An evaluation model for aboveground biomass Based on Hyperspectral Data from field and TM8 in Khorchin grassland, China

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 landscape in Inner Mongolia, China. Field experiments were carried out which collected hyperspectral data with a portable visible/NIR hyperspectral spectrometer (SOC 710), and collected aboveground net primary productivity (ANPP). Ground spectral models were then developed to estimate ANPP from the normalized difference vegetation index (NDVI), which was measured in the field following the same method as that of the Thematic Mapper(TM) from the Landsat 8 land imager (TM_NDVI). Regression analysis was used to assess the relationship between ANPP and NDVI based on coefficients of determination (R2) and error analysis. The estimation of ANPP had unique optimal regression models. By comparing the different spectral inversion models, we selected an exponential model associating ANPP with NDVI (ANPP = 12.523*e3.370*(0.462*TM_NDVI+0.413), standard error = 24.74 g m-2, R2 = 0.636, P < 0.001). This study suggests that the model can be used to monitor the condition and estimate the productivity of grassland at regional scales. The results still show a high potential to map grassland degradation proxies on the ground hyperspectral model. Thus, this study presents biomass hyperspectral inversion technology to remotely detect and monitor grassland degradation and productivity at high precision.