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<p>The remote sensing ecological index (RSEI) serves as a pivotal metric for evaluating the regional ecological environment quality (EEQ). Nevertheless, accurately quantifying and identifying its response to multi-factor coupling remain a considerable challenge. Therefore, in this study, an improved Remote Sensing Ecological Index with Local Adaptability (RSEILA) method was employed to analyze the EEQ’s spatiotemporal distribution pattern using the Google Earth Engine platform. Then, the Geodetector model was employed to identify the driving mechanisms responsible for EEQ variation under multi-factor coupling. The results show the following: (1) Over the past two decades, the EEQ has consistently achieved moderate to good levels and has exhibited an overall trend of improvement. (2) At the spatial scale, the distribution pattern of the RSEILA in Anhui Province was characterized by high values in the south and low values in the north, which was closely associated with the natural geographic conditions and land use patterns. (3) Multi-factor coupling exerted a significant spatiotemporal scale effect on the drivers of EEQ levels. At the temporal scale, EEQ levels were predominantly influenced by policy measures, while spatially topography and human activities were identified as the primary drivers of the EEQ changes. The findings of this research provide a theoretical foundation for enhancement and administration of the EEQ in Anhui Province and analogous regions.</p>

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Response mechanism of ecological environment quality variation to multifactor coupling at Spatiotemporal scales

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Xi Chen,
  • Yuqing Fang,
  • Yufeng He,
  • Wen Dai,
  • Ling Jiang,
  • Hong Wei,
  • Dong Wang,
  • Chun Wang

摘要

The remote sensing ecological index (RSEI) serves as a pivotal metric for evaluating the regional ecological environment quality (EEQ). Nevertheless, accurately quantifying and identifying its response to multi-factor coupling remain a considerable challenge. Therefore, in this study, an improved Remote Sensing Ecological Index with Local Adaptability (RSEILA) method was employed to analyze the EEQ’s spatiotemporal distribution pattern using the Google Earth Engine platform. Then, the Geodetector model was employed to identify the driving mechanisms responsible for EEQ variation under multi-factor coupling. The results show the following: (1) Over the past two decades, the EEQ has consistently achieved moderate to good levels and has exhibited an overall trend of improvement. (2) At the spatial scale, the distribution pattern of the RSEILA in Anhui Province was characterized by high values in the south and low values in the north, which was closely associated with the natural geographic conditions and land use patterns. (3) Multi-factor coupling exerted a significant spatiotemporal scale effect on the drivers of EEQ levels. At the temporal scale, EEQ levels were predominantly influenced by policy measures, while spatially topography and human activities were identified as the primary drivers of the EEQ changes. The findings of this research provide a theoretical foundation for enhancement and administration of the EEQ in Anhui Province and analogous regions.