Low-Cost Automatic Slope Monitoring Using Vector Tracking Analyses on Live-Streamed Time-Lapse Imagery

Abstract

Identifying precursor events that allow the timely forecasting of landslides, thereby enabling risk reduction, is inherently difficult. Here we present a novel, low cost, flow visualization technique using time-lapsed imagery (TLI) that allows real time analysis of slope movement. This approach is applied to the Rest and Be Thankful slope, Argyle, Scotland, where past debris flows have blocked the A83 or forced preemptive closure. TLI of the Rest and Be Thankful are taken from a fixed station, 28 mm lens, time lapse camera every 15 min. Imagery is filtered to counter the effects of misalignment from wind induced vibration of the camera, asymmetric lighting, and fog. Particle image velocimetry (PIV) algorithms are then run to produce slope movement velocity vectors. PIV generated vectors are automatically post-processed to separate vectors generated by slope movement from false positives generated by harsh environmental conditions. Results for images over a 20-day period indicated precursor slope movement initiated by a rainfall event, a period of quiescence for 10 days, followed by a large landslide failure during proceeding rainfall where over 3000 tons of sediment reached the road. Results suggest low cost, live streamed TLI and this novel PIV approach correctly detect and, importantly, report precursor slope movement, allowing early warning, effective management and landslide impact mitigation. Future applications of this technique will allow the development of an effective decision-making tool for asset management of the A83, reducing the risk to life of motorists. The technique can also be applied to other critical infrastructure sites, allowing hazard risk reduction.

Detection of slow‐moving landslides through automated monitoring of surface deformation using Sentinel‐2 satellite imagery

“…True landslides will be missed in two main situations: either their movement is too slow or too intermittent to be detected by a feature tracking‐derived median velocity map or rapid displacement and/or other surface change caused rapid surface decorrelation. Landslides with velocities too low for detection using feature tracking might be identifiable using alternative methods such as InSAR (Ferrigno et al, 2017; Wasowski & Bovenga, 2014) or ground‐based monitoring (Khan et al, 2021). This workflow may work less well in areas of the globe with conditions commonly leading to decorrelation, most commonly due to persistent snow cover, but this is likely to be the case with any optical feature‐tracking workflow.…”

Real-time shallow landslide hazard assessment on a regional scale

“…Indeed, five cases including the standard case, Case 2a with reduced c, Case 2b with reduced 𝜙, Case 3d with a shallower initial water table and Case 4 with 2-hour antecedent rainfall have similar values of δ FS as their ΔFS curves are almost parallel to each other. In this regard, it is a reliable way to use the δ FS to identify the high-hazard areas and could for instance be used to increase the frequency of slope movement monitoring at key sites (Khan et al 2021).…”

A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces

“…The transformed image is then segmented by threshold value, and the road main body is extracted after the small area spot is removed and the adaptive structural element morphology closing operation. In [15], [16]. it has been proved that there has been much research on slope monitoring and picture digitization technologies.…”