifa et al., 2007; Sierra & Russman, 2006). Previously to 

receive the PES economic benefits, the landowners need sign a 5-10 year contract with the

Efficiency of the Strategies to Prevent and Mitigate the Deforestation in Costa Rica 

government, agreeing to either protect forest cover or engage in reforestation. Funding for 

PES program comes from a 3.5% fossil fuel tax, private-sector and international donor 

contributions, a World Bank loan, and the sale of carbon offsets to industrialized countries 

established in the Kyoto Protocol (Kull et al., 2007). 

The percent of the country territory receiving PES increase from 5.5% (representing 4400 

contracts) in 2001 to 7.3% (representing 4600 contracts) in 2010 (National Forestry Financing 

Fund [FONAFIFO], 2011). According with the same source, currently PES covers 373,074 ha 

of forests in different succession stages and protects ca. 2.7 million of trees throughout the 7 

provinces of the country. Although this program might facilitate field abandonment in some 

marginal zones already affected by agricultural liberalization (Sierra & Russman, 2006), its 

relative efficiency as deforestation-avoiding strategy is even polemic. 

1.3 Private nature reserves 

Traditionally, conservation efforts to preserve the Costa Rican’ natural legacy (i.e., 

ecosystems and species) have been focused on the establishment of government-controlled 

areas such as national parks, wildlife refuges, biological reserves, and other management 

categories (Food and Agriculture Organization of the United Nations [FAO], 2010b). 

However, with the boom of the ecotourism at the end of 1980s and into the 1990s (see 

above), this fact has changed noticeably. Thus, the number of private reserves registered 

increases from 65 reserves in 2005 to ca. 200 reserves in 2011 (Red Costarricense de Reservas 

Naturales, 2011). 

Overall, private reserves range from 20 to 1500 ha in size (Herzog & Vaughan, 1998; Red 

Costarricense de Reservas Naturales, pers. comm.) and together cover over 81,429 ha of 

forests along the country (Red Costarricense de Reservas Naturales, 2011). This fact 

highlights the importance of private landholdings in the fate of the Costa Rican forests, 

particularly in the trends in the forest cover along the time. 

1.4 Environmental Education (EE) 

Environmental education is the process of recognizing values and clarifying concepts in order 

to develop skills and attitudes necessary to understand and appreciate the interrelatedness 

among men, his culture and his biophysical surroundings (International Union for the 

Conservation of Nature [IUCN], 1970). Overall, acording to Jacobson (1991) a well-designed 

and well-implemented EE program have the potential to increase ecological awareness, foster 

favorable attitudes toward the environment, and promote the conservation of the natural 

resources (including discourage of deforestation and/or selective logging). Even, some 

authors consider that an appropriate EE might produce significant behavioral changes in the 

people and hence, may be more crucial to successful long-term conservation than a strictly 

scientific work (Blum, 2008; Jacobson, 1991; Palmer, 1998). 

In this sense, since 1980s Costa Rica has been one of the world-leaders in efforts to promote 

environmental learning and national policies integrating education, conservation, and 

different sustainable activities related to ecotourism (Blum, 2008). For instance, as early as 

1975, the Universidad Nacional (one of the most important public universities in the country) 

established a School of Environmental Sciences which included an environmental education 

program. Similarly in 1994, the National Council of Vice-Chancellors created an Inter- 

University Commission for Environmental Education which works to ‘environmentalise’ all of 

the state universities (Oficina de Educación Ambiental, 2002). Currently, most of the 

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322 


environmental education in Costa Rican schools and/or communities is supported by the 

Ministry of Environment and diverse non-governmental organizations (Blum, 2008). 

Nevertheless, to date the controversy on the relative efficiency of the aforementioned factors 

as deforestation-avoiding strategies still persist. Some authors concludes that the current 

expansive network of efforts in conservation, environmental management, education and 

ecotourism contribute directly to prevent and minimize the deforestation in the country 

(e.g., Blum, 2008). Conversely, other authors support that the forest cover recovery in some 

areas of Costa Rica have been result of a process referred as “forest transition” (i.e., 

deforestation trends are replaced with reforestation following the trends in the economic 

development and urbanization), rather a direct effect of the environmental policies (Calvo- 

Alvarado et al., 2009; Schelhas & Sánchez-Azofeifa, 2006). Therefore, in this chapter I 

reviewed the available literature to assess the relative efficiency of the ecotourism, PES, 

private reserves and environmental education to prevent, mitigate, and/or reverts the 

deforestation in different regions of Costa Rica. Finally, I synthesize the major strengths and 

weaknesses of these strategies and suggest future directions to minimize the deforestation 

and to improve the environmental performance in the country. 

2. Methods 

2.1 Literature review 

To achieve the main objectives of this study, I conducted a systematic review of published 

articles, book chapters and dissertations up to August 2011 using ISI Web of Science, 

Biological Abstracts, Google Scholar, and Costa Rican environmental agencies online data 

bases. Overall, I amassed a total of 90 studies on the study topic distributed as follow: 36 

studies on ecotourism, 17 studies on payment for environmental services, 8 studies on 

private nature reserves, and 29 studies on environmental education. 

2.2 Efficiency indicators 

To determine the efficiency of the above mentioned strategies I used changes in deforestation 

rates and/or forest cover before the implementation of each strategy and 4-20 years after its 

implementation in the same region. As sources of the information on the deforestation rates 

and/or forest cover in the sites in which a particular strategy was implemented, I used the 

information available in the literature and in online data bases from the Ministerio del 

Ambiente, Energía y Tecnología (MINAET) de Costa Rica (http://www.minae.go.cr/), 

Sistema de Infomación de Recursos Forestales (http://www.sirefor.go.cr), Fondo Nacional de 

Financiamiento Forestal (http://www.fonafifo.com/), Oficina Naciona Forestal (ONF), 

Tribunal Ambiental Administrativo (http://www.tribunalambiental.org), and Centro de 

Derecho Ambiental y de Recursos Naturales (http://www.cedarena.org/). 

2.3 Statistical analysis 

To evaluate if forest area covers by private and public forests differed between years, I used 

generalized linear models (GLM: Lehman et al., 2005). I constructed the following model: 

AREA = FOREST TYPE (PRIVATE AND PUBLIC) nested within YEAR + FOREST 

TYPE*YEAR. Data were first arcsine transformed, and tested for a normal distribution with 

a Shapiro-Wilk test (passed, P>0.05). I then selected Normal distribution with an identity 

link-function to the response variable. I performed the statistical analyses using JMP 

(version 7.0, SAS Institute, Cary, NC).


Table 1. Three study case on ecotourism efficiency in different top tourism areas of Costa 

Rica. 

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324 



3.1 Relative efficiency of ecotourism 

Overall, the findings for the three analyzed case studies (Table 1) concur with previous 

studies suggesting that ecotourism represent a valuable tool for conservation and 

sustainable development (e.g., Almeyda et al., 2010; Ceballos-Lascurain, 1998; Wearing & 

Neil, 2009). Although in the three case studies there are some negative aspects, the general 

balance undoubtedly can favors the forest protection and the ecosystem regeneration (Table 

1). Similarly, there is evidence indicating that the expansion of the ecotourism plays a 

relevant role in the reduction of the deforestation in Costa Rica. Thus, considering forests on 

farmlands and non-farmland areas, the deforestation rates decreased from 47,219 ha/year 

during the 1950-1961 decade to 15,677 ha during 1973-1984 (reviewed by Harrison, 1989), 

which correspond to the boom of ecotourism in the country (see above). 

However, other studies do not support that ecotourism per se have a relevant role as 

deforestation-avoiding strategy. For instance, Kruger (2005) in his review of 251 ecotourism 

case studies found that ecotourism did not create enough revenues to prevent ‘consumptive’ 

land use (e.g., forest conversion to agriculture or pasture) among households. Furthermore, 

the increasing development in the ecotourism “hotspots” of Costa Rica (e.g., Nicoya, Monte 

Verde, Siquirres, Motezuma, Quepos) might result in a higher deforestation in theses areas. 

Thus, Almeyda et al. (2010) mention that uncontrolled development of standard hotel 

operations and large condo developments in Nicoya, seeks to capitalise on the region’s 

natural beauty and may reverse land cover trends if they are not accompanied by adequate 

forest conservation strategies and government monitoring. 

3.2 Relative efficiency of the PES 

Since the implementation of PES the amount of industrial wood extracted from forests 

gradually decreased from 248,362 m 3 in 1998 to ca. 50,000 m 3 in the years 1999-2006 (Figure 1). 

Conversely, wood derived from agroforestry increased from 458,538 m 3 in 1998 to a maximum 

value of 673,426 m 3 in 2001 and two years latter this value drop to 205,401 m 3 and remained 

relatively constant until 2007 (Figure 1). Furthermore, during this period occurred a noticeable 

increase in the amount of wood derived from forest plantations (mainly Smelina arborea and 

Tectona grandis, National Forestry Financing Fund, pers. comm.) (Figure 1). Similarly, during the 

period 2000-2010 (i.e., 15 year after the implementation of PES), the forest area of Costa Rica 

increased from 2.37 to 2.60 million hectares (Food and Agriculture Organization of the United 

Nations [FAO], 2010a). However, the history of the forest cover was quite different during the 

1980s when the nation was losing 4% of its forest cover annually—the highest deforestation 

rate in the western hemisphere at the time (Carriere, 1991). Indeed, between 1970 and 1980 

more than 7000 km 2 were cleared, and by 1987 total forest cover had been reduced to only 31% 

of the land mass or approximately 16,000 km 2 (Carriere, 1991). 

Currently there are 4599 PES contracts covering 373,074 ha of lands along the country. Most 

of the area covered by PES (70%) was devoted to forest protection. Similarly, from the four 

main PES categories, most contracts were devoted to forest protection (54.2%), following by 

reforestation (29.8%), protection of wildlife areas (6.4%), and forest management (1.5%) 

(Table 2). This suggests that PES can promotes the protection of the forest cover in different 

private nature lands, at least during the contract period (i.e., 5-years). However, there was a 

large variation in the number of contracts and area covered by PES among provinces (Table 

2). Alajuela province presented the larger number of contracts (1045 contracts) and


cumulative number of hectares covered by PES (73,516 ha), while the lower values were 

found in Cartago province (130 contracts and 28,822 ha, respectively) (Table 2). 

Volume of industrial wood (m3) 

1,000,000 

900,000 

800,000 

700,000 

600,000 

500,000 

400,000 

300,000 

200,000 

100,000 

0 

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 

Year 

Fig. 1. Amount of industrial wood extracted from agroforestry, forests, and forest 

plantations during a 10-year period. Source: Oficina Nacional Forestal (ONF) (2009). 

Province 

No. 

contracts 

Total area 

(ha) 

325 

Agroforestry 

Forest 

Forest plantation 

Hectares covered by PES category (No. contracts) a 

Forest protection Reforestation PWA FM Total 

Alajuela 1045 73,516.67 38,292.08 (447) 

21,255.07 

(443) 

6786.8 

(63) 

2351.82 68,685.77 

(31) (984) 

Limón 652 70,894.68 52,346.37 (389) 

3099.17 

(149) 

9645.7 

(44) 

587.32 

(10) 

65,678.56 

(592) 

Puntarenas 670 69,662.64 56,742.44 (422) 

3705.7 

(146) 

5928 

(62) 

0 

66,376 

(630) 

Guanacaste 1027 63,290.57 46,582.38 (541) 

11,130.89 

(375) 

547.5 

(10) 

0 

58,260.77 

(926) 

Heredia 484 35,722.00 18,989.42 (213) 

3885.51 

(113) 

8745.9 

(83) 

1792.66 33,413.49 

(30) (439) 

San José 590 31,165.95 24,462.32 (392) 

2430.23 

(122) 

1785.6 

(18) 

0 

28,678.15 

(532) 

Cartago 130 28,822.38 24,074.58 (90) 

848.4 

(21) 

3429.1 

(14) 

0 

28,352.08 

(124) 

Total 4599 373,074.90 261,489.59(2494) 

46,354.98 

(1369) 

36,868.60 4731.8 

(294) (71) 349,444.96 

aPWA= Protection of wildlife area, FM= forest management. 

