Mapping and Monitoring of Landslides using LIDAR

Abstract : During the past 20 years, the fast development of several remote sensors (e.g. Optical imaging, LIDAR, RADAR, etc.) assembled on satellite, aerial or terrestrial platforms is changing our perception and analysis of the Earth’s surface processes. Particularly, LIDAR technology is capable of acquiring faster, denser and more precise information about land terrain surface, allowing the 3D geometrical modelling of mountains, valleys and cliffs at different scales and with an unprecedented level of detail. Consequently, LIDAR applications on landslide mapping, characterization, monitoring and modelling are shedding a light into how landslides behave and evolve along space and time. Some examples include: (1) the precise landslide mapping through the identification of geomorphological features, (2) the accurate extraction of discontinuities characteristics that play a key role in slope stability, (3) the monitoring of landslides in order to analyse 3D displacements, (4) the magnitude/frequency quantification of fallen volumes along time in order to study erosion rates, (4) the improvement of rockfall hazard assessment, (5) the modelling of mass movements run-out, etc.

Keywords : LIDAR, Laser scanner, landslide, mapping, monitoring, structural analysis.

Jaboyedoff, M., Abellán, A., Carrea, D., Derron, M.H., Matasci, B. and Michoud, C., 2018. Mapping and monitoring of landslides using LiDAR. In Natural Hazards (pp. 397-420). CRC Press. DOI : 10.1201/9781315166841-17


During the last decades, the research on surface processes has taken its lead  from two main developments: (1) faster and more portable remote sensing devices and (2) more powerful computer processors and graphic capabilities. One of these remote sensing techniques  the light detection and ranging (LIDAR, all acronyms are found in Table 1), also known as laser scanner – has provided detailed and accurate topographic surveys since the beginning of the twenty-first century (Carter et al., 2001). LIDAR  allows us to acquire more than 10.000 points per second from fixed or moving positions, leading to huge sets of data in a three-dimensional (3D) Cartesian coordinate system (i.e. X, Y, and Z coordinates), normally referred to as “point clouds”. This detailed and accurate representation of the terrain surface has been extensively used to characterize, monitor and model landslides (Rowlands et al. 2003; Mc Kean and Roering, 2004; SafeLand Deliverable 4.1. 2010; Jaboyedoff et al., 2012; Abellán et al., 2014).

The two main categories of LIDAR for mapping and monitoring landslides are static and mobile devices. Static devices are commonly named ground-based LIDAR or terrestrial laser scanners (TLSs).  Regarding mobile devices, the different instruments are normally classified depending on the platform configuration: either boats (Alho et al., 2009, Michoud et al. 2015), cars (Lato et al., 2009) or flying devices (Shan and Toth, 2008). The latter, also known as airborne laser scanners (ALSs), include LIDAR assembled on airplanes, but the device can also be mounted over unmanned aerial vehicles (UAVs), and helicopters, and can be adjusted to the shooting direction (Vallet and Skaloud, 2004, Li et al. 2011). Examples of point cloud density that can be derived from  TLS systems normally range from 100 to 500 points/m2, whereas ALS systems range from 0.5 to 50 points/m2.

Point clouds can be converted to surfaces through different surface reconstruction processes, usually involving Delaunay triangulation. For instance, the point cloud produced by ALS is normally transformed into digital elevation models (DEMs) or high-resolution DEM (HRDEMs), which provide very detailed geomorphological information (Chigira et al., 2004; Ardizzone et al., 2007).

The main achievements on the application of the LIDAR technique to landslide investigations include: (1) the improvement of landslide mapping and inventories using HRDEM (Abdulwahid and Pradhan, 2017); (2) a complete characterization of the 3D slope deformation and signs of precursory activity, allowing the forecasting of landslides failures (Rosser et al., 2007; Collins and Sitar, 2008; Abellan et al., 2010; Pedrazzini et al., 2011); (3) an accurate and detailed monitoring of rock failures (Rosser et al. 2005; Teza et al. 2007; Collins and Sitar 2008; Oppikofer et al. 2008; Prokop and Panholzer 2009; Abellan et al. 2010; Kromer et al. 2017; Caudal et al. 2017); (4) the improvement in rock slope characterization in inaccessible areas thanks to the automatic extraction of discontinuity orientation (Bornaz et al., 2002; Kemeny and Turner, 2008; Gigli and Casagli, 2011, Riquelme et al. 2014); and (5) the identification of geomorphic signatures of debris flows based on HRDEM and accurate erosion and deposit quantification (Cavalli and Marchi, 2008; Bremer and Sass, 2012).

