Open AccessAn evaluation model for aboveground biomass based on hyperspectral data from field and TM8 in Khorchin grassland, China
Author(s) -
XiaoHua Zhang,
Xiuli Chen,
Mingwei Tian,
Yida Fan,
MA Jian-jun,
Danlu Xing
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0223934
Subject(s) - normalized difference vegetation index , hyperspectral imaging , remote sensing , environmental science , grassland , biomass (ecology) , enhanced vegetation index , vegetation (pathology) , thematic mapper , leaf area index , satellite imagery , vegetation index , ecology , geography , medicine , pathology , biology
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× e 3.370×(0.462× TM_NDVI +0.413) , standard error = 24.74 g m -2 , R 2 = 0.636, p < 0.001). The model accuracy verification results show that the correlation between the measured value and the predicted value of biomass was better with low model standard error. The model could make up for the lack of timeliness and comprehensiveness of conventional ground biomass survey, and provide technical support for high-precision large-area productivity estimation and ecological degradation diagnosis of regional scale grassland.