Landslide analysis using laser scanners

The advent of light detection and ranging (LiDAR) (See Table 1 for acronyms) has revolutionized the study of landslides  and geomorphology because it provides an extraordinarily fine resolution topography (Carter et al., 2001). Electronic components and computers at affordable prices have made this technique widely available since the beginning of the 21st century. The number of geoscientific publications is increasing at a nearly exponential rate since around 1990  demonstrating its impact on geosciences (Abellan et al., 2016).

There are several types of LiDAR and several configurations available on the market such as the terrestrial LiDAR or terrestrial LASER scanning (TLS), handy portable and mobile LiDAR, LiDAR mounted on drones, and airborne LASER scanners (ALS), of which the LiDAR instruments mounted on  unmanned aerial vehicles (UAV)  is the latest advancement in LiDAR technologies. We use the term drone for UAV. These devices produce data in the form of point clouds (PC), which are composed of  three-dimensional (3D) Cartesian coordinates X, Y, Z, and the intensity of the reflected signal.  Associated sensors may provide other attributes such as RGB colors. These 3D points are obtained by processing the returned signal, i.e., the part of the emitted pulse backscattered towards the device. Several peaks of varying intensities are typically extracted from the returned signal (which is in the form of a full wave). These peaks correspond to echoes from different reflectors, such as the ground or vegetation (Figure 1). In landslide research, the last pulse, i.e., in principle, the one corresponding to the ground, is typically used. For this purpose, the signal is automatically treated to record only the last pulse, or the last pulse is extracted during postprocessing. A PC can be projected on a horizontal grid to obtain a digital elevation model (DEM) by interpolating the PC points. If only the last pulses are kept, and if they cleaned from nonground points, then the DEM is a digital terrain model (DTM). In that case, DEM or DTM can be considered to be a 2.5D PC because no overhangs can be represented.

Due to their resolution and precision, LiDAR techniques (Shan and Toth, 2018; Vosselman and Maas, 2010) represent a major advance for landslide sciences in recent decades (SafeLand Deliverable 4.1., 2010; Jaboyedoff et al., 2012, 2018; Abellan et al., 2014; Pradhan, 2017). Landslide mapping and characterization have been greatly improved with the use of hillshades or slope angle maps of LiDAR DTMs (Chigira et al., 2004; Gleen et al., 2006; Ardizzone et al., 2007). It has enabled the  extraction and characterization of the micromorphology of landslides (McKean and Roering, 2004). One of the main advances is the ability to characterize structures of rock faces that are not easily accessible and to provide joint characteristics (Kemeny and Post, 2003; Oppikofer et al., 2008; Sturzenegger and Stead, 2009). The high accuracy and resolution of LiDAR data provide an easy way to quantify volumes that are deposited by debris flows (Bull et al., 2010; Bremer and Saas, 2012) or removed by rockfalls (Carrea et al., 2014; Rosser et al., 2007). This data also enables the  quantification of changes in topography from erosion, mass movements (Collins and Sitar, 2008; Michoud et al., 2015) and rockfalls (Lim et al., 2010).

The velocity or/and direction of slope movements can also be extracted from the comparison of PCs (Teza et al., 2007; Travelletti et al., 2014; Fey et al., 2015). This data  provide displacement vector fields, which hast the potential to  lead to the development of near real-time continuous monitoring using LiDAR (Kromer et al., 2015, 2017) to forecast failure. The study of several rockfall deformations have allowed for the characterization of deformations before failure (Oppikofer et al., 2008; Abellan et al., 2010; Royan et al., 2014).

The modeling of landslides has benefitted from the input of LiDAR data. It has improved rockfall modelling, by adding more detail to the terrain roughness (Agliardi and Crosta, 2003), and provided input data for slope stability analysis (Brideau et al., 2011). Analysis of the susceptibility of hillslopes to shallow landslides (Dietrich et al., 2001) was also greatly improved by using high-resolution DTM from LiDAR data. For rockfalls, Matasci et al. (2018) developed a 3D susceptibility assessment based on PCs.

Additional examples of applications are given below, following a review of the evolution of LiDAR in recent decades. The basics of LiDAR techniques are described, because they give insights  into the capabilities and drawbacks of LiDAR technologies.

