Aboveground forest biomass based on OLSR and an ANN model integrating LiDAR and optical data in a mountainous region of China

ABSTRACTAboveground forest biomass (Bagf) and height of forest canopy (Hfc) are of great significance for the determination of carbon sources and sinks, carbon cycling and global change research. In this paper, Bagf of coniferous and broadleaf forest in the Chinese Three Gorges region is estimated by integrating light detection and ranging (LiDAR) and Landsat derived data. For a better Bagf estimation, a synergetic extrapolation method for regional Hfc is explored based on a specific relationship between LiDAR footprint Hfc and optical data such as vegetation index (VI), leaf area index (LAI) and forest vegetation cover (FVC). Then, an ordinary least squares regression (OLSR) and a back propagation neural network (BP-NN) model for regional Bagf estimation from synergetic LiDAR and optical data are developed and compared. Validation results show that the OLSR can achieve higher accuracy of Hfc estimation for all forest types (R2 = 0.751, Root mean square error (RMSE) = 5.74 m). The OLSR estimated Bagf show...