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

2. Fact about Katrina  Category 3 on the Saffir-Simpson scale when it landed (windspeed140 mph, central pressure 920 mb)  The date of Landfall: Aug.29.2005  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)

3. Hurricane Katrina Image  NOAA Satellite image (Aug.29.2005) <http://www.srh.noaa.gov/hgx/gifs/Katrina.jpg>

4. 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

5. Damage Assessment Methodology  The flowchart of the suggested approach

6. Submergence Area Estimation at State Level  Input data: Landsat 7, 5 images <http://eros.usgs.gov/katrina/products.html>

7. 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

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

9. 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

10. 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

11. Assessment of Damage in New Orleans  The supervised classification result (Overall Accuracy: 84.29 %, (Overall Accuracy: 83.82%, Kappa Statistics: 0.8056) Kappa Statistics: 0.8003)

12. Assessment of Damage in New Orleans Change Detection No. of Cells Pre Katrina (No. of cells) Post Katrina (No. of cells) Change Area change (km 2 ) Change Rate (%) Building4,803,2614,133,656 - 669,605 -3.86-13.94 Road3,511,4991,433,871-2,077,628-11.97-59.17 Bare land 933,339248,826-684,513-3.94-73.34 Tree2,735,1891,167,207-1,567,982-9.03-57.33 Grass1,607,435701,376-906,059-5.22-56.37 Water2,667,1687,885,543+5,218,37530.06+195.65

13. 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

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

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

16. Assessment of Damage in New Orleans Urban Area No. of cells Pre Katrina Post Katrina Change (No.of cells) Area change (km 2 ) Building15599021104244 -455658 -455658-0.45 Road852990221400 -631590 -631590-0.63 Bare land 234,0450 -234045 -234045-0.23 Tree76831584191 -684124 -684124-0.68 Grass78450223874 -760628 -760628-0.76 Water 216502 216502 2997937 2997937 2781435 27814352.8  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

17. Assessment of Damage in New Orleans Urban Area  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, grass & trees, water & building caused low classification accuracy

18. 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

19. 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.

20. 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

21. Object Based Classification  Classification Result of IKONOS image

22. 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. BuildingRoadTreeGrassBarelandWaterSum Building148472138118222490018481 Road127405900004186 Tree650036100004260 Grass0006449006449 Bareland7900188364203909 Water0000030043004 Sum1570361974792686137323004 Producer’s0.94550.65500.75330.94000.97591 User’s0.80340.96970.847410.93171 Overall0.8839 KIA0.8454

23. Object Based Classification  The error matrix after Katrina   The classification accuracy was increased from 78.79% to 92.4%. BuildingRoadWaterTreeGrassBarelandSum Building1321746405400014221 Road2256759006607050 Water0098223710010193 Tree10709012530003538 Grass00009601111071 Bareland09800017991897 Sum13549732110723344110261910 Producer’s0.97550.92320.91600.73530.93570.9419 User’s0.92940.95870.96360.7150.89640.9483 Overall0.924 KIA0.8978

24. 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 EX) Building skeletons (square), W/L=1.6 EX) road skeletons (long), W/L=4.9

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

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

27. 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

28. 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

29. Change Detection in New Orleans  Object Based Classification using Shape membership function was used for Change Detection in New Orleans  By trial and error, scale, color & shape, compactness & smoothness factor was determined like below table ParameterScaleColorShape CompactnessSmoothness 100.5

30. Change Detection in New Orleans  Decision of membership function L/W = 1.5 is found out to be optimal value to divide building and road classes L/W = 1.5 is found out to be optimal value to divide building and road classes

31. Change Detection in New Orleans

32. Watergrasstreebareland White roof buildingroad Non white Roof bldg Sum Water68831930225001937494 Grass042330000574290 Tree0016120001151727 Bareland351002737002853373 White roof Bldg00001184715031412311 Road1780001722943917411513 Non white Roof bldg 0029600057486044 Sum74124426190829621356995896886 Producer’s0.92860.95640.84490.9240.8730.98440.8347 User’s0.91850.98670.93340.81140.96230.81990.951 Overall0.909 KIA0.8882  Contingency Matrix before Katrina