Table 2. Current area covered by PES category in the 7 provinces of Costa Rica. Data come 

from the National Forestry Financing Fund of Costa Rica, FONAFIFO, (2011). Data are 

ordered according to the total area protected.

326 


The aforementioned trends in forest cover recovery might be related to the different 

environmental restrictions and government incentives related to the PES established in the 

Forestry Law 7575. However, other authors suggest that the increase in the forest cover 

observed in different areas of Costa Rica are in fact a result of a process of “forest transition” 

(Calvo-Alvarado et al., 2009; Schelhas & Sánchez-Azofeifa, 2006). For instance, Calvo- 

Alvarado et al. (2009) analyzed the process of deforestation and restoration of the tropical 

dry forest in Guanacaste, Costa Rica, using socioeconomic data and satellite images of the 

forest cover from 1960 to 2005. They concluded that the restoration of the Guanacaste′s 

forest cover after the 1980s was the result of multiple socioeconomic factors rather than the 

efficiency of the PES. Similarly, Almeyda et al. (2010) found that the proportion of forest 

cover in the Nicoya peninsula decrease from 0.51 in 1987 to 0.36 in 2008, indicating a poor 

efficiency of the PES or any other potential deforestation-avoiding strategies implemented 

by the government during that period. 

For this reason, Calvo-Alvarado et al. (2009) mentions that neither the PES nor the other 

conservation policies implemented in the country are enough to protect this tropical dry 

forest at the long-term. This statement might be particularly true if we take in consideration 

the multiple financial limitations of the MINAET and the limited number of environmental 

inspectors of the Secretaría Técnica Nacional Ambiental, the public institution responsible 

for authorizing the construction and monitoring the environmental performance of the 

diverse public and private infrastructures along the country (MINAET, pers. comm.). 

3.3 Relative efficiency of private nature reserves 

During the decades 1990s and 2000s the forest area on control of private landholdings was 

noticeable greater than the forest area on control of the government (Figure 2). Nevertheless, 

the latter forest area was duplicated from 1990 to 2005 (Figure 2). However, until today, most 

of the Costa Rican forests (55%) are on control of private landholdings (Food and Agriculture 

Organization of the United Nations [FAO], 2010b). Overall, the forest cover protected by the 

private reserves increased ca. 2.5 times from 1995 (32,895 ha) to 2010 (81,429 ha) (Programa 

Estado de la Nación, 2011). Interestingly, from the 200 private reserves recorded until 2010 

only a minimal part is managed by individual landowners. Thus, 52% of private reserves are 

managed by non-government organizations (mainly public universities and non-profit 

conservationist institutions), 46% are managed by profit organizations, and only 2% are 

managed by individual landowners (Programa Estado de la Nación, 2011). This fact 

undoubtedly can benefit the long-term conservation of these areas because contrasting with 

individual landowners, the activities of the non-profit organizations and universities are 

frequently monitored by different government institutions and hence, it is more difficult to 

they change the land use of the forested areas. The deforestation and/or selective logging in 

reserves management by individual landowners is hard to detect and penalize (particularly 

when there is not a formal denounce) (Tribunal Ambiental Administrativo, 2011 ). 

On the other hand, considering both private reserves and mixed public-private areas, they 

cover over 13% of the total continental area of the country while the government-protected 

forests cover 21% of this area (Table 3). Additionally, as in most America Latina, the main 

uses of these reserves are ecotourism and investigation (Mesquita, 1999). Furthermore, since 

private reserves often are located in ecoregions poorly represented in the government 

system and in regions of the country without existing reserves, they might also contributes 

to protects a number of threatened animals such as agouti, peccary, jaguar, and puma


(Herzog & Vaughan, 1998). These facts strongly suggest that the private reserves play an 

important role in the protection of the forest cover in Costa Rica. Even, some of these reserves 

are also important research centers and frequently receive researches and students of diverse 

countries of the world. For instance, the La Selva Biological Station protects 1600 ha of primary 

tropical rainforest, (including 1000 tree species and 420 bird species), and also is one of the 

most well-studied and recognized tropical rainforest around the world with hundreds of 

scientific publications in high-impact international journals. 

Management category N Area (ha) %STAa % of the countryb Goverment-protected areas 

National Parks 28 629,121 37.1 12.3 

Biological Reserve 8 22,036 1.3 0.4 

Wildlife Refugie 12 61,708 3.6 1.2 

Absoult Natural Reserve 2 1,369 0.8 0 

Indigenous Reserves 24 335,851 19.8 6.6 

National Monument and 

other reserves 

3 23,768 1.4 0.5 

subtotal 

Mixed (Public-Private) 

77 1,073.853 64 21 

Wildlife Refugie 25 106,572 6.3 2.1 

Protected Zone 31 157,715 9.3 3.1 

Forest Reserve 9 221,239 13.0 4.3 

Wetland 13 68,543 4.0 1.3 

subtotal 

Private Reserves 

78 554,069 32.66 10.8 

Wildlife Refuges 34 68,447 4.03 1.3 

Natural Reserves 72 52,156 NA 1 

subtotal 106 120,603 4.03 2.3 

aPercent of total protected area. 

bPercent of the continental area of the country (i.e., 51,100 km2). 

Table 3. Area covered by public and private protected areas in Costa Rica during 2007. 

Modified from Food and Agriculture Organization of the United Nations FAO (2010b). 

3.4 Relative efficiency of environmental education 

The findings indicated that most environment education programs (EEP) in Costa Rica are 

linked to different ecotourism activities (79% of 29 analyzed studies), following by activities 

promoted by government and/or non-profit organizations. Currently, most Costa Rican 

eco-lodges, private reserves, and ecoturism companies have EEPs for employees, visitants 

and/or local inhabitants (Instituto Costarricense de Turismo [ICT], pers. comm.). Even, the 

EEP is an essential requirement for all those companies and institutions procuring the 

Certification for Sustainable Tourism (CST, see below). Currently, 178 eco-lodges, 59 

tourism agencies, and 4 car rental agencies are certificated (Instituto Costarricense de 

Turismo [ICT], 2011b), and hence with some type of EEP in course. 

Unfortunately, in most cases it is not possible to evaluate the relative efficiency of 

environmental education as deforestation-avoiding strategy due that its effect is difficult to 

separate from other complementary conservation strategies. Nevertheless, evidence 

327

328 


suggests that it contributes significantly to reinforce conservation attitudes of the people 

respect to the deforestation and other environmental problems (Blum, 2008; Koens et al., 

2009; Palmer, 1998). Further studies in particular communities of Costa Rica are necessary to 

evaluate if the environmental education per se is able to minimize, or even revert, the 

deforestation at the long-term. 

Forest area (x 1000 ha) 

3000 

2500 

2000 

1500 

1000 

500 

0 

Aa 

Aa 

Ab Ab 

1990 2000 

Year 

2005 

Fig. 2. Changes in the number of forest hectares under government and private management 

in Costa Rica. Different capital letters indicate significant differences among years, and 

different lowercase letters indicate differences among reserve type (contrast tests, P


vulnerable forested areas (i.e., those that contain threated species of animals and plants 

and/or water reservoir). When necessary, the government should consider the 

expropriation of some of these areas to guarantee their ecological integrity. 

Furthermore, to a more efficient control of the deforestation and/or logging along the 

country, it is important increase the resources invested in the environmental monitoring. In 

this sense, are necessary more private and public initiatives to monitoring and penalize the 

deforestation and other environmental crimes and/or reinforce those initiatives that have 

showed be successful. For instance, one successful initiative is the 2008′ public program 

namely “barridas ambientales” from the Tribunal Ambiental Admistrativo. In this initiative, 

a multidisciplinary team of biologists, forest engineers, chemistries, and lawyers carried out 

sudden visits (5-10 times/year) to different areas of the country and, with the cooperation of 

the local inhabitants, they detect, record, and sanction any environmental crime (including 

illegal logging). As result, the number of denounces has been increased by a 100% (Tribunal 

Ambiental Administrativo, 2011). Other interesting initiative is the program Certification for 

Sustainable Tourism (CST), from the Instituto Costarricense de Turismo. This program was 

designed to differentiated businesses of the tourism sector, based on way that they interact 

with the nature, local communities, and social resources in general (Instituto Costarricense 

de Turismo [ICT], 2011b). 


I thank Júlio César Bicca-Marques for logistical support for the redaction of this chapter in 

Brazil. The Ministerio del Ambiente, Energía y Telecomunicaciones de Costa Rica 

(MINAET) provided important information on changes in the forest cover in different 

regions of Costa Rica. 

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16 

Agroforestry Systems 

and Local Institutional Development 

for Preventing Deforestation in Chiapas, Mexico 


Lorena Soto-Pinto 1, 

Miguel A. Castillo-Santiago 2 and Guillermo Jiménez-Ferrer 3 

1 Institut de Cíencia i Tecnologia Ambientals, Universitat Autonoma Barcelona, 

2 El Colegio de la Frontera Sur (ECOSUR). Unidad San Cristóbal, Chiapas, 

3 Veterinary Faculty, Universitat Autonoma Barcelona, 

1,3 Spain 

2 Mexico 

The transformation of natural forest to secondary forest and pastures has been the most 

common process of land use change in tropical countries in recent decades (FAO, 2010). The 

main causes of deforestation include institutional factors, markets, public policies and global 

forces, which often act synergistically (Deininger and Minten, 1999; Bocco et al. 2001; 

Lambin et al., 2001). 

Mexico is a country with 64,802x10 3 ha of forested land, and it is one of the ten countries 

with the largest area of primary forest (3% of total). The annual net loss of deforestation in 

Mexico has been estimated to be 0.52% for the period of 1990-2010, but the net loss, on 

average, has decreased over the past few years (FAO, 2010). The highest deforestation rates 

are concentrated in the south and central regions of the country, as documented elsewhere: 

8.4% in el Nevado de Toluca, state of Mexico (1972-2000) (Maass et al., 2006); 8% in 

Patzcuaro, Michoacan (1960-1990) (Klooster, 2000); 6.9% in some areas of Campeche (Reyes- 

Hernández et al., 2003); 6.1% in the highlands of the state of Chiapas (Cayuela et al., 2006; 

Echeverría et al., 2007); and 2-6.7% in Selva Lacandona, also in Chiapas (Ortiz-Espejel & 

Toledo, 1998; de Jong et. al., 2000). Precisely, the states of Chiapas and Yucatan have 

registered the highest rate of forest conversion to grasslands and slash-and-burn cultivation 

over the past two decades, and Chiapas alone has contributed towards 12% of national 

deforestation during the period 1993-2007 (De Jong et al, 2010; Díaz-Gallegos et al., 2010). 

In Mexico, deforestation occurs because forests become converted to agriculture, livestock 

and urban areas. But also because logging activities fail to meet the requirements of forest 

management plans. All these processes result in the loss of forest goods and services 

(Lambin et al. 2003), and they contribute to ecosystem fragmentation (Ochoa-Gaona & 

González Espinosa, 2000; Cayuela et al., 2006), biological invasions (Hobbs, 2000), 

greenhouse gas emissions (Watson et al., 2000), biodiversity loss (Lugo et al., 1993), soil 

degradation (Lal, 2004) and water siltation (Sweeney et al., 2004).

334 


Deforestation is one of the key contributors to greenhouse gas (GHG) concentrations in the 

atmosphere (IPCC, 2000; Canadell & Raupach, 2008). Between 2003 and 2008, GHG 

deforestation-related emissions in Chiapas were estimated to be 16,477 (±7,299) Gg 

CO2/year, while 414 Gg CO2–eq were attributed to forest fires during the same period. 