Table 1: List of acronyms

Lidar and laser scanning techniques

1. History

The history of LIDAR technology is directly linked to the Light Amplification by Stimulated Emission of Radiation (LASER) evolution. In 1957, the physicists Charles Townes and Arthur Shawlow at Bell Labs investigated the development of optical Microwave Amplification by Stimulated Emission of Radiation (MASER), which later on became LASER in 1959 (Brooker, 2009). In May 1960, the first instrument which successfully produced a series of pulsed lasers was designed (Hecht, 1994). Less than one year later, the first LIDAR was manufactured for military purposes (Brooker, 2009). Then in the seventies, laser technologies became generalized for civilian applications such as surveying. Some examples include the use of electronic distance meter (EDM) in quarries and tunnel environments (Dallaire, 1974; Petrie, 1990; Petrie and Toth, 2009a).

At the same time, first ALS systems were able to measure the range of the emitted pulse with a precision of 1 m (Miller, 1965; Krabill et al., 1984), for altimetry and bathymetry purposes (Sheperd, 1965; Hoge et al., 1980,). Nevertheless, this technology was limited by the poor accuracy on the position and orientation of the airplane given by the Inertial Navigation Systems (INS). Afterwards, laser devices became more accurate, less heavy, less expensive and eye-safe. Moreover in the early nineties, the improvements on the navigation satellite constellations GPS-NAVSTAR (USA) and GLONASS (USSR) allowed for the overcoming of the limitations of former INS, considerably improving the ALS accuracy (Petrie and Toth, 2009b; Beraldin et al., 2010). In the late nineties, theoretical and practical aspects of ALS techniques were well known and several important works were published (Baltsavias, 1999a; Wehr and Lohr, 1999). Early applications of ALSs consisted in the production of DEM (Baltsavias, 1999b; Carter et al., 2001), the mapping of topographical changes of Greenland’s ice sheet (Krabill, et al., 1995), and the creation of a 3D model of an urban environment (Haala and Brenner, 1999). The mapping and modelling of landslides from HRDEM at regional scale started at the beginning of the twenty-first century century (Haugerud et al., 2003; Schulz, 2004, 2007; Van Den Eeckhaut et al., 2007; Haneberg et al., 2009).

During the last decade, the Geoscience Laser Altimeter System (GLAS) sensor illuminated the Earth with three lasers. One of these sensors was especially designed to monitor different environmental variables, such as polar ice sheet mass balances, vegetation canopy and land elevation with a vertical accuracy of 3 cm (Zwally et al., 2002; Abshire et al., 2005; Schutz et al., 2005). One of the most relevant topographic products derived from the ICESat mission was the 500 m pixel resolution DEM of Antarctica (DiMarzio, 2007; Shan and Toth, 2008). Meanwhile, a space-borne EDM device named Mars Orbiter Laser Altimeter (MOLA) was developed to observe Mars from 1999 to 2001 in order to survey its topography and atmosphere (Smith et al., 2001). This sensor  measured the surface of Mars during 15 months, obtaining a DEM of Mars with a resolution of a few square kilometres and a vertical accuracy of 1 m (Smith et al., 2001).

At the same time, pioneering triangulation-based TLS systems were designed and used for archeological purposes (Beraldin et al., 2000). Later on, ground-based LIDAR sensors have been used for surveying and monitoring deformations of urban structures (Gordon et al., 2001; Lichti, et al., 2002). Geological applications of TLSs have been experiencing fast developments since 2002, including: (1) extraction of orientations and roughness of rock slope discontinuities (Slob et al., 2002; Kemeny and Post, 2003; Fardin et al., 2004; Feng and Röschoff, 2004; Jaboyedoff et al., 2007; Sturzenegger and Stead, 2009; Garcia-Selles et al., 2011; Riquelme et al., 2014; ); (2) monitoring of volcanic activity (Hunter et al., 2003); (3) litho-stratigraphic modelling (Bellian et al., 2005; Buckley et al., 2008; Kurz, et al., 2008; Humair et al. 2015); (4) detection of rockfalls (Lim et al., 2005; Rosser et al., 2005; Collins and Sitar, 2008; Abellan et al., 2010); and (5) landslide monitoring (Teza et al., 2007; Collins and Sitar, 2008; Monserrat and Crosetto, 2008; Fey et al. 2015).

2. Instrument principle

2.1 LiDAR functioning

A LIDAR device consists of a combination of a laser rangefinder and a scanning mechanism which allows for the precise measurement of a distance to a target, and the orientation of the laser beam. The scanner device works through the internal rotation of one or two mirrors and/or the rotation of the whole device. Additional components normally include an electronic unit, an imaging device (e.g. digital camera) and specific software to control the whole system.

There are three main types of LIDAR, corresponding to three different ways of measuring the range; for instance, the distance along the line of sight (LOS) between the sensor and the terrain (see Fig. 1): (1) pulse LIDAR measures the time of flight (TOF) of a laser pulse, (2) phase LIDAR uses phase shift between emitted and received signal and (3) triangulation LIDAR uses a camera to locate the laser spot on the scanned surface (Vosselman and Maas, 2010).

