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8 November 2014 Estimation of forest biomass by integrating ALOS PALSAR And HJ1B data
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Abstract
The use of the optical and microwave remote sensing in combination with field measured data can provide an effective way to improve the estimation of forest biomass over large regions. In order to improve the accuracy of biomass estimation from remotely sensed data in mountainous terrain, the methods for obtaining above-ground biomass (AGB) from forest canopy structure estimates based on a physically-based canopy reflectance model estimation approach was introduced in this paper. A geometric-optical canopy reflectance model was run in multiple-forward mode (MFM) using HJ1B imagery to derive forest biomass at Helan Mountain nature reserve region in the northwest of China. Simultaneously, the multiple regression model was also developed to estimate the forest above-ground biomass by integrating field measurements of 30 sample plots with ALOS/PALSAR Synthetic Aperture Radar (SAR) backscatter remotely sensed data. The estimation biomass of two methods was evaluated with 20 field validation sites. MFM predictions of AGB from HJ1B imagery were compared with the results from PALSAR regression model, respectively. Error levels for two model and field measured data were also analyzed. The result shows that a good fit can be found between AGB estimated by geometric-optical canopy reflectance model and ground measured biomass with a R2 (Coefficient of Determination) and RMSE (Root Mean-Square Error) of 0.61 and 8.33 t/ha respectively. MFM provides lower error for all validation plots and its estimated accuracy is better than PALSAR regression model, whick has less accuracy estimation (R2=0.39, RMSE=14.89 t/ha). Consequently, it can conclude that geometric-optical canopy reflectance model was considerably more suitable for estimating forest biomass in mountainous terrain.
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X. Y. Wang, Y. G. Guo, and J. He "Estimation of forest biomass by integrating ALOS PALSAR And HJ1B data", Proc. SPIE 9260, Land Surface Remote Sensing II, 92603I (8 November 2014); https://doi.org/10.1117/12.2069071
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