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

Remote Sensing TechniqueRemote Sensing Technique

May, 2007May, 2007

Jie Shan, Jae Sung KimJie Shan, Jae Sung KimDept. 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

Pre KatrinaPre Katrina(No. of cells)(No. of cells)

Post KatrinaPost Katrina(No. of cells)(No. of cells)

ChangeChange(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, building & trees, The low signature separability between building & road, building & trees, grass & trees, water & building caused low classification accuracygrass & trees, water & building caused low classification 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.

BuildingBuilding RoadRoad TreeTree GrassGrass BarelandBareland WaterWater SumSum

BuildingBuilding 1484714847 21382138 11821182 224224 9090 00 1848118481

RoadRoad 127127 40594059 00 00 00 00 41864186

TreeTree 650650 00 36103610 00 00 00 42604260

GrassGrass 00 00 00 64496449 00 00 64496449

BarelandBareland 7979 00 00 188188 36423642 00 39093909

WaterWater 00 00 00 00 00 30043004 30043004

SumSum 1570315703 61976197 47924792 68616861 37323732 30043004

Producer’sProducer’s 0.94550.9455 0.65500.6550 0.75330.7533 0.94000.9400 0.97590.9759 11

User’sUser’s 0.80340.8034 0.96970.9697 0.84740.8474 11 0.93170.9317 11

OverallOverall 0.88390.8839

KIAKIA 0.84540.8454

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%.

BuildingBuilding RoadRoad WaterWater TreeTree GrassGrass BarelandBareland SumSum

BuildingBuilding 1321713217 464464 00 540540 00 00 1422114221

RoadRoad 225225 67596759 00 00 6666 00 70507050

WaterWater 00 00 98229822 371371 00 00 1019310193

TreeTree 107107 00 901901 25302530 00 00 35383538

GrassGrass 00 00 00 00 960960 111111 10711071

BarelandBareland 00 9898 00 00 00 17991799 18971897

SumSum 1354913549 73217321 1072310723 34413441 10261026 19101910

Producer’sProducer’s 0.97550.9755 0.92320.9232 0.91600.9160 0.73530.7353 0.93570.9357 0.94190.9419

User’sUser’s 0.92940.9294 0.95870.9587 0.96360.9636 0.7150.715 0.89640.8964 0.94830.9483

OverallOverall 0.9240.924

KIAKIA 0.89780.8978

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

EX) Building skeletons (square), EX) Building skeletons (square), W/L=1.6W/L=1.6

EX) road skeletons (long), EX) road skeletons (long), W/L=4.9W/L=4.9

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

Change Detection in New OrleansChange Detection in New Orleans

Object Based Classification using Shape membership function was Object Based Classification using Shape membership function was used for Change Detection in New Orleansused for Change Detection in New Orleans

By trial and error, scale, color & shape, compactness & smoothness By trial and error, scale, color & shape, compactness & smoothness factor was determined like below tablefactor was determined like below table

Parameter Scale Color Shape Compactness Smoothness

10 0.5 0.5 0.5 0.5

Change Detection in New OrleansChange Detection in New Orleans Decision of membership functionDecision of membership function

L/W = 1.5 is found out to be optimal value to divide building and road L/W = 1.5 is found out to be optimal value to divide building and road classesclasses

Change Detection in New OrleansChange Detection in New Orleans

Change Detection in New OrleansChange Detection in New Orleans

WaterWater grassgrass treetree barelandbareland White roofWhite roof

buildingbuilding

roadroad Non whiteNon white

Roof bldgRoof bldg

SumSum

WaterWater 68836883 193193 00 225225 00 00 193193 74947494

GrassGrass 00 42334233 00 00 00 00 5757 42904290

TreeTree 00 00 16121612 00 00 00 115115 17271727

BarelandBareland 351351 00 00 27372737 00 00 285285 33733373

White roofWhite roof

BldgBldg

00 00 00 00 1184711847 150150 314314 1231112311

RoadRoad 178178 00 00 00 17221722 94399439 174174 1151311513

Non whiteNon white

Roof bldgRoof bldg

00 00 296296 00 00 00 57485748 60446044

SumSum 74127412 44264426 19081908 29622962 1356913569 95899589 68866886

Producer’sProducer’s 0.92860.9286 0.95640.9564 0.84490.8449 0.9240.924 0.8730.873 0.98440.9844 0.83470.8347

