资源环境科技发展态势分析平台

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.