Ecological vulnerability assessment integrating multi-source remote sensing data

Abstract

Assessing ecological vulnerability is essential for identifying high-risk areas, guiding restoration strategies, and enhancing ecological security. Although the traditional Sensitivity–Resilience–Pressure (SRP) framework provides a comprehensive structure, its application is limited by data constraints, subjective indicator selection, and insufficient representation of human disturbance. This study integrates multi-source MODIS remote sensing data and anthropogenic disturbance indicators to construct an improved SRP-based assessment framework. Combined with geostatistical analysis and the Multi-scale Geographically Weighted Regression (MGWR) model, the study examines the spatiotemporal evolution and driving mechanisms of ecological vulnerability in the Huai River Economic Belt from 2002 to 2022. Results show that ecological vulnerability displayed an overall declining trend, with a mean Ecological Vulnerability Index (EVI) of 0.50, indicating moderate vulnerability. High-vulnerability areas are mainly distributed in the northwest and parts of the southwest, whereas low-vulnerability areas are concentrated in western and southwestern mountainous regions and portions of the east. MGWR results identify the Normalized Difference Vegetation Index (NDVI) as the dominant driver (average coefficients −0.933 in 2017 and −0.974 in 2022), followed by nighttime light intensity and PM2.5 concentrations (0.134 and 0.104, respectively). The MGWR model outperforms the traditional Geographically Weighted Regression (GWR) model by more accurately capturing spatial heterogeneity and providing better goodness-of-fit and predictive performance.