Damage Assessment of Hurricane Katrina using Remote Sensing Technique

1. Damage Assessment of Hurricane Katrina using Remote Sensing Technique March 13th, 2007 Jae Sung Kim, Jie Shan 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 • 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 km2

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) GSD: 2.45m <Credit to Digital Globe >

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 • Non parametric rule: feature space • Parametric rule : maximum likelihood for unclassified & overlap rule

11. Assessment of Damage in New Orleans • The supervised classification result <Pre Katrina> <Post Katrina> (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

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 • Bare lands are completely disappeared in this area • and most of grasses are submerged. • The amount of water increased more than 2.8km2 • 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=0.7, Color=0.3 • Accuracy=0.70, Kappa=0.63 • Water was misclassfied to Road and Bareland • Road & building was misclassified to water • 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=0.3, 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

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.

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

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 Building skeletons (square), W/L=1.692 road skeletons (long), W/L=4.922

25. Use of shape membership function • The membership function of building & road Building Road

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

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

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

29. 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.

30. 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 • Digital Globe (2005), Katrina Gallery, retrived September, 2005 from Digital Globe website: http://www.digitalglobe.com/katrina_gallery.html • Lilesand, T.M., Kiefer, R. W, Chipman J. W. (2004), Remote Sensing and Image Interpretation (5th 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 • 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 • 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