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Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data

Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Rd., Beijing 100094, China
University of Chinese Academy of Sciences, No. 19 Yuquan Rd., Beijing 100049, China
Department of Geography, University of North Texas, Denton, TX 76203-5017, USA
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(4), 45;
Received: 6 November 2015 / Revised: 15 March 2016 / Accepted: 15 March 2016 / Published: 28 March 2016
PDF [2996 KB, uploaded 28 March 2016]


Forest biomass is an important parameter for quantifying and understanding biological and physical processes on the Earth’s surface. Rapid, reliable, and objective estimations of forest biomass are essential to terrestrial ecosystem research. The Geoscience Laser Altimeter System (GLAS) produced substantial scientific data for detecting the vegetation structure at the footprint level. This study combined GLAS data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass (AGB) in Xishuangbanna, Yunnan Province, China. The GLAS waveform characteristic parameters were extracted using the wavelet method. The ASTER DEM was used to compute the terrain index for reducing the topographic influence on the GLAS canopy height estimation. A neural network method was applied to assimilate the MODIS BRDF data with the canopy heights for estimating continuous forest heights. Forest leaf area indices (LAIs) were derived from Landsat TM imagery. A series of biomass estimation models were developed and validated using regression analyses between field-estimated biomass, canopy height, and LAI. The GLAS-derived canopy heights in Xishuangbanna correlated well with the field-estimated AGB (R2 = 0.61, RMSE = 52.79 Mg/ha). Combining the GLAS estimated canopy heights and LAI yielded a stronger correlation with the field-estimated AGB (R2 = 0.73, RMSE = 38.20 Mg/ha), which indicates that the accuracy of the estimated biomass in complex terrains can be improved significantly by integrating GLAS and optical remote sensing data. View Full-Text
Keywords: GLAS; forest aboveground biomass; MODIS BRDF; Landsat TM; canopy height; LAI; Xishuangbanna GLAS; forest aboveground biomass; MODIS BRDF; Landsat TM; canopy height; LAI; Xishuangbanna

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Xi, X.; Han, T.; Wang, C.; Luo, S.; Xia, S.; Pan, F. Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data. ISPRS Int. J. Geo-Inf. 2016, 5, 45.

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