Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps: (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness; (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Model (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangitikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. We also demonstrate that for high precision survey scanners, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases.

https://nicolas.brodu.net/common/recherche/publications/M3C2.pdf

Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)

Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer-grade camera. The approach integrates structure-from-motion (SfM) and multi-view-stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM-MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close-range photogrammetry. The relative precision ratio achieved by SfM-MVS (measurement precision : observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e. centimeter-level precision over measurement distances of 10s of meters). We apply SfM-MVS at an intermediate scale, to determine erosion rates along a ~50-m-long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70±0.05 m a-1. Sequential erosion maps (at ~0.05 m grid resolution) highlight the spatio-temporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM-MVS produced comparable data and reduced data collection time by ~80%.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2011JF002289

Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application

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.

https://link.springer.com/content/pdf/10.1007%2Fs11069-010-9634-2.pdf

Use of LIDAR in landslide investigations: a review

Debris flows typically occur when intense rainfall or snowmelt triggers landslides or extensive erosion on steep, debris-mantled slopes. The flows can then grow dramatically in size and speed as they entrain material from their beds and banks, but the mechanism of this growth is unclear. Indeed, momentum conservation implies that entrainment of static material should retard the motion of the flows if friction remains unchanged. Here we use data from large-scale experiments to assess the entrainment of bed material by debris flows. We find that entrainment is accompanied by increased flow momentum and speed only if large positive pore pressures develop in wet bed sediments as the sediments are overridden by debris flows. The increased pore pressure facilitates progressive scour of the bed, reduces basal friction and instigates positive feedback that causes flow speed, mass and momentum to increase. If dryer bed sediment is entrained, however, the feedback becomes negative and flow momentum declines. We infer that analogous feedbacks could operate in other types of gravity-driven mass flow that interact with erodible beds. The mechanisms by which debris flows acquire mass and momentum as they entrain material are unclear. Large-scale experiments suggest that the pore pressure of wet bed sediment increases as the flow moves over the bed, leading to reduced friction and progressive scouring of the base.

Positive feedback and momentum growth during debris-flow entrainment of wet bed sediment

. Despite the large number of recent advances and developments in landslide susceptibility mapping (LSM) there is still a lack of studies focusing on specific aspects of LSM model sensitivity. For example, the influence of factors such as the survey scale of the landslide conditioning variables (LCVs), the resolution of the mapping unit (MUR) and the optimal number and ranking of LCVs have never been investigated analytically, especially on large data sets. In this paper we attempt this experimentation concentrating on the impact of model tuning choice on the final result, rather than on the comparison of methodologies. To this end, we adopt a simple implementation of the random forest (RF), a machine learning technique, to produce an ensemble of landslide susceptibility maps for a set of different model settings, input data types and scales. Random forest is a combination of Bayesian trees that relates a set of predictors to the actual landslide occurrence. Being it a nonparametric model, it is possible to incorporate a range of numerical or categorical data layers and there is no need to select unimodal training data as for example in linear discriminant analysis. Many widely acknowledged landslide predisposing factors are taken into account as mainly related to the lithology, the land use, the geomorphology, the structural and anthropogenic constraints. In addition, for each factor we also include in the predictors set a measure of the standard deviation (for numerical variables) or the variety (for categorical ones) over the map unit. As in other systems, the use of RF enables one to estimate the relative importance of the single input parameters and to select the optimal configuration of the classification model. The model is initially applied using the complete set of input variables, then an iterative process is implemented and progressively smaller subsets of the parameter space are considered. The impact of scale and accuracy of input variables, as well as the effect of the random component of the RF model on the susceptibility results, are also examined. The model is tested in the Arno River basin (central Italy). We find that the dimension of parameter space, the mapping unit (scale) and the training process strongly influence the classification accuracy and the prediction process. This, in turn, implies that a careful sensitivity analysis making use of traditional and new tools should always be performed before producing final susceptibility maps at all levels and scales.

/pdf/landslide-susceptibility-estimation-by-random-forests-4u1b4pm5im.pdf

Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues

A terrestrial laser scanner (TLS) allows the generation of a detailed model of a landslide surface. In this way, when two or more georeferenced models obtained by multi-temporal scans are available, the landslide displacement field can be computed. Nevertheless, such a computation is a relatively complex task because the recognition of correspondences among the multi-temporal models is required. The Iterative Closest Point (ICP) algorithm allows the alignment of two 3D objects having a common part by iterative shape matching. A new method for the automatic calculation of a landslide displacement field is presented here. It is based on a piecewise application of the ICP algorithm and is made possible by the robustness of this algorithm against noise and small morphological modifications. After a series of numerical experimentations, this method was successfully applied to two test sites located in the North-Eastern Italian Alps affected by high-risk landslides of the slump type (Perarolo di Cadore and Lamosano) with very different observational conditions.

Terrestrial laser scanner to detect landslide displacement fields: a new approach

Field observations of a debris flow event in the Dolomites

In 1997 a field monitoring system was installed in Acquabona Creek in the Dolomites (Eastern Italian Alps) to observe the hydrologic conditions for debris flow occurrence and some dynamic properties of debris flow. The monitoring system consists of three remote stations: an upper one located at the head of a deeply-incised channel and two others located downstream. The system is equipped with sensors for measuring rainfall, pore pressures in the mobile channel bottom, ground vibrations, debris flow depth, total normal stress and fluid pore-pressure at the base of the flow. Two video cameras record events at the upper channel station and one video is installed at the lowermost station. During summer 1998, three debris flows (volumes from less than 1000 m3 up to 9000 m3) occurred at Acquabona. The following results were obtained from a preliminary analysis of the data: 1) All of the flows were triggered by rainfalls of less than 1 hour duration, with peak rainfall intensities ranging from 4.8 to 14.7 mm / 10 minute. 2) Debris flows initiated in several reaches of the channel, including the head of the talus slope. 3) The initial surges of the mature flows had a higher solid concentration and a lower velocity (up to 4 m/s) than succeeding, more dilute surges (more than 7 m/s). 4) Total normal stress and pore fluid pressures measured at the base of the flow. (mean depth about 1.1 m) were similar (about 15 kPa), indicating a completely liquefied flow. 5) Peak flows entrained debris at a rate of about 6 m 3/m of channel length and channel bed scouring was proportional to the local slope gradient and was still evident in the lower channel where the slope was 7°.

Debris flow monitoring in the acquabona watershed on the Dolomites (Italian alps)

Characterization of landslide ground surface kinematics from terrestrial laser scanning and strain field computation

On the 9 th October 1963 a catastrophic landslide suddenly occ urred on the southern slope of the Vajont dam reservoir. A mass of approximately 270 million m 3 collapsed into the reservoir generating a wave whi ch overtopped the dam and hit the town of Longarone and other villages: almost 2000 people lost their lives. Many studies a nd researches were carried out on the geological and geomechanical asp ects and several attempts were made to explain the kinematics and dynamics of the landslide. This large mass of studie s and researches has greatly increased the understa nding of such phenomena, recognizing their precursory activity, p redicting their dynamic behaviour and identifying l ikely areas of triggering and deposition. This paper reports brief ly all the information reported in related papers , referring to the geological studies, to the chronology of events be fore 9 th October 1963 and to the different interpretations of the landslide triggering and actual mechanics and dynamics.

Rinaldo Genevois

Papers

Terrestrial laser scanner to detect landslide displacement fields: a new approach

Field observations of a debris flow event in the Dolomites

Debris flow monitoring in the acquabona watershed on the Dolomites (Italian alps)

Characterization of landslide ground surface kinematics from terrestrial laser scanning and strain field computation

The 1963 Vaiont Landslide