A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing

Abstract

Grassland degradation is an important research topic on a global scale, since it can severely restrict the development of animal husbandry and threaten ecological security. The proper monitoring of regional grassland degradation is the basis for strengthening grassland protection and restoration, as well as improving grassland ecology. In this study, the standards for monitoring grassland degradation at the regional level were established based on the field data measured in the study area and the data of a grazing-controlled experimental plot. We extracted the spectral characteristic parameters and carried out the spectral dimensionality reduction and accuracy evaluation using principal component analysis (PCA) and the multilayer perceptron neural network (MLPNN). Based on the EO-1 Hyperion images, multiple endmember spectral mixture analysis (MESMA) and the fully constrained least squares method pixel un-mixing (FCLS) were used to identify typical vegetation species and assess the degree of grassland degradation at the regional level per the established grassland degradation monitoring standards. This new method of monitoring grassland degradation from the perspective of the vegetation species composition not only makes grassland degradation monitoring more accurate, but also provides a reference for relevant studies.