Analysis of ecological quality driving factors in Liaoning Province based on Remote Sensing Ecological Index

Addressing the critical need for efficient, long-term monitoring of regional ecological dynamics, this study establishes a cloud-computing framework for systematic ecological assessment. Leveraging the Google Earth Engine (GEE) platform, we generated a high-resolution annual time series of the Remote Sensing Ecological Index (RSEI) for Liaoning Province, China, spanning from 2000 to 2024. A core methodological innovation involved the integration of spatial econometric techniques—specifically, the Geodetector method and bivariate Local Indicators of Spatial Association (LISA)—to rigorously identify and quantify the dominant drivers underlying environmental heterogeneity. The key findings demonstrate: (1) a significant province-wide trend of ecological improvement coexisting with persistent east-west disparities; (2) industrial land-use patterns and climatic factors as the predominant drivers of ecological change; and (3) the critical importance of implementing prioritized conservation measures in western development zones. This research provides a robust and transferable paradigm for regional ecological monitoring and offers actionable insights for decision-making aimed at promoting sustainable development in rapidly industrializing regions.