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

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.

Keywords

Forest biomass change / Gradient Boost Regression Tree / Landsat multi-temporal images / Permanent sample plots / Pinus densata / Shangri-La, China

References

[1] 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

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

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

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

[5] Badreldin N, Sanchezazofeifa A. 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, 2015, 7(3): 2832-2849.

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

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

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

[9] Breiman L. Random forest. Mach Learn, 2001, 45: 5-32.

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

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

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

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

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

[15] Colwell J. Vegetation canopy reflectance. Remote Sens Environ, 1974, 3(3): 175-183.

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

[17] Dube T, Mutanga O. 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, 2015, 108: 12-32.

[18] Dube T, Mutanga O, Dube T, Mutanga O. 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, 2015, 101: 36-46.

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

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

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

[22] Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat, 2001, 29(5): 1189-1232.

[23] Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal, 2002, 38(4): 367-378.

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

[25] 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. Generating global Leaf Area Index from Landsat: algorithm formulation and demonstration. Remote Sens Environ, 2012, 122: 185-202.

[26] Gómez C, White JC, Wulder MA, Alejandro P. Historical forest biomass dynamics modelled with Landsat spectral trajectories. ISPRS J Photogramm Remote Sens, 2014, 93: 14-28.

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

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

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

[30] He L. Shangri-La statistical yearbook, 2015, Kunming: Yunnan Science and Technology Press 33

[31] Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell, 1998, 20(8): 832-844.

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

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

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

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

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

[37] Kattenborn T, Maack J, Faßnacht F, Enßle F, Ermert J, Koch B. 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, 2015, 35(35): 359-367.

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

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

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

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

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

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

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

[45] Liu Q, Yang L, Liu QH, Li J. Review of forest above ground biomass inversion methods based on remote sensing technology. J Remote Sens, 2015, 19: 62-74.

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

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

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

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

[50] 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

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

[52] Morell V. Letting 1000 forests bloom. Science, 2008, 320(5882): 1442-1443.

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

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

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

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

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

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

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

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

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

[62] Richards JA. Remote sensing digital image analysis: an introduction, 1999, Berlin: Springer

[63] Shao GF. Richardson D. Optical remote sensing. International encyclopedia of geography: people, the earth, environment, and technology, 2017, Washington: Wiley 2390 2395

[64] Song Q. Estimating forest biomass based on microwave remote sensing, 2011, Harbin: Master, Northeast Forestry University.

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

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

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

[68] Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 1979, 8(2): 127-150.

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

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

[71] 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. Free access to Landsat imagery. Science, 2008 320 5879 1011

[72] 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

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

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

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

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

[77] Yan EP, Lin H, Wang GX, Sun H. 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, 2016 8 7 571

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

[79] Zandler H, Brenning A, Samimi C. Quantifying dwarf shrub biomass in an arid environment: comparing empirical methods in a high dimensional setting. Remote Sens Environ, 2015, 158: 140-155.

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

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

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

[83] Zhang JL, Pham TTH, Kalacska M, Turner S. 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, 2014, 31: 25-36.

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

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

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