Damage Assessment of Hurricane Katrina using Remote Sensing Technique March 13 th, 2007 Jae Sung Kim, Jie Shan Dept. of Civil Engineering Purdue University

Fact about Katrina  Category 3 on the Saffir-Simpson scale when it landed (windspeed140 mph, central pressure 920 mb)  The date of Landfall: Aug  Landfall site: Plaquemines Parish, LA  Damaged States: Louisiana, Mississippi, Florida, Alabama (Federally declared disaster states by FEMA)  Economic damage: more than $100 billion (Estimated by Risk Management Solutions, CA)

Hurricane Katrina Image  NOAA Satellite image (Aug ) <

Damages in New Orleans, LA  New Orleans urban area has elevation lower than the sea level  The collapse of the levee system caused submergence of the urban area of New Orleans  Damage to urban features: Building, Road, Tree, Grass, Bareland Building, Road, Tree, Grass, Bareland  The main purpose of this study is the estimation of the damage to earth surface features by the flood caused by Katrina and the decision of the best methodology in classification

Damage Assessment Methodology  The flowchart of the suggested approach

Submergence Area Estimation at State Level  Input data: Landsat 7, 5 images <

Submergence Area Estimation at State Level  The input images of before & after Katrina were reclassified with ArcGIS to estimate water class  Water class of pre- Katrina was clipped out from post-Katrina class  Total submerged area was estimated to 511 km 2

The Distribution of Water Depth  Estimated by DEM and water level data of USGS West-end stream flow gage site

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

Assessment of Damage in New Orleans  Type of classification Supervised classification  Training The number of training areas has to be more than 100 for complicated area (Lilesand et al., 2004) More than 100 samples were trained for building to include every possible colors of roof More than 100 samples were trained for building to include every possible colors of roof  Non parametric rule: feature space  Parametric rule : maximum likelihood for unclassified & overlap rule

Assessment of Damage in New Orleans  The supervised classification result (Overall Accuracy: %, (Overall Accuracy: 83.82%, Kappa Statistics: ) Kappa Statistics: )

Assessment of Damage in New Orleans Change Detection No. of CellsLegend Pre Katrina Post Katrina Change (No. of cells) Area change (km 2 ) Change Rate (%) Building4,803,2614,133, , Road3,511,4991,433,871-2,077, Bare land 933,339248, , Tree2,735,1891,167,207-1,567, Grass1,607,435701, , Water2,667,1687,885,543+5,218,

Assessment of Damage in New Orleans  The roads were severely damaged because most of the roads are below than the level of water  The submerged cells of buildings must be the low level structures such as single story building or low part of building such as edge of the roof  Most of low elevation classes such as road, grass, tree, and bare land are submerged more than half.  Submergence is more severe at northern New Orleans than southern part near Mississippi river, which has higher elevation

Assessment of Damage in New Orleans Urban Area  Input data : Ikonos images (Aug ‘02 & Sep.02 ’05, Space Imaging, GSD: 1m

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

Assessment of Damage in New Orleans Urban Area No. of cells Pre Katrina Post Katrina Change (No.of cells) Area change (km 2 ) Building Road Bare land 234, Tree Grass Water  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 2.8km 2 and this area is severely submerged. and this area is severely submerged.  Change Detection

Assessment of Damage in New Orleans Urban Area  Classification Accuracy (Before Katrina) Overall Classification Accuracy = 65.81% Overall Kappa Statistics =   Classificaiton Accuracy (After Katrina) Overall Classification Accuracy = 78.79% Overall Kappa Statistics =  The low signature separability between building & road, building & trees, grass & trees, water & building caused low classification accuracy

Assessment of Damage in New Orleans Urban Area  The example of building submergence  The example of road submergence  Building & road class has some pixels of opposite class because of signature separability matter

Object Based Classification  Compared to traditional pixel based classification, object based classification uses segmentation instead of pixel.  Definition of Segmentation: the search for homogeneous regions in an image and later the classification of these regions” (Mather, 1999)  Segmentation can be acquired adjusting the weight of color and shape.

