Flood detection from multi-temporal SAR data using harmonic analysis and change detection |
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| Institution: | 1. Vienna University of Technology, Department of Geodesy and Geoinformation, Gusshausstr. 27-29, 1040 Vienna, Austria;2. Centre de Recherche Public – Gabriel Lippmann, 41, rue du Brill, 4422 Belvaux, Luxembourg;1. Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles, Los Angeles, CA 90095-7228, USA;2. Remote Sensing Solutions Inc., Pasadena, CA 91107, USA;1. Centre de Recherche Public – Gabriel Lippmann, Belvaux, Luxembourg;2. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, 9000 Gent, Belgium;3. Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, 9000 Gent, Belgium;1. Environmental Systems Science Centre, Department of Meteorology, University of Reading, Reading RG6 6AL, UK;2. Departement Environnement et Agro-Biotechnologies, Centre de Recherche Public - Gabriel Lippmann, 4422 Belvaux, Luxembourg;3. Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK;1. Vienna University of Technology, Karlsplatz 13, 1040 Vienna, Austria;2. Luxembourg Institute of Science and Technology, 5, avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg |
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| Abstract: | Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in recent years. Most available algorithms typically focus on single-image techniques which do not take into account the backscatter signature of a land surface under non-flooded conditions. In this study, harmonic analysis of a multi-temporal time series of >500 ENVISAT Advanced SAR (ASAR) scenes with a spatial resolution of 150 m was used to characterise the seasonality in backscatter under non-flooded conditions. Pixels which were inundated during a large-scale flood event during the summer 2007 floods of the River Severn (United Kingdom) showed strong deviations from normal seasonal behaviour as inferred from the harmonic model. The residuals were classified by means of an automatic threshold optimisation algorithm after masking out areas which are unlikely to be flooded using a topography-derived index. The results were validated against a reference dataset derived from high-resolution airborne imagery. For the water class, accuracies > 80% were found for non-urban land uses. A slight underestimation of the reference flood extent can be seen, mostly due to the lower spatial resolution of the ASAR imagery. Finally, an outlook for the proposed algorithm is given in the light of the Sentinel-1 mission. |
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| Keywords: | ENVISAT Sentinel-1 Time series analysis Otsu Flood hazard HAND index |
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