Analyses of driving factors on the spatial variations in regional eco-environmental quality using two types of species distribution models: A case study of Minjiang River Basin, China

Abstract

Understanding the spatial variations in regional eco-environmental quality and their driving factors is essential for environmental management and protection. However, the lack of quantitative analyses in relevant studies has often hindered the formulation and implementation of effective eco-environmental policies. Taking Minjiang River Basin as an example, we used the remote sensing ecological indices (RSEI) to objectively and quantitatively characterize the eco-environmental quality. We also introduced and compared the maximum entropy (MaxEnt) and the random forest models to identify the critical driving factors. Results showed that the eco-environmental quality of Minjiang River Basin in 2020 was good overall, and the areas with good or excellent eco-environmental quality accounted for 58%. The areas with poor eco-environmental quality were mainly distributed in the periphery of the basin and the riverine areas. Both the MaxEnt and the random forest models were applicable to identify the critical driving factors and indicated that the degrees of landscape fragmentation and drastic land use were the main factors causing spatial variation of eco-environmental quality in the Minjiang River Basin. This study provides a new perspective and method for quantitative analysis of the driving factors of the spatial variations in regional eco-environmental quality for future eco-environmental management and protection.

Keywords

• Remote sensing ecological indices
• MaxEnt model
• Random forest model
• Minjiang River Basin
• Ecological and environmental quality