基于高光谱荒漠草原的微斑块光谱特征分析与提取

Abstract

In hyperspectral remote sensing images, desert steppe vegetation, bare soil, and rat holes appear as micro-patches. The spectral feature analysis of micro-patches is the basis for identification and classification and also the basis for quantitative remote sensing monitoring of ground objects. Inner Mongolia desert steppe micro-patch as the research object extracts the spectral reflectance of different micro-patches, performs various vegetation index calculations, quantitatively analyzes the spectral characteristics of different micro-patches, and proposes a micro-patch spectral analysis method. Classification of high-resolution hyperspectral images of desert steppe surface micropatches. The results show that: (1) There are pronounced differences in the spectral reflectance of the three types of surface micro-patches. The vegetation has apparent characteristics in the green wave reflection peak and the red wave absorption valley. The spectral reflectance of the bare soil is higher than that of the mouse hole, and the two have been increasing. The trend is increasing slowly; (2) The proposal and application of the MSA index can effectively realize the identification and classification of surface micropatches, and the Kappa coefficient has reached 0.906 through confusion matrix verification. The above spectral analysis method realizes the classification and identification of complex ground objects using near-ground remote sensing images. It provides new ideas and methods for accurate quantitative statistics of desert grassland ecological information.