These emissions represent 23.5% of national land-use change related emissions over the 

same period (Gobierno del Estado de Chiapas, 2011). In Chiapas, deforestation processes 

have affected highland, cloud and tropical forests. These forests have decreased in favour of 

agriculture, pastures and secondary vegetation. The original areas of some of these forest 

types have been reduced by 50% (de Jong et al, 2010). In particular, tropical mountain cloud 

forests and mangroves have been threatened by commercial agriculture, and considerable 

endemic biodiversity has subsequently been lost (Hirales-Cota, 2010; Toledo-Aceves et al., 

2011). The Selva Lacandona (Lacandon rainforest) is located in the southern region of the 

country next to Guatemala, and it has been severely deforested during the last 40 years. The 

rainforest area of Marques de Comillas lost 81,080 ha of tropical forests between 1986 and 

2005, which represents 48% of the original forest cover (Castillo-Santiago 2009). 

Deforestation and degradation have occurred despite the fact that a variety of policy tools 

and approaches have been developed by national and regional governments, as well as by 

civil organisations, in order to guarantee forest and biodiversity conservation, including 

people-oriented conservation areas; community-based sustainable resource use 

management approaches; technological innovations for improved forest and agricultural 

management practices; and payments for ecosystem services (PES) (Deininger & Minten, 

1999; Corbera, 2005; Cayuela et al, 2006). It is recognised that managed forests and 

agroforestry systems can contribute to conserve soil, regulate water flows, support 

biodiversity and sequester significant amounts of carbon by including timber-focused 

trees for durable products. Some of these land management systems can maintain biomass 

for longer, restore site capacity and increase economic benefits compared to a business-asusual 

scenario (Kotto-Same et al., 1997; De Jong et al., 2000; Albrecht & Kandji, 2003; 

Montagnini & Nair, 2004; Soto-Pinto et al., 2010). The evidence presented before, however, 

has demonstrated that most of these policy and project-based approaches have been far 

from successful, and have been unable to halt land-use change in the region as a whole. 

There are of course apparently successful experiences, which should help us to learn 

lessons and identify avenues for improving the design and effectiveness of existing 

policies and instruments. One of these, institutional development, aims to encourage and 

facilitate local inputs and experimentation, interaction and consultation of local actors 

among themselves and to interact with relevant external agents to increase opportunities 

for the poor; it has a crucial importance in territorial development (Evans, 2004; Schejtman 

and Berdegué, 2004). 

One of the latter experiences concerns the project being analysed in this chapter, which has 

been promoting agroforestry, reforestation and conservation activities for offsetting GHG 

emissions since 1994. The Scolel Té project has allowed carbon offsets to be sold through the 

voluntary market and will provide non-timber and timber products in the short, medium 

and long term. In the following sections, we examine the involvement in the project of three 

municipalities located in the northern-eastern tropical area of Chiapas, Mexico (i.e., Marqués 

de Comillas, Chilón and Salto de Agua) (Figure 1), and we discuss the potential of 

alternatives of avoided deforestation and agroforestry systems as well as institutional 

arrangements resulting from project development in these municipalities. The following 

section of the chapter describes the process of deforestation in Marqués de Comillas, a

Agroforestry Systems and Local 

Institutional Development for Preventing Deforestation in Chiapas, Mexico 335 

representative area of the state’s ongoing deforestation and degradation processes. The 

central part of the chapter highlights Scolel Te’s conservation and forest management 

approach in our selected municipalities, and it discusses the drivers and constraints for the 

successful realisation of environmental, economic and social benefits in the three selected 

cases. The last part of the chapter discusses the overall results in the context of evolving 

carbon forestry markets and the through the enhancement, conservation and sustainable 

management of forest stocks. 

Fig. 1. Study area in Chiapas, Mexico. 

2. The process of deforestation in Marqués de Comillas 

Marqués de Comillas is one of the four municipalities of La Selva Lacandona and it 

comprises a total area of 203,200 ha. In the last few four decades, this municipality showed 

one of the highest deforestation rates in Mexico. Between 1986 and 2005 it lost 

approximately 81,080 ha of tropical rainforest, which represents 48% of its original forest 

cover (Figure 2). This loss contributed to 1.5% of the total CO2 emissions from land use 

change in Mexico (Castillo-Santiago 2009). 

Settlements in La Selva Lacandona were promoted during 1970-1980, and they have been 

recognised as the main driver of deforestation in Marqués de Comillas. Population was 

relocated to resolve conflicts of land scarcity and social rebellion in other Mexican states. 

Consequently, land in Marques de Comillas, which was completely forested at the

336 


beginning of the 1970s, was distributed in 37 ejidos 1 (Mariaca, 2002). The colonisation 

process was complemented by the construction of a main road, which allowed people and 

goods to flow between rural areas and the main cities of Tabasco and Chiapas (Harvey, 

1998). Additionally, public policy favoured land use change from forest to maize 

agriculture, thereby financing deforestation in the 1970s. The process began with forest 

logging, followed by the use of fire for the cultivation of maize during three or four years. 

Once fertility was decreased and the land was weeded, due to intense cultivation, the 

natural steps were to intensify land use, fallow the land or let the grasses grow. Importantly, 

forested land was considered “idle land”, and the process of deforestation was actually 

funded by government programs that encouraged cattle ranching. In 1978, the protected 

area of Montes Azules was established adjacent to Marques de Comillas, covering and area 

of 331,200 ha. 

Subsequently, from 1992 to 1998, in Selva Lacandona six protected areas were established, 

five of them managed by the State, and other communal, with a total area of 123.660 ha: 

Chankin Protected Area (12.184 ha), Bonampak Natural Monument (4.357 ha), Biosphere 

Reserve Lacan-Tun (61.873 ha), areas of wildlife protection Nahá (3.847 ha), Metzabok (3.368 

ha), Natural Monument Yaxchilan (2.621 ha), and by agreement of the Lacandon 

Community, the Communal Reserve of Sierra Cojolita with 35.410 ha (INE 2000). 

Fig. 2. Land-use changes from 1986 to 2005 in Marques de Comillas Chiapas, México. 

In Marqués de Comillas, crops, such as cardamom, cocoa, rice, vanilla and rubber, were 

promoted by public and private investments to diversify the agricultural system and to 

employ local people who mainly participated as labourers in the 1980s and 1990s. Most of 

these projects were unsuccessful, due to their continuous requirements of external inputs, 

particularly technical support and capital. Forest initiatives were discouraging as well. In 

1996, the Pilot Forest Plan was launched to promote timber extraction and establish the basis 

for a rational use of community forests. However, only few ejidos have maintained the 

deforestation rate under the regional average. More recently, other public initiatives have 

favoured the growth of oil palm for biofuels, which are rapidly growing in this 

1 Ejido is a particular form of land tenure in which the State has given the land to a group of people 

called “ejidatarios”. The general use of forest and water is regulated by federal laws; land sale and use 

are decisions of the owner, this last is locally monitored and regulated by The Public Assembly of the 

community.



municipality, even at the expense of forests. Livestock has also gained ground. Livestock has 

very often represented the best economic option from the farmers’ point of view because it 

has a relatively stable national market and a low level of investment is required. Livestock is 

thus still the dominant activity in the area and it occupies most of the landscape in Marques 

de Comillas at present (Table 1). 

Type of vegetation Area (ha) Percentage 

Tropical rainforest 69,360 34 

Riparian forest 3,054 1 

Wetlands 3,387 2 

Secondary forest 16,544 8 

Secondary vegetation 

(shrubs and herbaceous lands) 

25,904 13 

Pastures for livestock 56,339 28 

Rainfed agriculture 24,324 12 

Human settlements 2,404 1 

Rivers 3,124 2 

Total 204,440 100 

Source: Castillo-Santiago, 2009 

Table 1. Land use in Marqués de Comillas (Chiapas, México). 

Livestock in Selva Lacandona has normally been characterised by its extensiveness, and it has 

been devoted mainly to grow calves that are fed with naturalised grasses of low nutritional 

value. Moreover, it has been characterised as having a low management level, high number of 

animals per land unity, low capital investments, poor infrastructure, scarce technical assistance 

and scarce financial support (Jiménez et al, 2008; Martínez & Ruiz de Oña, 2010). Table 2 

shows the main features of the livestock system in communities of Selva Lacandona. Most of 

the farmers (95%) have designed their systems to produce calves to sell them to intermediaries 

in the local and regional markets. The trees located in the pastures are mainly tropical forest 

remnant trees tolerated for shading cattle, including Blephariduim guatemalensis, Sabal 

mauritiformis, Vatairea lundellii, Guarea glabra, Albizia adinocephala, Bursera simaruba, Spondias 

mombin, and Swietenia macrophylla. Specific timber species, such as “popiste” (B. mexicanum) 

and guanacastle (Enterolobium cyclocarpum), are sometimes favoured with the purpose of being 

used as a lumber source in rural construction (local market or self-supply). 

Recently, several studies have highlighted the importance of silvopastoral systems and other 

agroforestry systems for conserving biodiversity and connecting countryside landscape with 

reserves (Harvey et al, 2006; Rice & Greenberg, 2004). Several institutions, including the 

Commission for Natural Protected Areas (CONANP), the Mesoamerican Biological Corridor 

(CBM) and the National Commission of Knowledge and Use of Diversity (CONABIO) have 

launched programs in Selva Lacandona, specifically in Marques de Comillas, to improve 

rural production and promote conservation. One of these initiatives is the Scolel Te’ project, 

which began in 1994 as a pilot experience in Chiapas and it is managed by the local 

organisation AMBIO cooperative. It originally involved a few dozens of farmers in the 

central highlands of the state and it has now grown to encompass more than 700 

participants and their families (3500 beneficiaries) in approximately 50 communities of 

Chiapas and the neighbouring states of Oaxaca and Tabasco. The project is built on a

338 

Characteristics of 

livestock 

production units 

La Siria, 

Ocosingo 

Ach lum Monte 

Libano, 

Ocosingo 

Amatitlan, 

Maravilla 

Tenejapa 


La Corona, Marques 

de Comillas 

Land use type Ejido Ejido Ejido Ejido 

Ethnic group Tseltal Tseltal Chol and 

Mestizo 

Mestizo 

Altitude (m a.s.l.) 150-200 300 - 500 275 - 590 75 – 125 

Land use Maize 

Maize 

Maize 

Maize agriculture 

agriculture, agriculture agriculture Livestock 

Fruits, 

Livestock Livestock Forestry 

Livestock 

Forestry 

Forestry PES-Carbon 

Average land 

area (ha/family) 

15 20 10 45 

Pasture area for 

cattle grazing 

(ha) 

10 15 5 25 

Stocking rate 

AU/ha 

Management 

system 

Product 

destination 

(mainly meat) 

Forage trees on 

pasture grazing 

areas 

1.9 2.1 1.5 2.7 

Livestock with 

improved 

pastures 

(Brachiaria 

brizantha, B 

humidicola). 

Rotations 

without 

technical 

assistance 

No supplement. 

Local market 

consumption 

G. sepium, 

Parmentiera 

aculeata, 

Brosimum 

allicastrum, 

Guazuma 

ulmifolia, Leucaena 

leuucocephala 


native grasses 

and “estrella” 

grass (Cynodon 

niemufensis). 

Without 

technical 

assistance and 

financial 

support. 

Breeding and 

sales of young 

calves 

Local market 

consumption 

Whiteringia 

meiantha, Thitonia 

diversifolia, G. 

sepium, G. 

ulmifolia, 

Eupatorium 

morifolium 


forest fallow 

grazing, and 

crop residues 

(maize stubbles) 

Local market 

consumption 

G. ulmifolia, 

Diphysa 

americana, 

Spondias mombin, 

Bahuinia herrerae 

Livestock on pastures 

with dispersed trees, 

live fences, and 

pastures with forest 

patches. Improved 

pastures (B. 

decumbens, B. 

humidicola, 

Andropogon gayanus). 

