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

Dong, Lixin; Tang, Shihao; Min, Min; Veroustraete, Frank; Cheng, Jie

doi:10.1080/01431161.2019.1587201

Title

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

Author

Dong, Lixin

Tang, Shihao

Min, Min

Veroustraete, Frank

Cheng, Jie

Abstract

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.

Language

English

Source (journal)

International journal of remote sensing. - London

Publication

London : 2019

ISSN

0143-1161

DOI

10.1080/01431161.2019.1587201

Volume/pages

40 :15 (2019) , p. 6059-6083