A tiered reconnaissance approach toward flood monitoring utilising multi-source radar and optical data

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1 5 th International Workshop on Remote Sensing for Disaster Response A tiered reconnaissance approach toward flood monitoring utilising multi-source radar and optical data Anneley McMillan Dr. Beverley Adams ImageCat & MCEER Remote Sensing Institute Benjamin Oates Dr. Jeremy Morley University College London MDA Geospatial Services

2 Tiered Reconnaissance System TIER 1. Regional Provides a quick look regional impact assessment, using change detection techniques. TIER 2. Neighborhood Locates communities experiencing different levels of impact, using analysis of highresolution satellite imagery TIER 3. Street/Per-building Identifies damage to individual structures and groups of structures, using remote sensing-based damage scales, VIEWS TM and in-field reconnaissance Disaster

3 Tier 3. Street to Per-building level Very rapid response High-resolution aerial data capture for flood damage estimation using VIEWS TM Field data collection & visualization system Incorporates high quality video feed and GPS technology Past deployments include: Hurricane Katrina Indian Ocean Tsunami Bam earthquake Vehicle On-foot Aerial Boat

4 Case Study: Gloucestershire Floods, July 2007

5 VIEWS provides GPS linked aerial video Methodology Image & GPS data downloaded to VIEWS visualization environment 1. Digitisation of flood vector boundary 2. Video and stills capture 3. Multimedia Data layer for Google Earth

6 Visualisation of Results Flood Proprietary extent around visualisation Tewksbury system and Gloucester integrated for into the Google 25 th June Earth Dark Flood blue vector, permanent with GPS water, path light and interactive blue flood digital water. stills. Issue: Could be hampered by weather and flight restrictions

7 Tiered Reconnaissance System TIER 1. Regional Provides a quick look regional impact assessment, using change detection techniques. TIER 2. Neighborhood Locates communities experiencing different levels of impact, using analysis of highresolution satellite imagery TIER 3. Street/Per-building Identifies damage to individual structures and groups of structures, using remote sensing-based damage scales, VIEWS TM and in-field reconnaissance Disaster

8 Tier 2. Neighbourhood Analysis RADARSAT flood mapping All weather, penetrates cloud Extensive swath widths covers large events Selected beam modes support neighbourhood-level analysis standard (25m res) fine beam (8m res) Regular overpass times Large archive of before (non- flood) images

9 Case 1 New Orleans, September 2005 On the 29th August 2005 Hurricane Katrina made landfall as a category 3/4 storm in Plaquemines Parish, Louisiana (USGS, 2005) The storm surge caused by Hurricane Katrina breached a number of the levees on canals linked to lake Pontchartrain, causing extensive flooding throughout the city, estimated at 80% (USGS, 2005). Two Radarsat scenes were available for flooded and non-flooded scenarios for both Standard beam, and fine beam modes.

10 Methodology 1. Images georeferenced, calibrated and co-registered. 2. False colour composite indicates flooding signature 3. Difference image used to accentuate difference 4. Flood extent for both standard and fine beam modes vectorized from difference image Flooded area Red at time of flood White Post flood 5. Thresholding also used to extract positive change of > 2-std. 2 between flooded and non-flooded image. 6. Morphological filters used to assimilate disparate speckled response. 7. Validation undertaken using optically derived boundary and depth maps provided by ImageCat and NOAA respectively. Non flooded area Red at time of flood White Post flood

11 Fine beam mode Validation 1: 2: Spatial Agreement offset between validation map & radarderived boundary Difference False colour position (Before-After) composite: Red = 13th April (no-flood) Green = 9th September (flood) Blue = 13th April (no-flood) Issue: Non-agreement due to difference in dates of radar and validation data

12 Standard mode Validation 1: 2: Spatial Agreement offset between validation map & radarderived boundary position Issue: Sometimes overpass is not quick enough for flash flood event. ent. May be a satellite eclipse or technical difficulties in processing ng

13 Case 2. Dresden, Germany. April Combination of rural and urban flooding The Dresden flood event under study occurred in early April 2006, when the Elbe River created 150-year record high River rose 13cm higher in some areas than the more widely reported flood of In particular, areas such as the historic town of Hitzacker and Lauenburg were damaged (ESA, 2006). Two standard beam mode RADARSAT scenes from during and after the flood were employed for change detection analysis.

14 Image Cat, Ltd. ImageCat, Methodology 1. First threshold misses urban saturation 2. After tiered thresholding 3. Generalisation 4. Composite of permanent water and flood 5. Validation compared to DLR (green within 100m) Issue: Radarsat data may not be available

15 Tiered Reconnaissance System TIER 1. Regional Provides a quick look regional impact assessment, using change detection techniques. TIER 2. Neighborhood Locates communities experiencing different levels of impact, using analysis of highresolution satellite imagery TIER 3. Street/Per-building Identifies damage to individual structures and groups of structures, using remote sensing-based damage scales, VIEWS TM and in-field reconnaissance Disaster

16 Tier 3. Regional Analysis Uses ERS-2 2 imagery from ESA Useful when RADARSAT imagery is unavailable or acquisition delayed ERS has different specifications compared with RADARSAT, particularly in terms of incidence angle, resolution and polarisation. This lead to a modified methodological approach 2 archive no-flood scenes utilised to eliminate baseline change Mask used to focus on urban areas SAR response assimilated into 5 x 5 neighbourhoods.

17 Case Study: Yorkshire Floods, June 2007 Estimated number of homes flooded

18 Methodology 1. Georeference and co-register imagery 2. Run change detection routine on 2 archive scenes to establish normal change. Consists of: Differencing Thresholding Aggregating mean pixel values into 5 x 5 grid squares 3. Run same change detection routine on flood and non-flood image 4. If normal change is negated from change between flood and non-flood imagery, all residual change can be said to have occurred around the time of the flood. 5. Particularly in urban areas this is likely to be flooding as the temporal gap between flood and archive non-flood is small (i.e. matter of weeks) which rules out changes due to new construction etc. 6. Validation using reports of road closures

19 Change detection in Hull 1. Baseline change detection 2. Flood change detection 3. Final flood map

20 Validation Compared with reports of road closures and flooding, this shows a close match. Issue: Rural flooding not well represented using single polarisation, at shallow incidence angles

21 Conclusions and Future Work The Tiered Reconnaissance System gives a flexible approach to rapid flood detection at street, neighbourhood and regional scales, which can be tailored to different circumstances and User needs. Being prepared with alternative resources and methodologies in case c of technical issues with satellites, data ordering issues, or adverse weather conditions ions is critical in operational disaster mapping. Future work includes investigating new sensors such as TerraSAR-X and RADARSAT-II, utilising other satellites available now, such as ENVISAT ASAR,, and extending current methodologies. Greater understanding of interactions of the urban environment and a radar beams, in terms of modelling and derivation of relationships, should help to improve operational methodologies for detecting flooding in urban environments. A major outcome of the New Orleans work is ongoing research undertaken by ImageCat in collaboration with UCL to examine the relationship between different urban parameters and radar backscatter, in order to understand how backscatter changes s in urban areas.

22 Urban parameter effect on radar images: Road angle

23 Urban parameter effect on radar images: Building height 3D-London model: Source ImageCat

24 Comparison of London and New Orleans: Road angle SITE SITE 21

25 Thank you!

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