Growing and sale of 

calves recently wean 

Local and regional 

market consumption 

G. sepium, Cecropia 

obtusifolia, Erythrina sp, 

L. leucocephala, P. 

aculeata 

Table 2. Socio-technical characteristics of livestock in four communities in Selva Lacandona 

(Chiapas, México).



participatory method that identifies rural development and forest management opportunities 

and constraints of each involved farmer and community. Subsequently, it helps farmers and 

communities to select and establish trees on individual or collective lands according to their 

preferences, providing technical support and paying participants for the provision of carbon 

offsets to national and mostly international buyers (Corbera 2010; Ruiz de Oña, 2011). Over 

time, the project has become a global landmark for the development of community-based 

payments for ecosystem services, and it has created its own design and implementation 

standard (www.planvivo.org). This approach has been extended to other similar projects in 

Uganda, Mozambique, Malawi and Cameroon 

3. Avoiding deforestation in La Corona, Marqués de Comillas 

In Ejido La Corona, a previous study estimated that 305 ha of forested land would have to 

be lost for agriculture purposes, as a baseline scenario during the period 2004-2009 

(Quechulpa, et al., 2010). With the support of the AMBIO cooperative and financial support 

from Pro-Árbol (i.e., a governmental PES program developed by Mexico’s National Forest 

Commission (CONAFOR) a participatory planning method was developed to design 

interventions for avoiding deforestation and increasing tree cover in deforested land. 

Concurrent financial resources were allocated from several programs, including Scolel Te, 

the Mexican Fund for Nature Conservation (FMCN) and Mesoamerican Biological Corridor 

(CBM). Financial resources were aimed to reduce pressure on forest land to avoid land-use 

change and to intensify cattle raising activities. 

The following two approaches were suggested by community members and selected for 

project development: 1) management of secondary vegetation and forest conservation and 2) 

establishment of agrosilvopastoral and agroforestry systems in open and grazing areas. As a 

result, secondary vegetation was managed by pruning and thinning trees to eliminate 

competition and favour growth of the most commercially valuable species. Activities for 

forest conservation included the opening and maintenance of 22 km of fire protection rifts, 

acquiring equipment for fires, combating brigades trained for fire control, supervising and 

regulating agricultural burns. Communal forest conservation incorporated a wider vision of 

the territory and collective agreements related to resource access into the working plan, 

thereby establishing rules and monitoring to regulate land use change according to the plan. 

The establishment of 45.5 km of live fences was carried out in accordance to the work plan 

(Figure 3) using “cocoite” forage trees (G. sepium) and other timber species, such as Tabebuia 

rosea, T. guayacan, C. odorata, S. macrophylla and Pachira aquatica. All forage trees were native 

species produced in communal nurseries. Other activities included the use of forage grasses 

such as Brachiaria, the promotion of cattle-feed supplements with multi-nutritional blocks, 

improvement of cattle breeds, establishment of livestock infrastructure, establishment of 

technical training and creation of a farmer-to-farmer exchange system (Ambio Cooperative, 

2010). In parallel, other projects particularly targeted towards women, such as the 

substitution of traditional open stoves by fuel wood-saving stoves, improvement of house 

conditions and improvement of other communal infrastructure, were also launched. 

In the first five years of the project, all of these activities resulted in 179 ha of prevented 

deforestation out of the 305 ha previously estimated to be lost, which translated in reduced 

CO2 emissions from land-use change. According to inventories from permanent plots, 407 ha 

of passively restored secondary forests accumulated biomass with a rate of 4.4 ton ha -1 year -1

340 


(approximately 3000 ton of CO2 per year), and it was estimated that the establishment of live 

fences fixed approximately 3200 tonnes of CO2. Other intangible benefits included the 

organisation of farmers for fire prevention; the organisation and training of brigades for 

carrying out forest inventories; and the improvement of productive systems. Above all, the 

recognition that forests may contribute to environmental and socioeconomic direct benefits 

was one of the main gains resulting from the project. 

Fig. 3. Participatory planning for designing forestry and agroforestry interventions in La 

Corona and Marques de Comillas in Chiapas, Mexico.



As a result of this management, Ejido La Corona, with a total area of 2254 ha, conserves 68% 

of the total rain forest (1530 ha), which includes primary and old secondary tropical rain 

forests. Nonetheless, livestock (528 ha), agriculture (177 ha) and urbanisation (20 ha) coexist 

with forests and the biological reserve, and these combined areas support 292 people 

(Quechulpa 2010). 

Subsequent studies have evaluated the performance of the systems established by producers 

in La Corona. An example is shown in Table 3, where the carbon components of pastures 

with and without trees are presented. 

Carbon components Pasture in Pasture with Pasture with 

monoculture live fences dispersed trees 

Trees (Mg C ha-1) 0.00 7.6 4.23 

Herbs (including grasses Mg C ha-1) 1.33 0.91 0.64 

Total roots (fine and coarse Mg C ha-1) 0.66 1.88 1.12 

Live biomass (Mg C ha-1) 1.99 10.4 5.99 

Soil organic matter 0-40cm (Mg C ha-1) 60.62 66.68 76.89 

Total carbon (Mg C ha-1) 64.62 87.5 88.89 

Source: modified from Aguilar-Argüello, 2007. 

Table 3. Carbon stocks in different components of monoculture pastures, live fences and 

dispersed trees in pastures in La Corona and Reforma Agraria in Chiapas (Mg C ha-1). 4. Agroforestry in Chilón and Salto de Agua, Chiapas 

Study communities of these municipalities are located in Chiapas tropical zone. Salto de 

Agua is located approximately 200 m a.s.l., and it has a warm and humid climate. In 

addition, Salto de Agua has a tropical rainforest. Meanwhile, Chilón is located in the 

intermediate tropical zone between 700 and 900 m a.s.l. It has a warm climate and abundant 

summer rains. Chilón has also a tropical rainforest, and the main soil types in this area are 

Regosols, Leptosols, and Cambisols (INEGI 1984; Soto-Pinto et al., 2010). Land is devoted 

mainly to agriculture, which is based on maize cultivation in both municipalities. While in 

Salto de Agua, maize cultivation is the key commercial and subsistence crop, Chilón farmers 

cultivate coffee as the main source of income. In both “ejidos”, farmers organise around 

small activity groups for establishing agroforestry systems. In Chilón, farmers established 

maize associated to trees (Taungya rotational systems), improved fallows and shaded coffee 

systems; while farmers in Salto de Agua established taungya systems converted finally to 

silvopastoral systems. Improved fallow consists of enriching fallow lands with secondary 

vegetation with timber trees, so far as the latter are planted during the first five years of the 

fallow period. Taungya, in turn, consists of enriching maize cultivated plots with timber 

trees in a rotational pattern; and coffee systems were enriched with timber trees in 

association to other variety of previously existing native trees as shading cover. The project 

Scolel Té has contributed to organizing agroforestry practices, training and monitoring. 

Previous evaluations by El Colegio de la Frontera Sur (ECOSUR) have shown that 

agroforestry systems provide multiple environmental services and can increase 

productivity, land and labour worth in relation to conventional land uses, such as extensive 

cattle farming and maize crops without trees (Soto-Pinto et al, 2010; Soto-Pinto, submitted). 

Coffee cultivation under the shade of trees conserves at least 40% of the total woody plant

342 


diversity in scarce neighbouring forests (Soto-Pinto et al, 2000; Romero-Alvarado et al, 2002; 

Peeters et al, 2003). Organic coffee cultivation translates into a higher carbon content in the 

upper soil layer (0-30 cm) than non-organic coffee systems, being able to store between 129.8 

and 215.6 Mg C ha -1 in their components, including soil C (Soto-Pinto et al., 2010). However, 

the amount of aboveground biomass depends on the structure and composition of shade 

vegetation (Table 4). Economic analyses comparing conventional coffee with and without 

enrichment of timber trees and PES and organic coffee with timber trees and PES have 

shown positive benefit/cost ratios, as follows: 0.8, 1.2 and 1.8 for conventional management, 

conventional management enriched with timber trees, and organic coffee plus carbon 

sequestration and timber, respectively (Table 5). 

Carbon components Natural traditional 

polyculture coffee 

Natural traditional Organic traditional 

polyculture coffee polyculture coffee 

enriched with timber enriched with timber 

trees 

trees 

Adult trees (≥10cm) 17.02±3.32 27.3±4.79 37.89±5.17 

Tree saplings (



Improved fallows have demonstrated to be an adequate alternative to slash-and-burn 

agriculture, due to their capacity to increase biomass, productivity, economic value, 

complexity, carbon and diversity (Roncal 2007; Soto-Pinto et al., submitted). Tables 6 and 7 

show structural and functional variables of these systems in the region. Results of these 

evaluations show that these systems may contribute to sedentarise the maize system, 

increase economic value and avoid deforestation while diversifying non-timber and timber 

related products. 

Both systems have shown their multifunctionality for improving productivity and restoring 

site features, environmental conditions and livelihood conditions (Soto-Pinto et al., 

Submitted). 

Land use systems 

Adult tree 

density 

(trees ha-1) 

Tree sapling 

density 

(100m2) 

Tree 

Height (m) 

Tree 

Diameter 

(cm) 

Taungya system 9-13years 520±218 31.9±19.0 8.8±1.4 16.2±3.9 

Improved Fallow at 9th year 623.3±106.3 4800±2361.0 8.26±1.3 16.88±3.16 

Traditional Fallow >30 years 463.8±191.8 3616.7±2700 7.4±1.3 17.7±3.47 

Inga-shaded organic coffee >10 

years 

79.3±79.3 25.1±7.5 5.6±1.0 19.2±13.0 

Polyculture-shaded organic 

coffee >10 years 

115.0±115.0 22.8±6.9 7.3±4.1 15.0±9.9 

Polyculture-shaded non organic 

coffee >10 years 

206.3±180.0 21.5±7.5 6.5±3.5 15.4±10.2 

Pasture with dispersed trees >10 

years 

20.0±10.0 112.0±16.0 6.1±3.4 14.8±4.5 

Pasture with live fences >10 

years 

56.0±37.3 116.0±68.7 4.5±1.5 10.3±4.5 

Pasture in monoculture >10 years 0 0 0 0 

Continuous maize 4-7 years 210.0±217.0 66.6±57.7 2.1±0.42 7.17±2.2 

Source: Aguilar-Argüello 2007; Roncal –García et al., 2007; Aguirre 2006; Soto-Pinto et al. 2010; Soto- 

Pinto et al. submitted; and other original data 

Table 6. Structural variables of agroforestry systems in Selva Lacandona (Mexico). 

In Salto de Agua and Chilón the decisions on land use are taken individually, often under 

the consensus of the family and the work group. The impact seems to be centered at the plot 

level. However the impact on the territory has not been evaluated. 

In each community, regional and local technicians act as training guides. Local decisions are 

taken individually or by group. Regional and state-level assemblies of technicians discuss, 

analyse problems, and propose solutions, new projects and financial supports to resolve 

specific problems. Civil and academic organizations and government dependencies 

accompany the process offering punctual technical assistance to resolve specific questions 

and financial support. Academy plays an important role in knowledge management, 

offering training and developing human resources. All of the sectors are involved in 

thematic networks contributing to the building of a public policy in the thematic of forestry 

programs, PES and territorial developing, among other issues.

344 

Land use systems 

Aboveground 

tree biomass 

(Mg ha-1) 


Soil Carbon 0- 

30cm in depth 

(Mg C ha-1) 

Tree Species 

Richness 

(number of 

species) 

Taungya system 9-13years 44.4±25.7 104.7±30.1 3.4±2.3 (500m2) Improved Fallow at 9th year 164.3±65.4 88.85±4.7 15.8±3.9 (1000m2) Traditional Fallow >30 years 109.35±66.7 120.4±7.0 19.7±3.8 (1000m2) Inga-shaded organic coffee >10 years 34.1±17.6 75.83±28.6 5.6±2.9 (1000m2) 

Polyculture-shaded organic coffee >10 

years 

75.8±27.4 131.13±23.4 15.7±3.3 (1000m2) 

Polyculture-shaded non organic coffee 

>10 years 

54.6±25.9 101.13±27.9 8.0±3.3 (1000m2) 

Pasture with dispersed trees >10 years 8.5±6.0 46.4±13.0 2.6±2.6 

Pasture with live fences >10 years 15.2±10.7 40.5±9.8 1±0 

Pasture in monoculture >10 years 0 50.2±14.6 0 

Continuous maize 4-7 years 4.38±3.62 115.0±12.3 14.0±4.8 (1000m2) 

Source: Aguilar-Argüello 2007; Roncal –García et al., 2007; Aguirre 2006; Soto-Pinto et al. 2010; Soto- 

Pinto et al. submitted; and other original data 

Table 7. Functional variables of agroforestry systems in Selva Lacandona (Chiapas). 

