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.

草原退化是全球范围内重要的研究课题，因为它会严重限制畜牧业的发展并威胁生态安全。适当监测区域草地退化是加强草地保护和恢复以及改善草地生态的基础。在这项研究中，根据研究区域实测数据和放牧控制实验区的数据，建立了区域级草地退化监测标准。我们提取了光谱特征参数，并使用主成分分析（PCA）和多层感知器神经网络（MLPNN）进行了光谱降维和准确性评估。根据EO-1 Hyperion图像，根据建立的草地退化监测标准，使用多端元光谱混合分析（MESMA）和完全约束最小二乘法像素分解（FCLS）来识别典型的植被物种，并在区域级别评估草地退化的程度。从植被物种组成的角度出发，这种监测草地退化的新方法不仅使草地退化监测更加准确，而且为相关研究提供了参考。