Sentinel-1-based flood mapping: a fully automated processing chain

This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-real-time (NRT). Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows deriving time-critical disaster information in les...

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Vydané v:International journal of remote sensing Ročník 37; číslo 13; s. 2990 - 3004
Hlavní autori: Twele, André, Cao, Wenxi, Plank, Simon, Martinis, Sandro
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Taylor & Francis 02.07.2016
Predmet:
data collection
Greece
Internet
monitoring
remote sensing
Turkey (country)
wind
Greece
Turkey (country)
ISSN:0143-1161, 1366-5901, 1366-5901
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Shrnutí:This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-real-time (NRT). Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows deriving time-critical disaster information in less than 45 min after a new data set is available on the Sentinel Data Hub of the European Space Agency (ESA). Due to the systematic acquisition strategy and high repetition rate of Sentinel-1, the processing chain can be set up as a web-based service that regularly informs users about the current flood conditions in a given area of interest. The thematic accuracy of the thematic processor has been assessed for two test sites of a flood situation at the border between Greece and Turkey with encouraging overall accuracies between 94.0% and 96.1% and Cohen's kappa coefficients (κ) ranging from 0.879 to 0.910. The accuracy assessment, which was performed separately for the standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that under calm wind conditions, slightly higher thematic accuracies can be achieved by using VV instead of VH polarization data.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2016.1192304