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

Muhammad Waqas Khan*, Stuart Dunning, Rupert Bainbridge, James Martin, Alejandro Diaz-Moreno, Hamdi Torun, Nanlin Jin, John Woodward, Michael Lim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 pre-emptive 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.
Original languageEnglish
Article number893
Number of pages17
JournalRemote Sensing
Volume13
Issue number5
DOIs
Publication statusPublished - 27 Feb 2021

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