School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, China.
Nanjing Institute of Environmental Science, Ministry of Ecology and Environment of China, Nanjing, China.
PLoS One. 2020 Feb 28;15(2):e0223934. doi: 10.1371/journal.pone.0223934. eCollection 2020.
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 < 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.
生物量是监测植被退化和生产力的重要指标。本研究在中国内蒙古的科尔沁草原地区，测试了高光谱遥感原位测量在高精度估算地上生物量（AGB）上的适用性。为了提高作为地上净初级生产力（ANPP）指标的 AGB 的预测精度，本文结合地面测量和遥感反演构建了光谱模型。利用便携式可见/近红外高光谱光谱仪（SOC 710）测量的草原植被地面光谱计算出地面归一化差异植被指数（SOC_NDVI）。同时，利用美国国家航空航天局（NASA）发射的陆地卫星 8 上的专题制图仪（TM）测量的草原植被的遥感归一化差异植被指数（TM_NDVI）进行计算。根据 AGB 与 SOC_NDVI、SOC_NDVI 与 TM_NDVI 的回归分析关系，建立了地上生物量评价模型（AGB = 12.523×e3.370×（0.462×TM_NDVI+0.413），标准误差 = 24.74 g m-2，R2 = 0.636，p < 0.001）。模型精度验证结果表明，生物量实测值与预测值的相关性较好，模型标准误差较低。该模型可以弥补常规地面生物量调查的时效性和全面性不足，为区域尺度草原高精度大面积生产力估算和生态退化诊断提供技术支持。
