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Abstract
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.
Highlights
Remote sensing has emerged as a privileged means of observing and studying natural disasters
Bahamas In the Marsh Harbour area, the normalized difference ratio (NDR) is calculated from images of 21 August, ten days before the evIenntt,haenMd a2rsShepHteamrbboeurr, tahreead, atyheafNteDrRthies ecvalecnutla(Fteigdufrreom6).imThagisesshoofr2t1tiAmuegduisffte, rteenncdeabyestwbeefeonre acthqeuiesviteionnt,saanldlo2wSseepltiemminbaetri,otnheofdaayll apfotetrenthtiealevvaernitat(iFoingsurwe h6i)c.hThcoisusldhobrte tdimuee dtoiffoetrheenrcesebnestiwtiveeen maiccqrou-iesviteionntss liaklleotwrospeicliaml rinaiant.ion of all potential variations which could be due to other sensitive micro-events like tropical rain
The algorithm presented in this paper permits Sentinel-1 data processing for detection of flood impacts from the download to the final product thanks to S1-tiling for the pre-treatment and NDR for flood detection
Summary
Remote sensing has emerged as a privileged means of observing and studying natural disasters. The capabilities of spaceborne sensors and platforms allow more accurate and frequent observations. Usually preferred because of its accessibility, is often unusable because of the bad weather conditions caused by the extreme weather events inducing natural disasters. As evidenced by the increasing number of satellites launched between 2007 and 2019 [1], synthetic aperture radar (SAR) imagery is becoming more and more popular. The numerous images produced offer new opportunities to be exploited in the field of natural hazards. Radar images are less impacted than optical imagery by cloudiness and the active sensor system allows for night-time snapshots [2]
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