Graduate Thesis Or Dissertation

 

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  • Landslides are an insidious natural hazard, which can result in significant damage to public infrastructure. Limited monitoring tools are available, particularly tools suitable for use in forested environments. These tools often only allow a few locations across the slide to be monitored. Terrestrial Laser Scanning (TLS) shows promise as a monitoring technique given the high spatial resolution and accuracy at which measurements can be made. However, current procedures can be time consuming, require advanced skill and judgment by the analyst, and typically require manual methods of feature extraction to quantify landslide movement. To overcome these limitations, this thesis presents and investigates a new methodology to detect and monitor landslide movement in a densely forested area using natural features such as tree trunks. The presented methodology searches through the noisy point cloud dataset to find trees and then fit circles to points sampled on the tree trunk. Next, comparing the movement of the circles between time series terrestrial laser scan surveys provides quantified displacements distributed across the landslide. For quality control purposes a parametric analysis was conducted and revealed that the root mean square error (RMS) of the circle fit and the difference in calculated tree radii between epochs are the dominant parameters in correctly pairing trees between epochs. For the test dataset, the optimal values were a RMS circle-fit of less than 1.5 cm and less than 1.0 cm for the calculated difference in tree radii. Application of the methodology to a case study of Johnson Creek Landslide (JCL) showed that TLS can determine landslide movement comparable to conventional monitoring methods. The displacements observed on markers were within 2 cm from the displacement observed from traditional methods such as total station monitoring. TLS also provides more samples than currently observed for this location, allowing increased detail for landslide modeling and monitoring.
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  • description.provenance : Submitted by Jeremy Conner (connerje@onid.orst.edu) on 2013-05-08T18:43:20Z No. of bitstreams: 2 ConnerJeremyC2013.pdf: 2074623 bytes, checksum: e740758c0c468503efcaad0845c034c2 (MD5) code_final.zip: 12543684 bytes, checksum: 676afa4c8669f0603daf800c14a42e9a (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2013-05-09T16:00:04Z (GMT) No. of bitstreams: 2 ConnerJeremyC2013.pdf: 2074623 bytes, checksum: e740758c0c468503efcaad0845c034c2 (MD5) code_final.zip: 12543684 bytes, checksum: 676afa4c8669f0603daf800c14a42e9a (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-05-09T17:53:01Z (GMT) No. of bitstreams: 2 ConnerJeremyC2013.pdf: 2074623 bytes, checksum: e740758c0c468503efcaad0845c034c2 (MD5) code_final.zip: 12543684 bytes, checksum: 676afa4c8669f0603daf800c14a42e9a (MD5)
  • description.provenance : Made available in DSpace on 2013-05-09T17:53:01Z (GMT). No. of bitstreams: 2 ConnerJeremyC2013.pdf: 2074623 bytes, checksum: e740758c0c468503efcaad0845c034c2 (MD5) code_final.zip: 12543684 bytes, checksum: 676afa4c8669f0603daf800c14a42e9a (MD5) Previous issue date: 2013-05-01

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