GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 151-5
Presentation Time: 2:30 PM


MITCHELL, Andrew D.1, LATO, Matthew1, MCDOUGALL, Scott2, PORTER, Michael1, BALE, Stephanie1 and WATSON, Andrew3, (1)BGC Engineering Inc., Suite 500 980 Howe Street, Vancouver, BC V6Z 0C8, Canada, (2)Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, 2020-2207 Main Mall, Vancouver, BC V6T 1Z4, Canada, (3)BC Hydro, Four Bentall Centre, 1100-1055 Dunsmuir Street, Vancouver, BC V7X 1V5, Canada,

Remote sensing techniques, such as airborne LiDAR, have become established in the assessment and monitoring of geohazards. Change detection analysis techniques utilizing LiDAR and photogrammetry data are maturing rapidly, but primarily focus on local areas of interest. This case study describes the application of change detection analysis using two airborne LiDAR datasets, collected nine years apart, to rapidly identify landslide and erosion events that happened within a 750 km2 area. Efficiencies in data processing were realized by subdividing the LiDAR data into smaller sub-regions, and utilizing automated processing techniques to perform the initial data alignment and change detection analysis. A more refined analysis was then conducted by realigning and reanalyzing areas of interest at a site-specific scale, which is more typical of routine change detection analysis. The validity of the change detection analysis was demonstrated through site visits to select areas within the monitoring area to confirm there was evidence of recent slope movement where the analysis indicated topographic changes.

The difference in point density between the two data collections, approximately 1 point/m2 for the baseline dataset versus 5 points/m2 for the new dataset, presented a challenge. The increased resolution of the new dataset meant small topographic features, such as ridge lines, were better defined, and were identified as areas of change in the analysis. The limit of detectable change was relatively high compared to smaller areas with more consistent data resolution, however, the results of the analysis were appropriate for identifying areas of significant movement or erosion. Communication of this amount of spatial data in a meaningful way was another project challenge. This challenge has been addressed by hosting the LiDAR data and change detection results on a web-based GIS platform, allowing users to interact with the data without needing specialized software.

  • Regional ALS Change Detection.pdf (3.8 MB)