Near real-time flood detection in urban and rural areas using TerraSAR-X

David Mason, Ian Davenport,

University of Reading

Guy Schumann, Jeff Neal, Paul Bates

University of Bristol

Need for real-time visualisation tools

• Pitt Commission set up by UK government recommended real-time visualisation tools be available for emergency reponders

• Vast majority of flooded area may be rural, but important to detect urban flooding due to increased risks/costs

• ASAR/ERS-2 have too low a resolution to detect urban floods – but high resolution SARs can.

Fusion of algorithms

• Near real-time algorithm for rural flood detection developed at DLR

• Non-real-time algorithm for urban flood detection developed at Reading

• Objective is to fuse and automate to develop near real-time algorithm for flood detection in urban and rural areas

• Algorithm assumes LiDAR available for urban area

2km

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B

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TerraSAR-X image of the Severn flood of July 2007

TerraSAR-X image of Tewkesbury flooding on 25th July 2007 showing

urban areas (3m resolution, dark areas are water).

ASAR image of 26th July 2007 (25m resolution).

Aerial photo mosaic of Tewkesbury flooding on 24 July 2007.

LiDAR DSM of Tewkesbury (2m resolution).

Layover (AB) and shadow (CD) in a flooded street between

adjacent buildings.

h1h2

A N B Y C D

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TerraSAR-X

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Regions unseen by TerraSAR-X in LiDAR DSM due to combined

shadow and layover (satellite looking West).

Detection of rural flooding

• Detect flood extent in rural areas, then in urban area guided by rural flood extent

• Rural flood detection achieved by segmenting SAR image into homogeneous regions (objects), then classifying them

• Use eCognition Developer software for multi-resolution segmentation and classification.

Threshold determination

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Object mean intensity threshold T

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Misclassified water (%)

Misclassified non-water (%)

Total misclassified (%)

Flood detection in urban areas

• Seed pixels identified with backscatter less than threshold, and heights less than or similar to adjacent rural flood

• Seed pixels clustered together if sufficiently close

• Shadow/layover masked out

Correspondence between the TerraSAR-X and aerial photo flood extents in main urban areas of Tewkesbury, superimposed on the LiDAR image (yellow = wet in SAR and aerial photos, red = wet in SAR only, green = wet in aerial

photos only). Flood detection accuracy = 75%.

Correspondence between the TerraSAR-X and aerial photo flood extents over the rural validation area (region B), superimposed on the TerraSAR-X image (blue = wet in SAR and aerial photos, red = wet in SAR only, green = wet in

aerial photos only). Flood detection accuracy = 89%.

(a)

(b)

Possible multi-scale visualisation of flood extents in (a) rural (blue = predicted flood), and (b) urban areas (yellow = predicted flood).

Operational considerations

• Ensure that –– can task a satellite in time to acquire image of developing flood– short time delay between image acquisition and production of

SAR flood extent

• Preprocessing operations can be carried out in parallel with tasking satellite e.g. generation of shadow-layover map

• Blueprint for operational system is ESA FAIRE system – produces multi-look geo-registered ASAR images 3 hours after acquisition

Conclusion

• Automatic near real-time algorithm developed that can detect rural flooding with good accuracy, urban flooding with less good accuracy

• Need to test on more flood events

• Need to improve urban classification accuracy