Pulse LIDAR is based on the measurement of the time delay (TOF) of a laser pulse travelling from the source (e.g. TLS) to a reflective target (e.g. ground surface) and back to the source, as follows (Petrie and Toth, 2009a):

d = 1/2 c * Δt

Where d is the range, c is the speed of light in the air (3×108 m/s) and Dt is the TOF. This technique allows for centimetre accuracy measurements at several kilometres’ distance. Phase LIDAR allows for a more precise acquisition (some millimetres), but it only works for short ranges (less than 100m). Phase LIDAR uses a continuous amplitude or/and frequency modulated beam instead of pulses. The range is deduced from the phase shifts between emitted and backscattered signal for a couple of frequencies (Petrie and Toth, 2009a). Triangulation LIDAR estimates the range from two angles: the illumination angle of the laser beam and the observation angle of the camera (Beraldin et al., 2010). With a high accuracy (around 0.1 mm), triangulation LIDAR has very short ranges (less than 5 m). Pulse (or TOF) LIDAR is the main device used for landslide monitoring and mapping, since it is the only one that makes possible acquisitions for ranges longer than 100 m (up to a couple of kilometres for the most advanced sensors). There are few applications of phase and triangulation LIDAR for landslides, such as very detailed characterization of sliding surfaces or analogue modelling in the lab (Carrea et al., 2012).

Figure 1: (a) Sketch of LIDAR functioning principle showing TLS device, LOS and spot dimension; (b) Signal intensity vs. TOF for two different methods of measurement: full wavelength method vs. pulse method.

The footprint size depends on the range and the  beam divergence angle. The shape of the footprint on the surface depends on the local incidence angle, which is the angle between the LOS and the normal to the surface (Fig. 2). The range is measured at the peak of intensity of the backscattered pulse. If the surface is normal to the laser beam, it corresponds to the centre of the footprint (point P in Fig. 2). If the incidence angle is high (more than 70°) or the local relief is rough, the recorded point of location (point P’ in Fig. 2) can lie anywhere within the spot (Schaer et al., 2007; Lichti and Skaloud, 2010).

2.2 Multiple echoes

Even if strongly collimated, laser beams of LIDARs are affected by divergence; that is, the beam width increases with the range. Typical diameters of beam footprints are 30-50 cm for ALS at 1 km range, and 10 cm for TLS at 500 m range (Lichti and Skaloud, 2010). As a result, the consequences are that several backscattered pulses can correspond to a single emitted pulse (multiple echoes). For instance, when a pulse hits a tree, part of the pulse can be reflected by the top of the canopy, another part by branches and the last one by the ground (see Fig. 2). This characteristic of the beam to “penetrate” the vegetation is a key advantage of LIDAR compared to photogrammetry, in order to get ground elevation models of vegetated areas. However, a dense forest may prevent the beam from reaching the ground surface. Full wave LIDAR devices, usually ALS, provide a continuous record of the backscattered signal (full waveform). Most TLSs record only one or several discrete pulses. In landslide studies, the last pulse, (i.e. the pulse backscattered by the ground surface) is generally used. But it may be convenient to have other return pulses for vegetation removal purposes (Petrie and Toth, 2009a).

2.3 Other parameters: intensity and color

In addition to 3D surface point locations, LIDAR devices measure the signal intensity of the backscattered laser signal. Intensity mainly depends on the type of material, orientation of the slope, range and laser wavelength. Additional external cameras can be coupled with LIDAR during acquisition, allowing the assignment of a color attribute (i.e. RGB) to each point of the LIDAR point cloud. During ALS acquisitions, natural colors and/or infrared images are usually taken simultaneously. A single-lens reflex (SLR) digital camera is frequently used to provide natural colours to TLS point clouds, but more advanced imaging devices, such as hyperspectral cameras, can be used too (Kurz et al., 2011). Some of the main uses of intensity values or colour attributes for landslide studies include bedrock mapping, vegetation removal and interactive visualization of point clouds (Alba et al., 2011).

Figure 2. LIDAR footprint deformation (θ , incidence angle; N: normal to the surface). The point position is attributed to the centre of the beam (point P), but the range is measured at the maximum intensity of the backscattered pulse. Then, the actually measured point P’ is located slightly before the rock face on the LOS.

