This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR-derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.

Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost.The increasing computing capabilities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice.This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.

Terrestrial laser scanning (TLS) is one of the most promising surveying techniques for rockslope characterization and monitoring. Landslide and rockfall movements can be detected by means of comparison of sequential scans. One of the most pressing challenges of natural hazards is combined temporal and spatial prediction of rockfall. An outdoor experiment was performed to ascertain whether the TLS instrumental error is small enough to enable detection of precursory displacements of millimetric magnitude. This consists of a known displacement of three objects relative to a stable surface. Results show that millimetric changes cannot be detected by the analysis of the unprocessed datasets. Displacement measurement are improved considerably by applying Nearest Neighbour (NN) averaging, which reduces the error (1σ) up to a factor of 6. This technique was applied to displacements prior to the April 2007 rockfall event at Castellfollit de la Roca, Spain. The maximum precursory displacement measured was 45 mm, approximately 2.5 times the standard deviation of the model comparison, hampering the distinction between actual displacement and instrumental error using conventional methodologies. Encouragingly, the precursory displacement was clearly detected by applying the NN averaging method. These results show that millimetric displacements prior to failure can be detected using TLS.