Automated quantification of distributed landslide movement using circular tree trunks extracted from terrestrial laser scan data

This manuscript presents a novel algorithm to automatically detect landslide movement in a forested area using displacements of tree trunks distributed across the landslide surveyed repeatedly using terrestrial laser scanning (TLS). Common landslide monitoring techniques include: inclinometers, global position system (GPS), and interferometric synthetic aperture radar (InSAR). While these techniques provide valuable data for monitoring landslides, they can be difficult to apply with adequate spatial or temporal resolution needed to understand complex landslides, specifically in forested environments. Comparison of the center coordinates (determined via least-squares fit of the TLS data) of a cross section of the tree trunk between consecutive surveys enable quantification of landslide movement rates, which can be used to analyze patterns of landslide displacement. The capabilities of this new methodology were tested through a case-study analyzing the Johnson Creek Landslide, a complex, quick moving coastal landslide, which has proven difficult to monitor using other techniques. A parametric analysis of fitting thresholds was also conducted to determine the reliability of tree trunk displacements calculated and the number of features that were extracted. The optimal parameters in selecting trees for movement analysis were found to be less than 1.5cm for the RMS residuals of the circle fit and less than 1.0cm for the difference in the calculated tree radii between epochs. Automated approach and algorithm using natural features to detect landslide movement.Case study highlighting use of terrestrial laser scanning to analyze landslide movement and coastal erosion.Parametric analysis determining optimal performance of the algorithm based on a few, intuitive user input parameters.