2.4 ALS vs TLS

ALSs are widely used, by either national topography agencies or private companies, to cover large areas for topographical surveying or forest management (Shan and Toth, 2008). The plane usually flies 1000 m above the ground, and the ALS records 1-2 points by ground square-metre (recent developments increase these values by one order of magnitude), with a position accuracy of 5-30 cm (Petrie and Toth, 2009b). Mobile techniques require a precise recording of the location and altitude of the scanning device during the acquisition, which is actually carried out by coupling the LIDAR with a Global Navigation Satellite System (GNSS) (usually a differential global positioning system [GPS]) and with an inertial measurement unit (IMU). Then the location of each point of a scan is recalculated as a function of the LIDAR position and orientation during a post-processing stage. Due to its heavy logistics, ALS is operated by professional surveying companies. Other smaller mobile platforms are also occasionally used for the laser scanning of landslides. For instance, LIDAR sensors can be assembled to helicopters, taking profit of its relatively low flying speed and flying heights close to terrain surface, which allows acquiring 3D information on cliffs from a an oblique view. This was used to scan a 2 km2 area around the Åknes rock slide (Norway), providing 20 measurement points per square meter  with a position precision of 5 cm (Blikra et al., 2006; Jaboyedoff et al., 2011). Also, offshore laser scanning from a boat is used for coastal cliff and river bank stability analysis, with a typical average point spacing of 10-20 cm (Alho et al., 2009). Finally, a fast growing technique is the setup of laser scanners on cars or trains in order to acquire high resolution ground models along transport corridors. This technique has been used to assess rock wall stability along roads and railways in Ontario (Canada) with an average point density of 100 points/m2 (Lato et al., 2009).

A TLS is a fixed position scanning technique that only requires the device setup on a tripod opposite the surface to scan within the maximum range limit. Because of this relatively easy implementation, TLS is usually operated directly by geoscientists in charge of the landslide investigation. With a high point density (up to 10,000 points/m2) and accuracy (about 1 cm), it is mostly limited by the availability of a good point of view, the maximum range of acquisition and the relatively small areas that can be covered in single acquisition due to a limited field of view (FOV) angle (for instance, 40° in TLS Ilris3D). Usually, in order to cover the whole area of interest, several scans have to be acquired from different points of view.

2.5 Spacing, accuracy, resolution and data types

The major differences between the two most commonly used techniques (i.e TLSs and ALSs) are: (1) average point spacing: metre scale for ALSs and centimetre scale for TLSs; (2) position and range accuracy: some tens of centimetres for ALS and about 1 cm for TLS; (3) point cloud geometry: as ALSs are usually shooting subvertically, they arevery efficient in order to produce horizontally projected surface, such as gridded DEMs. But it  provides a poor representation of steep areas like rock cliffs. In addition to this, as opposed to TLS devices,  ALSs are not able to have several ground surface elevation values of ground elevation (Z) for each horizontal position (X, Y), and thus they are unable to represent overhanging walls. Properly speaking, ALS products are 2.5-dimensional (2.5D) elevation surfaces and not full 3D representations of the terrain surface. As a result, most of ALS processing software is inefficient to work properly with point clouds from TLS acquisitions.

Measurement accuracy on single points depends on range, surface reflectivity and incident angle (Ingensand, 2006; Lichti, 2007; Abellán et al., 2009; Lichti and Skaloud, 2010). Inaccuracy of surface location and geometry is due not only to single measurement errors, but also to alignment and merging processes. This is particularly critical when several acquisitions are merged into one dataset. The quality of the final dataset must be carefully checked, because even very small inaccuracies can produce large uncertainties in change detection, as discussed by Schürch et al. (2011).

Point cloud resolution determines the level of detail that can be observed from a scanned point cloud (Pesci et al., 2011), where the laser beam width plays an important role (Lichti and Jamtsho, 2006). However, TLS resolution and point spacing are normally mixed up (Lichti and Jamtsho, 2006). It is recommended to place the optimal point spacing as 86% of the laser beam width at a given distance.

2.6 Data acquisition issues: occlusion and biases

Occlusion:  Due to relief roughness and natural variability, the acquisition of rock slope geometry from a single station is normally affected by the existence of shadow areas (also called occluded areas). In order to minimize occlusion issues, (Kemeny and Turner, 2008) different series of best practices and protocols are proposed for survey planning. For instance, occlusion can be minimized using data acquisition from multiple locations (Lato, et al., 2010; Stock et al., 2011).

Biases: Discontinuity characterization may be affected by biases depending on the intersection angle between discontinuity sets and the sensor’s LOS (Sturzenegger and Stead, 2009; Lato, et al., 2010): the density of those families with a normal vector parallel to the LIDAR’s LOS may be over-represented compared to the density of families with a normal vector perpendicular to the LOS of the instrument. Indeed, this is a similar effect observed by Terzaghi, (1962), in which the density of families with directions parallel to the slope orientation may be over represented compared with the families perpendicular to the slope.

