Spatiotemporal Variation and Influencing Factors of Ecological Quality in the Guangdong-Hong Kong-Macao Greater Bay Area Based on URSEI over the Past 30 Years

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of China’s three major urban agglomerations. Over the past thirty years, the region has undergone intensive economic development and urban expansion, resulting in significant changes in its eco-logical conditions. Due to the region’s humid and rainy climate, traditional remote sens-ing ecological index (RSEI) struggle to ensure consistency in long-term ecological quality assessments. To address this, the study developed a Unified RSEI (URSEI) model, incor-porating optimized data selection, composite index construction, normalization using invariant regions, and multi-temporal principal component analysis. Using Landsat im-agery from 1990 to 2020, the study examined the spatiotemporal evolution of ecological quality in the GBA. Building on this, spatial autocorrelation analysis was applied to ex-plore the distribution characteristics of URSEI, followed by Geodetector analysis to in-vestigate its driving factors, including temperature, precipitation, elevation, slope, land use, population density, GDP, and nighttime light. The results indicate that: (1) URSEI effectively mitigates the impact of cloudy and rainy conditions on data consistency, producing seamless ecological quality maps that accurately reflect the region’s ecological evolution; (2) Ecological quality showed a “decline-then-improvement” trend during the study period, with the URSEI mean dropping from 0.65 in 1990 to 0.60 in 2000, then rising to 0.63 by 2020. Spatially, ecological quality was higher in the northwest and northeast, and poorer in the central urbanized areas; (3) In terms of driving mechanisms, nighttime light, GDP, and temperature were the most influential, with the combined effect of “nighttime light + land use” being the primary driver of URSEI spatial heterogeneity. Human activity-related factors showed the most notable variation in influence over time.