A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

Autor: Alexandre, Cyprien, Johary, Rosa, Catry, Thibault, Mouquet, Pascal, Révillion, Christophe, Rakotondraompiana, Solofo, Pennober, Gwenaëlle
Přispěvatelé: UMR 228 Espace-Dev, Espace pour le développement, Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM), Institut et Observatoire de Géophysique et Astronomie d'Antananarivo, Madagascar (IOGA), Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Remote Sensing, Vol 12, Iss 2, p 252 (2020)
Remote Sensing
Remote Sensing, MDPI, 2020, 12 (2), pp.252. ⟨10.3390/rs12020252⟩
ISSN: 2072-4292
Popis: International audience; In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.
Databáze: OpenAIRE
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