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|Title:||Urban flood detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) observations in a Bayesian Framework : a case study for Hurricane Matthew||Authors:||Lin, Nina Yunung
Hill, Emma M.
|Keywords:||SAR Intensity Time Series
Urban Flood Mapping
|Issue Date:||2019||Source:||Lin, N. Y., Yun, S.-H., Bhardwaj, A., & Hill, E. M. (2019). Urban flood detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) observations in a Bayesian Framework : a case study for Hurricane Matthew. Remote Sensing, 11(15), 1778- doi:10.3390/rs11151778||Series/Report no.:||Remote Sensing||Abstract:||In this study we explored the application of synthetic aperture radar (SAR) intensity time series for urban flood detection. Our test case was the flood in Lumberton, North Carolina, USA, caused by the landfall of Hurricane Matthew on 8 October 2016, for which airborne imagery—taken on the same day as the SAR overpass—is available for validation of our technique. To map the flood, we first carried out normalization of the SAR intensity observations, based on the statistics from the time series, and then construct a Bayesian probability function for intensity decrease (due to specular reflection of the signal) and intensity increase (due to double bounce) cases separately. We then formed a flood probability map, which we used to create our preferred flood extent map using a global cutoff probability of 0.5. Our flood map in the urban area showed a complicated mosaicking pattern of pixels showing SAR intensity decrease, pixels showing intensity increase, and pixels without significant intensity changes. Our approach shows improved performance when compared with global thresholding on log intensity ratios, as the time series-based normalization has accounted for a certain level of spatial variation by considering the different history for each pixel. This resulted in improved performance for urban and vegetated regions. We identified smooth surfaces, like asphalt roads, and SAR shadows as the major sources of underprediction, and aquatic plants and soil moisture changes were the major sources of overprediction.||URI:||https://hdl.handle.net/10356/85554
|ISSN:||2072-4292||DOI:||http://dx.doi.org/10.3390/rs11151778||Rights:||© 2019 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ASE Journal Articles|
EOS Journal Articles
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