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

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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 (R 2 = 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.
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Document Type: Research Article

Affiliations: 1: Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, P. R. China 2: Faculty of Sciences, Department of Bioscience Engineering, University of Antwerp, Antwerp, Belgium 3: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, P. R. China

Publication date: August 3, 2019

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