5. Discussion 

In some places traditional communities have managed their resources sustainably for long 

time, even better than in many protected areas managed by the State, especially in Latin 

America (Bray et al., 2008; Porter-Bolland, et al., In Press). However, in the last years the 

effects of public policy, colonization process and urban development, among other land use 

change drivers led to high deforestation rates in sites which until four decades before were 

completely forested, this is the case of Selva Lacandona in Mexico where Marques de 

Comillas is a referent. 

Enriched shaded coffee, alternative rotational and silvopastoral systems have demonstrated 

benefits in the topics of food production, biodiversity and economy of livelihoods. Results 

demonstrate the value of agroforestry systems as a potential strategy for tree cover recovery 

and carbon sequestration (Haile et al., 2008; Nair et al., 2010; Soto-Pinto et al., 2010) and 

suggest that adequate planning, incentives and capacity-building efforts can lead to better 

conservation practices (Berkes, 2007). In Ejidos La Corona and communities in Chilón and 

Salto de Agua people have improved their local organisational activities, either in groups or 

collectively through preventing deforestation; intensifying the agriculture process; 

reforesting the deforested and open areas; controlling fire; acquiring new abilities; creating 

norms, sanctions, work plans, and social rearrangements; and reinforcing old capacities for 

developing a forest culture as a part of a new institutional development and good 

governance (Evans 2004; Corbera, 2005, 2010). All this, coupled with the accompaniment of 

the civil society and academic institutions, and the involvement of government in a network 

of ecosystem services has been key in order to begin a governance development (Ruiz de 

Oña et al., 2011). However, the scaling up of this process is a challenge since it represents a 

greater organizational complexity and negotiation, a matter of governance (Swiderska et al., 

2008).



In Marques de Comillas collective organization and decisions can impact broad and 

decisively on the territory in the short term, while in Chilón and Salto de Agua decisions 

taken by individuals or groups may be slower than collective decisions. Future studies on 

social resilience on both types of patterns should be of great importance. The lesson gained 

in this experience is that public programs must consider community priorities based on an 

integral regional vision, environmental education, strengthening of local capabilities and 

organisation. As a whole, public priorities may be a better appropriation and adaptation of 

programs in a new model of territorial development by considering institutional 

arrangements (Merino and Warnholtz, 2005). Farmers are the leading experts regarding 

their context and livelihood conditions and may or may not adopt and adapt programs that 

involve a territorial vision. The intense participation of communities may allow better 

monitoring, especially when these programs are integrated into their own communitymanagement 

plans (Franzel and Scherr, 2002). 

Territorial participatory planning of agroforestry and restoration systems, in addition to the 

institutional development have demonstrated their capacity to benefit environment, 

farmer’s livelihoods and social capital, through their contribution to increase productivity, 

complexity, diversity, economic value, organization capacities and knowledge, which as a 

whole may contribute to avoid deforestation (Alburquerque, 2002; Bray, 2008; Swiderska et 

al., 2008). However, other products, service, process and management innovations are 

required to have a broader menu of options for farming systems and livelihoods to ensure 

the permanence of forest systems in a competitive context. Some of the needs to be resolved 

in order to conceive this process as a territorial development are: the best practices to 

achieve food self-sufficiency, development of market chains, education, training, 

infrastructure, financial support, policy arrangements and skills of competitiveness for 

production systems (Alburquerque, 2002; Ruiz de Oña et al., 2011). An integration of an 

agroecological matrix which enables agricultural production and natural resource 

conservation and a new political vision may help facilitate social and environmental 

synergies in rural areas (Perfecto and Vandermeer, 2010) 

Although the experience run by the project Scolel te’ in Marqués de Comillas is not precisely 

REDD+, may offer a set of lessons learned about the conditions required for a good 

management for reducing deforestation. Some elements which need to be considered in order 

to contribute to the design of PES programs are the following: 1) technology adaptation, 2) 

ordering of land use with a territorial development vision, 3) broad participation of all 

stakeholders, 4) institutional development at local, regional levels and, 5) involvement in 

networks to launch processes of governance for the management of an adequate public policy 

of PES. Although these systems may contribute to avoiding deforestation, their potential to 

become part of REDD programs need to be better discussed since the relationship among 

markets and actors’ rights and duties, in addition to the uncertainty regarding the factors 

influencing effectiveness on deforestation is unclear (Coad et al., 2008). 

In Chiapas, a process of institutional development around the issue of PES is emerging. In the 

last years groups working on ecosystem services (State Group of Ecosystem Services, GESE by 

its acronyms in Spanish) and deforestation and degradation (REDD) have matured in the state 

of Chiapas; representatives of the “Camara de Diputados y Senadores del Estado de Chiapas” 

(State House of Representatives and Senate) have approved the Law for Mitigation and 

Adaptation to Climate Change in the State; meanwhile, a group of research institutions and 

organizations of the civil society carry out an effort of monitoring, reporting and verification

346 


(MRV) in rural communities as a basis for a REDD planning in Chiapas. Moreover, the 

Government of the States of Chiapas and Campeche has recently signed an agreement with 

Acre Brazil and the Government of California through The Governors’ Climate and Forests 

Task Force (GCF) for REDD + (http://tropicalforestgroup.blogspot.com/2010/11/text-of-cachiapas-acre-mou-on-redd.html) 

with the aim to “developing a common subnational REDD+ 

framework or patform for adoption and implementtion in GCF states and provinces” and “to 

building databases, developing options for linkage arrangements for getting financial support, 

technical assistance, capacity building, and advancing stakeholder involvement” 

(http://www.gcftaskforce.org). For its part, the Chiapas Government has allocated funds from 

the vehicle ownership tax payment to farmers focused on a Biodiversity Hotspot, the Selva 

Lacandona (Lacandon Community). This region has been widely supported by conservation 

projects, but from the point of view of other authors it lacks of organizational conditions for 

effective management of forest carbon (Castillo-Santiago et al 2009). 

At the national level, CONAFOR (Forestry National Comission) has convened a technical 

advisory board (Consejo Tecnico Consultivo CTC) aimed to “promote and deliver 

recommendations to the government institutions in order to influence the building of a 

functional mechanism for designing and implementing REDD+ in Mexico, guarantig the 

transference and maximization of environmental and social benefits” 

(http://www.reddmexico.org). However, groups of civil society organizations drew 

attention to the topic of indigenous peoples' rights. Hence, much effort is still needed to 

really get to have a shared vision. 

Forest alternatives will only be adopted by farmers if they respond to expressed needs of 

population, reduce risks, alleviate constraints, and increase productivity. Deforestation 

process seems to take its course if drivers persist (Cortina et al., in press). Farmers will be 

interested since they participate and design innovations from the beginning of the project 

and also if the initiatives constitute a good deal from the set of alternatives offered in the 

territory (Franzel & Scherr, 2002; Merino & Warnholtz, 2005; Bray, 2008). Mere academic 

exercises of land use ordering may be at risk of abandonment if local priorities are left out, 

so that the participation of farmers (men and women) in planning, designing and applying 

technology, norms, rules and sanctions among other local institutions must be established 

with broad participation of all of the actors involved. 

6. Acknowledgments 

We would like to thank CONACYT for financial support (projects SEP-2004-C01-46244 and 

FORDECYT 116306), and we would also like to thank El Colegio de la Frontera Sur 

(ECOSUR), Esteve Corbera from Universitat Autònoma de Barcelona (UAB), Cooperative 

Ambio, and anonymous reviewers. Sandra Roncal, Cecilia Armijo, Victor Aguilar Argûello 

and Carlos Mario Aguirre helped for fieldwork. 

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17 

Economic Models 

of Shifting Cultivation: A Review 

Yoshito Takasaki 

University of Tsukuba 

Japan 

Shifting cultivation is a dominant agricultural system in tropical forests. Shifting 

cultivators transform nutrients stored in standing forests to soils by slashing, felling, and 

burning forests (i.e., slash-and-burn); they regularly shift crop lands by replacing depleted 

plots with cleared forest lands (Denevan & Padoch, 1987; Kleinman et al., 1995; 

Ruthenberg, 1980). Approximately 300–500 million people practice slash-and-burn 

agriculture on almost one third of the planet’s 1,500 million ha of arable land (Giaradina 

et al., 2000; Goldammer, 1993). Shifting cultivation is central to the poverty-environment 

nexus in the tropics. On one hand, shifting cultivation is a dominant livelihood activity 

among small-scale tropical farmers with various cultural, ethnic, and social backgrounds, 

and thus it is tightly linked with poverty and development (Angelsen & Wunder, 2003; 

Byron & Arnold, 1999; Reardon & Vosti, 1995; Sunderlin et al., 2005; Wunder, 2001). On 

the other hand, not only is shifting cultivation one of the major causes of tropical 

deforestation, but also, the associated forest-cover change leads to multiple environmental 

problems, such as soil degradation, biodiversity loss, and reduced carbon sequestration 

(e.g., Chazdon et al., 2009; Dent & Wright, 2009; Kleinman et al., 1995; Lawrence et al., 

2005; Myers, 1992). As such, shifting cultivation can conflict with various conservation 

efforts, such as maintaining protected areas, engaging in community-based conservation, 

sustaining integrated conservation-development programs (ICDPs), making payments for 

environmental services (PES), and reducing emissions from deforestation and forest 

degradation (REDD) (e.g., Angelsen, 2008; Wilshusen et al., 2002; Wunder, 2006). A winwin 

goal of poverty alleviation and rainforest conservation in shifting cultivation systems 

is a global challenge of the first order. To design an effective policy mix, it is crucial to 

develop a better understanding of shifting cultivators’ decision making; to that end, 

economic modeling is a powerful tool. 

This chapter reviews economic models of shifting cultivation and those of deforestation 

and soil conservation related to shifting cultivation developed by economists over the last 

two decades. My goal is not to offer a comprehensive review, but to highlight key 

modeling approaches (what is modeled and what is not, and with what assumptions), 

clarify how they are useful and incomplete in efforts to examine shifting cultivators’ 

behaviors, and point to promising directions for future modeling. I encourage readers to

352 


see other reviews on economic models, such as Kaimowitz and Angelsen (1998) and 

Barbier and Burgess (2001) for deforestation and Barbier (1997) for land degradation in 

developing countries. As far as I know, no other reviews on economic models of shifting 

cultivation are available. 

I focus on farm-level models that characterize individual farmers’ behaviors (endogenous 

variables) under certain environmental and institutional conditions, such as resource 

stock, markets, and property rights (Binswanger & McIntire, 1987). 1 Farm models allow 

modelers to examine how farmers’ behaviors are affected by policy parameters 

(exogenous variables). Modelers usually focus on individual farmers’ key decisions that 

directly or indirectly determine environmental outcomes of interest (e.g., forest clearing in 

deforestation models). Although no models fully capture the complexity of the real world, 

economic models highlight key aspects of the reality to better understand causal 

mechanisms. 

1.1 Modeling approach 

Three important choices in modeling approaches require attention: static vs. dynamic 

modeling, market conditions, and policies. Economic models are generally classified into 

static or dynamic models; whereas static models capture economic agents’ decisions at a 

point in time, dynamic models consider the potentially changing path of their behaviors. 

The choice depends on whether agents’ decisions at a point in time affect their future 

decisions. This dynamic linkage is described by state equations, i.e., the law of motion of 

state variables, which can be the outcome of interest. Although static models characterize 

agents’ optimal decisions at a given point in time, dynamic models characterize the overtime 

path of their optimal decisions (control variables) and corresponding state variables. 

For example, in a soil-conservation model, the state variable can be soil stock (or fertility) 

and the control variables can be farmers’ choices that affect soil fertility, such as cultivation 

intensity and soil conservation input. The simplest dynamic model is a two-period model, 

although most dynamic models discussed below consider an infinite time horizon, while in 

this chapter, models are considered to be static when agents make current decisions based 

only on the present value of the net benefit/cost stream. 

