Researchers at Oregon State University have developed a streamlined method that uses software to process some basic bare earth lidar data inputs, returning identity and class of possible landslide locations, enabling improved resource allocation in landslide risk assessment and mitigation. The technology uses a novel algorithm to automatically and consistently detect landslide deposits on a landscape scale. The algorithm implements a Contour Connection Method (CCM) based primarily on bare earth lidar data while requiring minimal user input. The output is integrated with mapping software to quickly identify a variety of landslide features.
Features & Benefits
- Simplified input variables
- Can handle larger data sets, lead to lower computational cost
- Enhanced landslide inventorying and classification
- Detection and classification of historical slides
- Landslide risk assessment
- Decision making in construction and emergency response planning
Background of Invention
Landslides are a common, sometimes highly destructive environmental hazard. Despite the devastating effects, current methods for identifying the potential for hazardous landslides can be challenging, time-consuming and expensive. These methods include field inventorying, photogrammetric approaches, and use of bare-earth (BE) lidar digital terrain models (DTMs). The only method with sufficient resolution, detail and accuracy for mapping across landscape scale are BE DTMs. This method often involves a multitude of techniques including manual digitizing, statistical or machine learning approaches and use of alternate sensors. As a result, current methods for inventorying landslides and landslide risk is extremely expensive and time consuming, with suboptimal allocation of resources.
Fig. 3. Conceptual schematic of active landslide zone
Fig. 5. CCM detection and bare earth map of landslides surrounding Stillaguamish Valley.
Software has been developed and validated, ready for commercial applications