Abstract. 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.

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 OPEN ACCESS Remote Sens. 2015, 7 13030 in many disciplines, such as rock slope pre-failure deformation, deformation in civil infrastructure and small-scale geomorphological change.

Using change detection and semi-automated identification methods, it is possible to extract detailed rockfall information from terrestrial laser scanning data to build a database of events, which can be used in the development of the frequency-magnitude relationship for a slope. In this study, we have applied these methods to the White Canyon, a hazardous slope that presents rockfall hazards to the CN Rail line in British Columbia, to build a database of rockfalls including their locations, volumes, and block shapes. We identified over 1900 rockfall events during a 15-month period, ranging in volume from 0.01 to 45 m 3. The frequency of these events changed throughout the year, with the highest periods of activity occurring over the winter months. We investigated how the sampling interval, or duration between scans, can affect how the rockfalls are identified, and therefore the frequency-magnitude relationship for the slope using datasets with fewer scans. We show that as the duration between scans becomes larger, fewer rockfalls are detected, as multiple events that have occurred in the same location cluster together into a single event. The results of this study can be used to assist the railways in planning the appropriate number and duration between future scans, in order to capture frequency-magnitude data for the slope with a desired level of detail.

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