Although perfect markets enable an efficient allocation of resources, market imperfection is 

the norm in developing countries, where most tropical forests are situated. Better 

understanding market imperfection and non-market institutions has been a central theme of 

development economics over the last three decades (Bardhan & Udry, 1999; Ray, 1998). 

Although many shifting cultivation, deforestation, and soil conservation models in the 

literature assume perfect markets to examine price policies, such as those related to taxes 

and subsidies, some models consider imperfect factor markets. In particular, although with 

a perfect labor market a market price (wage) supports a separation of farm households' 

consumption (labor supply) and production (labor demand) decisions, market imperfection 

can break this separation (Singh et al., 1986); here wage represents the opportunity cost of 

1 Kaimowitz and Angelsen (1998) review deforestation models other than farm-level models, such as 

regional-level models and national and macro-level models, including general equilibrium models (see 

also Angelsen & Kaimowitz, 1999). Although tropical forests are often common property, soils are 

individual farmers’ private property; most soil conservation models are farm-level models.

Economic Models of Shifting Cultivation: A Review 353 

labor in the form of returns to any non-farm activities (Benjamin, 1992). Not surprisingly, 

market imperfection commonly gives rise to ambiguous policy impacts. In contrast, some 

models employ a framework that does not involve any factor markets (e.g., models focusing 

on fallow-cultivation cycle). 

Most models examine farm output price (mostly food price) and wage (opportunity cost 

of labor), which can be altered by various macroeconomic policies; some models also 

examine input price other than wage, technological progress, and property rights. 2 Many 

dynamic models highlight the role of the discount rate, which can be altered by credit 

policies. Some models that consider farmers’ decisions with uncertainty – especially in 

production and price – focus on the roles of risk and risk aversion. Most deforestation 

models show that promoting farming through price and technology leads to greater forest 

clearing as the farmers augment farm production; in contrast, promoting non-farm 

activities discourages forest clearing. Most dynamic models reveal that a lower discount 

rate encourages investment not only in soils (soil conservation), but also in land holdings 

(forest clearing). Other policy impacts are generally mixed, depending on modeling 

specification (assumption). Specific theoretical predictions of each model are not reviewed 

in this chapter. 

1.2 Organization of the chapter 

The remainder of the chapter is organized as follows. Sections 2, 3, and 4 review deforestation, 

soil conservation, and shifting cultivation models, respectively. The main papers cited in these 

sections are listed in chronological order in Tables 1, 2, and 3, respectively, which summarize 

decision variables, outcome variables, policy parameters, modeling frameworks (static vs. 

dynamic), and factor markets (perfect vs. imperfect vs. not modeled). 

The tables also report whether the modeling work is accompanied with a substantial 

empirical analysis; an empirical analysis can be a case study, a descriptive analysis of micro 

data, simulation work based on micro data, or a regression analysis (to test theoretical 

hypotheses). Whereas some models – especially those accompanied with an empirical 

analysis – consider specific empirical contexts (e.g., colonists in Amazonia), others are 

developed in general contexts. Although this distinction is not always clear, it is clarified 

when needed. In some models I show mathematical equations to highlight their key features 

in a concrete way; when I do so, I change original notations (and functions in some cases) to 

uniform notations for clarity and clear comparisons across models. 

Based on these reviews, Section 5 discusses major lacunae in extant shifting cultivation 

models and promising avenues for future modeling. Section 6 concludes. 

2. Deforestation models 

Most farm-level deforestation models examine forest-clearing labor as a key decision 

variable. Assuming a simple function of forest clearing with labor as a unique input (which 

is valid among small-scale farmers who do not use chainsaws), cleared forest is directly 

captured by forest-clearing labor. 

2Welfare-augmenting policies are usually considered. It is a straightforward process to examine welfare 

impacts of specific policies in dynamic models by applying the procedure developed by Caputo (1990) 

(see Takasaki, 2006 for an example).

354 

Southgate 

(1990) 

Larson 

(1991) 

DeShazo 

and 

DeShazo 

(1995) 

Bluffstone 

(1995) 

Angelsen 

(1999) 

Barrett 

(1999) 

Barbier 

(2000) 

Pendleton 

and Howe 

(2002) 

van Soest 

et al. 

(2002) 

Takasaki 

(2007) 

Delacote 

(2007) 

Main decision 

variables 

Forest-clearing 

labor, soil 

conservation labor 


labor, soil 

conservation labor 

Main outcome 

variables 


labor, soil 

conservation 

labor 


labor, soil 

conservation 

labor 

On-farm labor Forest clearing 

(land value) 

Labor for firewood 

collection 

Cleared forest 

(distance) 


labor 


labor 


labor 


labor 


labor 

Proportion of land 

cultivated 

Table 1. Deforestation models 

Firewood 

collection, forest 

stock 

Cleared forest 

(distance) 


labor 

Main policy 

parameters 

Output price, 

wage, interest 

rate 

Output price, 

wage, interest 

rate, 

technological 

progress 

Output price, 

input price, 

wage, cost of 

land clearing 


Static vs. 

dynamic 

Factor 

markets 

Empirics 

Static Perfect None 



Wage Dynamic Perfect, Nepal 

imperfect (simulation) 

Output price, 

wage, transport 

cost, discount 

rate, population 

Output price - 

mean and 

standard 

deviation 

Cultivated land Output price, 

wage 

Cleared forest Market 

integration 

(generated from 

price and wage), 

technological 

progress 

Cleared forest Technological 

progress, output 

price 

Static None, 

perfect 

None 

Static Perfect Madagascar 

(case study) 

Dynamic Perfect Mexico, 

Ghana 

(case study) 

Dynamic 

(2 periods) 

Cleared forest Output price, Dynamic 

wage, land price, (2 periods) 

discount rate 

Proportion of 

land cultivated 

Risk, risk 

aversion, 

population, 

forest 

profitability 

Perfect Bolivia 

(regression) 

Static Perfect, 

imperfect 

None 

Perfect, None 

imperfect 

Static Not 

modeled 

None


McConnell 

(1983) 

Main decision 

variables 

Soil loss, nonsoil 

input 

Main outcome 

variables 

Main policy 

parameters 

Static vs. 

dynamic 

Factor 

markets 

Empirics 

Soil depth Tenure Dynamic Perfect None 

Barbier (1990) Soil-degrading Soil depth Output price, 

input, soilconserving 

input 

input price 

Barrett (1991) Soil loss, nonsoil 

input 

Clarke (1992) Farm input, soil 

investment 

LaFrance 

(1992) 

Krautkraemer 

(1994) 

Cultivation 

input, soilconservation 

input 

Barrett (1996) Soil loss, soilconservation 

input 

Grepperud 

(1997a) 

Grepperud 

(1997b) 

Bulte and van 

Soest (1999) 

Grepperud 

(2000) 

Lichtenberg 

(2006) 

Graff-Zivin 

and Lipper 

(2008) 

Dynamic Perfect Indonesia 

(descriptive) 

Soil depth Output price Dynamic Perfect None 

Soil quality Output price, 

input price, 

discount rate 

Dynamic Perfect None 

Soil stock Output price Dynamic Perfect None 

Soil loss Soil fertility Population Dynamic Perfect None 

Soil depth Output price, 

discount rate 

Farming labor, Farming labor, Farming 

soil-conservation soil-conservation support, soil- 

labor 

labor 

conservation 

support, offfarm 

support 

Farm input, Soil stock Output price, 

investment in 

soil-conservation 

structure 

discount rate 

Soil loss, 

farming labor 

Farming 

intensity (soil 

depleting/ 

conserving) 

Soil loss, 

farming labor 

Soil carbonsequestration 

investment 

Table 2. Soil-conservation models 




Soil depth Output price Dynamic Perfect, 

imperfect 

None 

Soil fertility Risk aversion Dynamic Perfect None 

Soil depth Output price Dynamic Perfect None 

Soil carbonsequestration 

investment 

Sequestration 

cost, output 

price, discount 

rate, risk 

aversion 

Dynamic Perfect None

356 

Barrett 

(1991) 

Jones and 

O’Neill 

(1993) 

López 

(1997) 

Tachibana 

et al. (2001) 

Batabyal and 

Lee (2003) 

Sylwester 

(2004) 

Willassen 

(2004) 

Takasaki 

(2006) 

Pascual and 

Barbier 

(2006) 

Pascual and 

Barbier 

(2007) 

Balsdon 

(2007) 

Brown 

(2008) 

Main decision 

variables 

Cultivation 

length, fallow 

length 

Proportion of 

land 

cultivated 

Main outcome 

variables 

Fallowcultivation 

cycle 

Main policy 

parameters 

Fallow length Output price, 

wage, discount 

rate, population 


Static vs. 

dynamic 

Factor 

markets 

Output price Dynamic Not 

modeled 

Empirics 

None 


Cleared forest Cleared forest Output price Dynamic Perfect Ghana 

(regression) 

Proportion of 

upland land 

cultivated, 

upland forest 

cleared 

Proportion of 

upland 

cultivated, 

shifting 

cultivation area, 

upland forest 

cleared 

Lowland 

technological 

progress, 

lowland farm 

area, output 

price, forestclearing 

cost, 

tenure security 

Fallow length Fallow length Return to fallow, 

discount rate 

Proportion of 

land 

cultivated 


cycle 

Proportion of 

land cleared 

Farming labor 

(clearing and 

on-farm labor 

with a fixed 

proportion) 

Farming labor 

(clearing and 

on-farm labor 

with a fixed 

proportion) 

Cultivation 

length 

Proportion of 

land 

cultivated 

Land quality Income transfer, 

output price, 

population 


cycle, 

soil fertility 

(present value of 

gross output) 

Proportion of 

land cleared 

Fallow soil 

fertility, forest 

clearing 

Fallow soil 

fertility, forest 

clearing 

Cultivation 

length 

Proportion of 

land cultivated 

Table 3. Shifting cultivation models 

Dynamic Perfect Vietnam 

(regression) 

Dynamic Not 

modeled 

Dynamic Not 

modeled 

Output price Dynamic Not 

modeled 

Output price, 

wage, discount 

rate, soilregeneration 

rate, soil 

erosivity 

None 

None 

None 


Population Dynamic Perfect Mexico 

(simulation) 

Output price Dynamic Perfect Mexico 

(simulation) 

Output price, 

non-farm income 

Preference, 

spatial 

dependency 

Dynamic Not 

modeled 

None 

Dynamic Perfect Cameroon 

(regression, 

simulation)


2.1 Static deforestation models 

Early deforestation models are static. Southgate (1990), which is elaborated by Larson (1991), 

considers not only forest-clearing labor, but also soil-conservation labor among colonists in 

the forest frontier; 3 these two labors separately determine the present value of agricultural 

production (cropping and livestock) and soil conservation. DeShazo and DeShazo (1995) 

apply an agricultural household model (Singh et al., 1986) to forest clearing with a perfect 

labor market, though they capture forest clearing through the value of land (rent), not forest 

clearing itself. van Soest et al. (2002) directly extend the agricultural household model to 

forest clearing, comparing effects of farm technological progress on forest clearing under 

perfect and no labor-market conditions. 

Barrett (1999) and Delacote (2007), respectively, examine influences of price and production 

risk in farming on forest clearing in their static models; Delacote (2007) also addresses effects 

of risk aversion and returns to standing forest in the form of non-timber forest products 

(NTFPs). 4 

2.2 Discrete dynamic deforestation models 

Static deforestation models effectively treat cleared land as a variable input (produced by 

labor) for farming. This setup is valid if tropical farmers replace their old infertile plots with 

newly cleared forest lands every agricultural season or do not consider future production on 

their cleared lands because of insecure tenure. This is not a common practice among shifting 

cultivators, because (1) forest clearing is very costly to them (especially with no use of 

chainsaws), (2) they can employ a variety of traditional soil management techniques (in 

particular fallowing), and (3) forest clearing and cultivation often give them some claims to 

the land (Takasaki, 2007). Instead, shifting cultivators crop their cleared lands for more than 

one agricultural season over time. 

