Editorial Type:
Article
Article Type:
Research Article

Analysis of Tornado Damage Tracks from the 3 May Tornado Outbreak Using Multispectral Satellite Imagery

May Yuan Department of Geography, The University of Oklahoma, Norman, Oklahoma

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Melany Dickens-Micozzi Department of Geography, The University of Oklahoma, Norman, Oklahoma

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Michael A. Magsig National Weather Service Warning Decision Training Branch and Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

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Print Publication:
01 Jun 2002
DOI:
https://doi.org/10.1175/1520-0434(2002)017<0382:AOTDTF>2.0.CO;2
Page(s):
382–398
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Abstract

Remote sensing (RS) and geographic information systems (GIS) techniques are applied to high-resolution satellite imagery to determine characteristics of tornado damage from the 3 May 1999 tornado outbreak. Three remote sensing methods, including principal components analysis, normalized difference vegetation index (NDVI) analysis, and NDVI change analysis, elicit tornado damage paths at different levels of detail on the 23.5-m-resolution images captured by the Linear Imaging Self-Scanning III (LISS-3) sensor on the Indian Remote Sensing (IRS) satellite before and after the outbreak. Remote sensing results were spatially overlaid on F-scale contours compiled by the members of Oklahoma Weather Center. Spatial overlays reveal that results from the principal components analysis correlate well with F3 or greater damage. NDVI analysis shows signatures expanding to F2 damage, and NDVI change analysis is capable of detecting F1 damage in some instances. In general, results of these analyses correspond to more severe damage in rural areas than in urban areas. Comparison with detailed ground surveys shows that the spectral signatures of tornado damage are related to vegetation damage and large debris fields. Variations in spectral signatures with Fujita tornado damage intensity suggest that land cover characteristics may be just as important as tornado damage intensity in creating a track detectable by satellite. It is concluded that RS and GIS techniques on IRS LISS-3 imagery (an example of multispectral satellite imagery) can be useful in assessing tornado damage, particularly for extensive and intense events.

Corresponding author address: May Yuan, Sarkey Energy Center, Room 684, 100 East Boyd St., Norman, OK 73019. Email: myuan@ou.edu

Abstract

Remote sensing (RS) and geographic information systems (GIS) techniques are applied to high-resolution satellite imagery to determine characteristics of tornado damage from the 3 May 1999 tornado outbreak. Three remote sensing methods, including principal components analysis, normalized difference vegetation index (NDVI) analysis, and NDVI change analysis, elicit tornado damage paths at different levels of detail on the 23.5-m-resolution images captured by the Linear Imaging Self-Scanning III (LISS-3) sensor on the Indian Remote Sensing (IRS) satellite before and after the outbreak. Remote sensing results were spatially overlaid on F-scale contours compiled by the members of Oklahoma Weather Center. Spatial overlays reveal that results from the principal components analysis correlate well with F3 or greater damage. NDVI analysis shows signatures expanding to F2 damage, and NDVI change analysis is capable of detecting F1 damage in some instances. In general, results of these analyses correspond to more severe damage in rural areas than in urban areas. Comparison with detailed ground surveys shows that the spectral signatures of tornado damage are related to vegetation damage and large debris fields. Variations in spectral signatures with Fujita tornado damage intensity suggest that land cover characteristics may be just as important as tornado damage intensity in creating a track detectable by satellite. It is concluded that RS and GIS techniques on IRS LISS-3 imagery (an example of multispectral satellite imagery) can be useful in assessing tornado damage, particularly for extensive and intense events.

Corresponding author address: May Yuan, Sarkey Energy Center, Room 684, 100 East Boyd St., Norman, OK 73019. Email: myuan@ou.edu

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