3. Data treatment

A series of pre-processing steps are necessary in order to transform the RAW LIDAR data (unfiltered point cloud both in ALSs and TLSs) into real terrain points, as follows (see Fig. 3):

3.1 Full-waveform and automatic filtering

The first truly operational full-waveform (FW) LIDAR systems for airborne applications appeared in 1999 (Mallet and Bretar, 2009). This new generation of instruments is able to capture not only the discrete signal return but also the entire waveform. The first experimental full-waveform LIDAR system was originally developed for forestry canopy and building segmentation. At present, full-waveform data are mainly used for ALS applications in order to discriminate the surface terrain from forestry, which is of great interest for vegetation removal and DEM generation. Current research on full-waveform LIDAR data includes signal decomposition for object classification: terrain, vegetation, wires, buildings, and so forth.

The integration of LIDAR-derived 3D data with complementary information registered by other remote sensing methods is also in development. For instance, the coupling of LIDAR and InSAR data was successfully explored for displacement monitoring (Farr et al. 2008; Lingua et al. 2008; Roering et al. 2009). Other spectral attributes provided by multispectral imaging can also help improve interpretation and segmentation of 3D LIDAR data (Kurz et al. 2011).

3.2 Non ground points filtering (including vegetation removal)

Non-ground-point (e.g., vegetation, cables, birds and dust) filtering is a time-consuming task that is commonly performed manually. Although this approach may be straightforward, when non-ground points appear at small portions of the terrain, new semi-automatic methods aiming to classify ground points are being currently developed. These methods of classification, which are normally supervised by the user, are based on the identification of different values of the point cloud that allow us to separate ground from non-ground points. For instance, while some methods use the colour-coded value (RGB) or intensity of the laser return (e.g. Lichti, 2005; Franceschi et al., 2009), other approaches include the study of some geometrical parameters as single point statistics (e.g. Huang et al., 2000), local 3D point cloud statistics (e.g. Lalonde et al., 2006), multi-scale dimensionality criteria (Brodu and Lague, 2012) or using classifiers as support vector machine (Cristianini and Shawe-Taylor, 2000), expectation–maximization (EM) algorithms and/or Gaussian Mixture Models (Xu and Jordan, 1996). Although these methods are highly promising, manual vegetation removal is still currently necessary in most of the cases.

Figure 3. Workflow showing the main steps on data treatment.
3.3 Point cloud comparison

Point cloud comparison between sequential LIDAR datasets allows the detection of temporal differences in terrain morphology at multiple scales (Lim et al., 2005, Kromer et al., 2015). In order to make a comparison, several steps are required: (1) acquisition of a reference point cloud at initial time (t0); (2) construction of a reference surface at t0; (3) acquisition of successive data point cloud at different time lapses (t1, t2, etc.); (4) co-registration between reference surface and successive data point clouds (Besl and McKay, 1992; Chen and Medioni, 1992); and (5) change detection through the calculation of the differences for each period of comparison.

The single point distances between reference and successive point clouds are normally computed along a user-defined vector in one dimension. This vector can be defined both using a fixed orientation (Rosser et al., 2005; Girardeau-Montaut, 2006) or using different local vectors defined perpendicular to the rock face at each section of the slope, as described in Brodu and Lague (2012). Another method of comparison includes the use of the “shortest distance” between the data point cloud and the reference model. The real 3D deformation of single blocks in terms of rotations and translations can be quantified by using the roto-translational (RT) matrix technique (Collins and Sitar, 2008; Monserrat and Crosetto, 2008). More recently, a series of innovative approaches for surface change detection utilize semi-automatic techniques for 4D investigation of slope failures (Kromer et al. 2015; Williams et al. under review).

Regarding sign criteria, a positive value is normally interpreted as an increase of material (e.g. deposition) or a displacement toward the external part of the slope (pre-failure deformation). Similarly, negative values are normally interpreted as a loss of material on a given period (e.g. soil erosion and rockfalls).

Landslide applications

1. Landslides

Since the beginning of the 2000s, the ALS system has been widely used to produce HRDEMs at regional scale (Petrie and Toth, 2009b). This technique quickly became an essential document of a regional landslide inventory map (Mc Kean and Roering, 2004; Jaboyedoff et al., 2012; Petschko et al. 2016). By creating a hill-shaded surface, it is possible to highlight structures that cannot be seen from aerial photos or in field observations (e.g. Schulz, 2004; Van Den Eeckhaut et al., 2007; Haneberg et al., 2009; Burns et al., 2010) either due to landslide scale (from metric to kilometric), or location (in inaccessible or densely vegetated areas) (Fig. 4). Different approaches are usually employed for landslide mapping and identification, with various degrees of automation: (1) manual or expert-based approach, which focuses on the identification of landslides based on geomorphological evidences of crowns, main scarps, flanks, foot or toe and vegetation cover (Zaruba and Mencl, 1982; Cruden and Varnes, 1996; Soeters and Van Westen, 1996; Agliardi et al., 2001; Braathen et al., 2004); (2) automatic geomorphological features recognition, which extracts different information derived from HRDEMs, such as slope angle, curvature or roughness (Glenn et al., 2006).

