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Evaluating the landscape ecological risk index (LERI) at the landscape level and identifying its driving mechanism is crucial for ensuring regional ecological stability. Firstly, we developed an LERI evaluation model to comprehensively evaluate its spatial-temporal evolution in the Nanjing Metropolitan Area from 2000 to 2020. Secondly, we applied the geographically weighted random forest model to explore the driving mechanisms of different factors on LERI. Finally, we analyzed the correlations between various driving factors. The results indicated that: (1) the spatial distribution pattern of LERI remained stable, with a general “high in the north, low in the south” pattern. The area of medium-risk zones decreased by 1.25 × 103 km2 (a decline of 5.76%), while the area of high-risk zones increased by 0.39 × 103 km2 (a rise of 8.11%), with the areas of extremely low, low, and extremely high-risk zones changing relatively steadily; (2) the normalized difference vegetation index (NDVI), annual average precipitation (AAP), and net primary productivity (NPP) are the main driving factors of LERI evolution, with population density (PD) playing a secondary role. The effects of annual average temperature (AAT) and nighttime lights (NTL) are relatively small, and the spatial distribution of factor importance varies significantly; (3) there is a consistently strong positive correlation between driving factors such as AAP, NDVI, NPP, and AAT. These findings aim to provide scientific support for regional landscape ecological risk management.

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LERI Evaluation and Driving Mechanism Analysis via GWRF Model

  • 会议论文

  • 发表日期：

    2026年

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  • Chenfeng Xu,
  • Zhihao Kang,
  • Min Li,
  • Yike Hu,
  • Zhengyang Zou,
  • Xing Geng,
  • Haolan Huang,
  • Zibo Zhu,
  • Fenglei Chen,
  • Ziruo Feng,
  • Yan Cheng

摘要

Evaluating the landscape ecological risk index (LERI) at the landscape level and identifying its driving mechanism is crucial for ensuring regional ecological stability. Firstly, we developed an LERI evaluation model to comprehensively evaluate its spatial-temporal evolution in the Nanjing Metropolitan Area from 2000 to 2020. Secondly, we applied the geographically weighted random forest model to explore the driving mechanisms of different factors on LERI. Finally, we analyzed the correlations between various driving factors. The results indicated that: (1) the spatial distribution pattern of LERI remained stable, with a general “high in the north, low in the south” pattern. The area of medium-risk zones decreased by 1.25 × 103 km2 (a decline of 5.76%), while the area of high-risk zones increased by 0.39 × 103 km2 (a rise of 8.11%), with the areas of extremely low, low, and extremely high-risk zones changing relatively steadily; (2) the normalized difference vegetation index (NDVI), annual average precipitation (AAP), and net primary productivity (NPP) are the main driving factors of LERI evolution, with population density (PD) playing a secondary role. The effects of annual average temperature (AAT) and nighttime lights (NTL) are relatively small, and the spatial distribution of factor importance varies significantly; (3) there is a consistently strong positive correlation between driving factors such as AAP, NDVI, NPP, and AAT. These findings aim to provide scientific support for regional landscape ecological risk management.