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<p>Flood risk assessment is essential for mitigating the impacts of extreme hydrological events, particularly in regions prone to recurrent flooding. This study introduces a novel, scalable GIS-based framework that integrates LiDAR-derived terrain models, Sentinel-2 multispectral imagery, and high-resolution bluespot analysis for rural flood risk mapping. The proposed methodology advances current practices by combining morphometric terrain indicators with vegetation influence through NDVI (Normalized Difference Vegetation Index) and a refined hydrological depression model, enabling improved detection of microtopographic vulnerabilities. Applied to Mihăești commune (Romania), the analysis revealed that 48.49% of the area falls under high or very high flood susceptibility, and 36.76% of the analyzed buildings are located within flood-prone zones. The approach demonstrates strong predictive capacity through robust statistical validation. Results confirm that low elevation, steep slopes, and sparse vegetation increase flood susceptibility, while NDVI integration enhances the spatial differentiation of risk. Beyond its local relevance, the method provides a transferable workflow suitable for broader rural and peri-urban flood assessment applications. The findings highlight the importance of high-resolution remote sensing in developing data-driven, adaptive flood risk models to inform climate resilience planning.</p>

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Integrating LiDAR, Sentinel-2 data and Bluespot modeling for flood risk mapping in Mihăești, Romania

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Ioana Vizireanu,
  • Georgiana Grigoraș,
  • Dan Răducanu

摘要

Flood risk assessment is essential for mitigating the impacts of extreme hydrological events, particularly in regions prone to recurrent flooding. This study introduces a novel, scalable GIS-based framework that integrates LiDAR-derived terrain models, Sentinel-2 multispectral imagery, and high-resolution bluespot analysis for rural flood risk mapping. The proposed methodology advances current practices by combining morphometric terrain indicators with vegetation influence through NDVI (Normalized Difference Vegetation Index) and a refined hydrological depression model, enabling improved detection of microtopographic vulnerabilities. Applied to Mihăești commune (Romania), the analysis revealed that 48.49% of the area falls under high or very high flood susceptibility, and 36.76% of the analyzed buildings are located within flood-prone zones. The approach demonstrates strong predictive capacity through robust statistical validation. Results confirm that low elevation, steep slopes, and sparse vegetation increase flood susceptibility, while NDVI integration enhances the spatial differentiation of risk. Beyond its local relevance, the method provides a transferable workflow suitable for broader rural and peri-urban flood assessment applications. The findings highlight the importance of high-resolution remote sensing in developing data-driven, adaptive flood risk models to inform climate resilience planning.