We present an automated terrestrial laser scanning (ATLS) system with automatic near-real-time change detection processing. The ATLS system was tested on the Séchilienne landslide in France for a 6-week period with data collected at 30 min intervals. The purpose of developing the system was to fill the gap of high-temporal-resolution TLS monitoring studies of earth surface processes and to offer a cost-effective, light, portable alternative to ground-based interferometric synthetic aperture radar (GB-InSAR) deformation monitoring. During the study, we detected the flux of talus, displacement of the landslide and pre-failure deformation of discrete rockfall events. Additionally, we found the ATLS system to be an effective tool in monitoring landslide and rockfall processes despite missing points due to poor atmospheric conditions or rainfall. Furthermore, such a system has the potential to help us better understand a wide variety of slope processes at high levels of temporal detail.

An automated, fixed-location, time lapse camera system was developed as an alternative to monitoring geological processes with lidar or ground-based interferometric synthetic-aperture radar (GB-InSAR). The camera system was designed to detect fragmental rockfalls and pre-failure deformation at rock slopes. It was implemented at a site along interstate I70 near Idaho Springs, Colorado. The camera system consists of five digital single-lens reflex (DSLR) cameras which collect photographs of the rock slope daily and automatically upload them to a server for processing. Structure from motion (SfM) photogrammetry workflows were optimized to be used without ground control. An automated change detection pipeline registers the point clouds with scale adjustment and filters vegetation. The results show that if a fixed pre-calibration of internal camera parameters is used, an accuracy close to that obtained using ground control points can be achieved. Over the study period between March 19, 2018 and June 24, 2019, a level of detection between 0.02 to 0.03 m was consistently achieved, and over 50 rockfalls between 0.003 to 0.1 m³ were detected at the study site. The design of the system is fit for purpose in terms of its ground resolution size and accuracy and can be adapted to monitor a wide range of geological and geomorphic processes at a variety of time scales.

This study presents a point cloud de-noising and calibration approach that takes advantage of point redundancy in both space and time (4D). The purpose is to detect displacements using terrestrial laser scanner data at the sub-mm scale or smaller, similar to radar systems, for the study of very small natural changes, i.e., pre-failure deformation in rock slopes, small-scale failures or talus flux. The algorithm calculates distances using a multi-scale normal distance approach and uses a set of calibration point clouds to remove systematic errors. The median is used to filter distance values for a neighbourhood in space and time to reduce random type errors. The use of space and time neighbours does need to be optimized to the signal being studied, in order to avoid smoothing in either spatial or temporal domains. This is demonstrated in the application of the algorithm to synthetic and experimental case examples. Optimum combinations of space and time neighbours in practical applications can lead to an improvement of an order or two of magnitude in the level of detection for change, which will greatly improve our ability to detect small changes in many disciplines, such as rock slope pre-failure deformation, deformation in civil infrastructure and small-scale geomorphological change.

While terrestrial laser scanning and photogrammetry provide high quality point cloud data that can be used for rock slope monitoring, their increased use has overwhelmed current data analysis methodologies. Accordingly, point cloud processing workflows have previously been developed to automate many processes, including point cloud alignment, generation of change maps and clustering. However, for more specialized rock slope analyses (e.g., generating a rockfall database), the creation of more specialized processing routines and algorithms is necessary. More specialized algorithms include the reconstruction of rockfall volumes from clusters and points and automatic classification of those volumes are both processing steps required to automate the generation of a rockfall database. We propose a workflow that can automate all steps of the point cloud processing workflow. In this study, we detail adaptions to commonly used algorithms for rockfall monitoring use cases, such as Multiscale Model to Model Cloud Comparison (M3C2). This workflow details the entire processing pipeline for rockfall database generation using terrestrial laser scanning.

We present an Automated Terrestrial Laser Scanning (ATLS) system with automatic near real-time change detection processing. The ATLS system was tested on the Séchilienne Landslide in France for a six-week period with data collected at 30 minute intervals. The purpose of developing the system was to fill the gap of high temporal resolution TLS monitoring studies of earth surface processes and to offer a cost effective, light, portable alternative to GB-InSAR deformation monitoring. During the study, we detected the flux of talus, displacement of the landslide and pre-failure deformation of discrete rockfall events. We also defined a distance spatio-temporal confidence interval and achieved measurement confidence at 95 % that varied between 2 to 10 mm at target scanner distances greater than 1000 m. Additionally, we found the ATLS system is still an effective tool in monitoring landslide and rockfall processes despite missing points due to poor atmospheric conditions or rainfall. Furthermore, such a system has the potential to help us better understand a wide variety of slope processes at high levels of temporal detail.