Takasaki (2007) treats forest clearing as both an input for current production and an 

investment in future production in his two-period model. Quality-adjusted land for 

cultivation at period t is given by: 

 

A aL 

(1.1) 

1 1 

1 

A A a L 

(1.2) 

2 1 2 

where Lt is labor allocated to clear forest at period t, a is forest-clearing function, and ρ 

captures fertility decline through cultivation (depreciation rate). van Soest et al. (2002) use 

the same forest-clearing function as in equation (1.1); equation (1.2) is a state equation of 

3Although conflicts over property rights are central issues among colonists in the forest frontier (e.g., 

Alston et al., 2000; Anderson & Hill, 1990; Hotte, 2001; Mueller, 1997), related theoretical modeling is 

not reviewed in this chapter. 

4The potential role of NTFPs for sustainable development and poverty alleviation in the tropics is often 

emphasized (e.g., Arnold & Perez, 2001; Coomes et al., 2004; Wunder, 2001); at the same time, 

overexploitation of forest resources as local commons among poor populations has been a major 

concern (i.e., poverty-environment trap) (Barbier, 2010; Dasgupta, 1993, 2001; Jodha, 1986). In particular, 

firewood collection and associated forest degradation have received much attention. Bluffstone (1995), 

for example, examines firewood/fodder collection and forest biomass evolution.

358 


crop land. Takasaki (2007) considers not only labor-market conditions, but also land-market 

conditions, comparing four distinct market institutions (Latin America vs. Sub-Saharan 

Africa), including the effects of land price. 

Some static models, such as Southgate (1990), Larson (1991), and Angelsen (1999), jointly 

address input and investment aspects of forest clearing by considering the benefit/cost 

stream over time generated by current forest clearing; such models capture neither farmers’ 

behaviors over time nor the evolution of land assets. 5 

Pendleton and Howe (2002) develop a two-period model for Amerindians in Bolivia, 

capturing forest clearing in the dry season (period 1) for production in the wet season 

(period 2). Distinct from other modeling works, Pendleton and Howe (2002) distinguish 

between primary and secondary forests; they also construct a measure of market integration 

from market prices. 

2.3 Continuous dynamic deforestation models 

Following a standard capital model, dynamic farm-level deforestation models consider 

forest clearing as a pure investment in land capital for future production. This modeling is 

commonly used to examine a society's optimal deforestation – i.e., exploitation of tropical 

forests as the commons – in the literature (e.g., Barbier & Burgess, 1997; Ehui et al., 1990; 

López, 1994; López & Niklitschek, 1991); most models employ control theory in a 

continuous time framework (e.g., Kamien & Schwartz, 1991; Seietstad & Sydsaeter, 1987). 

Assuming that a fixed proportion of arable land (δ) is fallowed in each time period, Barbier 

(2000) considers the following state equation: 

 

A a L A 

(2) 

where time index is suppressed and A dA dt . The depreciation rate δ is effectively the 

same as ρ in equation (1) in the discrete-time framework. 

3. Soil-conservation models 

Soil-management measures are classified into two groups based on their costs: one with 

reduced current output levels, such as less intensified cultivation, forest fallowing, and 

perennial systems, and the other with input use, which can take various forms, such as 

mulching, composting, terracing, and creating hedgerows, depending on agroecological 

conditions in specific locales. Although fertilizer is an essential input in other agricultural 

systems, fertilizer use is very limited in shifting cultivation that relies heavily on forestbased 

measures (forest clearing and fallowing) (Nicholaides et al., 1983; Sanchez et al., 1982). 

Grepperud (1997a) examines how programs supporting farming, soil conservation, and nonfarm 

activities affect labor allocations for these three activities in his static model, in the 

same spirit as Southgate (1990) and Larson (1991). 

5The key decision variable in Angelsen’s model (1999) is the distance to forest cleared. Such spatial 

modeling, which is common among geographers, is not reviewed in this chapter (other examples of 

spatial farm-level deforestation models developed by economists include Angelsen, 1994; Chomitz & 

Gray, 1996; Mendelsohn, 1994). Angelsen (1999) compares four models under distinct modeling 

assumptions and property rights, not market conditions, in a unified framework.


All soil conservation models developed in the literature examine continuous cultivation 

with fixed land size. 

3.1 Canonical soil dynamics 

McConnell (1983) models the dynamics of soil depth x as follows: 

x s 

(3) 

where is natural soil regeneration and s is soil loss associated with cultivation; farm 

output is a function of soil loss, soil depth (fertility), and non-soil inputs (evaluated at factor 

price). 6 This model captures only the adjustment of cultivation intensity among soilmanagement 

measures. 

3.2 Input-based soil-conservation models 

Economists have extended McConnell’s (1983) dynamic model by incorporating input-based 

soil-conservation measures in various ways. Clarke (1992) adds soil investment as a choice 

variable to equation (3); Barbier (1990) and LaFrance (1992) consider inputs for (soil 

degrading) cultivation and soil conservation separately; Barrett (1996) adds a soilconservation 

measure as a function of conservation input to equation (3); and Grepperud 

(1997b) considers an investment in soil-conservation structure, such as terraces, modeling 

the joint evolution of soil stock and conservation structure. 

Bulte and van Soest (1999) examine the soil dynamics with no labor market, using the 

following state equation: 

 

x l s 

(4) 

where l is labor for soil conservation. Equation (4) captures labor-intensive soil 

conservation. 7 

Grepperud (2000) examines how risk aversion influences soil conservation with production 

and price uncertainty. Graff-Zivin and Lipper (2008) examine the farmer’s decision on 

investment in soil carbon sequestration by explicitly modeling soil carbon as well as soil 

fertility with production risk; they examine effects of sequestration cost and risk aversion, as 

well as output price and discount rate. 

3.3 Continuous vs. cyclical farming 

Assuming stock-dependent soil regeneration (cf. equations 3 and 4), 

 

x x s 

(5) 

Krautkraemer (1994) shows that in the presence of nonconvexity in the net benefit function, 

a non-continuous farming strategy – periodic cycles of cultivation and fallow – can be an 

6Barrett (1991) compares McConnell’s (1983) models with and without non-soil inputs. 

7Using equation (4), Bulte and van Soest (2001) examine an environmental Kuznets curve for land 

degradation with no labor market. Lichtenberg (2006) demonstrates that ambiguous impacts of output 

price found by Bulte and van Soest (1999) is not attributable to labor-market failure, but can occur 

depending on the labor supply's wage elasticity.

360 


equilibrium (Lewis & Schmalensee, 1977, 1979) and that population growth leads to a shift 

from cyclical cultivation to continuous cultivation (sensu Boserup, 1965). 

4. Shifting cultivation models 

Shifting cultivation models in the economics literature can be classified into four: the fallowcultivation 

cycle model, the forest-fallow model, the cultivation-intensity model, and the 

land-replacement model. 8 Almost all models are dynamic; all models except for Tachibana 

et al. (2001) assume a fixed land size. 

4.1 Fallow-cultivation cycle models 

Fallow-cultivation cycle models focus on fallow and/or cultivation length as decision 

variables, ignoring all other decisions, such as labor allocation. Barrett (1991) extends the 

optimal forest-rotation problem (Faustmann, 1995) to fallow-cultivation cycles by treating 

both fallow and cultivation lengths as choice variables. This rotation problem does not 

explicitly capture soil dynamics. In contrast, Willassen (2004) models the cyclical evolution 

of soil fertility in the cultivation and fallow phases; the farmer chooses only the phase – 

binary choice q = 0 (fallow) or 1 (cultivation) – over time, and distinct from soil conservation 

models (e.g., equation 3), soil dynamics under cultivation as well as fallow are assumed to 

be determined by soil fertility level x only. 

In these cyclical models, the farmer does no cultivation in the fallow phase. This 

simplification is for analytical tractability. Of course, in practice, shifting cultivators mix 

different stages of cultivation and fallow across plots. 

Assuming fixed fallow length and on-farm soil dynamics characterized by equation (5), 

Balsdon (2007) focuses on cultivation length as a choice variable; distinct from other cyclical 

models, the termination of the cultivation phase in one plot is instantly followed by 

cultivation on the next plot. Batabyal and Lee (2003), in contrast, focus on the choice of 

fallow length. 

4.2 Cultivation-intensity models 

Cultivation-intensity models capture soil degradation resulting from shortened fallow 

through the cultivation-intensity measure without explicitly modeling fallow dynamics. 

Although cultivation-intensity models differ depending on their focus, their common 

feature is to capture cultivation intensity through the proportion of land cultivated (b). For a 

given land size, 1 – b is the proportion of fallow land and 1/b represents fallow length. For 

example, for b = .1, fallow length is 10 (years). 

4.2.1 Early cultivation-intensity models 

Larson and Bromley (1990) develop a dynamic model with a fixed cultivation intensity. 

Jones and O’Neill (1993) develop a static model using cultivation intensity b as a key 

decision variable. 9 

8Batabyal and Beladi (2004) and Batabyal and Nijkamp (2009) apply stochastic modeling to shifting 

cultivation, which is not reviewed in this chapter. 

9Jones and O’Neill (1993) extend their model to a spatial model.


4.2.2 Cultivation-intensity models with soil dynamics 

In Sylwester’s (2004) model, the soil dynamics under cultivation follows equation (5), with 

soil loss s replaced with a function of cultivation intensity b; distinct from other cultivationintensity 

models, Sylwester does not model factor markets as in fallow-cultivation cycle 

models. 

Whereas Brown (2008) considers a binary choice between cultivation and fallow – on each 

plot over time – as in fallow-cultivation cycle models, he solves the dynamic problem by 

treating this binary variable q as continuous; that is, he effectively uses cultivation intensity 

b as a choice variable. His focus is to examine the roles of preference (measured by the 

revealed preference approach) and spatial dependency in farmers’ forest clearing using 

simulation (see also Brown, 2006). 

4.2.3 Cultivation-intensity models with land dynamics 

Tachibana et al. (2001) develop a cultivation-intensity model that endogenizes the evolution 

of upland holdings (T) among Vietnamese farmers who combine upland shifting cultivation 

and lowland paddy cultivation: 

T a( b) bT 

(6) 

where a is (upland) forest cleared and endogenized depreciation rate δ(b) (cf. equation 2) 

captures soil degradation through shortened fallow (higher b captures depriving 

intensification). Note that distinct from equation (2), T is total land holdings, consisting of 

cultivated land bT (=A) and fallow land (1-b)T (= T – A). Furthermore, fallow land is under 

the risk of being grabbed by neighbors. Tachibana et al. (2001) examine how the proportion 

of cultivated upland land (inverse of fallow length), shifting cultivation area, and upland 

forest clearing are affected by a rich set of policies, such as lowland technological progress, 

lowland farm area, forest clearing cost, and upland tenure security, as well as output price. 

4.3 Forest-fallow models 

4.3.1 Forest-fallow models with communal fallow forest 

Forest-fallow models endogenize the dynamics of biomass accumulation in fallow forest as 

a soil builder. Fallow forest is explicitly or implicitly assumed to be communally owned by 

villagers. López (1997) introduces the following dynamics of fallow biomass density η: 

i i a 

 

Q 

(7) 

where γ is the intrinsic growth of secondary vegetation, ai is cleared forest by household i, 

and Q is total land area under both cultivation and fallow – of the village. Equation (7) 

assumes that fallow biomass density is determined by the proportion of cleared forest land 

for cultivation, i.e., village-level cultivation intensity. 10 

Assuming equation (7) and a simple conversion of biomass to soil fertility on cleared fallow 

forest, Pascual and Barbier (2006; 2007) derive the dynamics of soil fertility on cleared forest 

(Pascual & Barbier, 2006, equation 5). They assume that in each period of time the farmer 

10In the forest-fallow model, adding NTFPs collected from secondary fallow forest as an additional 

benefit of fallowing is a straightforward extension.