33. Change Detection in New Orleans pixels object class pixels object class  Before Katrina (Building)  After Katrina (Building) pixels object class pixels object class

34. Change Detection in New Orleans  Before Katrina (Road)  After Katrina (Road) pixel object class pixel object class

35. Change Detection in New Orleans  Contingency Matrix after Katrina WatertreegrassbarelandRoad Non white Roof bldg White Roof bldg Sum Water13383250000208013841 Tree12014221000005422 Grass00104400001044 Bareland000806000806 Road00008003018929895 Non white Roof bldg 001221520493705211 White roof bldg00002310993510166 Sum14584447111669588234514511827 Producer’s0.91760.9440.89540.84130.97190.95960.84 User’s0.96690.7785110.80880.94740.9773 Overall0.9126 KIA0.889

36. Change Detection in New Orleans No. of Cells Pre Katrina (No. of cells) Post Katrina (No. of cells) Change Area change (km 2 ) Change Rate (%) Building64917325298472-1193260-4.783-18.38 Road34502081938369-1511839-8.204-43.82 Bare land 32463119996-304635-1.820-93.84 Tree23186922090765-227927-0.429-9.83 Grass969647585613-384034-2.042-39.61 Water247842661494793637105324.797+148.12

37. Conclusion  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.  Pixel based classification for Quickbird and IKONOS images were performed.  Object based classification for IKONOS without shape fuzzy rule and Quickbird with shape fuzzy rule were performed.  Roads are harshly damaged because most of them are located in low elevation.  About 13%, 18% of buildings were estimated to be submerged in each pixel based and object based classification and they are believed to be low level structures such as single story building or edge of the roof.  Optimal decision of the weight between color & shape during segmentation, a proper shape-membership function enhanced the classification accuracy.  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 fuzzy membership function inside the super class  The membership value of L/W=1.5 was found out optimal value to divide the white roof building and the road.  Object based classification enhanced the classification accuracy compared to pixel based classification.

38. 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 21-25 2003, IGARSS ’03, Proceedings, 2003 IEEE International, Vol(3): 2039-2041  Department of Homeland Security’s Federal Emergency Management Agency (FEMA) (2005), retrieved September, 2005 from FEMA website: http://www.fema.gov/news/disasters.fema?year=2005 http://www.fema.gov/news/disasters.fema?year=2005  Digital Globe (2005), Katrina Gallery, retrived September, 2005 from Digital Globe website: http://www.digitalglobe.com/katrina_gallery.html http://www.digitalglobe.com/katrina_gallery.html  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: http://www.srh.noaa.gov/hgx/gifs/Katrina.jpg  Space Imaging (2005), Image Gallery, retrieved September, 2005 from Space Imaging website: http://www.spaceimaging.com/gallery/hurricanes2005/katrina/newOrleansViewer.htm http://www.spaceimaging.com/gallery/hurricanes2005/katrina/newOrleansViewer.htm  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: http://eros.usgs.gov/katrina/products.html http://eros.usgs.gov/katrina/products.html  U.S. Geological Survey (2005) USGS 073802331 (COE) Lake Pontchartrain at West End, LA, gage height, retrieved September, 12, 2005, from USGS website: http://waterdata.usgs.gov/la/nwis/uv?dd_cd=01&format=gif&period=31&site_no=073802331  U.S. Geological Survey (2005) National Map Seamless Data Distribution System, DEM file, retrieved September, 12, 2005, from USGS website: http://seamless.usgs.gov  Wikipedia (2005) Economic Effect of Hurricane Katrina, retrieved September, from Wikepedia website: http://en.wikipedia.org/wiki/Economic_effects_of_Hurricane_ Katrina  Wikipedia (2005) Meteorological history of Hurricane Katrina, retrieved September, from Wikepedia website:http://en.wikipedia.org/wiki/Meteorological_history_of_Hurricane Katrina