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Predicting ground-based damage states from windstorms using remote-sensing imagery

  • Brown, Tanya M. (Insurance Institute for Business and Home Safety) ;
  • Liang, Daan (Department of Construction Engineering and Engineering Technology, Texas Tech University) ;
  • Womble, J. Arn (Wind Science & Engineering Research Center, Texas Tech University)
  • Received : 2011.01.10
  • Accepted : 2011.10.12
  • Published : 2012.09.25

Abstract

Researchers have recently begun using high spatial resolution remote-sensing data, which are automatically captured and georeferenced, to assess damage following natural and man-made disasters, in addition to, or instead of employing the older methods of walking house-to-house for surveys, or photographing individual buildings from an airplane. This research establishes quantitative relationships between the damage states observed at ground-level, and those observed from space using high spatial resolution remote-sensing data, for windstorms, for individual site-built one- or two-family residences (FR12). "Degrees of Damage" (DOD) from the Enhanced Fujita (EF) Scale were determined for ground-based damage states; damage states were also assigned for remote-sensing imagery, using a modified version of Womble's Remote-Sensing (RS) Damage Scale. The preliminary developed model can be used to predict the ground-level damage state using remote-sensing imagery, which could significantly lessen the time and expense required to assess the damage following a windstorm.

Keywords

References

  1. Adams, B.J., Womble, J.A., Mio, M.Z. and Mehta, K.C. (2004), Collection of satellite referenced building damage information in the aftermath of Hurricane Charley, MCEER Response Report Series MCEER-04-SP04, Buffalo, NY.
  2. Adams, B.J. (2005), Remote sensing technology-a coming of age, Natural Hazards Observer, University of Colorado, 29(4), 1-3.
  3. Brown, T.M. Liang, D. and Womble, J.A. (2010), Development of a statistical relationship between groundbased and remotely-sensed damage in windstorms, EF Scale Stakeholders Forum, Norman, OK, March.
  4. Bunting, W.F. and Smith, B.E. (1993), A guide for conducting damage surveys, NOAA Technical Memorandum, NWS SR-146.
  5. Fujita, T.T., Bradbury, D.A. and Van Thullenar, C.F. (1970), "Palm sunday tornadoes of April 11, 1965", Mon. Weather Rev., 98(1), 29-69. https://doi.org/10.1175/1520-0493(1970)098<0029:PSTOA>2.3.CO;2
  6. Fujita, T.T. (1971), Proposed characterization of tornadoes and hurricanes by area and intensity, SMRP Paper 91, Department of Geophysical Science, University of Chicago, IL.
  7. Fujita, T.T. and Smith, B.E. (1993), Aerial survey and photography of tornado and microburst damage, The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monog., AGU, 79, 479-493. https://doi.org/10.1029/GM079p0479
  8. He, H., Yin, J., Mehta, K.C. and Chen, D. (2005), "Implementation of statistical documentation algorithm and data Format to Hurricanes Charley and Ivan", Proceedings of the 10th Americas Conference on Wind Engineering, Baton Rouge, LA, June.
  9. Knabb, R.D., Rhome, J.R. and Brown, D.P. (2006), Tropical cyclone report Hurricane Katrina, National Hurricane Center, Miami, FL. .
  10. Marshall, T.P. and McDonald, J.R. (1982), "An engineering analysis of the grand island, NE Tornadoes", Proceedings of the 12th Conference on Severe Local Storms, San Antonio, TX, January.
  11. Marshall, T.P. (2002), "Tornado damage survey at Moore, Oklahoma", Weather Forecast., 17(3), 582-598. https://doi.org/10.1175/1520-0434(2002)017<0582:TDSAMO>2.0.CO;2
  12. McMillan, A., Adams, B.J., Reynolds, A.E., Brown, T.M., Liang, D. and Womble, J.A. (2008), Advanced technology for rapid tornado damage assessment following the 'Super Tuesday' Tornado Outbreak of February 2008, MCEER Response Report Series MCEER-08-SP01, Buffalo, NY.
  13. Mehta, K.C., Minor, J.E. and McDonald, J.R. (1976), "Windspeed analysis of April 3-4 1974 Tornadoes", J. Struct. Eng.- ASCE, 102(ST9), 1709-1724.
  14. Mehta, K.C. (2009), Texas Tech University Wind Science and Engineering Research Center, Personal Communication, November 11, 2009.
  15. Minor, J.E. (2005), "Lessons learned from failures of the building envelope in windstorms", J. Archit.Eng.- ASCE, 11(1), 10-13. https://doi.org/10.1061/(ASCE)1076-0431(2005)11:1(10)
  16. Singh, A. (1989), "Digital change detection techniques using remotely sensed data", Int. J. Remote Sens., 10(6), 989-1004. https://doi.org/10.1080/01431168908903939
  17. Speheger, D.A., Doswell III, C.A. and Stumpf, G.J. (2002), "The Tornadoes of 3 May 1999: event verification in central oklahoma and related issues", Weather Forecast., 17(3), 362-381. https://doi.org/10.1175/1520-0434(2002)017<0362:TTOMEV>2.0.CO;2
  18. Thompson, J.N., Kiesling, E.W., Goldman, J.L, Mehta, K.C., Wittman, J. and Johnson, F.B. (1970), The Lubbock Storm of May 11, 1970, Report prepared for the Committee on Earthquake Inspection, National Academy of Engineering, Published by the National Academy of Sciences, Washington, D.C.
  19. Visser, S.J. and Dawood, A.S. (2004), "Real-time natural disasters detection and monitoring from smart earth observation satellite", J. Aerospace Eng.,17(1), 10-19. https://doi.org/10.1061/(ASCE)0893-1321(2004)17:1(10)
  20. WISE Research Center (2006), A recommendation for an enhanced fujita scale (EF-Scale), Revision 2 October 10, 2006, Texas Tech University, Lubbock, TX.
  21. Womble, J.A. (2005), Remote-sensing applications to windstorm damage assessment, PhD Dissertation, Texas Tech University, Lubbock, TX.
  22. Womble, J.A., Adams, B.J. and Mehta, K.C. (2005), "Windstorm damage surveys using high-resolution satellite images", Proceedings of the 10th Americas Conference on Wind Engineering, Baton Rouge, LA.
  23. Wurman, J. and Alexander, C.R. (2005), "The May 30 1998 Spencer, South Dakota, Storm. Part II: comparison of damage and radar-derived winds in the Tornadoes", Mon. Weather Rev., 133(1), 97-119. https://doi.org/10.1175/MWR-2856.1
  24. Yuan, M., Dickens-Micozzi, M. and Magsig, M.A. (2002), "Analysis of tornado damage tracks from the 3 May Tornado Outbreak using multispectral satellite imagery", Weather Forecast.,17(3), 382-398. https://doi.org/10.1175/1520-0434(2002)017<0382:AOTDTF>2.0.CO;2

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