[Ecological Environment Monitoring and Driving Factors Analysis of the Greater Bay Area Based on Improved Remote...

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most economically vibrant regions in China. Analyzing the temporal-spatial variation patterns and driving mechanisms of its ecological environment quality is of significant importance for implementing the strategy of ecological civilization construction during the process of high-quality economic development. Based on the Google Earth Engine (GEE) cloud platform and MODIS remote sensing data, this study constructs an improved remote sensing ecological index (KRSEI) suitable for high-vegetation areas using principal component analysis (PCA), incorporating greenness (KNDVI), humidity (WET), heat (LST), and dryness (NDBSI). The Sen+Mann-Kendall method, Hurst index, coefficient of variation (CV), and parameter-optimized geographical detector model are employed to analyze the temporal-spatial changes and future trends of ecological environment quality in the GBA from 2000 to 2020 and to explore its influencing mechanisms. The results show that: ① The contribution rate of the model's first principal component (PC1) exceeded 83.91%, which better integrated the characteristics of each indicator compared to the traditional RSEI. ② The average KRSEI values in the study area from 2000 to 2020 were 0.56, 0.49, 0.57, 0.57, and 0.55, respectively, showing an overall fluctuating downward trend. The "good" ecological grade accounted for the largest area (23.31%-40.42%), while the "poor" grade accounted for 8.39%-15.65%. The combined area proportion of "excellent" and "good" regions increased by 6.62%, while that of "poor" and "very poor" regions increased by 4.75%. The regional habitat quality exhibited a spatial pattern of "high in the periphery, low in the center," with ecological degradation expected to dominate future changes. ③ The overall ecological quality of the region showed good stability, but high-intensity development zones such as economic belts and free trade zones exhibited high variability. ④ Optimal parameter geographical detector analysis indicated that elevation was the primary factor in the spatial analysis of ecological environment quality, and the interaction between elevation and land use had the strongest driving force on the spatial differentiation of KRSEI. This study provides scientific references for the sustainable development of the GBA and the improvement of ecological environment monitoring mechanisms, facilitating the coordinated development of the economy and ecological environment in the region.