Damage Assessment of Damage Assessment of Hurricane Katrina using Hurricane Katrina using

Remote Sensing TechniqueRemote Sensing Technique

March 13March 13thth, 2007, 2007

Jae Sung Kim, Jie ShanJae Sung Kim, Jie ShanDept. of Civil EngineeringDept. of Civil Engineering

Purdue UniversityPurdue University

Fact about KatrinaFact about Katrina

Category 3 on the Saffir-Simpson scale when it Category 3 on the Saffir-Simpson scale when it landed (windspeed140 mph, central pressure 920 landed (windspeed140 mph, central pressure 920 mb)mb)

The date of Landfall: Aug.29.2005The date of Landfall: Aug.29.2005 Landfall site: Plaquemines Parish, LA Landfall site: Plaquemines Parish, LA Damaged States: Louisiana, Mississippi, Florida, Damaged States: Louisiana, Mississippi, Florida,

Alabama (Federally declared disaster states by Alabama (Federally declared disaster states by FEMA) FEMA)

Economic damage: more than $100 billion Economic damage: more than $100 billion (Estimated by Risk Management Solutions, CA)(Estimated by Risk Management Solutions, CA)

Hurricane Katrina ImageHurricane Katrina Image NOAA Satellite image (Aug.29.2005)NOAA Satellite image (Aug.29.2005)

<http://www.srh.noaa.gov/hgx/gifs/Katrina.jpg><http://www.srh.noaa.gov/hgx/gifs/Katrina.jpg>

Damages in New Orleans, LADamages in New Orleans, LA New Orleans urban area has elevation lower than the sea New Orleans urban area has elevation lower than the sea

levellevel

The collapse of the levee system caused submergence of The collapse of the levee system caused submergence of the urban area of New Orleansthe urban area of New Orleans

Damage to urban features: Damage to urban features: Building, Road, Tree, Grass, BarelandBuilding, Road, Tree, Grass, Bareland

The main purpose of this study is the estimation of the The main purpose of this study is the estimation of the damage to earth surface features by the flood caused by damage to earth surface features by the flood caused by Katrina and the decision of the best methodology in Katrina and the decision of the best methodology in classificationclassification

Damage Assessment MethodologyDamage Assessment Methodology

The flowchart of the suggested approachThe flowchart of the suggested approach

Submergence Area Estimation at State LevelSubmergence Area Estimation at State Level

Input data: Landsat 7, 5 imagesInput data: Landsat 7, 5 images

<http://eros.usgs.gov/katrina/products.html><http://eros.usgs.gov/katrina/products.html>

Submergence Area Estimation at State LevelSubmergence Area Estimation at State Level

The input images of The input images of before & after Katrina before & after Katrina were reclassified with were reclassified with ArcGIS to estimate ArcGIS to estimate water classwater class

Water class of pre- Water class of pre- Katrina was clipped Katrina was clipped out from post-Katrina out from post-Katrina classclass

Total submerged area Total submerged area was estimated to 511 was estimated to 511 kmkm22

The Distribution of Water DepthThe Distribution of Water Depth

Estimated by DEM and water level data of USGS Estimated by DEM and water level data of USGS West-end West-end stream flow gage sitestream flow gage site

Assessment of Damage in New OrleansAssessment of Damage in New Orleans Input dataInput data Quickbird images (March ‘04 & Quickbird images (March ‘04 & SepSep. 03 ‘05). 03 ‘05) GSD: 2.45m GSD: 2.45m

<<Credit to Digital GlobeCredit to Digital Globe > >

Assessment of Damage in New OrleansAssessment of Damage in New Orleans

Type of classificationType of classificationSupervised classificationSupervised classification

TrainingTrainingThe number of training areas has to be more than 100 for The number of training areas has to be more than 100 for complicated area (complicated area (Lilesand et al., 2004) Lilesand et al., 2004)

More than 100 samples were trained for building to include More than 100 samples were trained for building to include every possible colors of roofevery possible colors of roof

Non parametric rule: feature spaceNon parametric rule: feature space

Parametric rule : maximum likelihood for unclassified & Parametric rule : maximum likelihood for unclassified & overlap ruleoverlap rule

Assessment of Damage in New OrleansAssessment of Damage in New Orleans The supervised classification resultThe supervised classification result

<Pre Katrina> <Post Katrina><Pre Katrina> <Post Katrina>(Overall Accuracy: 84.29 %, (Overall Accuracy: 83.82%,(Overall Accuracy: 84.29 %, (Overall Accuracy: 83.82%,Kappa Statistics: 0.8056) Kappa Statistics: 0.8003)Kappa Statistics: 0.8056) Kappa Statistics: 0.8003)

