Climate change has increased the frequency and intensity of extreme weather events, leading to greater risks for vulnerable urban areas. Urban sprawl and inadequate infrastructure exacerbate the vulnerability of the territory, resulting in significant socioeconomic losses from climate-related hazards and in particular flooding. Cities in small size watersheds with short response time are particularly vulnerable also because effects of floods must be detected very quickly and effectively monitored to implement effective interventions to mitigate their impact on humans, properties, infrastructures, and environment. To enhance climate resilience in cities, smart technologies such as GIS-based Digital Twin help to simulate and monitor flooding scenarios to support urban planning and decision-making. The validation of the outputs of these virtual systems relies on flood maps from past events generated from satellite-based data and mainly Synthetic Aperture Radar (SAR) images, which are effective in various meteorological conditions. Although SAR-based methods are effective for flood detection, their application in urban areas is challenging due to the complexity of radar backscattering effects caused by built environments. The under-detection caused by the limitations of using intensity data alone, can be reduced by InSAR coherence but not always interferometric imagery is available. This study focuses on developing and validating flood maps basically obtained from single intensity SAR images based on a consolidated approach that combines clustering and fuzzy logic approaches and here improved to better reproduce flooding in urban areas. The method is applied to a severe flood event that occurred in the Florence-Prato-Pistoia plain area in Tuscany (Italy) in November 2023. A comparison with the Copernicus Emergency Management Service maps demonstrated good correspondence with the maps generated by the methodology. This work addresses the challenges of flood detection in urban areas, emphasizing the importance of reliable and prompt satellite-based flood mapping procedures. However, it also highlights some operational limitations due to the likely unavailability of their outputs during the early stages of flood events.
Enhancing climate resilience in urban areas through satellite data: A case study of flood mapping during a rainfall event in Tuscany, Italy
Serafino, Giovanni;
2025-01-01
Abstract
Climate change has increased the frequency and intensity of extreme weather events, leading to greater risks for vulnerable urban areas. Urban sprawl and inadequate infrastructure exacerbate the vulnerability of the territory, resulting in significant socioeconomic losses from climate-related hazards and in particular flooding. Cities in small size watersheds with short response time are particularly vulnerable also because effects of floods must be detected very quickly and effectively monitored to implement effective interventions to mitigate their impact on humans, properties, infrastructures, and environment. To enhance climate resilience in cities, smart technologies such as GIS-based Digital Twin help to simulate and monitor flooding scenarios to support urban planning and decision-making. The validation of the outputs of these virtual systems relies on flood maps from past events generated from satellite-based data and mainly Synthetic Aperture Radar (SAR) images, which are effective in various meteorological conditions. Although SAR-based methods are effective for flood detection, their application in urban areas is challenging due to the complexity of radar backscattering effects caused by built environments. The under-detection caused by the limitations of using intensity data alone, can be reduced by InSAR coherence but not always interferometric imagery is available. This study focuses on developing and validating flood maps basically obtained from single intensity SAR images based on a consolidated approach that combines clustering and fuzzy logic approaches and here improved to better reproduce flooding in urban areas. The method is applied to a severe flood event that occurred in the Florence-Prato-Pistoia plain area in Tuscany (Italy) in November 2023. A comparison with the Copernicus Emergency Management Service maps demonstrated good correspondence with the maps generated by the methodology. This work addresses the challenges of flood detection in urban areas, emphasizing the importance of reliable and prompt satellite-based flood mapping procedures. However, it also highlights some operational limitations due to the likely unavailability of their outputs during the early stages of flood events.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
