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

Fangfang Sun, Chengcheng Dong, Longlong Zhao, Jinsong Chen, Li Wang, Ruixia Jiang () and Hongzhong Li ()
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Fangfang Sun: Shenzhen Academy of Environmental Science, Shenzhen 518001, China
Chengcheng Dong: Shenzhen Academy of Environmental Science, Shenzhen 518001, China
Longlong Zhao: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Jinsong Chen: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Li Wang: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Ruixia Jiang: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Hongzhong Li: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Land, 2025, vol. 14, issue 5, 1-25

Abstract: 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 ecological conditions. Due to the region’s humid and rainy climate, traditional remote sensing ecological indexes (RSEIs) struggle to ensure consistency in long-term ecological quality assessments. To address this, this study developed a unified RSEI (URSEI) model, incorporating optimized data selection, composite index construction, normalization using invariant regions, and multi-temporal principal component analysis. Using Landsat imagery from 1990 to 2020, this study examined the spatiotemporal evolution of ecological quality in the GBA. Building on this, spatial autocorrelation analysis was applied to explore the distribution characteristics of the URSEI, followed by geodetector analysis to investigate its driving factors, including temperature, precipitation, elevation, slope, land use, population density, GDP, and nighttime light. The results indicate that (1) the 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; and (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.

Keywords: ecological quality; URSEI; spatial autocorrelation; influencing factors; geographical detector; Guangdong-Hong Kong-Macao Greater Bay Area (GBA) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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