Legend

afterclass.img

Class_Names

bareland

building

cloud

grass

road

tree

water

Assessment of Damage in New OrleansAssessment of Damage in New Orleans

Change DetectionChange Detection

No. ofNo. ofCellsCells

LegendLegend Pre KatrinaPre Katrina Post KatrinaPost KatrinaChangeChange

(No. of cells)(No. of cells)Area changeArea change

(km(km22))Change RateChange Rate

(%)(%)

BuildingBuilding 4,803,2614,803,261 4,133,6564,133,656 - 669,605- 669,605 -3.86-3.86 -13.94-13.94

RoadRoad 3,511,4993,511,499 1,433,8711,433,871 -2,077,628-2,077,628 -11.97-11.97 -59.17-59.17

Bare landBare land 933,339933,339 248,826248,826 -684,513-684,513 -3.94-3.94 -73.34-73.34

TreeTree 2,735,1892,735,189 1,167,2071,167,207 -1,567,982-1,567,982 -9.03-9.03 -57.33-57.33

GrassGrass 1,607,4351,607,435 701,376701,376 -906,059-906,059 -5.22-5.22 -56.37-56.37

WaterWater 2,667,1682,667,168 7,885,5437,885,543 +5,218,375+5,218,375 30.0630.06 +195.65+195.65

Assessment of Damage in New OrleansAssessment of Damage in New Orleans

The roads were severely damaged because most The roads were severely damaged because most of the roads are below than the level of waterof the roads are below than the level of water

The submerged cells of buildings must be the low The submerged cells of buildings must be the low level structures such as single story building or low level structures such as single story building or low part of building such as edge of the roofpart of building such as edge of the roof

Most of low elevation classes such as road, grass, Most of low elevation classes such as road, grass, tree, and bare land are submerged more than half.tree, and bare land are submerged more than half.

Submergence is more severe at northern New Submergence is more severe at northern New Orleans than southern part near Mississippi river, Orleans than southern part near Mississippi river, which which hashas higher elevation higher elevation

Assessment of Damage in New Orleans Assessment of Damage in New Orleans Urban AreaUrban Area

Input data : Ikonos images (Aug ‘02 & Sep.02 ’05, Space Imaging, Input data : Ikonos images (Aug ‘02 & Sep.02 ’05, Space Imaging, GSD: 1m GSD: 1m

Assessment of Damage in New Orleans Urban AreaAssessment of Damage in New Orleans Urban Area The supervised classification resultThe supervised classification result

Assessment of Damage in New Orleans Assessment of Damage in New Orleans Urban AreaUrban Area

No. ofNo. ofcellscells

Pre KatrinaPre Katrina Post KatrinaPost KatrinaChangeChange

(No.of cells)(No.of cells)Area changeArea change

(km(km22))

BuildingBuilding 15599021559902 11042441104244 -455658-455658 -0.45-0.45

RoadRoad 852990852990 221400221400 -631590-631590 -0.63-0.63

Bare landBare land 234,045234,045 00 -234045-234045 -0.23-0.23

TreeTree 768315768315 8419184191 -684124-684124 -0.68-0.68

GrassGrass 784502784502 2387423874 -760628-760628 -0.76-0.76

WaterWater 216502216502 29979372997937 27814352781435 2.82.8

Bare lands are completely disappeared in this area Bare lands are completely disappeared in this area and most of grasses are submerged.and most of grasses are submerged.

The amount of water increased more than 2.8kmThe amount of water increased more than 2.8km22 and this area is severely submerged.and this area is severely submerged.

Change DetectionChange Detection

Assessment of Damage in New Orleans Assessment of Damage in New Orleans Urban AreaUrban Area

Classification Accuracy (Before Katrina)Classification Accuracy (Before Katrina)Overall Classification Accuracy = 65.81%Overall Kappa Statistics = 0.5568

Classificaiton Accuracy (After Katrina)Overall Classification Accuracy = 78.79%Overall Kappa Statistics = 0.6970

The low signature separability between building & road, The low signature separability between building & road, building & trees, grass & trees, water & building caused low building & trees, grass & trees, water & building caused low classification accuracyclassification accuracy

Assessment of Damage in New Orleans Urban AreaAssessment of Damage in New Orleans Urban Area

The example of building submergenceThe example of building submergence

The example of road submergenceThe example of road submergence

Building & road class has some pixels of opposite class Building & road class has some pixels of opposite class because of signature separability matterbecause of signature separability matter

Object Based ClassificationObject Based Classification Compared to traditional pixel based classification, object Compared to traditional pixel based classification, object

based classification uses segmentation instead of pixel. based classification uses segmentation instead of pixel. Definition of Segmentation: the search for homogeneous Definition of Segmentation: the search for homogeneous

regions in an image and later the classification of these regions in an image and later the classification of these regions” (Mather, 1999) regions” (Mather, 1999)

