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

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This article describes a study combining Geoscience Laser Altimeter System (GLAS) data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass in Xishuangbanna, Yunnan Province, China.

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12 p.

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Xi, Xiaohuan; Han, Tingting; Wang, Cheng; Luo, Shezhou; Xia, Shaobo & Pan, Feifei March 28, 2016.

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Description

This article describes a study combining Geoscience Laser Altimeter System (GLAS) data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass in Xishuangbanna, Yunnan Province, China.

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12 p.

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Abstract: 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.

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  • International Journal of Geo-Information, 2016. Basel, Switzerland: Multidisciplinary Digital Publishing Institute

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  • Publication Title: International Journal of Geo-Information
  • Volume: 5
  • Issue: 45
  • Pages: 1-12
  • Peer Reviewed: Yes

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Submitted Date

  • November 6, 2015

Accepted Date

  • March 15, 2016

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  • March 28, 2016

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  • Aug. 29, 2017, 9:38 a.m.

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Xi, Xiaohuan; Han, Tingting; Wang, Cheng; Luo, Shezhou; Xia, Shaobo & Pan, Feifei. Forest above Ground Biomass Inversion by Fusing GLAS with Optical Remote Sensing Data, article, March 28, 2016; Basel, Switzerland. (digital.library.unt.edu/ark:/67531/metadc991002/: accessed December 18, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.