Ecological assessment and driver analysis of high vegetation cover areas based on new remote sensing index

Ecosystem degradation and decline are central issues that urgently require resolution within global environmental protection efforts. Accordingly, accurately analyzing the spatiotemporal evolution of regional ecological environmental quality and exploring its natural and anthropogenic driving factors of ecological environmental quality are crucial for protecting regional ecological environments and advancing sustainable development strategies. Therefore, this study created a new remote sensing ecological index to investigate the patterns of ecological quality change in vegetation-covered areas over a long time series and identified the intensity and local response relationships of various driving factors, including climate, topography, soil, and urbanization. The results revealed an upward trend in the ecological quality of the Heilongjiang region, with a high degree of spatial autocorrelation in ecological quality. The ecological grades in forest-covered areas significantly surpassed those in other vegetated, urban, and desert regions. Through a collaborative analysis of various geographical statistical methods, the intensities of the driving factors and their local response relationships were determined. This study provides a method for accurately and rapidly assessing regional ecological environmental quality and exploring the complex interactions of driving factors, thus offering a theoretical basis for monitoring regional-scale ecological conditions, balancing ecological and economic development, and informing environmental protection policies. • GPP and kNDVI introduction enhances ecological quality assessment for accuracy and comprehensiveness. • Counted the area percentage in various ecological classes and plotted trends in ecological quality. • Identified clustered distribution patterns and prepared maps for ecological quality in diverse regions. • Forest-covered areas have the highest ecological level. • Drivers interactions exert greater ecological impact than individual factors.