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Estimating aboveground biomass of broadleaf, needleleaf, and mixed forests in Northeastern China through analysis of 25-m ALOS/PALSAR mosaic data

文献类型： 外文期刊

作者： Ma, Jun 1 ; Xiao, Xiangming 1 ; Qin, Yuanwei 2 ; Chen, Bangqian 1 ; Hu, Yuanman 4 ; Li, Xiangping 1 ; Zhao, Bin 1 ;

作者机构： 1.Fudan Univ, Inst Biodivers Sci, Minist Educ, Key Lab Biodivers Sci & Ecol Engn, Shanghai 200433, Peoples R China

2.Univ Oklahoma, Dept Microbiol & Plant Biol, Ctr Spatial Anal, Norman, OK 73019 USA

3.Chinese Acad Trop Agr Sci, Danzhou Invest & Expt Stn Trop Crops, Rubber Res Inst, Minist Agr, Danzhou 571737, Peoples R China

4.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110016, Peoples R China

关键词： ALOS/PALSAR;Aboveground live biomass;Northeastern China;Nonlinear regression models;Boosted regression tree;Topographical and stand structure factors

期刊名称：FOREST ECOLOGY AND MANAGEMENT （ 影响因子：3.558； 五年影响因子：4.039 ）

ISSN：

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收录情况： SCI

摘要： Aboveground biomass (AGB) of temperate forest plays an important role in global carbon cycles and needs to be estimated accurately. ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar) data has recently been used to estimate forest AGB. However, the relationships between AGB and PALSAR backscatter coefficients of different forest types in Northeastern China remain unknown. In this study, we analyzed PALSAR data in 2010 and observed AGB data from 104 forest plots in 2011 of needleleaf forest, mixed forest, and broadleaf forest in Heilongjiang province of Northeastern China. "Poisson" regression in generalized linear models (GLMs) and BRT (boosted regression tree) analysis in generalized boosted models (GBMs) were used to test whether the constructed PALSAR/AGB models based on individual forest types have better performance. We also investigated whether adding topographical and stand structure factors into the regression models can enhance the model performance. Results showed that GBM model had a better performance in fitting the relationships between AGB and PALSAR backscatter coefficients than did GLM model for needleleaf forest (RMSE = 3.81 Mg ha(-1), R-2 = 0.98), mixed forest (RMSE = 17.72 Mg ha(-1), R-2 = 0.96), and broadleaf forest (RMSE = 19.94 Mg ha(-1), R-2 = 0.96), and performance of nonlinear regression models constructed on individual forest types were higher than that on all forest plots. Moreover, fitting results of GLM and GBM models were both enhanced when topographical and stand structure factors were incorporated into the predictor variables. Regression models constructed based on individual forest types outperform than that based on all forest plots, and the model performance will be enhanced when incorporating topographical and stand structure factors. With information of forest types, topography, and stand features, PALSAR data can express its full ability in accurate estimation of forest AGB. (C) 2017 Elsevier B.V. All rights reserved.

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[1]Application of the space-for-time substitution method in validating long-term biomass predictions of a forest landscape model. Ma, Jun,Xiao, Xiangming,Chen, Bangqian,Li, Xiangping,Zhao, Bin,Xiao, Xiangming,Doughty, Russell,Bu, Rencang,Hu, Yuanman,Chen, Bangqian.

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