Categorizing natural disaster damage assessment using satellite-based geospatial techniques
- 1School of Geographical Sciences, Arizona State University 600 E. Orange St. SCOB Bldg Rm 330, Tempe,\newline AZ 85287-0104, USA
- 2Department of Geography, University of Oklahoma 100 East Boyd St., Norman, OK 73019, USA
- 3School of Geographical Sciences, Arizona State University 600 E. Orange St. SCOB Bldg Rm 330, Tempe,\newline AZ 85287-0104, USA
- 4Science Applications International Corp (SAIC) Contractor USGS Center for Earth Resources Observation and Science (EROS), Sioux Falls, SD, 57198, USA
Abstract. Remote sensing of a natural disaster's damage offers an exciting backup and/or alternative to traditional means of on-site damage assessment. Although necessary for complete assessment of damage areas, ground-based damage surveys conducted in the aftermath of natural hazard passage can sometimes be potentially complicated due to on-site difficulties (e.g., interaction with various authorities and emergency services) and hazards (e.g., downed power lines, gas lines, etc.), the need for rapid mobilization (particularly for remote locations), and the increasing cost of rapid physical transportation of manpower and equipment. Satellite image analysis, because of its global ubiquity, its ability for repeated independent analysis, and, as we demonstrate here, its ability to verify on-site damage assessment provides an interesting new perspective and investigative aide to researchers. Using one of the strongest tornado events in US history, the 3 May 1999 Oklahoma City Tornado, as a case example, we digitized the tornado damage path and co-registered the damage path using pre- and post-Landsat Thematic Mapper image data to perform a damage assessment. We employed several geospatial approaches, specifically the Getis index, Geary's C, and two lacunarity approaches to categorize damage characteristics according to the original Fujita tornado damage scale (F-scale). Our results indicate strong relationships between spatial indices computed within a local window and tornado F-scale damage categories identified through the ground survey. Consequently, linear regression models, even incorporating just a single band, appear effective in identifying F-scale damage categories using satellite imagery. This study demonstrates that satellite-based geospatial techniques can effectively add spatial perspectives to natural disaster damages, and in particular for this case study, tornado damages.