A SYNERGETIC USE OF ACTIVE MICROWAVE OBSERVATIONS, OPTICAL IMAGES AND TOPOGRAPHY

DATA FOR IMPROVED FLOOD MAPPING IN THE GULF OF MEXICO AREA

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, FL4NOAA / National Ocean Service / Office of Coast Survey / Coastal Survey Development Lab

IGARSS 2011

Overland

Coastal Erosion

Tidal

Wave

Bay

Inundation

Sediment

Shorelines

Tides

Marsh, Oyster & SAV Assessments

Coastal DynamicAssessments

Management Actions

Integrated Models

Field/Lab Experiments

Salinity

Dynamic Results

Societal and Coastal Ecosystem Benefits

Earth Data

Global ClimateChange Scenarios

Management Tools

Biological Biotic

Moving Towards Spatial Storm Surge Model Validation

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

Station °W °N RMS (%) K1obs (m) K1sim (m) O1obs (m) O1sim (m) M2obs (m) M2sim (m)Apalachicola River –84.98167 29.72667 11.5 0.130 0.146 0.112 0.138 0.116 0.114Turkey Point –84.51167 29.91500 8.5 0.172 0.172 0.157 0.161 0.256 0.251Shell Point –84.29000 30.06000 7.9 0.183 0.189 0.162 0.177 0.362 0.362Apalachee Bay –84.17833 30.07833 7.8 0.173 0.190 0.153 0.177 0.356 0.365Cedar Key –83.03167 29.13500 15.1 0.177 0.181 0.163 0.170 0.386 0.361Shark River –82.72333 28.87000 12.7 0.136 0.180 0.150 0.166 0.378 0.373St. Andrew Bay –85.66667 30.15167 12.7 0.141 0.148 0.135 0.146 0.026 0.027Panama City Beach –85.87833 30.21333 10.5 0.145 0.150 0.141 0.148 0.034 0.029

(a) Apalachicola

(b) Turkey Point

(c) Shell Point

(d) Apalachee Bay

Comparison of Simulated and Measured Tidal Signals

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.

•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

Radarsat-1 03/03/2004 – low tide conditions

Apalachicola Radarsat Scene

CO-register and re-sample to the same projection and pixel size

Radarsat 1 data LiDAR-derived DEMLandsat 7 image

(low tide)

Speckle filtering High contour line Low contour line

RGB colorcompositing

Flood-prone areasmask

Change detection within flood-prone areas

Flooded / non-flooded areas map

Validation with aerial photography

Acquisition date* 01/20/2003 09/17/2003 03/03/2004 07/25/2004

Water level (m) -0.064 0.24 -0.24 0.278

Wind speed (m/s) 1.1 4.3 1.1 2.3

Radarsat Imagery

*Acquisition time for all the Apalachicola scenes was 11h:40 GMT

Hours

0 2 4 6 8 10 12 14 16 18 20 22 24

Met

ers

Rel

ativ

e to

NA

DV

-0.6

-0.4

-0.2

0.0

0.2

0.4

20 Jan 200317 Sep 20033 Mar 200425 Jul 2004

Historic Observed Water Level (Apalachicola, FL)

(from NOAA Tides & Currents)

MHHW

-0.459

Low-water levelLandsat 7 scene (2/2/03; 15:56)

Radarsat Apalachicola Scene Dates andCorresponding Water Level

Radarsat Scene Color Composites 3/3/04 as low tide conditionRed = change to flooded

backscatter ↓Cyan = change from flooded

backscatter ↑White = unchanged pixels

Intertidal Zone Composites 3/3/04 as low tide conditionRed = change to flooded

backscatter ↓Cyan = change from flooded

backscatter ↑White = unchanged pixels

Flooded areas (red color)

RGB image within the potential flooded area

Estimated Flooded Areas along St. James Peninsula

1/20/2003

9/17/2003 7/25/2004

Frequency of Pixel Values within Flood-Prone Mask

Frequency of Pixel Values within Flood-Prone Mask

25

30.1

25.04

Water Level (m)

Num

ber o

f Flood

ed P

ixel

s

03/03/04

01/20/03 09/17/03

07/25/04

Franklin County - FLDOT

Comparison with Historic Aerial Photographs

Green – detected by SAR (3/3/04) & aerials

Yellow – detected only bySAR (3/3/04)

Blue – detected only by aerials

Site 101/04/10

12:12

Site 201/05/10

14:53

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

Site 1 Site 2

Water Level (m) -0.184 -0.222

POD (%) 58 83.8

Summary and Future DirectionsThe 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 processes2) extend this approach temporally to include more dates and spatially across the northern Gulf of Mexico coast to include Alabama and Mississippi

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).