A new algorithm for automated landslide inventorying, named the Contour Connection Method (CCM), has been developed that relies on simplified morphological features for analysis. Previous landslide mapping techniques have included 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 delineation, where a geologist must painstakingly mark hundreds, maybe thousands of landslide features using GIS tools, only to have inventoried slope failures in a small area of land. Statistical or machine learning approaches have been used to train computers to use complex parameters to find landslides using a DEM, but this often requires an exceptional amount of experience, computer training or complex parameters.
These approaches are important to defining an inventory, yet there are drawbacks manual inventorying is extremely time-consuming and subjective; machine-learning approaches are not necessarily intuitive or simple to apply. Despite current collaborative efforts between computer scientists, geographers, engineers and geologists to expediently inventory current landslides, we are at a bottleneck. That is, the public sees the importance of inventorying past landslides, the same way flood maps or tsunami zones are widely available, but we are not yet of capacity to do so.
The Contour Connection Method (CCM) utilizes bare earth lidar to detect landslide deposits on a landscape scale in an automated manner. This approach requires less user input than other mapping algorithms, and focuses on general landslide geometry – such as the slope of landslide scarps and deposits. 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. 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.
CCM has shown good agreement with manually inventoried landslide maps provided by the Oregon Department of Geology and Mineral Industries (DOGAMI), which serves as a leader in using BE lidar to delineate landslide features throughout the state of Oregon a daunting task. Comparisons of manually delineated quadrants have shown up to 90% agreement (pixel-pixel) with geologist-inventoried landslides in a matter of minutes, presenting a means of expediting the important process of inventorying the ghosts of past landslides that surround all hilly or mountainous terrain. This presents a promising tool for a consistent means of inventorying existing landslides, especially in consideration of a federal effort to map all of the United States with high-resolution, aerial lidar, known as the 3D Elevation Program (3DEP).
In the wake of tragic landslide events of the past year, it is evident that inventories of past landslides should be public information, like flood maps or faults zones. This insufficient availability of information is not due to a lack of motivated geologists, but a daunting, enormous landscape that presents a severe logistical challenge for manual inventorying. However, the increasing prevalence of lidar and use of landslide inventorying tools like CCM may present just the catalyst that geologists need to begin to win this battle.
Details of CCM can be found through a peer-reviewed article published in Computers and Geosciences:
Ben A. Leshchinsky, Michael J. Olsen, Burak F. Tanyu, Contour connection method for automated identification and classification of landslide Deposits, Computers & Geosciences, ISSN 0098-3004, http://dx.doi.org/10.1016/j.cageo.2014.10.007.