When several generations of ALS scans are available, then change detection between HRDEMs can be used to characterize topographic changes. This technique has been employed to detect slope failures, soil erosion, slope displacements and terrain deformation (Burns et al., 2010; DeLong et al., 2012; Fey et al. 2015). Some examples of ALSs for coastal cliff monitoring and erosion analysis are discussed in Young and Ashford (2008) and Reineman et al. (2009). Furthermore, high resolution topography from airborne LIDAR can also be compared with less accurate historical aerial photographs, in order to analyse the kinematics of the landslide over a long-term period.

Figure 4. Mapping of a large-scale landslide detected using a LIDAR-based HRDEM. Bern Canton, northwest Switzerland (Data from Géodonnées © Swisstopo, DV084371)

2. Rock slopes

2.1 Structural analysis

The aim of the structural study is the detection and characterization of the main joint sets responsible for the fracturing of the rock mass. The analysis of the network of discontinuities is an essential step to study a rockfall scar or to assess the potential unstable areas of a rock slope. The measurements of the orientation (Fig. 5), undulation, persistence, spacing, opening, roughness and water presence of the main joint sets are usually taken in the field with classical methods, such as photography, direct observations and compass measurements (ISRM, 1978). TLS allows to perform most of these tasks faster, with a high accuracy, and even in inaccessible vertical areas (Kemeny and Post 2003; Jaboyedoff et al. 2009; Lato et al. 2010; Gigli and Casagli 2011; Longoni et al. 2012). Georeferenced 3D TLS point clouds allow us to extract the orientation of structures and to perform distance or volume measurements (Oppikofer et al., 2009; Olsen et al. 2015). Similar results can be obtained from HRDEM generated from aerial LIDAR, but with significant limitations imposed by the vertical shooting direction and by the size of the cells. The representation of structural features like planes or lines, which is classically done with stereoplots (ISRM, 1978), can also be automatically extracted using modern algorithms. Early studies used a manually selected section of the point cloud and calculated the best-fitting plane (Oppikofer et al., 2009; Sturzenegger and Stead, 2009), which can be laborious and time-consuming. Later on, some authors proposed the construction of 2.5D surfaces such as Triangular Irregular Networks (TIN) for the automatic calculation of the discontinuity orientation (Slob et al., 2002; Kemeny and Turner, 2008). More recently, techniques allow automatically computing local plane orientations directly from the point cloud (Ferrero et al., 2009; Garcia-Selles et al., 2011; Gigli and Casagli, 2011; Riquelme et al., 2014). In order to facilitate structural interpretations, the orientation and inclination of each point of a TLS point cloud, or of a DEM cell, can be represented using a unique colour (Jaboyedoff et al., 2007). The resolution of the datasets plays an important role in the analysis of discontinuity orientation: as discussed in Sturzenegger and Stead (2009), low-resolution datasets may lead to (1) a truncation of non-persistent discontinuity sets due to a lack of information and (2) a shifting of discontinuity orientation due to a non-realistic smoothening of the surface geometry.

Figure. 5. Discontinuity mapping on a Terrestrial LIDAR point cloud, rendered using greyscale intensity values (From Sturzenegger and Stead, 2009; with permission)

2.2 Monitoring of fragmental rockfalls

Due to the steepness of rock slopes, terrestrial LIDAR is more extensively used than airborne LIDAR for the monitoring of fragmental rockfalls. Moreover, terrestrial LIDAR provides an unprecedented level of detail at a site-specific slope, both resolution and accuracy ranging from several millimetres to a few centimetres. A pioneer approach for monitoring fragmental rockfalls along a section of marine cliffs in northeast England was developed by Lim et al. (2005) and Rosser et al., (2005). Since then, TLSs have become a widely used tool for automatic rockfall detection and volume calculation (Abellan et al., 2010; Stock et al., 2011; Collins and Stock, 2012; Carrea et al. 2014; Tonini and Abellan, 2014; Micheletti et al. 2017; Williams et al. under review). Using the TLS technique, Stock, et al., (2012) detected a series of progressive rockfalls that occurred in exfoliated granitic rocks, analysing how a given fracture event initiated subsequent adjacent failures. A common application of TLSs for rockfall monitoring includes the analysis of Magnitude-Frequency (MF) characterization of fragmental rock falls. Indeed, several authors have observed a scale-invariant pattern in a MF dimension under log-log scale (Rosser et al., 2005; ; Dewez et al., 2009; Lim et al., 2010; Santana et al., 2012) (Fig. 6), suggesting that a self-organized criticality (SOC) may rule rockfall phenomena (Hergarten, 2003). Some other applications include the monitoring of several million cubic metres of rockslides (Fig. 7) in order to quantify deformation rates and failure mechanisms (Collins and Sitar, 2008; Avian et al., 2009; Oppikofer et al., 2009; Ravanel et al., 2010), and the failure forecasting based on a given precursory indicator: either minor scale rockfalls or tertiary creep deformation (Rosser et al., 2007; Abellan et al., 2010; Royan et al., 2013; Kromer et al. 2017). More recently, the combination of web-retrieved images and LIDAR data for rock slope monitoring has been explored (Guerin et al. 2017; Voumard et al. 2017).

