Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences and its aftereffects are ever more dire. Hence, water as a fundamental land cover type needs to be constantly monitored under urban context, but the presiding methodologies which uses SAR data require substantial improvements. This work is aiming to explore the performance Sentinel-1 constellation, to map and analyze urban floods on test sites having different climate environments and mountainous terrains. And to provide a comprehensive understanding of urban vulnerability and resilience during and after flood events by overlaying flood maps onto extracted urban extents, which serve as the foundational layer of built-up areas. The experimental results state that the existing methodology for automated, high resolution urban flood mapping using multitemporal Sentinel-1 SAR data has overall accuracy of 87.1% in a mountainous and hilly urban territory. Moreover, by overlaying flood impacts on extracted urban extents, the research highlights the relationship between urban infrastructure and flood dynamics, providing valuable information for disaster management and urban resilience strategies. Additionally, the research highlights that substantial improvement of the accuracy in Globcover datasets, and the challenges posed by complex terrains and the limitations of SAR in detecting finer urban and flood details, offering avenues for future research.

Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences and its aftereffects are ever more dire. Hence, water as a fundamental land cover type needs to be constantly monitored under urban context, but the presiding methodologies which uses SAR data require substantial improvements. This work is aiming to explore the performance Sentinel-1 constellation, to map and analyze urban floods on test sites having different climate environments and mountainous terrains. And to provide a comprehensive understanding of urban vulnerability and resilience during and after flood events by overlaying flood maps onto extracted urban extents, which serve as the foundational layer of built-up areas. The experimental results state that the existing methodology for automated, high resolution urban flood mapping using multitemporal Sentinel-1 SAR data has overall accuracy of 87.1% in a mountainous and hilly urban territory. Moreover, by overlaying flood impacts on extracted urban extents, the research highlights the relationship between urban infrastructure and flood dynamics, providing valuable information for disaster management and urban resilience strategies. Additionally, the research highlights that substantial improvement of the accuracy in Globcover datasets, and the challenges posed by complex terrains and the limitations of SAR in detecting finer urban and flood details, offering avenues for future research.

MAPPING URBAN FLOODS IN PAKISTAN AND INDIA USING THE EUROPEAN SENTINEL-1 CONSTELLATION

MIAKHIL, SHAFI ULLAH
2023/2024

Abstract

Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences and its aftereffects are ever more dire. Hence, water as a fundamental land cover type needs to be constantly monitored under urban context, but the presiding methodologies which uses SAR data require substantial improvements. This work is aiming to explore the performance Sentinel-1 constellation, to map and analyze urban floods on test sites having different climate environments and mountainous terrains. And to provide a comprehensive understanding of urban vulnerability and resilience during and after flood events by overlaying flood maps onto extracted urban extents, which serve as the foundational layer of built-up areas. The experimental results state that the existing methodology for automated, high resolution urban flood mapping using multitemporal Sentinel-1 SAR data has overall accuracy of 87.1% in a mountainous and hilly urban territory. Moreover, by overlaying flood impacts on extracted urban extents, the research highlights the relationship between urban infrastructure and flood dynamics, providing valuable information for disaster management and urban resilience strategies. Additionally, the research highlights that substantial improvement of the accuracy in Globcover datasets, and the challenges posed by complex terrains and the limitations of SAR in detecting finer urban and flood details, offering avenues for future research.
2023
MAPPING URBAN FLOODS IN PAKISTAN AND INDIA USING THE EUROPEAN SENTINEL-1 CONSTELLATION
Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences and its aftereffects are ever more dire. Hence, water as a fundamental land cover type needs to be constantly monitored under urban context, but the presiding methodologies which uses SAR data require substantial improvements. This work is aiming to explore the performance Sentinel-1 constellation, to map and analyze urban floods on test sites having different climate environments and mountainous terrains. And to provide a comprehensive understanding of urban vulnerability and resilience during and after flood events by overlaying flood maps onto extracted urban extents, which serve as the foundational layer of built-up areas. The experimental results state that the existing methodology for automated, high resolution urban flood mapping using multitemporal Sentinel-1 SAR data has overall accuracy of 87.1% in a mountainous and hilly urban territory. Moreover, by overlaying flood impacts on extracted urban extents, the research highlights the relationship between urban infrastructure and flood dynamics, providing valuable information for disaster management and urban resilience strategies. Additionally, the research highlights that substantial improvement of the accuracy in Globcover datasets, and the challenges posed by complex terrains and the limitations of SAR in detecting finer urban and flood details, offering avenues for future research.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14239/33283