Comprehensive degradation index for monitoring desert grassland using UAV multispectral imagery

Abstract

Desert grassland is an essential component of the northern grasslands of China, representing an ecosystem that transitions from grassland to desert. However, it is a fragile ecological environment. Evaluating degraded grassland ecosystems is a prerequisite for degradation prevention and management. However, a systematic and standardized evaluation system for the fragile ecosystems of desert grassland ecological regions in China is lacking. Therefore, to accurately identify the degree of desert grassland degradation, this study selected 66 sample plots in Otog Banner and, using the analytic hierarchy process-entropy method, established a comprehensive evaluation system for desert grasslands. Using nine vegetation indices extracted from unmanned aerial vehicle multispectral imagery, we employed machine learning regression algorithms to develop a model for assessing grassland degradation features. The results indicated that the primary factor influencing grassland degradation was the land condition layer, followed by the resilience layer. The regression model constructed using the random forest algorithm demonstrated optimal performance in grassland degradation evaluation, with correlation coefficients of 0.981 and 0.942. Finally, we established a high-precision grassland degradation assessment model with an average relative error of 9.49%, offering a novel approach for the rapid diagnosis of grassland degradation through remote sensing macroscopic evaluation.

Keywords

• Comprehensive degradation index
• Machine learning
• UAV
• Desert grassland
• Multispectral