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Open AccessArticle

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

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UMR 228 Espace-Dev, SEAS-OI, 97410 Saint-Pierre, Reunion Island, France
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IOGA, Institut et Observatoire Géophysique d’Antananarivo, Antananarivo 101, Madagascar
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UMR 228 Espace-Dev, Maison de la télédétection, 34090 Montpellier, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 252; https://doi.org/10.3390/rs12020252
Received: 29 November 2019 / Revised: 6 January 2020 / Accepted: 8 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue SAR for Natural Hazard )
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. View Full-Text
Keywords: hurricane; cyclone; flood; Sentinel 1 time series; change detection; NDR; SAR hurricane; cyclone; flood; Sentinel 1 time series; change detection; NDR; SAR
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MDPI and ACS Style

Alexandre, C.; Johary, R.; Catry, T.; Mouquet, P.; Révillion, C.; Rakotondraompiana, S.; Pennober, G. A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts. Remote Sens. 2020, 12, 252.

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