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| Titel |
Categorizing natural disaster damage assessment using satellite-based geospatial techniques |
| VerfasserIn |
S. W. Myint, M. Yuan, R. S. Cerveny, C. Giri |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 8, no. 4 ; Nr. 8, no. 4 (2008-07-17), S.707-719 |
| Datensatznummer |
250005643
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| Publikation (Nr.) |
 copernicus.org/nhess-8-707-2008.pdf |
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| Zusammenfassung |
| 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. |
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