Aboveground forest biomass based on OLSR and an ANN model integrating LiDAR and optical data in a mountainous region of China
2019
Dong, Lixin | Tang, Shihao | Min, Min | Veroustraete, Frank | Cheng, Jie
Aboveground 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 shows a good agreement with field measurements. The accuracy of regional Bₐgf 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.
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