Impact of color & shape factor  Decision of color & shape factor  Shape=0.3, Color=0.7  Accuracy=0.89 Kappa=0.87  Accuracy enhanced by 0.02  Water on the road disappeared  Shape=0.1, Color=0.9  Accuracy=0.91 Kappa=0.88  Accuracy is over 0.9  Lot of road & bareland classes disappeared from water class  Shape=0.5, Color=0.5  Accuracy=0.87, Kappa=0.84  Accuracy enhanced by 0.17  Water was misclassfied to Road and Bareland  Shape=0.7, Color=0.3  Accuracy=0.70, Kappa=0.63  Water was misclassfied to Road and Bareland  Road & building was misclassified to water

Object Based Classification  Classification Result of IKONOS image

Object Based Classification  The 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 Classification  The error matrix after Katrina   The classification accuracy was increased from 78.79% to 92.4%.

Use of shape membership function  Object based classification adapts fuzzy approach using shape membership function such as length, width, area, the ratio of length & width and the longest edge of object, etc.  Shape membership function will solve the problem of low accuracy of road class for pre Katrina IKONOS image  The difference of Length/Width between building and road Building skeletons (square), W/L=1.692 road skeletons (long), W/L=4.922

Use of shape membership function  The membership function of building & road BuildingRoad

Use of shape membership function IKONOS Image of New Orleans W/O Shape Membership Function With Shape Membership Function

Use of shape membership function Example image of road W/O Shape Membership Function With Shape Membership Function  EX) The building objects in the road and grass classes were removed

Example image of building W/O Shape Membership Function With Shape Membership Function Use of shape membership function  EX) The road objects in building class were removed

Conclusion & Future Work  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 impact of natural disaster.  Real time imagery will provide quick response to the emergency unit.  Roads are harshly damaged because most of them are located in low elevation.  About 10% of buildings were estimated to be submerged and they are believed to be low level structures such as single story building or edge of the roof.  Object based classification enhanced the classification accuracy compared to pixel based classification.  Optimal decision of the weight between color & shape during segmentation, a proper shape-membership function will enhance the classification accuracy.

Reference  Baatz, M. et al. (2004), eCognition User Guide 4, Definiens Imaging, Munchen, Germany  Darwish, A., Leukert, K., Reinhardt, W. (2003), Image Segmentation for the Purpose of Object Based Classification, Geoscience and Remote Sensing Symposium, July , IGARSS ’03, Proceedings, 2003 IEEE International, Vol(3):  Department of Homeland Security’s Federal Emergency Management Agency (FEMA) (2005), retrieved September, 2005 from FEMA website:  Digital Globe (2005), Katrina Gallery, retrived September, 2005 from Digital Globe website:  Lilesand, T.M., Kiefer, R. W, Chipman J. W. (2004), Remote Sensing and Image Interpretation (5 th ed.), John Wiley & Sons, Inc., NewYork  Mather, P.(1999) Computer Processing of Remotely Sensed Images, Chichester, Wiley  Renyi, L, Nan, L. (2001), Flood Area and Damage Estimation in Zhejiang, China. Journal of Environmental Management, 66:1-8  National Oceanic & Atmospheric Administration (2005), Hurricane Katrina Image, retrieved November, 9, 2005 from NOAA website:  Space Imaging (2005), Image Gallery, retrieved September, 2005 from Space Imaging website:  Tsoukalas L. H., Uhrig R. E. (1997) Fuzzy and Neural Approaches in Engineering, John Wiley & Sons, Inc., NewYork  U.S. Geological Survey (2005), Hurricane Katrina Disaster Response, Hurricane Katrina Posters, retrieved September, 12, 2005 from USGS website:  U.S. Geological Survey (2005) USGS (COE) Lake Pontchartrain at West End, LA, gage height, retrieved September, 12, 2005, from USGS website:  U.S. Geological Survey (2005) National Map Seamless Data Distribution System, DEM file, retrieved September, 12, 2005, from USGS website:  Wikipedia (2005) Economic Effect of Hurricane Katrina, retrieved September, from Wikepedia website: Katrina  Wikipedia (2005) Meteorological history of Hurricane Katrina, retrieved September, from Wikepedia website: Katrina