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Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon

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Title: Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon
Authors: Booth, Adam M.1 abooth@uoregon.edu, Roering, Josh J.1, Perron, J. Taylor2
Source: Geomorphology. Aug2009, Vol. 109 Issue 3/4, p132-147. 16p.
Subjects: Geological mapping, Landslides, Spectrum analysis, Geological statistics, Mountains, Surface of the earth, Earth (Planet)
Geographic Terms: Puget Sound (Wash.), Washington (State), Oregon
Abstract: Abstract: Landslide inventory maps are necessary for assessing landslide hazards and addressing the role slope stability plays in landscape evolution over geologic timescales. However, landslide inventory maps produced with traditional methods — aerial photograph interpretation, topographic map analysis, and field inspection — are often subjective and incomplete. The increasing availability of high-resolution topographic data acquired via airborne Light Detection and Ranging (LiDAR) over broad swaths of terrain invites new, automated landslide mapping procedures. We present two methods of spectral analysis that utilize LiDAR-derived digital elevation models of the Puget Sound lowlands, Washington, and the Tualatin Mountains, Oregon, to quantify and automatically map the topographic signatures of deep-seated landslides. Power spectra produced using the two-dimensional discrete Fourier transform and the two-dimensional continuous wavelet transform identify the characteristic spatial frequencies of deep-seated landslide morphologic features such as hummocky topography, scarps, and displaced blocks of material. Spatial patterns in the amount of spectral power concentrated in these characteristic frequency bands highlight past slope instabilities and allow the delineation of landslide terrain. When calibrated by comparison with detailed, independently compiled landslide inventory maps, our algorithms correctly classify an average of 82% of the terrain in our five study areas. Spectral analysis also allows the creation of dominant wavelength maps, which prove useful in analyzing meter-scale topographic expressions of landslide mechanics, past landslide activity, and landslide-modifying geomorphic processes. These results suggest that our automated landslide mapping methods can create accurate landslide maps and serve as effective, objective, and efficient tools for digital terrain analysis. [Copyright &y& Elsevier]
Copyright of Geomorphology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 41239342
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon
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  Data: <searchLink fieldCode="JN" term="%22Geomorphology%22">Geomorphology</searchLink>. Aug2009, Vol. 109 Issue 3/4, p132-147. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Puget+Sound+%28Wash%2E%29%22">Puget Sound (Wash.)</searchLink><br /><searchLink fieldCode="DE" term="%22Washington+%28State%29%22">Washington (State)</searchLink><br /><searchLink fieldCode="DE" term="%22Oregon%22">Oregon</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Abstract: Landslide inventory maps are necessary for assessing landslide hazards and addressing the role slope stability plays in landscape evolution over geologic timescales. However, landslide inventory maps produced with traditional methods — aerial photograph interpretation, topographic map analysis, and field inspection — are often subjective and incomplete. The increasing availability of high-resolution topographic data acquired via airborne Light Detection and Ranging (LiDAR) over broad swaths of terrain invites new, automated landslide mapping procedures. We present two methods of spectral analysis that utilize LiDAR-derived digital elevation models of the Puget Sound lowlands, Washington, and the Tualatin Mountains, Oregon, to quantify and automatically map the topographic signatures of deep-seated landslides. Power spectra produced using the two-dimensional discrete Fourier transform and the two-dimensional continuous wavelet transform identify the characteristic spatial frequencies of deep-seated landslide morphologic features such as hummocky topography, scarps, and displaced blocks of material. Spatial patterns in the amount of spectral power concentrated in these characteristic frequency bands highlight past slope instabilities and allow the delineation of landslide terrain. When calibrated by comparison with detailed, independently compiled landslide inventory maps, our algorithms correctly classify an average of 82% of the terrain in our five study areas. Spectral analysis also allows the creation of dominant wavelength maps, which prove useful in analyzing meter-scale topographic expressions of landslide mechanics, past landslide activity, and landslide-modifying geomorphic processes. These results suggest that our automated landslide mapping methods can create accurate landslide maps and serve as effective, objective, and efficient tools for digital terrain analysis. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Geomorphology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.geomorph.2009.02.027
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 16
        StartPage: 132
    Subjects:
      – SubjectFull: Geological mapping
        Type: general
      – SubjectFull: Landslides
        Type: general
      – SubjectFull: Spectrum analysis
        Type: general
      – SubjectFull: Geological statistics
        Type: general
      – SubjectFull: Mountains
        Type: general
      – SubjectFull: Surface of the earth
        Type: general
      – SubjectFull: Earth (Planet)
        Type: general
      – SubjectFull: Puget Sound (Wash.)
        Type: general
      – SubjectFull: Washington (State)
        Type: general
      – SubjectFull: Oregon
        Type: general
    Titles:
      – TitleFull: Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon
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            NameFull: Booth, Adam M.
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            NameFull: Roering, Josh J.
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            NameFull: Perron, J. Taylor
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              Text: Aug2009
              Type: published
              Y: 2009
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              Value: 109
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              Value: 3/4
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            – TitleFull: Geomorphology
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