An evaluation model for aboveground biomass based on hyperspectral data from field and TM8 in Khorchin grassland, China

Xiaohua Zhang, Xiuli Chen, Meirong Tian, Yongjun Fan, Jianjun Ma and Danlu Xing

PLOS ONE, 2020, vol. 15, issue 2, 1-12

Abstract: Biomass is an important indicator for monitoring vegetation degradation and productivity. This study tests the applicability of Hyperspectral Remote-Sensing in situ measurements for high-precision estimation aboveground biomass (AGB) on regional scales of Khorchin grassland in Inner Mongolia, China. In order to improve prediction accuracy of AGB which is frequently used as an indicator of aboveground net primary productivity (ANPP), this paper combined ground measurement with remote sensing inversion to build the spectral model. The ground normalized difference vegetation index (SOC_NDVI) calculated from ground spectral of grassland vegetation which was measured by a portable visible/NIR hyperspectral spectrometer (SOC 710). Meanwhile, the remote normalized difference vegetation index (TM_NDVI) calculated from remote spectral of grassland vegetation which was measured by Thematic Mapper (TM) from Landsat 8 which launched by National Aeronautics and Space Administration (NASA). According to regression analysis for the relationship between AGB and SOC_NDVI, SOC_NDVI and TM_NDVI, the evaluation model for aboveground biomass was developed (AGB = 12.523×e3.370×(0.462×TM_NDVI+0.413), standard error = 24.74 g m-2, R2 = 0.636, p

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0223934

DOI: 10.1371/journal.pone.0223934

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