A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing. (July 2020)

• Record Type: Journal Article
• Authors: Lyu, Xin; Li, Xiaobing; Dang, Dongliang; Dou, Huashun; Xuan, Xiaojing; Liu, Siyu; Li, Mengyuan; Gong, Jirui
• Is Part Of: Ecological indicators. Volume 114(2020)
• Publication Date: 2020-07
• Subjects: Vegetation composition -- Grassland degradation -- EO-1 Hyperion -- Spectral un-mixing -- Method innovation; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.106310
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Monitoring grassland degradation from the perspective of the vegetation species composition can make up for the shortcomings of conventional methods. The grassland degradation monitoring standards were established in the study based on the measured data. The different typical plants responded differently for spectral bands, which provide the theoretical basis for species identification and grassland degradation evaluation. 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.