2014 GSA Annual Meeting in Vancouver, British Columbia (19–22 October 2014)

Paper No. 248-11
Presentation Time: 3:45 PM


LESHCHINSKY, Ben, Forest Engineering, Resources and Management, Oregon State University, 273 Peavy Hall, Oregon State University, Corvallis, OR 97331, OLSEN, Michael J., School of Civil and Construction Engineering, Oregon State University, 220 Owen Hall, Corvallis, OR 97331 and TANYU, Burak, Civil, Environmental, and Infrastructure Engineering, George Mason University, 1409, Nguyen Engineering Building, 4400 University Drive, MS 6C1, Fairfax, VA 22030, ben.leshchinsky@oregonstate.edu

Landslides are a common hazard worldwide that result in major economic, environmental and social impacts. Despite their devastating impact, inventorying and mapping existing landslides, often the areas at the highest risk of reoccurrence, is a challenging, time-consuming, and expensive process. Current landslide mapping techniques include field inventorying, photogrammetry, and use of bare-earth (BE) lidar Digital Elevation Models (DEMs) to highlight regions of instability. However, many of these techniques do not have sufficient accuracy, resolution or consistency for inventorying landslide deposits on a landscape scale - with the exception being use of lidar bare earth digital elevation models (DEMs). These DEMs can reveal the landscape beneath vegetation and other obstructions, highlighting landslide features, including scarps, deposits, fans and more. Current approaches to landslide inventorying with lidar include manual digitizing, statistical or machine learning approaches, and use of alternate sensors (e.g., hyperspectral imaging) with lidar, which may be inconsistent and subjective to human judgment, or require complex datasets. Presented is a new algorithm, called the Contour Connection Method (CCM), which utilizes bare earth lidar to consistently detect landslide deposits on a landscape scale in an automated fashion. This approach requires minimal user input that is related to general landslide geometry - such as the landslide scarp and deposit gradients. The CCM algorithm functions by applying contours and nodes to a map, and using vectors connecting the nodes to evaluate gradient and associated landslide features based on the user defined input criteria. This process not only highlights deposits, but it yields a unique signature for each landslide feature that may be used to classify different landscape features. This is possible because each landslide feature has a distinct set of metadata – specifically, density of connection vectors on each contour – that provides a unique signature for each landslide. In this study, demonstrations of using CCM are presented by applying the algorithm to the regions that have been manually delineated by landslides and comparing the results.
  • CCM_GSA_final.pdf (6.5 MB)