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 realtime 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
N
B
B
A
A
2km
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).
TerraSAR-X
θ
M
O
R
h1
A
N
h2
B
Y
C
D
Layover (AB) and shadow (CD) in a flooded street between
adjacent buildings.
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
80
70
Percentage
60
50
Misclassified water (%)
40
Misclassified non-water (%)
30
Total misclassified (%)
20
10
0
40
50
60
70
80
Object mean intensity threshold T
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%.
(b)
(a)
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