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Updated: Jun 28, 2026

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Identifying priority urban ecological restoration areas using an integrated machine-learning and multi-dimensional

Yuxia Hu1, Zhaowu Yu1, Jinyu Hu1

  • 1Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China.

Journal of Environmental Management
|June 26, 2026
PubMed
Summary
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Identifying priority areas for urban ecological restoration is crucial. This study introduces an integrated framework for targeted restoration, focusing on transitional zones with high ecological value and resistance.

Area of Science:

  • Urban Ecology
  • Ecological Restoration
  • Spatial Planning

Background:

  • Rapid urbanization increases pressure on urban ecosystems, necessitating effective restoration strategies.
  • Previous studies often separate structural optimization from functional recovery and lack dynamic assessments.
  • A disconnect exists between static ecological assessments and spatially variable anthropogenic pressures.

Purpose of the Study:

  • To develop an integrated framework for identifying priority areas and classifying restoration types in urban ecosystems.
  • To address limitations in previous studies by combining machine learning, ecological unit identification, and multi-dimensional diagnosis.
  • To provide a spatially explicit and transferable tool for guiding differentiated ecological restoration strategies.

Main Methods:

Keywords:
Ecological restorationHigh-density citiesMachine learningResistance surfaceUrban ecosystem

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  • Developed an integrated framework combining machine-learning-based resistance modeling, ecological unit identification, and five-dimensional ecological diagnosis.
  • Applied the framework in Shanghai to identify key ecological restoration areas.
  • Classified ecological units into four restoration types: quality improvement, connectivity enhancement, stability enhancement, and ecosystem function improvement.

Main Results:

  • Identified key restoration areas exhibiting a core-periphery pattern along urban-exurban gradients.
  • Restoration priorities were concentrated in transitional zones with important ecosystem services and relatively high ecological resistance.
  • High and very high priority units constitute 44% of ecological space, with 26.1% classified as medium priority.

Conclusions:

  • The proposed framework offers a mechanism-driven, spatially explicit approach for prioritizing ecological restoration in urban regions.
  • The five-dimensional diagnostic framework enables differentiated restoration strategies based on specific ecological unit needs.
  • The findings provide a structured basis for sequencing and intensifying restoration actions in high-density urban areas.