Abstract
Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. High resolution Synthetic Aperture Radar (SAR) sensors are able to detect flood extents in urban areas during both day- and night-time. If obtained in near real-time, these flood extents can be used for emergency flood relief management or as observations for assimilation into flood forecasting models. In this paper a method for detecting flooding in urban areas using near real-time SAR data is developed and extensively tested under a variety of scenarios involving different flood events and different images. The method uses a SAR simulator in conjunction with LiDAR data of the urban area to predict areas of radar shadow and layover in the image caused by buildings and taller vegetation. Of the urban water pixels visible to the SAR, the flood detection accuracy averaged over the test examples was 83%, with a false alarm rate of 9%. The results indicate that flooding can be detected in the urban area to reasonable accuracy, but that this accuracy is limited partly by the SAR’s poor visibility of the urban ground surface due to shadow and layover.
Highlights
Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the risks to people and the economic impacts are most severe
In Ref. 20, a reasonable urban flood detection accuracy of 75% was achieved in urban areas that were visible to the synthetic aperture radar (SAR), with a false alarm rate of 19%
Flood detection in urban areas: A revised approach to that of Ref. 20 was developed for flood detection in urban areas, which in the analysis proved superior to the original method
Summary
Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the risks to people and the economic impacts are most severe. Giustarini et al.[15] detected urban flooding using a change detection technique, in which an SAR image containing flooding was normalized using a second image acquired during dry conditions, with the second image having the same look angle, orbit inclination, frequency, and resolution as the first This enabled the identification of regions not visible to the SAR (e.g., shadow) or that systematically behaved as specular reflectors (e.g., smooth tarmac and permanent water bodies). Mason et al.: Robust algorithm for detecting floodwater in urban areas using synthetic aperture radar images the catchment that caused the particular flood being investigated is the same as that used to calculate the flood return period data. We find that a number of improvements can be made, in particular to the estimation of the flood elevations and to the method of delineating the flooding in the urban area
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