IGARSS 2011_

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  • 1. A SYNERGETIC USE OF ACTIVE MICROWAVE OBSERVATIONS, OPTICAL IMAGES AND TOPOGRAPHY DATA FOR IMPROVED FLOOD MAPPING IN THE GULF OF MEXICO AREA
    IGARSS 2011
    Marouane Temimi1, Naira Chaouch1, Scott Hagen2, John Weishampel3, Stephen Medeiros2, Jesse Feyen4, Yuji Funakoshi4, Reza Khanbilvardi1
    1NOAA- Cooperative Remote Sensing Science and Technology (CREST) Center,City University of New York, New York, NY
    2Civil, Environmental and Construction Engineering Department, University of Central Florida, Orlando, FL
    3Department of Biology, University of Central Florida, Orlando, FL
    4NOAA / National Ocean Service / Office of Coast Survey / Coastal Survey Development Lab

2. Moving Towards Spatial Storm Surge Model Validation
Integrated Models
Dynamic Results
Earth
Data
Tides
Wave
Tidal
Inundation
Overland
Sediment
Field/Lab Experiments
Bay
Salinity
Global Climate
Change Scenarios
Coastal Erosion
Shorelines
Biological
Biotic
Management
Tools
Coastal Dynamic
Assessments
Marsh, Oyster & SAV Assessments
Societal and Coastal Ecosystem Benefits
Management
Actions
3. Northeastern Gulf of Mexico Study Area
Apalachee Bay
Shell Point
Turkey Point
Apalachicola River
St. Andrew Bay
Cedar Key
Panama City Beach
Shark River
National Ocean Service Tidal Gaging Stations
4. Comparison of Simulated and Measured Tidal Signals
(a) Apalachicola
(c) Shell Point
(d) Apalachee Bay
(b) Turkey Point
5. Project Sub-Objective
To demonstrate the efficacy of employing high resolution imagery to improve coastal inundation models that are presently employed by NOAA (NWS and NOS), USACE, and FEMA, and those soon to be applied operationally. Imagery will enable the assessment of wetting/drying algorithms and general spatial validation.
6.

  • Radar is sensitive to water, due to its high dielectric constant, and hence valuable in characterizing wetlands

7. It differentiates between moist soil and standing water 8. Standing water interacts with the radar differently depending on vegetation structure 9. When exposed to open water without (or submerged) vegetation, specular reflection occurs.Double bounce backscattering
Specular scattering
10. Apalachicola Radarsat Scene
Radarsat-1 03/03/2004 low tide conditions
11. CO-register and re-sample to the same projection and pixel size
Landsat 7 image (low tide)
Radarsat 1 data
LiDAR-derived DEM
Speckle filtering
Low contour line
High contour line
RGB colorcompositing
Flood-prone areasmask
Change detection within flood-prone areas
Flooded / non-flooded areas map
Validation with aerial photography
12. Radarsat Imagery
*Acquisition time for all the Apalachicola scenes was 11h:40 GMT
Historic Observed Water Level (Apalachicola, FL)
(from NOAATides & Currents)
13. Radarsat Apalachicola Scene Dates andCorresponding Water Level
MHHW
-0.459
Low-water levelLandsat 7 scene(2/2/03; 15:56)
14. Radarsat Scene Color Composites
3/3/04 as low tide conditionRed = change to floodedbackscatter
Cyan = change from floodedbackscatter White = unchanged pixels
15. Intertidal Zone Composites
3/3/04 as low tide conditionRed = change to floodedbackscatter
Cyan = change from floodedbackscatter White = unchanged pixels
16. RGB image within the potential flooded area
Flooded areas (red color)
17. Estimated Flooded Areas along St. James Peninsula
1/20/2003
9/17/2003
7/25/2004
18. Frequency of Pixel Values within Flood-Prone Mask
19. Frequency of Pixel Values within Flood-Prone Mask
20. 07/25/04
01/20/03
09/17/03
Number of Flooded Pixels
03/03/04
Water Level (m)
21. Comparison with Historic Aerial Photographs
Franklin County - FLDOT
Site 101/04/1012:12
Green detected by SAR (3/3/04) & aerials
Yellow detected only bySAR (3/3/04)
Blue detected only by aerials
Site 201/05/1014:53
22. Agreement between SAR and Aerials
Probability of Detection (POD)
POD = A / (A +C)
A = number of pixels of class X (flooded) which were identified correctly as class X
C = number of pixels of class X which were notclassified as X
23. Summary and Future Directions
The multi-temporal composited SAR images clearly show flooded and non-flooded areas during both high tide and low tide conditions.
These results show potential for high resolution remotely sensed imagery to: monitor coastal flooding, delineate inundated areas, and improve hydrodynamic model verification/validation across a variety of coastal landscapes.
We will: 1) evaluate model spatial flood predictions and guide improvements in the simulation of the wetting/ drying processes
2) extend this approach temporally to include more dates and spatially across the northern Gulf of Mexico coast to include Alabama and Mississippi
24. Acknowledgments
Support for this part of the project was provided by the NASA Program in Earth Science for Decision Making - Gulf of Mexico Region (Grant #NNX09AT44G) awarded to S. Hagen (PI-UCF).
NASA Applied Sciences Program
http://games.bio.ucf.edu