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Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

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Abstract

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration. Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation. Southwest China is characterized by complex topographic features and forest canopy structures, complicating methods for mapping aboveground biomass and its dynamics. The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics. This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM time-series images. This method was formulated by comparing two parametric methods: Linear Regression for Multiple Independent Variables (MLR), and Partial Least Square Regression (PLSR); and two nonparametric methods: Random Forest (RF) and Gradient Boost Regression Tree (GBRT) based on the state of forest aboveground biomass and change models. The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la, Yunnan, China. Landsat images and national forest inventory data were acquired for 1987, 1992, 1997, 2002 and 2007. The results show that: (1) correlation and homogeneity texture measures were able to characterize forest canopy structures, aboveground biomass and its dynamics; (2) GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR; (3) GBRT was the most reliable approach in the estimation of aboveground biomass and its changes; and, (4) the aboveground biomass change models showed a promising improvement of prediction accuracy. This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.

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References

  • Anys H, Bannari A, He DC, Morin D (1994) Texture analysis for the mapping of urban areas using airborne MEIS-II images. In: proceedings of the first international airborne remote sensing conference and exhibition. Strasbourg, France, pp 231–245

  • Atzberger C, Richter K, Vuolo F (2011) Why confining to vegetation indices? Exploiting the potential of improved spectral observations using radiative transfer models. Remote Sens Agric Ecosyst Hydrol XIII 8174(1):466–471

    Google Scholar 

  • Avitabile V, Baccini A, Friedl MA, Schmullius C (2012) Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sens Environ 117:366–380

    Article  Google Scholar 

  • Babcock C, Finley AO, Cook BD, Weiskittel A, Woodall CW (2016) Modeling forest biomass and growth: coupling long-term inventory and LiDAR data. Remote Sens Environ 182:1–12

    Article  Google Scholar 

  • Badreldin N, Sanchezazofeifa A (2015) Estimating forest biomass dynamics by integrating multi-temporal landsat satellite images with ground and airborne LiDAR data in the Coal Valley Mine, Alberta, Canada. Remote Sens 7(3):2832–2849

    Article  Google Scholar 

  • Basuki TM, Skidmore AK, van Laake PE, Hussin Y, van Duren IC (2012) The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass. Geocarto Int 27(4):329–345

    Article  Google Scholar 

  • Berk A, Bernstein LS, Anderson GP, Acharya PK, Robertson DC, Chetwynd JH, Adler-Golden SM (1998) MODTRAN cloud and multiple scattering upgrades with application to AVIRIS. Remote Sens Environ 65(3):367–375

    Article  Google Scholar 

  • Brandt JS, Kuemmerle T, Li HM, Ren GP, Zhu JG, Radeloff VC (2012) Using Landsat imagery to map forest change in southwest China in response to the national logging ban and ecotourism development. Remote Sens Environ 121:358–369

    Article  Google Scholar 

  • Breiman L (2001) Random forest. Mach Learn 45:5–32

    Article  Google Scholar 

  • Cao L, Coops NC, Innes JL, Sheppard SRJ, Fu LY, Ruan HH, She GH (2016) Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data. Remote Sens Environ 178:158–171

    Article  Google Scholar 

  • Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113(5):893–903

    Article  Google Scholar 

  • Chi H, Sun GQ, Huang JL, Li RD, Ren XY, Ni WJ, Fu AM (2017) Estimation of forest aboveground biomass in Changbai Mountain region using ICESat/GLAS and Landsat/TM data. Remote Sens 9(7):707

    Article  Google Scholar 

  • Cohen WB, Goward SN (2004) Landsat’s role in ecological applications of remote sensing. Bioscience 54(6):535–545

    Article  Google Scholar 

  • Cohen WB, Yang ZG, Kennedy R (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—tools for calibration and validation. Remote Sens Environ 114(12):2911–2924

    Article  Google Scholar 

  • Colwell J (1974) Vegetation canopy reflectance. Remote Sens Environ 3(3):175–183

    Article  Google Scholar 

  • Czerwinski CJ, King DJ, Mitchell SW (2014) Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987–2010. Remote Sens Environ 141(2):188–200

    Article  Google Scholar 

  • Dube T, Mutanga O (2015) Investigating the robustness of the new Landsat-8 operational land imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas. ISPRS J Photogramm Remote Sens 108:12–32

    Article  Google Scholar 

  • Dube T, Mutanga O, Dube T, Mutanga O (2015) Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS J Photogramm Remote Sens 101:36–46

    Article  Google Scholar 

  • Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77(4):802–813

    Article  CAS  Google Scholar 

  • Fassnacht FE, Hartig F, Latifi H, Berger C, Hernández J, Corvalán P, Koch B (2014) Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sens Environ 154:102–114

