New Techniques to Monitor and Characterize Landslide Behavior and Controls Using Digital Terrain Models

Coastal landslides and erosion are major natural hazards resulting in unstable slopes, constituting immense challenges to modern infrastructure. Developing, maintaining, and performing risk assessments of infrastructure on, or close to, these hazards require a detailed understanding of the geophysical processes destabilizing the slope. These efforts start with the collection of high-quality monitoring data to assess the current rates of slope deformation. In the last several decades, geospatial technologies including lidar (Light Detection and Ranging), and photogrammetric SfM-MVS (Structure from Motion – Multi View Stereopsis) have been used to perform high-resolution surveys of unstable slopes. Such technologies have proven to be effective, capable of detecting slope deformation at the cm-level scale. Improvements in initial processing techniques, decreasing sensor costs, and wide-scale acceptance have made these approaches more accessible than ever. However, after initial processing of data, present day methodologies are often overly simplistic, or require expert design of site-specific algorithms with fine-tuning of non-intuitive parameters that are time consuming to produce. In addition, current remote-sensing based workflows often do not utilize data from in-situ monitoring sensors, such as GNSS (Global Navigation Satellite System), inclinometers, or extensometers. Rather, survey data collected is processed and analyzed in isolation missing an opportunity to leverage the advantages of each data source. Lastly, there are still major gaps in our understanding of the environmental and morphological processes driving sea cliff erosion, especially at the hyperspatial scale. This work advances the current state of slope monitoring techniques to enable improvements in the use of remote-sensing data to quantify and model the geophysical processes of slope deformation through three fundamental contributions. First, a new automated method to derive dense landslide surface displacement information from sequential DEMs (Digital Elevation Models) that is easy to implement and requires no parameter tuning is presented. Second, leveraging this approach, a computationally efficient novel method of fusing remotely-sensed and in-situ monitoring data to produce a high-spatial high-temporal interpolation of deformation is developed. Finally, this dissertation contributes to the overall understanding of the governing geophysical processes driving coastal erosion using a unique monitoring dataset compiled on the Oregon Coast. In summary, this work presents and rigorously evaluates a new framework and associated tools for slope deformation monitoring and contributes to the overall understanding of the complex processes governing coastal erosion behavior over time

SlideSim: 3D Landslide Displacement Monitoring through a Physics-Based Simulation Approach to Self-Supervised Learning

Displacement monitoring is a critical step to understand, manage, and mitigate potential landside hazard and risk. Remote sensing technology is increasingly used in landslide monitoring. While significant advances in data collection and processing have occurred, much of the analysis of remotely-sensed data applied to landslides is still relatively simplistic, particularly for landslides that are slow moving and have not yet “failed”. To this end, this work presents a novel approach, SlideSim, which trains an optical flow predictor for the purpose of mapping 3D landslide displacement using sequential DEM rasters. SlideSim is capable of automated, self-supervised learning by building a synthetic dataset of displacement landslide DEM rasters and accompanying label data in the form of u/v pixel offset flow grids. The effectiveness, applicability, and reliability of SlideSim for landslide displacement monitoring is demonstrated with real-world data collected at a landslide on the Southern Oregon Coast, U.S.A. Results are compared with a detailed ground truth dataset with an End Point Error RMSE = 0.026 m. The sensitivity of SlideSim to the input DEM cell size, representation (hillshade, slope map, etc.), and data sources (e.g., TLS vs. UAS SfM) are rigorously evaluated. SlideSim is also compared to diverse methodologies from the literature to highlight the gap that SlideSim fills amongst current state-of-the-art approaches

SlideSim: 3D Landslide Displacement Monitoring through a Physics-Based Simulation Approach to Self-Supervised Learning

Displacement monitoring is a critical step to understand, manage, and mitigate potential landside hazard and risk. Remote sensing technology is increasingly used in landslide monitoring. While significant advances in data collection and processing have occurred, much of the analysis of remotely-sensed data applied to landslides is still relatively simplistic, particularly for landslides that are slow moving and have not yet “failed”. To this end, this work presents a novel approach, SlideSim, which trains an optical flow predictor for the purpose of mapping 3D landslide displacement using sequential DEM rasters. SlideSim is capable of automated, self-supervised learning by building a synthetic dataset of displacement landslide DEM rasters and accompanying label data in the form of u/v pixel offset flow grids. The effectiveness, applicability, and reliability of SlideSim for landslide displacement monitoring is demonstrated with real-world data collected at a landslide on the Southern Oregon Coast, U.S.A. Results are compared with a detailed ground truth dataset with an End Point Error RMSE = 0.026 m. The sensitivity of SlideSim to the input DEM cell size, representation (hillshade, slope map, etc.), and data sources (e.g., TLS vs. UAS SfM) are rigorously evaluated. SlideSim is also compared to diverse methodologies from the literature to highlight the gap that SlideSim fills amongst current state-of-the-art approaches

Forecasting Post-Earthquake Rockfall Activity

[EN] Important infrastructure such as highways or railways traverse unstable terrain in many mountainous and scenic parts of the world. Rockfalls and landslides result in frequent maintenance needs, system unreliability due to frequent closures and restrictions, and safety hazards. Seismic activity significantly amplifies these negative economic and community impacts by generating large rockfalls and landslides as well as weakening the terrain. This paper interrogates a rich database of repeat terrestrial lidar scans collected during the Canterbury New Zealand Earthquake Sequence to document geomorphic processes as well as quantify rockfall activity rates through time. Changes in the activity rate (spatial distribution) and failure depths (size) were observed based on the Rockfall Activity Index (RAI) morphological classification. Forecasting models can be developed from these relationships that can be utilized by transportation agencies to estimate increased maintenance needs for debris removal to minimize road closures from rockfalls after seismic events.Olsen, M.; Massey, C.; Leshchinsky, B.; Wartman, J.; Senogles, A. (2023). Forecasting Post-Earthquake Rockfall Activity. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/19202

The Hooskanaden Landslide: Historic and Recent Surge Behavior of an Active Earthflow on the Oregon Coast

This paper presents an analysis of the Hooskanaden Landslide, an earthflow, which experienced a dramatic surge event beginning on February 24, 2019, closing US Highway 101 near mile point 343.5 for nearly 2 weeks. This ~ 1 km long surge event resulted in horizontal displacements of up to 45 m and uplift of 6 m at the toe located on a gravel beach adjacent to the Pacific Ocean. The Hooskanaden Landslide, likely active since the eighteenth century, exhibits regular activity with a recurrence interval of major surge events of approximately every 20 years, transitioning from slow to relatively rapid velocities. During the 2019 event, maximum displacement rates of approximately 60 cm/h were observed, slowly decreasing to 15 cm/h for a sustained period of approximately 2 weeks before the eventual return to baseline conditions (\u3c 0.02 cm/h)