Forest Aboveground Biomass Estimation and Response to Climate Change Based on Remote Sensing Data

Abstract

As the largest and most important natural terrestrial ecosystem, forest plays a crucial role in reducing the concentrations of greenhouse gases in the atmosphere, mitigating global warming, maintaining the global ecological balance, and promoting global biological evolution and community succession. The accurate and rapid assessment of forest biomass is highly significant for estimating the regional carbon budget and monitoring forest change. In this study, Landsat images and China’s National Forest Continuous Inventory data of 1999, 2004, 2009, and 2014 were used to establish extreme gradient boosting (XGBoost) models for forest aboveground biomass (AGB) estimation based on forest type in the Xiangjiang River Basin, Hunan Province, China. Kriging interpolation of the AGB residuals was used to correct the error of AGB estimation. Then, a new XGBoost model was established using the final corrected AGB maps and climate data to estimate the AGB under different climate scenarios during the 2050s and 2070s. The results indicated that AGB estimation using the XGBoost model with correction via Kriging interpolation of the AGB residuals can significantly improve the accuracy of AGB estimation. The total AGB of the study area increased over time from 1999 to 2014, indicating that the forest quality improved in the study area. Under the different climate scenarios, the total AGB during the 2050s and 2070s was predicted to decline continuously with increasing of greenhouse gas emissions, indicating that greenhouse gas emissions have a negative impact on forest growth. The results of this study can provide data support for evaluating the ecological function and value of forest ecosystems, and for formulating reasonable forest management measures to mitigate the effects of climate change.