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

2. OverlandOverland Coastal Erosion TidalTidal Wave Bay Inundation Sediment Shorelines Tides Marsh, Oyster & SAV Assessments Coastal Dynamic Assessments Coastal Dynamic Assessments Management Actions Management Actions Integrated Models Field/Lab Experiments Salinity Dynamic Results Societal and Coastal Ecosystem Benefits Earth Data Data Global Climate Change Scenarios Management Tools Management Tools Biological Biotic Moving Towards Spatial Storm Surge Model Validation

3. Apalachicola River Turkey Point Shell Point Apalachee Bay Cedar Key Shark River St. Andrew Bay Panama City Beach National Ocean Service Tidal Gaging Stations Northeastern Gulf of Mexico Study Area

4. Station°W°NRMS (%)K1 obs (m)K1 sim (m)O1 obs (m)O1 sim (m)M2 obs (m)M2 sim (m) Apalachicola River–84.9816729.7266711.50.1300.1460.1120.1380.1160.114 Turkey Point–84.5116729.915008.50.172 0.1570.1610.2560.251 Shell Point–84.2900030.060007.90.1830.1890.1620.1770.362 Apalachee Bay–84.1783330.078337.80.1730.1900.1530.1770.3560.365 Cedar Key–83.0316729.1350015.10.1770.1810.1630.1700.3860.361 Shark River–82.7233328.8700012.70.1360.1800.1500.1660.3780.373 St. Andrew Bay–85.6666730.1516712.70.1410.1480.1350.1460.0260.027 Panama City Beach–85.8783330.2133310.50.1450.1500.1410.1480.0340.029 (a) Apalachicola (b) Turkey Point (c) Shell Point (d) Apalachee Bay Comparison of Simulated and Measured Tidal Signals

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 It differentiates between moist soil and standing water Standing water interacts with the radar differently depending on vegetation structure When exposed to open water without (or submerged) vegetation, specular reflection occurs. Double – bounce backscattering Specular scattering

7. Radarsat-1 03/03/2004 – low tide conditions Apalachicola Radarsat Scene

8. CO-register and re-sample to the same projection and pixel size Radarsat 1 dataLiDAR-derived DEM Landsat 7 image (low tide) Speckle filtering High contour line Low contour line RGB color compositing Flood-prone areas mask Change detection within flood-prone areas Flooded / non-flooded areas map Validation with aerial photography

9. Acquisition date*01/20/200309/17/200303/03/200407/25/2004 Water level (m)-0.0640.24-0.240.278 Wind speed (m/s)1.14.31.12.3 Radarsat Imagery *Acquisition time for all the Apalachicola scenes was 11h:40 GMT Historic Observed Water Level (Apalachicola, FL) (from NOAA Tides & Currents)

10. MHHW -0.459 Low-water level Landsat 7 scene (2/2/03; 15:56) Radarsat Apalachicola Scene Dates and Corresponding Water Level

11. Radarsat Scene Color Composites 3/3/04 as low tide condition Red = change to flooded backscatter ↓ Cyan = change from flooded backscatter ↑ White = unchanged pixels

12. Intertidal Zone Composites 3/3/04 as low tide condition Red = change to flooded backscatter ↓ Cyan = change from flooded backscatter ↑ White = unchanged pixels

13. Flooded areas (red color) RGB image within the potential flooded area

14. Estimated Flooded Areas along St. James Peninsula 1/20/2003 9/17/20037/25/2004

15. Frequency of Pixel Values within Flood-Prone Mask

17. Water Level (m) Number of Flooded Pixels 03/03/04 01/20/03 09/17/03 07/25/04

18. Franklin County - FLDOT Comparison with Historic Aerial Photographs Green – detected by SAR (3/3/04) & aerials Yellow – detected only by SAR (3/3/04) Blue – detected only by aerials Site 1 01/04/10 12:12 Site 2 01/05/10 14:53

19. 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 not classified as X Site 1Site 2 Water Level (m)-0.184-0.222 POD (%)5883.8

20. 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

21. NASA Applied Sciences Program http://games.bio.ucf.edu 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).