| 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 timeseries 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. |