    Article  Google Scholar 

  • Foody GM, Boyd DS, Cutler MEJ (2003) Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sens Environ 85(4):463–474

    Article  Google Scholar 

  • Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    Article  Google Scholar 

  • Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

    Article  Google Scholar 

  • Ganguly S, Samanta A, Schull MA, Shabanov NV, Milesi C, Nemani RR, Knyazikhin Y, Myneni RB (2008) Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: implementation, analysis and validation. Remote Sens Environ 112(12):4318–4332

    Article  Google Scholar 

  • Ganguly S, Nemani RR, Zhang G, Hashimoto H, Milesi C, Michaelis A, Wang WL, Votava P, Samanta A, Melton F, Dungan JL, Vermote E, Gao F, Knyazikhin Y, Myneni RB (2012) Generating global Leaf Area Index from Landsat: algorithm formulation and demonstration. Remote Sens Environ 122:185–202

    Article  Google Scholar 

  • Gómez C, White JC, Wulder MA, Alejandro P (2014) Historical forest biomass dynamics modelled with Landsat spectral trajectories. ISPRS J Photogramm Remote Sens 93:14–28

    Article  Google Scholar 

  • Güneralp I, Filippi AM, Randall J (2014) Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling. Int J Appl Earth Obs Geoinf 33(1):119–126

    Article  Google Scholar 

  • Hall RJ, Skakun RS, Arsenault EJ, Case BS (2006) Modeling forest stand structure attributes using Landsat ETM + data: application to mapping of aboveground biomass and stand volume. For Ecol Manag 225(1–3):378–390

    Article  Google Scholar 

  • Haralick R, Shanmugan KS, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  • He L (2015) Shangri-La statistical yearbook. Yunnan Science and Technology Press, Kunming, p 33

    Google Scholar 

  • Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

    Article  Google Scholar 

  • Houghton RA, Hall F, Goetz SJ (2009) Importance of biomass in the global carbon cycle. J Geophys Res Biogeosci 114(G2):1–13

    Article  Google Scholar 

  • Huang EH, Pan DL, Li SJ, He XQ (2006) Comparing methods for identifying the outliers in the in-water profile spectral data. J Mar Sci 24(1):91–96

    Google Scholar 

  • Huete A, Justice C, Liu H (1994) Development of vegetation and soil indexes for MODIS-EOS. Remote Sens Environ 49(3):224–234

    Article  Google Scholar 

  • Jesúsa A, Emilio C, Alicia PO (2009) Above ground biomass assessment in Colombia: a remote sensing approach. For Ecol Manag 257(4):1237–1246

    Article  Google Scholar 

  • Jordan CF (1969) Derivation of leaf-area index from quality of light on the forest floor. Ecology 50(4):663–666

    Article  Google Scholar 

  • Kattenborn T, Maack J, Faßnacht F, Enßle F, Ermert J, Koch B (2015) Mapping forest biomass from space—fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models. Int J Appl Earth Obs Geoinf 35(35):359–367

    Article  Google Scholar 

  • Kelsey KC, Neff JC (2014) Estimates of aboveground biomass from texture analysis of Landsat imagery. Remote Sens 6(7):6407–6422

    Article  Google Scholar 

  • Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens Environ 110(3):370–386

    Article  Google Scholar 

  • Kennedy RE, Yang ZG, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. Remote Sens Environ 114(12):2897–2910

    Article  Google Scholar 

  • Koch B (2010) Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens 65(6):581–590

    Article  Google Scholar 

  • Lawrence R, Bunn A, Powell S, Zambon M (2004) Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sens Environ 90(3):331–336

    Article  Google Scholar 

  • Liang TG, Yang SX, Feng QS, Liu BK, Zhang RP, Huang XD, Xie HJ (2016) Multi-factor modeling of above-ground biomass in alpine grassland: a case study in the Three-River Headwaters Region, China. Remote Sens Environ 186:164–172

    Article  Google Scholar 

  • Liu JG, Li SX, Ouyang ZY, Tam C, Chen XD (2008) Ecological and socioeconomic effects of China’s policies for ecosystem services. Proc Natl Acad Sci USA 105(28):9477–9482

    Article  PubMed  Google Scholar 

  • Liu Q, Yang L, Liu QH, Li J (2015) Review of forest above ground biomass inversion methods based on remote sensing technology. J Remote Sens 19:62–74

    Google Scholar 

  • Lu DS (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27(7):1297–1328

    Article  Google Scholar 

  • Lu DS, Mausel P, Brondizio E, Moran E (2002) Above-ground biomass estimation of successional and mature forests using TM images in the Amazon Basin. Springer, Berlin