362 


cultivates only the cleared land; then, on-farm soil conservation is irrelevant. In Pascual and 

Barbier (2006; 2007), the only decision variable is farm labor, which is assumed to be 

allocated between forest clearing and cultivation with a fixed proportion. Pascual and 

Barbier (2006; 2007) examine impacts of population density (n/Q, where n is the number of 

households in the village) and output price on forest clearing and fallow soil fertility. 

4.3.2 Forest-fallow models with private fallow forest 

Shifting cultivators commonly have usufruct of not only the cultivated land they have 

cleared, but also their fallow land; customary tenure of fallow land tends to be insecure, 

however, and this tenure insecurity influences their forest clearing and fallowing decisions 

(Otsuka & Place, 2001; Place & Otsuka, 2001; Tachibana et al., 2001). It is straightforward to 

revise equation (7) to characterize such an alternative customary tenure setting; then, soil 

fertility of cleared fallow forest is effectively determined by fallow length or the inverse of 

cultivation intensity, 1/b. In this way, the fallow-forest model with private fallow forest 

directly corresponds to the cultivation-intensity model; a key difference is that the former 

focuses on fallow dynamics and the latter highlights other dynamics, such as on-farm soil or 

land holdings. 

4.4 Land-replacement models 

Fallow-cultivation cycle models assume a cyclical switch of the whole land between 

cultivation and fallow; fallow-forest models assume that the farmer cultivates cleared forest 

land only in each period of time. In practice, shifting cultivators replace some depleted plots 

with cleared forest land each time, while continuing to cultivate the remaining plots; 

replacing all plots simultaneously is a polar case. 

This aspect is explicitly captured in the land-replacement model (with fixed land size) 

introduced by Takasaki (2006). The key choice variable is the proportion of cultivated land, 

not total land, replaced with cleared forest land (c). This modeling approach highlights the 

tension between replaced (cleared) and non-replaced (remaining) plots – the former is more 

fertile but clearing is costly. It also directly captures new soils on cleared forest land added 

to soils on remaining plots. Specifically, the dynamics of on-farm soil stock is obtained by 

extending equation (3): 

1 

x c c s 

(8) 

where φ is soil stock (per unit of land) of cleared forest (see Takasaki, 2006, Figure 1 for 

derivation). Note that for c = 0 (continuous cultivation), equation (8) is the same as (3); for c 

= 1 (complete replacement), equation (8) corresponds to forest-fallow models, though fallow 

dynamics is not modeled (φ is not endogenized). Takasaki (2006) examines effects on forest 

clearing (measured by c) of soil-regeneration rate and soil erosivity altered by soil 

conservation programs, as well as output price, wage, and discount rate. 

5. Discussion 

5.1 Primary vs. secondary forests 

The review in the last section indicates two significant lacunae in the extant shifting 

cultivation models. The first lacuna is that the extant models do not distinguish between


primary and secondary forests. 11 This distinction is critically important for both 

environmental and economic reasons. First, in general, protecting primary forest with 

greater biodiversity needs to be given a higher priority than secondary forest protection. 

At the same time, as primary forest becomes scarce in the tropics, researchers and 

practitioners pay greater attention to secondary fallow forest (Coomes et al., 2000). In 

particular, short fallow results in less matured secondary forest with limited biomass 

accumulation and poor protection of erodible soils, as well as low biodiversity, weak 

carbon sequestration, and limited timber and NTFPs (Brown & Lugo, 1990; Chazdon et 

al., 2009; Dalle & de Bois, 2006; Dent & Wright, 2009; Lawrence et al., 2005). Shifting 

cultivation models need to jointly address cleared primary forest and fallow length of 

secondary forest as key environmental outcomes. 

Second, the choice between primary and secondary forest is determined by farmers’ 

decisions under specific environmental and economic conditions: In particular, secondary 

forest is less fertile but easier to clear than primary forest (Scatena et al., 1996), and this 

comparison depends on fallow length (farmer’s decision) (Dvořàk, 1992) and the availability 

of primary forest (determined by population growth, etc.). This choice also has a direct 

implication for asset accumulation: Although clearing secondary forest does not alter total 

land holdings (only the plot phase changes from fallow to cultivation), clearing new 

primary forest augments land holdings. That is, although secondary forest brings fertile soil, 

primary forest brings both more fertile soil and new land itself. Shifting cultivation models 

need to capture these key differences. 

Pendleton and Howe (2002) address the choice between primary and secondary forests as a 

pure forest-clearing problem; they neither model the role of secondary fallow forest as a soil 

builder nor consider soil addition through primary forest clearing. No other deforestation 

models distinguish or specify the type of cleared forest; this is also true in dynamic 

deforestation models, which necessarily involve land accumulation (Barbier, 2000; Takasaki, 

2007). Not only all soil conservation models but also most shifting cultivation models 

assume fixed land holdings, and thus implicitly focus on secondary forest; Tachibana et al. 

(2001) do not distinguish or specify the type of cleared forest, either. 

This lacuna in the theoretical literature is in contrast to the considerable number of empirical 

studies on primary and secondary forests. Smith et al. (1999), for example, show that the 

relative importance of secondary forest to primary forest increases over time among 

Amazonian colonists; Coomes et al. (2000; 2011) also find this pattern over a longer time 

span among Amazonian peasants (in their study village in Peru, primary forest has virtually 

disappeared). 

5.2 On-farm soil conservation in shifting cultivation 

Supporting non-farm activities discourages farming, thereby releasing pressure on forests. 

This policy option becomes available and significant only after non-agricultural sectors 

sufficiently develop, often following massive deforestation and forest degradation. What 

policies can slow down this trend along the development path? 

11 Primary forest “has had little or no anthropogenic intervention” and secondary forest is “woody 

successional vegetation that regenerates after the original forest cover has been removed for agriculture 

or cattle ranching” (Smith et al., 1999, p.86).

364 


The second lacuna not only in the extant theoretical works on shifting cultivation, but also in 

related empirical works is the investigation into potential roles of on-farm soil conservation. 

Among poor shifting cultivators, forest-based soil-management options (forest clearing and 

fallowing) outweighs on-farm soil conservation (Barbier, 1997); when degraded land can be 

easily replaced, farmers have little incentive to adopt expensive input-based soilconservation 

measures. Then, the question is whether policy makers can alter shifting 

cultivators’ benefit-cost calculations by introducing effective soil-conservation programs, as 

discussed by Takasaki (2006) (see also Grepperud, 1997a). 

Although developing locally adoptable, effective soil-conservation measures in tropical 

forests has been a daunting task (Lal, 1995), soil scientists' recent growing interest in biochar 

in Amazonia may lead to significant improvement in soil fertility and soil carbon 

sequestration in shifting cultivation systems (Glaser, 2007; Marris, 2006; Steiner et al., 2004). 

Biochar, also known as black carbon, is the residue of organic matter that has been 

pyrolyzed (partially combusted in a low-oxygen environment). Research indicates that 

Amazonian black carbon (terra preta) has, on average, three times more soil organic matter 

(SOM) content, higher nutrient levels, and a better nutrient retention capacity than 

surrounding infertile soils (Glaser, 2007). How the labor-intensive alternative “slash-andchar” 

system, combined with sustainable charcoal production, can be promoted among poor 

shifting cultivators is still an open question, however (Swami et al., 2009) (see Coomes & 

Burt, 2001 for charcoal production among Amazonian peasants). 

Soil-conservation models extensively developed in the literature can well capture various 

input-based soil-conservation measures; in particular, equation (4) or its variant can be 

applied to labor-intensive conservation like biochar. 

5.3 Shifting cultivation regimes 

It is very useful to differentiate two regimes of shifting cultivation. In regime 1, where 

primary forest is available, farmers choose to clear primary or secondary forest. Although 

the extant deforestation and shifting cultivation models effectively capture primary forest 

clearing and secondary fallow forest clearing (cyclical cultivation), respectively, neither of 

them addresses the choice of these two. As primary forest becomes scarce (deforestation), 

cultivation shifts to regime 2, in which only secondary forest is cleared; in another words, 

primary forest has been so degraded that clearing primary forest is too costly or simply not 

an available option. Policies effectively protecting primary forest (in particular, protected 

areas with compliance) can also make this regime shift. 12 Although the extant shifting 

12 Migration can also significantly affect the regime shift. Coomes et al. (2011) find that urban migration 

plays an important role in lowering pressure on diminishing forest land among shifting cultivators in 

their study village. The extensive migration option in the forest frontier, however, may allow farmers to 

clear forest – both primary and secondary – without employing fallowing practices; this is possible 

among colonists in land-abundant areas in Latin America, especially in locales where selling cleared 

lands is an additional motive for forest clearing (Barbier, 2004; Binswanger, 1991; Takasaki, 2007). 

Conceptually, further regime shifts following regime 2 can be considered. Once shifting cultivators start 

to employ continuous cultivation on some plots, regime 3 emerges; in this new regime, in addition to 

forest fallow management, farmers make a key choice between shifting and continuous. Lastly, regime 

3 is followed by the complete shift to continuous cultivation, i.e., abandonment of shifting cultivation 

(Krautkraemer, 1994).


cultivation models essentially focus on regime 2, protecting remaining primary forest and 

promoting sustainable secondary forest management (long fallow) in regime 1 should be 

given a higher priority for conservation and development in shifting cultivation systems. 

5.4 Future modeling 

It is now clear that a promising avenue for future modeling of shifting cultivation is to 

extend extant models for secondary fallow forest in regime 2 by adding primary forest 

clearing to capture regime 1 and by endogenizing on-farm soil conservation to examine its 

effects on forest outcomes. That is, a unified farm model of primary forest clearing, forest 

fallowing, and on-farm soil conservation is needed to examine effective policies for 

protecting primary forest and maintaining sustainable long fallow. 

Two extensions toward such a unified model are suggested. The first is to augment a 

cultivation-intensity model so that it captures the dynamics of both on-farm soil and land 

holdings (through primary forest clearing). Such an augmented model could explicitly 

capture the mechanism of depriving intensification embedded in δ(b) in equation (6). 

The second extension is to augment Takasaki’s (2006) land-replacement model by 

endogenizing cultivation intensity and capturing acquisition of new land and soil through 

primary forest clearing. The proportion of total land, not cultivated land, replaced with 

fallow forest is bc, and fallow length 1/bc determines the soil stock of cleared fallow forest φ 

in equation (8). 

5.5 Hypothetical effects of on-farm soil conservation 

How does better on-farm soil conservation affect forest outcomes? On one hand, in regime 2 

with no primary forest clearing, it is expected that shifting cultivators intensify on-farm soil 

conservation and rely less on fallow soils (less frequent clearing), resulting in longer fallow. 

On the other hand, in regime 1, better on-farm soil conservation encourages shifting 

cultivators to clear more primary forest with increased returns to farming; at the same time, 

primary forest clearing (land accumulation) is balanced with secondary forest clearing 

(fallow management). A well-designed soil conservation program might result in longer 

fallow at the cost of primary forest; then, it becomes crucial to combine the soil program 

with other measures to protect primary forest, such as protected areas. 

The unified farm model proposed above can dissect shifting cultivators’ benefit-cost 

calculations, shedding light on an effective policy mix for conservation and development 

and pointing to promising avenues for empirical research. 

6. Conclusion 

This chapter reviewed farm-level economic models of shifting cultivation, as well as those of 

deforestation and soil conservation related to shifting cultivation. Although economists 

have made significant progress in modeling shifting cultivation over the last two decades, 

extant economic models neither clearly distinguish between primary and secondary forests 

nor address potential roles of on-farm soil conservation in shifting cultivation. Developing a 

unified farm model of primary forest clearing, forest fallowing, and on-farm soil 

conservation is needed to examine effective policies for protecting primary forest and 

maintaining sustainable secondary fallow forest. The chapter pointed to promising avenues 

for future modeling.

366 



This chapter has benefited significantly from the comments and suggestions of Oliver 

Coomes. This research has been made possible through financial support provided by the 

Japan Society for the Promotion of Science and the Ministry of Education, Culture, Sports, 

Science and Technology in Japan. Any errors of interpretation are solely the author’s 

responsibility. 

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