Image-based correlation of Laser Scanning point cloud time series for landslide monitoring

• We present a methodology to monitor the 3D displacements from Terrestrial Laser Scanning point cloud time series using an image-based approach.
• We propose a Normalized Cross Correlation technique to analyze the point cloud time series, and evaluate its performance against other LiDAR processing approach (ICP) and dGPS surveys.
• We propose a method to estimate the strain and shear deformation from the point cloud time series for a better understanding of the landslide mechanics.
• The sources of error affecting the results are presented and discussed.

Very high resolution monitoring of landslide kinematics is an important aspect for a physical understanding of the failure mechanisms and for quantifying the associated hazard. In the last decade, the potential of Terrestrial Laser Scanning (TLS) to monitor slow-moving landslides has been largely demonstrated but accurate processing methods are still needed to extract useful information available in point cloud time series. This work presents an approach to measure the 3D deformation and displacement patterns from repeated TLS surveys. The method is based on the simplification of a 3D matching problem in a 2D matching problem by using a 2D statistical normalized cross-correlation function. The computed displacement amplitudes are compared to displacements (1) calculated with the classical approach of Iterative Closest Point and (2) measured from repeated dGPS observations. The performance of the method is tested on a 3 years dataset acquired at the Super-Sauze landslide (South French Alps). The observed landslide displacements are heterogeneous in time and space. Within the landslide, sub-areas presenting different deformation patterns (extension, compression) are detected by a strain analysis. It is demonstrated that pore water pressure changes within the landslide is the main controlling factor of the kinematics.