Segmentation can be acquired adjusting the weight of color Segmentation can be acquired adjusting the weight of color and shape.and shape.

shapecolor hw)(hwf 1

Impact of color & shape factor Impact of color & shape factor Decision of color & shape factorDecision of color & shape factor

ShapeShape=0.3, =0.3, ColorColor=0.7=0.7

Accuracy=0.89 Accuracy=0.89 Kappa=0.87Kappa=0.87

Accuracy Accuracy enhanced by 0.02enhanced by 0.02

Water on the road Water on the road disappeared disappeared

ShapeShape=0.1, =0.1, ColorColor=0.9=0.9

Accuracy=0.91 Accuracy=0.91 Kappa=0.88Kappa=0.88

Accuracy is over Accuracy is over 0.90.9

Lot of road & Lot of road & bareland classes bareland classes disappeared from disappeared from water class water class

ShapeShape=0.5, =0.5, ColorColor=0.5=0.5

Accuracy=0.87, Accuracy=0.87, Kappa=0.84Kappa=0.84

Accuracy Accuracy enhanced by enhanced by 0.170.17

Water was Water was misclassfied to misclassfied to Road and Road and BarelandBareland

ShapeShape=0.7, =0.7, ColorColor=0.3=0.3

Accuracy=0.70, Accuracy=0.70, Kappa=0.63Kappa=0.63

Water was Water was misclassfied to misclassfied to Road and Road and BarelandBareland

Road & building Road & building was misclassified was misclassified to waterto water

Object Based ClassificationObject Based Classification

Classification Result of IKONOS imageClassification Result of IKONOS image

Object Based ClassificationObject Based Classification The error matrix before KatrinaThe error matrix before Katrina

The classification accuracy has increased from 65.81% to 88.39%. But road is still more misclassified than other features.

Object Based ClassificationObject Based Classification The error matrix after KatrinaThe error matrix after Katrina

The classification accuracy was increased from 78.79% to 92.4%.

Use of shape membership functionUse of shape membership function Object based classification adaptObject based classification adaptss fuzzy approach using fuzzy approach using

shape membership function such as length, width, area, shape membership function such as length, width, area, the ratio of length & width the ratio of length & width andand the longest edge of object, the longest edge of object, etc.etc.

Shape membership function will solve the problem of low Shape membership function will solve the problem of low accuracy of road class for pre Katrina IKONOS imageaccuracy of road class for pre Katrina IKONOS image

The difference of Length/Width between building and roadThe difference of Length/Width between building and road

Building skeletons (square), Building skeletons (square), W/L=1.692W/L=1.692

road skeletons (long), road skeletons (long), W/L=4.922W/L=4.922

Use of shape membership functionUse of shape membership function The membership function of building & roadThe membership function of building & road

BuildingBuilding RoadRoad

Use of shape membership functionUse of shape membership function

IKONOS Image of New OrleansIKONOS Image of New Orleans W/O Shape Membership FunctionW/O Shape Membership Function With Shape Membership FunctionWith Shape Membership Function

Use of shape membership functionUse of shape membership function

Example image of roadExample image of road W/O Shape Membership FunctionW/O Shape Membership Function With Shape Membership FunctionWith Shape Membership Function

EX) The building objects in the road and grass EX) The building objects in the road and grass classes were removedclasses were removed

Example image of buildingExample image of building W/O Shape Membership FunctionW/O Shape Membership Function With Shape Membership FunctionWith Shape Membership Function

Use of shape membership functionUse of shape membership function EX) The road objects in building class were removedEX) The road objects in building class were removed

Conclusion & Future WorkConclusion & Future Work The damaged object such as building and roads could be The damaged object such as building and roads could be

detected with remote sensing technique which is time and detected with remote sensing technique which is time and cost-effective approach to assess the impact of natural cost-effective approach to assess the impact of natural disaster.disaster.

Real time imagery will provide quick response to the Real time imagery will provide quick response to the emergency unit.emergency unit.

Roads are harshly damaged because most of them are Roads are harshly damaged because most of them are located in low elevation.located in low elevation.

AAbout 10%bout 10% of buildings of buildings wewere re estimated to be estimated to be submerged submerged and and they arethey are believed to be low level structures such as believed to be low level structures such as single story building or edge of the roof.single story building or edge of the roof.

Object based classification enhanced the classification Object based classification enhanced the classification accuracy compared to pixel based classification.accuracy compared to pixel based classification.

Optimal decision of the weight between color & shape Optimal decision of the weight between color & shape during segmentation, a proper shape-membership function during segmentation, a proper shape-membership function will enhance the classification accuracy.will enhance the classification accuracy.

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