Chen Xi, Fang Yuqing, He Yufeng, Dai Wen, Jiang Ling, Wei Hong, Wang Dong, Wang Chun
School of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239000, China.
Anhui Province Key Laboratory of Physical Geographic Environment, Chuzhou, 239000, China.
Sci Rep. 2025 Aug 19;15(1):30460. doi: 10.1038/s41598-025-13944-x.
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.
遥感生态指数(RSEI)是评估区域生态环境质量(EEQ)的关键指标。然而,准确量化和识别其对多因素耦合的响应仍然是一项重大挑战。因此,在本研究中,采用了一种具有局部适应性的改进遥感生态指数(RSEILA)方法,利用谷歌地球引擎平台分析EEQ的时空分布格局。然后,运用地理探测器模型识别多因素耦合下导致EEQ变化的驱动机制。结果表明:(1)在过去二十年中,EEQ一直处于中等至良好水平,并呈现出总体改善趋势。(2)在空间尺度上,安徽省RSEILA的分布格局表现为南高北低,这与自然地理条件和土地利用模式密切相关。(3)多因素耦合对EEQ水平的驱动因素具有显著的时空尺度效应。在时间尺度上,EEQ水平主要受政策措施影响,而在空间上,地形和人类活动被确定为EEQ变化的主要驱动因素。本研究结果为安徽省及类似地区EEQ的提升和管理提供了理论基础。