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

Aboveground forest biomass (B_agf) and height of forest canopy (H_fc) are of great significance for the determination of carbon sources and sinks, carbon cycling and global change research. In this paper, B_agf 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 B_agf estimation, a synergetic extrapolation method for regional H_fc is explored based on a specific relationship between LiDAR footprint H_fc 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 B_agf estimation from synergetic LiDAR and optical data are developed and compared. Validation results show that the OLSR can achieve higher accuracy of H_fc estimation for all forest types (R² = 0.751, Root mean square error (RMSE) = 5.74 m). The OLSR estimated B_agf shows a good agreement with field measurements. The accuracy of regional B_agf estimated by the BP-NN model (RMSE = 12.23 t ha^–1) is superior to that estimated by the OLSR method (RMSE = 17.77 t ha^–1) especially in areas with complex topography. [ABSTRACT FROM AUTHOR]