Improving Flood Modelling and Visualisation using Remote Sensing

Transcription

1 Improving Flood Modelling and Visualisation using Remote Sensing David Mason 1, Paul Bates 2, Johanna Dall Amico 1, Matt Horritt 3, Jeff Neal 2, Guy Schumann 2, Rainer Speck 4. 1 Environmental Systems Science Centre, University of Reading, UK 2 School of Geographical Sciences, University of Bristol, UK 3 Halcrow Group Ltd. 4 DLR Oberpfaffenhofen, Wessling, Germany

2 Predictions of flood extent Used - for maintaining flood defences for emergency flood relief management for risk assessment Object is to improve flood models by using remotely sensed data (SAR, LiDAR) to validate and parameterise the models.

3 ERS-1 SAR image of 1992 Thames flood, with flood extent (waterline) from snake superimposed (green).

4 Laser Altimetry - LiDAR Scanning pulsed laser Horizontal resolution ~1m Vertical height accuracy 10-15cm Sawtooth pattern of heights

5 Uses of LiDAR for model parameterisation Providing an accurate DTM of the floodplain. Estimating vegetation heights from which floodplain friction can be derived. Generating unstructured grids incorporating buildings and taller vegetation.

6 Improving SAR flood extent using LiDAR SAR image west of Oxford with snake superimposed

7 LiDAR image west of Oxford, with relative SAR snake heights superimposed

8 Height differences (cm) Downstream distance (m) Height differences between pairs of corresponding points on the snake and aerial photo waterlines versus distance downstream

9 Snake conditioned on SAR and LiDAR, superimposed on SAR Snake conditioned on SAR and LiDAR, superimposed on LiDAR

10 Height differences (cm) Height differences (cm) Downstream distance (m) Downstream distance (m) (a) SAR (b) SAR and LiDAR Paired height differences versus distance downstream for snake conditioned on (a) SAR, (b) SAR and LiDAR.

11 Improved performance measure for model validation (a) Traditional areal method F = A / (A + B + C) where A = area correctly predicted as wet by the model B = area predicted as wet that is actually dry C = area predicted as dry that is actually wet F = 1 when observed and predicted flood extents coincide exactly, 0 when no overlap (b) Mean height difference method Select pairs of corresponding points on model and SAR waterlines in areas of low slope. Use a paired t-test to estimate the probability P(t> t 0 ) that their mean height difference is not significantly non-zero (if it is, model run is non-behavioural).

12 Flow (cu.m/s) Flow modelling Modelled the 1992 Thames flood using LISFLOOD-FP (12km reach, 50m grid size). Flood extent sensitive to channel friction but insensitive to floodplain friction ERS Time (hours) Hydrograph at upstream end of reach

13 Performance measure Channel friction (Manning's n) P(t> t0 ) F measure Comparison of performance measures for snake conditioned on SAR and LiDAR.

14 Obtaining validation data of urban flood extent In urban areas flooding impacts most severe Observations of urban flood extents are needed for validation of modelled flood extents In rural areas, 2D models validated using ERS/ASAR but these have too low a resolution for urban areas But several high resolution SARs recently launched (TerraSAR-X, RADARSAT-2, ALOS PALSAR and COSMO- SkyMed)

15 N 2km TerraSAR-X image of the lower Severn flood of July 2007, with DLR flood extent (blue) overlain ( DLR 2007). The rectangle covers Tewkesbury.

16 TerraSAR-X image of Tewkesbury flooding on 25 th July 2007 showing urban areas (3m resolution, dark areas are water).

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

18 Aerial photo mosaic of Tewkesbury flooding on 24 July 2007.

19 LiDAR DSM of Tewkesbury (2m resolution).

20 TerraSAR-X θ M O R h h 2 1 A N B Y C D Layover (AB) and shadow (CD) in a flooded street between adjacent buildings.

21 DLR SAR End-to-End simulator (SETES )

22 Regions unseen by TerraSAR-X in LiDAR DSM due to shadow (satellite looking West).

23 Regions unseen by TerraSAR-X in LiDAR DSM due to layover (satellite looking West).

24 Regions unseen by TerraSAR-X in LiDAR DSM due to combined shadow and layover (satellite looking West).

25 A B TerraSAR-X image of Tewkesbury with flood extent (blue) predicted by snake superimposed (shadow/layover masked out).

26 Delineation of flood in rural areas using snake algorithm applied to SAR and LiDAR data Supervised classification of urban flood seed regions Determination of spatially-varying urban water height threshold Seed region growing Flowchart of method of flood detection in urban areas.

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

28 Near real-time flood extent for flood relief management The Pitt Report concluded that some decision-making was hampered by lack of information about the flood. Need for near real-time visualisation tools to enable the emergency services to react to and manage fast-moving events, and target their limited resources at the highest priority areas. Useful if near real-time visualisation of the flood extent could be made available overlayed on map data in a simple GIS.

29 International Charter for Space and Major Disasters Charter has been set up to provide exactly this type of data. EA invoked the Charter for the first time in the UK in June EA unable to use the ERS-2 SAR image supplied because not geometrically corrected. TerraSAR-X images can be registered to single pixel accuracy rapidly and automatically.

30 TerraSAR-X image of the Cockermouth flood (21/11/09)

31 Flood extent produced by the DLR algorithm overlayed on TerraSAR-X image of the Severn flood of July 2007 ( DLR 2007).

32 Combined algorithm for rural and urban flooding DLR algorithm will not work in urban areas due to radar shadow/layover. Combine algorithm for rural flooding with that for urban flooding. Need to automate a number of steps in urban algorithm. Resulting algorithm could only be used where urban areas mapped with LiDAR. Radar shadow/layover calculation done in parallel with processing TerraSAR-X data.

33 Urban flood extent (water = yellow) Rural flood extent (water = blue, rectangle = Tewksbury) Possible multi-scale visualisation of flood extent