Details of Award
NERC Reference : NE/I000658/1
Near real-time flood detection in rural and urban areas using high resolution Synthetic Aperture Radar images
Grant Award
- Principal Investigator:
- Dr DC Mason, University of Reading, Environmental Systems Science Centre
- Grant held at:
- University of Reading, Environmental Systems Science Centre
- Science Area:
- Freshwater
- Overall Classification:
- Freshwater
- ENRIs:
- Global Change
- Environmental Risks and Hazards
- Science Topics:
- Survey & Monitoring
- Hydrological Processes
- Regional & Extreme Weather
- Climate & Climate Change
- Abstract:
- Flooding is a major hazard in both rural and urban areas worldwide, and has occurred regularly in the UK in recent times. A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The latest generation of very high resolution Synthetic Aperture Radar (SAR) satellites now make such technology a real possibility. The vast majority of a flooded area may be rural rather than urban, but it is important to detect the urban flooding because of the increased risks and costs associated with it. Flood extent can be detected in rural floods using SARs such as ERS and ASAR, but these have too low a resolution (25m) to detect flooded streets in urban areas. However, a number of SARs with spatial resolutions as high as 1m have recently been launched that are capable of detecting urban flooding. They include TerraSAR-X, RADARSAT-2, ALOS PALSAR, and the first three of the COSMO-SkyMed satellites. An important factor making near real-time operation possible is that accurate geo-registration can now be performed rapidly. For example, the images from TerraSAR-X can be made available in geo-registered form to better than one pixel locational accuracy using precise knowledge of the orbit parameters. In the absence of significant wind or rain, river flood-water generally appears dark in a SAR image because the water acts as a specular reflector. A near real-time flood detection algorithm using a split-based automatic thresholding procedure applied to multi-look single-polarisation TerraSAR-X data has been implemented at DLR Oberpfaffenhofen's Centre for Satellite-Based Crisis Information. This searches for water as regions of low SAR backscatter using a region-growing iterated segmentation/classification approach, and requires minimal user intervention. However, the algorithm would require modification to work in urban areas containing radar shadow and layover. In contrast, a semi-automatic algorithm for the detection of floodwater in urban areas using TerraSAR-X has also been developed previously. It uses the DLR SAR End-To-End simulator (SETES) in conjunction with LiDAR data to estimate regions of the image in which water would not be visible due to radar shadow or layover caused by buildings and taller vegetation. The algorithm is aimed at detecting flood extents for calibrating and validating an urban flood inundation model in an offline situation, and requires user interaction at a number of stages. This invariably introduces an element of delay into the production of the final product. The proposal is to revise and combine the existing algorithms to automate the steps requiring manual interaction and to take advantage of the availability of LiDAR data in the urban area, to lead to a near real-time algorithm. This would be tested on the TerraSAR-X image of the Tewkesbury 2007 flood.
- NERC Reference:
- NE/I000658/1
- Grant Stage:
- Completed
- Scheme:
- Directed (Research Programmes)
- Grant Status:
- Closed
- Programme:
- Tech Proof of Concept
This grant award has a total value of £54,901
FDAB - Financial Details (Award breakdown by headings)
| Indirect - Indirect Costs | DA - Investigators | DI - Staff | DA - Estate Costs | DI - Equipment | DI - T&S | DA - Other Directly Allocated |
|---|---|---|---|---|---|---|
| £17,948 | £5,207 | £22,285 | £6,065 | £2,400 | £560 | £437 |
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