Landslides are geohazards that result in major economic, environmental and social impacts for operation, maintenance and construction of highways. Current databases of landslides are limited and fragmented since they are based on a variety of inventories/geologic maps from various sources, and have been mapped sporadically over time. Landslides that occur on or near highways may be classified in terms of size and type, proximity to the highway and cost to fix. However, there is often very little information about whether it was an existing, dormant slide or a new slope failure, highlighting a need for a baseline inventory. Establishing a baseline inventory of landslides along highway corridors is can be a challenging, time-consuming process due to large road networks, complex terrain, and large quantities of slope failures, but is a critical component of asset management due to the likely reoccurrence of landslides at areas of previous failure. Knowledge of existing slides and their proximity to infrastructure with quantifiable costs (e.g. bridges, walls, traffic signals) and risks (e.g. traffic) may serve as an objective management tool regarding repairs and maintenance prioritization. Current landslide mapping techniques include roadside inventories or basic surveying techniques to highlight regions of risk, which are likely also the areas that require more resources for maintenance. However, many of these techniques do not have sufficient accuracy, resolution or consistency for inventorying landslide deposits on a landscape scale. Fortunately, use of remote sensing tools like lidar, photogrammetric point clouds, or basic digital elevation models (DEMs) provide sufficient accuracy to begin a baseline inventory of landslides surrounding state highways. These tools are especially promising as they may reveal the actual terrain beneath vegetation, highlighting landslide features like steep headscarps and the hummocks of landslide deposits. Currently, these mapping techniques are applied for asset management, but are often done using subjective interpretation of hazard features and asset values. Although this manual interpretation is valuable, large networks of highways and an ever-changing landscape of geo-hazards necessitates semi-automated approaches to landslide mapping around state highway assets as a baseline now, and repeatedly over the future. A new algorithm, called the Contour Connection Method (CCM), will enable rapid and consistent mapping of geohazards in context of existing asset management programs.

Lidar, photogrammetry or other digitized remote sensing tools have developed rapidly in past years, presenting new techniques to mapping landslides with objective and consistent computing tools, a promising technique when combined with the overlapping asset information presented using Geographic Information Systems (GIS). CCM, a simplified automated algorithm has been developed to utilize a given DEM, derived from lidar, photogrammetry or existing contour maps to detect landslide deposits and evaluate their risk to right-of-way. Using geology and soil maps for a given region, this algorithm can be tailored to the mapping and classification of landslide deposits for given DEMs. This algorithm requires little user input, simply parameters to define headscarps and resolution, and functions by focusing on generalized landslide geometry. Not only does the simplified process highlight landslide deposits, but it provides unique data about each mapped landslide deposit that can aid in classification of geo-hazards. Combination of landslide inventorying, classification, and their proximity to assets will expedite risk assessment and prioritization of assets, especially when integrating state-specific specific GIS layers for asset management. Most advantageous is the consistency and speed of analysis, typically requiring only minutes to run a preliminary analysis. Specific objectives include: 1. Modify DEM-based analysis to include state-specific soil, geology, lithology maps and/or existing landslide inventories. Integration of these region-specific parameters would assist in refined mapping and asset management. If unavailable, a preliminary analysis will provide a starting point for landslide maps. The landslide classification scheme (rotational, translational, flow, etc.) will be refined based on state-specific inventories or geology. nventorying and classification schemes will be connected to geohazard age and activity. 2. Modify analysis to have a feature to iteratively perform analysis to create probability maps of geohazards. 3. Create state-specific asset management tool that will use existing infrastructure network (e.g. GIS inventory) and their proximity to mapped geohazards to evaluate risk based on infrastructure value (e.g. bridge cost, maintenance cost, etc.) or risk (e.g. highway closure, traffic counts, etc.). 4. Provide a free-standing GIS tool that utilizes available DEMs (lidar, photogrammetry, contour maps) and is tailored to fit within the framework of state-specific asset and geohazards management and prioritization objectives. 5. Perform a preliminary inventory of landslides along a portion of a state highway corridor. All maps and inventories would be provided in GIS-compatible files that would fit within existing asset management programs. Deliverables could be tailored to state-specific GIS platforms.

The use of this DEM-based geohazard mapping approach would enable a new means of asset management using fast and consistent, semi-automated tools. Such a technique would fit within existing state asset management frameworks, providing enhanced measures to assess management costs, prioritize areas of concern, decide to expand or reduce potential site investigation, and assess potential future route planning. A consistent, fast and superior framework for identifying and classifying landslides and their respective activity would further inform prioritization and mitigation needs over time, a critical consideration for future asset management with limited resources. This project will enable states to establish a baseline hazard inventory and provide them with a tool for updating their databases within their current asset management framework in the future.

Participant's Funding Request: $20,000/year for 3 years