25 October 2012 Flood delineation from synthetic aperture radar data with the help of a priori knowledge from historical acquisitions and digital elevation models in support of near-real-time flood mapping
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Abstract
The monitoring of flood events with synthetic aperture radar (SAR) sensors has attracted a considerable amount of attention during the last decade, owing to the growing interest in using spaceborne data in near-real time flood management. Most existing methods for classifying flood extent from SAR data rely on pure image processing techniques. In this paper, we propose a method involving a priori knowledge about an area taken from a multitemporal time series and a digital elevation model. A time series consisting of ENVISAT ASAR acquisitions was geocoded and coregistered. Then, a harmonic model was fitted to each pixel time series. The standardised residuals of the model were classified as flooded when exceeding a certain threshold value. Additionally, the classified flood extent was limited to flood-prone areas which were derived from a freely available DEM using the height above nearest drainage (HAND) index. Comparison with two different reference datasets for two different flood events showed that the approach yielded realistic results but underestimated the inundation extent. Among the possible reasons for this are the rather coarse resolution of 150 m and the sparse data coverage for a substantial part of the time series. Nevertheless, the study shows the potential for production of rapid overviews in near-real time in support of early response to flood crises.
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Stefan Schlaffer, Markus Hollaus, Wolfgang Wagner, Patrick Matgen, "Flood delineation from synthetic aperture radar data with the help of a priori knowledge from historical acquisitions and digital elevation models in support of near-real-time flood mapping", Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 853813 (25 October 2012); doi: 10.1117/12.974503; https://doi.org/10.1117/12.974503
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