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<p>Urban riparian corridors are increasingly degraded due to rapid urbanization. Yet spatial tools for prioritizing restoration remain limited, especially in data-poor regions. This study presents an Integrated Spatial Decision-Support Framework (ISDSF) that integrates high-resolution Sentinel-2, terrain and soil datasets within a principal component analysis (PCA)-weighted multi-criteria framework across a 500 m corridor of the river Gomati in Lucknow, India to generate a Composite Restoration Index (CRI) at 10 m resolution. The CRI revealed that 36.4% of the study area fell under high or very-high restoration priority classes, characterized by low NDVI/LAI, elevated LST, depleted SOC and increased bare/impervious surfaces. Ground surveys showed that higher CRI values were associated with reduced native species richness and increased invasive dominance, while Random Forest-based internal consistency assessment showed strong agreement between the CRI and its component indicators (R² = 0.97; RMSE = 0.01). By combining objective weighting, internal model checking and floristic ground-truthing, the framework provides a locally calibratable decision-support tool for targeting riparian restoration under urban pressure.</p>

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Prioritizing restoration hotspots in riparian corridors using integrated spatial decision-support framework for targeted urban greening initiatives

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Priya Verma,
  • Satyabhan Singh,
  • Ratna Katiyar,
  • Dibyendu Adhikari

摘要

Urban riparian corridors are increasingly degraded due to rapid urbanization. Yet spatial tools for prioritizing restoration remain limited, especially in data-poor regions. This study presents an Integrated Spatial Decision-Support Framework (ISDSF) that integrates high-resolution Sentinel-2, terrain and soil datasets within a principal component analysis (PCA)-weighted multi-criteria framework across a 500 m corridor of the river Gomati in Lucknow, India to generate a Composite Restoration Index (CRI) at 10 m resolution. The CRI revealed that 36.4% of the study area fell under high or very-high restoration priority classes, characterized by low NDVI/LAI, elevated LST, depleted SOC and increased bare/impervious surfaces. Ground surveys showed that higher CRI values were associated with reduced native species richness and increased invasive dominance, while Random Forest-based internal consistency assessment showed strong agreement between the CRI and its component indicators (R² = 0.97; RMSE = 0.01). By combining objective weighting, internal model checking and floristic ground-truthing, the framework provides a locally calibratable decision-support tool for targeting riparian restoration under urban pressure.