Figure 6.  Model comparison showing the changes (i.e., rockfalls) along time on La Cornalle study area (Vaud, Switzerland)(2) .

2.3 Rockfall susceptibility assessment

The simplest way to detect the potential rockfall source areas is by defining a slope angle threshold above which rockfalls are more susceptible to occur (Guzzetti et al., 2003). This method can be optionally coupled with other criteria such as the presence of cliff areas (Jaboyedoff and Labiouse, 2003). The slope threshold can be gathered from a detailed statistical analysis of slope angles, thus allowing to identify the cliff area (Strahler, 1954; Baillifard et al., 2004; McKean and Roering, 2004; Loye et al. 2009). A simple GIS approach developed by Baillifard et al. (2003) allows identifying potential rockfall source areas along roads according to the presence or absence of four parameters: faults, scree slopes, rock cliffs, and steep slopes. A great improvement related to the use of DEMs for the detection of rockfall-prone areas consists in the automatic realization of a kinematic analysis along the whole area (Wagner et al., 1988; Gokceoglu et al., 2000). This analysis allows the detection the topography areas where a discontinuity of a given orientation can lead to an instability. Using a standard classic stability criterion (Norrish and Wyllie, 1996) together with a statistical analysis of the kinematic tests, Gokceoglu et al. (2000) were able to produce probability maps of different failure mechanisms such as planar sliding, toppling and wedge sliding. The kinematic analysis criterion was applied to accurately detect the most unstable areas from TLS data (Fanti et al., 2013). Spacing and persistence of the joint sets, which can be determined both in the field and on LIDAR point clouds (Sturzenegger and Stead, 2009; Lato, et al., 2010; Riquelme et al. 2015), should also be taken into account in the stability calculations, as suggested by Jaboyedoff et al. (2004). The number of potential failures per cliff area can be calculated based on the spacing and on the trace length values, which considerably improves the rockfall susceptibility maps (Brideau et al., 2011; Matasci et al. accepted).

Figure 7. Different techniques for the quantification of a mass movement located on Montatuay mountain (Valais, Switzerland) using a ground-based LIDAR. (a) Picture of the study area; (b) Filtered point cloud of the study area; each point is coloured according to the differences calculated between the 2009 ALS DEM and the TLS point cloud captured in April 2011. (c) Rotated perspective of the LIDAR point cloud, including the manual selection of different blocks for subsequent tracking. (d) Velocity computation for each of the previously selected blocks; the straight line in log-log scale shows a progressive stabilization of the slope.

3. Debris flows

LIDAR is used for various tasks related to debris flows mapping, characterization and modelling. HRDEMs from ALSs are frequently used to identify and characterize initiation and propagation zones of debris flows (Staley et al., 2006; Conway et al., 2010; Lopez Saez et al., 2011; Lancaster et al., 2012) anddeposition fans (Staley et al., 2006), or to estimate the eroded and deposited volumes (Breien et al., 2008; Scheidl et al., 2008; Bull et al., 2010). Remarkably, Schürch et al. (2011) have used TLSs to detect surface changes along a debris flow channel in Switzerland, and Cavalli and Marchi (2008) have combined TLS and ALS to quantify sediment transport for debris flow events in the Austrian Alps, observing several metre changes between the two epochs (Fig. 8). ALS data are also used to estimate the volume of sediments that can be potentially entrained along the channels by a future event (Abancó and Hürlimann, 2014). Apart from geomorphic analysis, a DEM from laser scanning can also be used in debris flow propagation modelling. For instance, Stolz and Huggel (2008) have assessed the impact of HRDEM cell sizes (1, 4 and 25 m.) on modelled propagations, in particular the effect of resolution on debris flow confinement along recent channels.

4. Input for modelling

Mass movement modelling is the process of simulating its stability and/or its propagation across time. This analysis can be carried out using conventional representations of a terrain’s surface, either DEM or HRDEM. Note: current models do not utilize real 3D point clouds but 2.5D projections of the topography.

One of the first applications of LIDAR topographic data for landslide modelling was presented by Dietrich et al. (2001) using the SHALSTAB model. These authors showed that susceptibility mapping of shallow landslides can be considerably improved when using a HRDEM of 1 m cell size obtained from ALS. The results of this simulation demonstrated that the distribution of potentially unstable areas was closer to the inventoried landslides than when using low resolution DEM. In addition, the total surface of affected terrain was smaller. The same trend has also been observed in other available models such as TRIGRS, which includes more sophisticated infiltration models (Savage et al., 2003; Godt et al., 2008).