    Book  Google Scholar 

  • Lu DS, Chen Q, Wang GX, Liu LJ, Li GY, Moran E (2014) A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int J Digit Earth 9(1):63–105

    Article  Google Scholar 

  • Main-Knorn M, Cohen WB, Kennedy RE, Grodzki W, Pflugmacher D, Griffiths P, Hostert P (2013) Monitoring coniferous forest biomass change using a Landsat trajectory-based approach. Remote Sens Environ 139(4):277–290

    Article  Google Scholar 

  • Matthew MW, Adler-Golden SM, Berk A, Richtsmeier SC, Levine RY, Bernstein LS, Acharya PK, Anderson GP, Felde GW, Hoke MP, Ratkowski A, Burke HH, Kaiser RD, Miller DP (2000) Status of atmospheric correction using a MODTRAN4-based algorithm. In: Shen SS, Descour MR (eds) Algorithms for multispectral, hyperspectral, and ultraspectral imagery VI. SPIE-International Society for Optical Engineering, Bellingham, pp 199–207

  • Moisen GG, Freeman EA, Blackard JA, Frescino TS, Zimmermann NE, Edwards TC Jr (2006) Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecol Model 199(2):176–187

    Article  Google Scholar 

  • Morell V (2008) Letting 1000 forests bloom. Science 320(5882):1442–1443

    Article  PubMed  Google Scholar 

  • Naesset E, Gobakken T, Bollandsas OM, Gregoire TG, Nelson R, Stahl G (2013) Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway. Remote Sens Environ 130:108–120

    Article  Google Scholar 

  • Nichol J, Hang LK, Sing WM (2006) Empirical correction of low Sun angle images in steeply sloping terrain: a slope—matching technique. Int J Remote Sens 27(3):629–635

    Article  Google Scholar 

  • Peichl M, Arain MA (2007) Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. For Ecol Manag 253(1):68–80

    Article  Google Scholar 

  • Peregon A, Yamagata Y (2013) The use of ALOS/PALSAR backscatter to estimate above-ground forest biomass: a case study in Western Siberia. Remote Sens Environ 137(251):139–146

    Article  Google Scholar 

  • Pflugmacher D, Cohen WB, Kennedy RE (2012) Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens Environ 122:146–165

    Article  Google Scholar 

  • Pflugmacher D, Cohen WB, Kennedy RE, Yang ZQ (2014) Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics. Remote Sens Environ 151(8):124–137

    Article  Google Scholar 

  • Phua MH, Saito H (2003) Estimation of biomass of a mountainous tropical forest using Landsat TM data. Can J Remote Sens 29(4):429–440

    Article  Google Scholar 

  • Powell SL, Cohen WB, Yang ZQ, Pierce JD, Alberti M (2008) Quantification of impervious surface in the Snohomish water resources inventory area of Western Washington from 1972–2006. Remote Sens Environ 112(4):1895–1908

    Google Scholar 

  • Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL (2010) Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens Environ 114(5):1053–1068

    Article  Google Scholar 

  • Richards JA (1999) Remote sensing digital image analysis: an introduction. Springer, Berlin

    Book  Google Scholar 

  • Shao GF (2017) Optical remote sensing. In: Richardson D (ed) International encyclopedia of geography: people, the earth, environment, and technology. Wiley, Washington, pp 2390–2395

    Google Scholar 

  • Song Q (2011) Estimating forest biomass based on microwave remote sensing. Master, Northeast Forestry University, Harbin

    Google Scholar 

  • Sun X (2016) Study on biomass estimation of Pinus densata in Shangri-La based on Landsat8 - OLI. Master, Southwest Forestry University, Kunming

  • Tang SZ, Zhang HR, Xu H (2000) Study on establish and estimate method of compatible biomass model. Sci Silvae Sin 36(z01):19–27

    Google Scholar 

  • Tang LN, Gao LJ, Shi LY (2015) Sustainable management and protection of ecosystems in Shangri-La County, Yunnan Province, China: introduction. Int J Sustain Dev World Ecol 22(2):99–102

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150

    Article  Google Scholar 

  • Vierling KT, Bässler C, Brandl R, Vierling LA, Weiss I, Müller J (2011) Spinning a laser web: predicting spider distributions using LiDAR. Ecol Appl 21(2):577–588

    Article  CAS  PubMed  Google Scholar 

  • Wang JL, Cheng PF, Xu S, Wang XH, Cheng F (2013) Forest biomass estimation in Shangri-La based on the remote sensing. J Zhejiang A&F Univ 30(3):325–329

