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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

Author

Listed:
  • Fangfang Sun

    (Shenzhen Academy of Environmental Science, Shenzhen 518001, China
    These authors contributed equally to this work.)

  • Chengcheng Dong

    (Shenzhen Academy of Environmental Science, Shenzhen 518001, China
    These authors contributed equally to this work.)

  • 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
    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China)

  • Hongzhong Li

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

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.

Suggested Citation

  • Fangfang Sun & Chengcheng Dong & Longlong Zhao & Jinsong Chen & Li Wang & Ruixia Jiang & Hongzhong Li, 2025. "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," Land, MDPI, vol. 14(5), pages 1-24, May.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:1117-:d:1660332
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    References listed on IDEAS

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