The optimal accuracy and resolution of the DEM for modelling should be carefully considered. Although HRDEM usually improves the modelling of the phenomena, most of the conventional modelling packages do not necessarily require more than a 5 to 10 m DEM resolution (Liao et al., 2011). Modelling using a higher resolution DEM can even introduce artefacts and non-realistic noises that can mask the main terrain features (Tarolli and Tarboton, 2006). For instance, ALS derived DEM used for mapping debris flow kinematics have a strong dependency on lateral spreading on the DEM resolution (Stolz and Huggel, 2008; Horton et al., 2013). It was found that the fine resolution produced geometric artefacts, degrading the quality of the modelling results. Similarly, the use of HRDEMs for the simulation of rockfall propagation can lead to significant differences when compared with lower resolution DEMs (Agliardi and Crosta, 2003; Janeras et al., 2004). For instance, the lateral dispersion of the trajectories and the kinetic energy vary with DEM resolution that controls the roughness of the terrain surface (Agliardi and Crosta, 2003). According to Agliardi and Crosta (2003), the maximum kinetic energy normally increases with the DEM resolution. However, the opposite result is presented in Jaboyedoff et al. (2006). This discrepancy may be explained by the fact that a higher resolution DEM has little impact on the accuracy of the results when other parameters (geology, vegetation, etc.) are poorly described (Fuchs et al., 2014). The resolutions of the LIDAR DEM are often too fine compared to the other required input data (Brideau et al., 2011).

Erosion and hillslope evolution modelling has been greatly enhanced by using HRDEM (Roering, 2008; Neugirg et al. 2016). However, most of the current packages for landslide modelling are not able to make the best use of high-resolution LIDAR data. The increasing power of computers will certainly lead to an extensive use of HRDEM as input for stability and run-out modelling. One can expect that the exploitation of full 3D data will be attained in the near future.

Figure 8. Surface differences between pre-event (ALS) and post-event (TLS) datasets (negative values for erosion). ( From Bremer and Sass, 2012; with permission).


The current knowledge concerning the mapping and monitoring of landslides is now far beyond what was expected by the end of the nineties. This is mainly due to the recent advances in remote sensing techniques (including LIDAR) together with the exponential growth of computational capabilities. For instance, laser scanning has allowed for the development of new techniques to monitor slope changes and slope movements, providing innovative ways for hazard qualification and a better understanding of the surface processes such as rockfall failures.

Dense 3D data has become popular thanks to LIDAR. Nevertheless, one of the present-day key issues is the scarce software to extract all the landslide-relevant information from the unprocessed LIDAR data, especially from full waveform data. At present, different modelling techniques benefit only partially from the full resolution of the LIDAR derived DEMs. This is linked to the fact that the density and the quality of the geometrical information are much higher than other parameters required for hazard assessment, such as geological maps. Despite this, the recent advances achieved on the study of rockfalls, and rock slopes indicate that HRDEMs possess a great potential for the development of new methods and techniques for the analysis of landslide phenomena.

New applications and techniques are now emerging and will be used as a routine in a near future: (1)  the development of new algorithms for the analysis of complex slope deformations in space and time (Kromer et al. 2015; Williams et al. under review); (2) the detection of slope movement in real-time and its integration on early warning systems (Olsen et al., 2012; Kromer et al. 2015; Williams et al. under review); (3) the possibility to rapidly acquire accurate 3D data along transport corridors using mobile mappers (Fig. 9); (4) the improvement of TLS technical capabilities, including maximum range, accuracy and point density; (5) the merging of web-retrieved images and terrestrial LIDAR data for slope monitoring (Guerin et al. 2017; Voumard et al. 2017);  (6) the acquisition of Earth surface topography by a new generation of satellite LIDAR such as ICESat-2 planned for 2018 and (7) the releasing of LIDAR data to the public domain in open repositories, including ‘Open Topography’ and ‘Point Cloud Library’ is helping raising awareness on the importance of 3D and 4D data treatment.

In conclusion, we can say that  LIDAR technology  has completely revolutionized the characterization, mapping and monitoring of landslides during the last decades. However, a series of great challenges still remain on the development of innovative tools to rapidly extract geohazard related information from LIDAR data.

Figure 9. (a) Example of discontinuities mapping along a highway at Pontarlier (East France) using an Optech Lynx Mobile Mapper LIDAR. (b) Picture of the study area.


The present work has been partially supported by the Swiss National Foundation project numbers 138015 (Understanding landslide precursory deformation from superficial 3D data), 127132 (Rockfall sources: toward accurate frequency estimation and detection) and 144040 (Characterizing and analysing 3D temporal slope evolution). In addition, the second author was funded by the H2020 Program of the European Commission during manuscript review [Ref: MSCA-IF-2015-705215].


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