    CAS  Google Scholar 

  • Woodcock CE, Allen R, Anderson Belward A, Bindschadler R, Cohen W, Gao F, Goward SN, Helder D, Helmer E, Nemani R, Oreopoulos L, Schott J, Thenkabail PS, Vermote EF, Vogelmann J, Wulder MA, Wynne R, Team LS (2008) Free access to Landsat imagery. Science 320(5879):1011

    Article  CAS  PubMed  Google Scholar 

  • Xiao G, Chen Q, Zo Lu (2014) Analysis on extreme precipitation characteristics of Shangri-La in recent 54 years. In: The 31st annual meeting of china meteorological society, Beijing, pp 1–6

  • Xu H (1998) Studies on standing tree biomass models and the corresponding parameter estimation. Doctor, Beijing Forestry University, Beijing

  • Xu H, Yue CR (2014) Study on forest landscape change and forest biomass estimation in Shangri-La based on remote sensing technology. Yunnan Science and Technology Press, Kunming

    Google Scholar 

  • Xu ZG, Bennett MT, Tao R, Xu JT (2004) China’s sloping land conversion programme four years on: current situation and pending issues. Int For Rev 6(3–4):317–326

    Google Scholar 

  • Xu JT, Yin RS, Li Z, Liu C (2006) China’s ecological rehabilitation: unprecedented efforts, dramatic impacts, and requisite policies. Ecol Econ 57(4):595–607

    Article  Google Scholar 

  • Yan EP, Lin H, Wang GX, Sun H (2016) Multi-resolution mapping and accuracy assessment of forest carbon density by combining image and plot data from a nested and clustering sampling design. Remote Sens 8(7):571

    Article  Google Scholar 

  • Zald HSJ, Wulder MA, White JC, Hilker T, Hermosilla T, Hobart GW, Coops NC (2016) Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada. Remote Sens Environ 176:188–201

    Article  Google Scholar 

  • Zandler H, Brenning A, Samimi C (2015) Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting. Remote Sens Environ 158:140–155

    Article  Google Scholar 

  • Zhang M, Yuan H (1997) The PauTa criterion and rejecting the abnormal value. J Zhengzhou Univ Technol 18(1):84–88

    Google Scholar 

  • Zhang JL, Xu H, Yue CR, Yuan H (2013) Change and prediction of forest landscape pattern in Shangri-La county based on CA-Markov. J Northeast For Univ 41(6):46–49

    Google Scholar 

  • Zhang G, Ganguly S, Nemani RR, White MA, Milesi C, Hashimoto H, Wang WL, Saatchi S, Yu YF, Myneni RB (2014a) Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sens Environ 151(8):44–56

    Article  Google Scholar 

  • Zhang JL, Pham TTH, Kalacska M, Turner S (2014b) Using Landsat thematic mapper records to map land cover change and the impacts of reforestation programmes in the borderlands of southeast Yunnan, China: 1990–2010. Int J Appl Earth Obs Geoinf 31:25–36

    Article  CAS  Google Scholar 

  • Zhou JJ, Zhao Z, Zhao QX, Zhao J, Wang HZ (2013) Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data. J Appl Remote Sens 7(1):073484

    Article  Google Scholar 

  • Zhu XL, Liu DS (2015) Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. ISPRS J Photogramm Remote Sens 102:222–231

    Article  Google Scholar 

  • Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1(1):3–14

    Article  Google Scholar 

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Acknowledgements

We thank Dr. Nicholas C. Coops for technical suggestions and editing, Dr. Mingliang Wang for editing, and Mr. Xuehua Wang for language improvement.

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Authors and Affiliations

  1. Faculty of Forestry, Southwest Forestry University, 300 Bailongsi, Kunming, 650224, Yunnan, People’s Republic of China

    Jialong Zhang, Chi Lu, Hui Xu & Guangxing Wang

  2. Department of Geography, Southern Illinois University Carbondale, Carbondale, IL, USA

    Guangxing Wang

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  1. Jialong Zhang
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  2. Chi Lu
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Correspondence to Hui Xu.

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Project funding: This work was supported by the State Forestry Administration of China under the national forestry commonwealth project grant #201404309, the Expert Workstation of Academician Tang Shouzheng of Yunnan Province, the Yunnan provincial key project of Forestry, and the Research Center of Kunming Forestry Information Engineering Technology.

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Corresponding editor: Tao Xu.

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Zhang, J., Lu, C., Xu, H. et al. Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data. J. For. Res. 30, 1689–1706 (2019). https://doi.org/10.1007/s11676-018-0713-7

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