Comparison of three modeling methods for estimating forest biomass using TM, GLAS and field measurement data

Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest aboveground biomass (AGB), many difficulties remain in data resources and prediction approaches [1, 2]. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF))were used to estimate forest AGB over the Daxing'anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown to estimate AGB reliably by regression (R
²
=0.96 RMSE=17.73Mg/ha) and cross validation (R
²
=0.71 RMSE=39.60 Mg/ha).