Landslide deformation character inferred from terrestrial laser scanner data

Landslides are ubiquitous and cause thousands of deaths and injuries each year. Achieving a better understanding of landslide stability and governing processes requires good knowledge of ground surface displacements but acquiring this information is challenging. Three dimensional point-cloud data from terrestrial laser scanning (TLS) show potential for obtaining ground displacements accurately. Problems arise, however, when estimating continuous displacement fields from TLS data because reflecting points from sequential scans of moving ground are non-unique, thus repeat TLS surveys typically do not track individual reflectors. In this dissertation, the cross-correlation-based Particle Image Velocimetry (PIV) method is implemented to derive 3D surface deformation fields using TLS point-cloud data. Associated errors are estimated and the method's performance is tested with synthetic displacements applied to a TLS dataset. The method is applied to the toe of the episodically active Cleveland Corral landslide in northern California using six different TLS scans acquired between June 2005 and April 2012. Estimated displacements agree well with independent measurements at better than 9% root mean squared (RMS) error and permit further analysis to infer the subsurface deformation characteristics of the landslide. The hypothesis that the depth and orientation of the buried slip surface and the subsurface slip rate can be estimated using the surface displacement field is tested. To estimate slip depth and slip rate of the slide, a 2D balanced cross-section (BC) method commonly applied to landslides and an elastic dislocation (ED) model widely applied to study geologic faults are performed. The BC method provides slip-surface depth; the ED model determines the slip-surface depth as well as orientation and slip magnitude. The estimated slip-surface depths using both methods agree with direct measurements of depth. This indicates that these two approaches may offer more efficient and less costly remote means of inferring landslide geometry and slip behavior. The PIV method is also compared with the iterative closest point method and the efficacy of using these two methods to estimate 3D displacement fields using TLS data are discussed. The estimated surface displacement and the inferred subsurface deformation enable assessment of the hazards posed by large, slow-moving landslides.