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Estimation of forest aboveground biomass from HJ1B imagery using a canopy reflectance model and a forest growth model |
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| Authors: | Xinyun Wang Yige Guo Jie He Lingtong Du Tianhua Hu |
| Institution: | 1. State Key Laboratory Breeding Base of Land Degradation and Ecological Restoration of Northwest China, Ningxia University, Yinchuan, China;2. Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwestern China of Ministry of Education, Ningxia University, Yinchuan, China;3. School of Resources and Environment, Ningxia University, Yinchuan, China;4. The Forest Ecosystem Research Station, Helan Mt. National Nature Reserve Administration, Yinchuan , China |
| Abstract: | Accurately estimating the spatial distribution of forest aboveground biomass (AGB) is important because of its carbon budget forms part of the global carbon cycle. This paper presented three methods for obtaining forest AGB based on a forest growth model, a Multiple-Forward-Mode (MFM) method and a stochastic gradient boosting (SGB) model. A Li-Strahler geometric-optical canopy reflectance model (GOMS) with the ZELIG forest growth model was run using HJ1B imagery to derive forest AGB. GOMS-ZELIG simulated data were used to train the SGB model and AGB estimation. The GOMS-ZELIG AGB estimation was evaluated for 24 field-measured data and compared against the GOMS-SGB model and GOMS-MFM biomass predictions from multispectral HJ1B data. The results show that the estimation accuracy of the GOMS-MFM model is slightly higher than that of the GOMS-SGB model. The GOMS-ZELIG and GOMS-MFM models are considerably more accurate at estimating forest AGB in arid and semiarid regions. |
| Keywords: | Forest aboveground biomass (AGB) HJ1B canopy reflectance model ZELIG model stochastic gradient boosting model (SGB) |