Automated Means of Identifying Landslide Deposits using LiDAR Data using the Contour Connection Method Algorithm

Thursday, 18 December 2014
Ben Adam Leshchinsky, Oregon State University, Corvallis, OR, United States, Michael J Olsen, Oregon State University, School of Civil and Construction Engineering, Corvallis, OR, United States and Burak F Tanyu, George Mason University Foundation Inc., Fairfax, VA, United States
Landslides are a global natural hazard, resulting in severe economic, environmental and social impacts every year. Often, landslides occur in areas of repeated slope instability, but despite these trends, significant residential developments and critical infrastructure are built in the shadow of past landslide deposits and marginally stable slopes. These hazards, despite their sometimes enormous scale and regional propensity, however, are difficult to detect on the ground, often due to vegetative cover. However, new developments in remote sensing technology, specifically Light Detection and Ranging mapping (LiDAR) are providing a new means of viewing our landscape. Airborne LiDAR, combined with a level of post-processing, enable the creation of spatial data representative of the earth beneath the vegetation, highlighting the scars of unstable slopes of the past. This tool presents a revolutionary technique to mapping landslide deposits and their associated regions of risk; yet, their inventorying is often done manually, an approach that can be tedious, time-consuming and subjective. However, the associated LiDAR bare earth data present the opportunity to use this remote sensing technology and typical landslide geometry to create an automated algorithm that can detect and inventory deposits on a landscape scale. This algorithm, called the Contour Connection Method (CCM), functions by first detecting steep gradients, often associated with the headscarp of a failed hillslope, and initiating a search, highlighting deposits downslope of the failure. Based on input of search gradients, CCM can assist in highlighting regions identified as landslides consistently on a landscape scale, capable of mapping more than 14,000 hectares rapidly (<30 minutes). CCM has shown preliminary agreement with manual landslide inventorying in Oregon’s Coast Range, realizing almost 90% agreement with inventorying performed by a trained geologist. The global threat of landslides necessitates new and effective tools for inventorying regions of risk to protect people, infrastructure and the environment from landslide hazards. Use of the CCM algorithm combined with judgment and rapidly developing remote sensing technology may help better define these regions of risk.