Land cover classification and grassland biomass monitoring model in alpine pastoral area based on HJ1A hyperspectral image

Using hyperspectral image of HJ1A satellite, the standard spectral curves of different objects in the study area were analyzed; land cover classification accuracy between supervised classification and spectral angle mapping method were compared; and the grassland biomass monitoring models based on hyperspectral remote sensing in alpine pastoral area were also studied. The results show that:1) the absorption position, absorption depth and other spectral characteristics of the standard spectral curves of different objects are different in the visual bands, but they are similar in nearinfrared bands. In the visual bands, there is only one absorption position in the standard spectral curves of clouds and vegetation, and the absorption depth of clouds is less than that of vegetation. In the standard spectral curves of bare lands and water areas, there are five and six absorption positions, respectively. 2) Both the spectral angle mapping and supervised classification methods are suitable for hyperspectral image classification. The overall classification accuracy of spectral angle mapping method reaches to 85.9% and is much higher than that of supervised classification approach. The spectral angle mapping method can recognize the objects under thin clouds, cloud shadows and mountain shadows. 3) Based on regression analyzed results between grassland biomass and 9 vegetation indices, two vegetation indices of Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (SR) are suitable for grassland biomass monitoring in the study area. HJ1A hyperspectral data has been successfully applied in classification of land cover of alpine pastoral areas. This research laid foundations of further studying in HJ1A hyperspectral imaging data.