Abstract
This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission’s six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency’s (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission.
Highlights
As floods presently rival earthquakes and hurricanes in terms of economic losses [1], insurance companies face the problem of a substantial increase in flood claims
Several international Earth Observation (EO) initiatives have set up a research and development agenda to propose new products to serve this user community and more in general to improve the services provided for crisis response and mitigation
The algorithm was tested using the images acquired during the hurricane season in 2017, which caused large-scale flooding in and around the city of Houston (Texas)
Summary
As floods presently rival earthquakes and hurricanes in terms of economic losses [1], insurance companies face the problem of a substantial increase in flood claims. The Working group on Disasters of the Committee on Earth Observation Satellites (CEOS) highlighted the need to exploit the full potential of new EO data sets to support flood management in its various phases [3] In this context, SAR data play a major role in operational services for flood risk management thanks to their high sensitivity to water and the ability to provide data during day and night, regardless of cloud cover. Water bodies appear as dark areas in SAR images, being smooth surfaces that typically reflect the radar signal in the specular direction away from the antenna, thereby producing a very low backscatter For this reason, a single radar observation of a flood can detect floodwater in such an environment [7,12,13], change detection approaches are often used to mask out permanent water or false alarms caused by shadows or smooth surfaces such as tarmac [8,14,15].
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