Scholarlink

<p>As a critical ecological barrier in eastern China, the Huai River urban agglomeration (HRUA) necessitates research on the spatiotemporal dynamics, drivers, and future scenarios of Habitat Quality (HQ). This study established an integrated “Scenario Simulation-HQ Assessment-Dual perspective Factor Analysis” framework. We integrated Multiscale Geographically Weighted Regression (MGWR) with explainable machine learning to explore the influencing factors, as these two methods are highly complementary. Key findings indicate: (1) The area with median-or-higher HQ decreased by 3% (2000–2020). Multi-scenario simulations (MS) project the 2035 Urban Development Scenario (UDS) will cause the most severe HQ degradation. HQ’s overall spatial pattern remained stable, with persistent, significant hot spot clustering and strong habitat resilience in the southwest. (2) Elevation, NDVI, slope, and population density were key factors influencing HQ spatial differentiation, showing significant effect heterogeneity. (3) Urban expansion and cropland loss were the primary land-use drivers of HQ degradation, jointly accounting for 85.7% of land-use change impact and exhibiting a significant synergistic effect. The research framework provides practical value for exploring the changes of HQ in the HRUA, exploring the influencing factors of HQ from multiple perspectives, and carrying out zoning planning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Urban expansion and cropland loss drive habitat quality decline in the Huai River Urban agglomeration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

查看全文
  • Junhao Cheng,
  • Wenfeng Hu,
  • Mengtian Zheng,
  • Xiaolong Jin,
  • Junqiang Yao,
  • Lei Li,
  • Shuangmei Tong,
  • Zhouwei Yuan,
  • Ping Han

摘要

As a critical ecological barrier in eastern China, the Huai River urban agglomeration (HRUA) necessitates research on the spatiotemporal dynamics, drivers, and future scenarios of Habitat Quality (HQ). This study established an integrated “Scenario Simulation-HQ Assessment-Dual perspective Factor Analysis” framework. We integrated Multiscale Geographically Weighted Regression (MGWR) with explainable machine learning to explore the influencing factors, as these two methods are highly complementary. Key findings indicate: (1) The area with median-or-higher HQ decreased by 3% (2000–2020). Multi-scenario simulations (MS) project the 2035 Urban Development Scenario (UDS) will cause the most severe HQ degradation. HQ’s overall spatial pattern remained stable, with persistent, significant hot spot clustering and strong habitat resilience in the southwest. (2) Elevation, NDVI, slope, and population density were key factors influencing HQ spatial differentiation, showing significant effect heterogeneity. (3) Urban expansion and cropland loss were the primary land-use drivers of HQ degradation, jointly accounting for 85.7% of land-use change impact and exhibiting a significant synergistic effect. The research framework provides practical value for exploring the changes of HQ in the HRUA, exploring the influencing factors of HQ from multiple perspectives, and carrying out zoning planning.