User’sUser’s 0.91850.9185 0.98670.9867 0.93340.9334 0.81140.8114 0.96230.9623 0.81990.8199 0.9510.951

OverallOverall 0.9090.909

KIAKIA 0.88820.8882

Contingency Matrix before KatrinaContingency Matrix before Katrina

Change Detection in New OrleansChange Detection in New Orleans

pixels object classpixels object class

Before Katrina (Building)Before Katrina (Building)

After Katrina (Building)After Katrina (Building)

pixels object classpixels object class

Change Detection in New OrleansChange Detection in New OrleansBefore Katrina (Road)Before Katrina (Road)

After Katrina (Road)After Katrina (Road) pixel object classpixel object class

pixel object classpixel object class

Change Detection in New OrleansChange Detection in New OrleansContingency Matrix after KatrinaContingency Matrix after Katrina

WaterWater treetree grassgrass barelandbareland RoadRoad Non whiteNon white

Roof bldgRoof bldg

White Roof White Roof bldgbldg

SumSum

WaterWater 1338313383 250250 00 00 00 208208 00 1384113841

TreeTree 12011201 42214221 00 00 00 00 00 54225422

GrassGrass 00 00 10441044 00 00 00 00 10441044

BarelandBareland 00 00 00 806806 00 00 00 806806

RoadRoad 00 00 00 00 80038003 00 18921892 98959895

Non whiteNon white

Roof bldgRoof bldg

00 00 122122 152152 00 49374937 00 52115211

White roofWhite roof

bldgbldg

00 00 00 00 231231 00 99359935 1016610166

SumSum 1458414584 44714471 11661166 958958 82348234 51455145 1182711827

Producer’sProducer’s 0.91760.9176 0.9440.944 0.89540.8954 0.84130.8413 0.97190.9719 0.95960.9596 0.840.84

User’sUser’s 0.96690.9669 0.77850.7785 11 11 0.80880.8088 0.94740.9474 0.97730.9773

OverallOverall 0.91260.9126

KIAKIA 0.8890.889

Change Detection in New OrleansChange Detection in New Orleans

No. ofNo. ofCellsCells

Pre KatrinaPre Katrina(No. of cells)(No. of cells)

Post KatrinaPost Katrina(No. of cells)(No. of cells)

ChangeChange(No. of cells)(No. of cells)

Area changeArea change(km(km22))

Change RateChange Rate(%)(%)

BuildingBuilding 64917326491732 52984725298472 -1193260-1193260 -4.783-4.783 -18.38-18.38

RoadRoad 34502083450208 19383691938369 -1511839-1511839 -8.204-8.204 -43.82-43.82

Bare landBare land 324631324631 1999619996 -304635-304635 -1.820-1.820 -93.84-93.84

TreeTree 23186922318692 20907652090765 -227927-227927 -0.429-0.429 -9.83-9.83

GrassGrass 969647969647 585613585613 -384034-384034 -2.042-2.042 -39.61-39.61

WaterWater 24784262478426 61494796149479 3637105336371053 24.79724.797 +148.12+148.12

ConclusionConclusion The damaged object such as building and roads could be detected with remote The damaged object such as building and roads could be detected with remote

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

Pixel based classification for Quickbird and IKONOS images were performed.Pixel based classification for Quickbird and IKONOS images were performed. Object based classification for IKONOS without shape fuzzy rule and Quickbird Object based classification for IKONOS without shape fuzzy rule and Quickbird

with shape fuzzy rule were performed.with shape fuzzy rule were performed. Roads are harshly damaged because most of them are located in low Roads are harshly damaged because most of them are located in low

elevation.elevation. AAbout 13%bout 13% , 18% of buildings , 18% of buildings wewere re estimated to be estimated to be submerged in each pixel submerged in each pixel

based and object based classification and based and object based classification and they arethey are believed to be low level believed to be low level structures such as single story building or edge of the roof.structures such as single story building or edge of the roof.

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

For Quickbird images, the subclass of white roof building and road were For Quickbird images, the subclass of white roof building and road were created under the super class of white urban and they were classified by shape created under the super class of white urban and they were classified by shape fuzzy membership function inside the super classfuzzy membership function inside the super class

The membership value of L/W=1.5 was found out optimal value to divide the The membership value of L/W=1.5 was found out optimal value to divide the white roof building and the road.white roof building and the road.

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

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