The principle of LASERs was established by Einstein (1917). The first functional LASER was designed by Theodor Maiman from the Hughes Research Laboratory in 1960. The first experiment of distance measurement by a range finder or “LiDAR” was performed a few months after construction of the LASER (Brooker, 2009). In the 1970s, LASER techniques were used to measure distance precisely using EDM (electronic distance measuring); the first stand-alone devices coupled with a theodolite (Petrie and Toth, 2018a) that often required a reflector, which is not the case for present-day LiDAR.

The first topographic survey of the Earth’s surface  was performed by a LASER altimeter deployed on an airplane, providing measurements with an accuracy greater than 30 cm at 300 m elevation (Miller, 1965). In 1984, a topographic survey along a profile was performed by NASA using an airborne oceanic LiDAR (Krabill et al., 1984). The major issue at this time was the positioning. The appearance of Global Positioning Systems (GPS) allowed workers to acquire topographic  transects over the Greenland ice sheet with an accuracy of  <20 cm (Krabill et al., 1995). The late 1990s experienced the rapid development of the use and appropriation of ALS (Baltsavias, 1999; Wehr and Lohr, 1999; Carter et al., 2001). For instance, ALS was applied to urban 3D data extraction (Haala and Brenner, 1999) and forest stand research (Naesset, 1997; Means et al., 1999).

The TLS developed slightly more slowly than ALS (Petrie and Toth, 2018b). TLS emerged in the late 1990s for surveying and monitoring structures (Gordon et al., 2001; Lichti et al., 2002). Closed-range TLS appeared slightly earlier (Kanade, 1987). Lichti et al. (2000) tested the capabilities of 3D surveys of TLS on a rock dam. After 2002, the number of publications based on that technology increased drastically (Abellan et al., 2016).

Progressively, TLS devices were coupled with global poisoning satellite systems (GNSS) and inertial measurement units (IMU), aiding the development of mobile LiDAR (Manandhar and Shibasaki, 2001). From 2010, the miniaturization of LiDAR, reduced instrument cost, and drone development made drone-LiDAR available and very powerful for geoscience applications (Lin et al., 2011; Petrie and Toth, 2018a). Furthermore, for a few years, handheld portable LiDAR using IMU has permitted short-range acquisition (10-50 m) by simply walking (ZEB-REVO by geoslam.com) (Figure 2). It may also include a GNSS such as the Pegasus Backpack system (Petrie and Toth, 2018c).

The “boom” of applications for landslide studies mainly began after the year 2000. The first development of slope characterization occurred before 1990 based on LASER-EDM instruments, which were used to monitor rock faces along profiles in mines (Petrie, 1990). One of the first applications of LiDAR was dedicated to the characterization of rock discontinuity orientations and sizes based on TLS (Slob et al., 2002; Kemeny and Post, 2003). The roughness of rock discontinuities was studied by Fardin et al. (2004). ALS hillshade DEMs were used, once sufficiently high-resolution DEMs were available to map and characterize landslides (Carter et al., 2001; Chigira et al., 2004; Haugerud et al., 2003; McKean and Roering, 2004; Ardizzone et al., 2007). Detection of sequences of rockfalls has been performed by calculating the differences between successive TLS PCs (Lim et. al., 2005; Rosser et al., 2005; Collins and Sitar, 2008; Abellan et al., 2010, Williams et al. 2018). Some authors monitored landslide movements and slope evolution based on time series TLS scans (Teza et al., 2007; Collins and Sitar, 2008).

The rich history of LASER scanning has been described in more detail by  Shan and Toth (2018), Brooker (2009) and Jaboyedoff et al. (2018).

Lasers and safety

The LiDAR technology is based on a LASER (light amplification by stimulated emission of radiation) with wavelengths from approximately 300 nm to 2000 nm. LASERs are based on electron excitation of specific atoms (often impurities) by lights. The electrons in atoms have specific energy levels that are called E0, E1, E2, and so on. LASERS  are able to excite electrons in a metastable energy level from E0 to E1 by transitions from a higher energy state E2, or by simulating another electron with the exact E1 energy level. This leads to the so-called population inversion, i.e., the number of excited electrons exceeds the number of electrons in the ground state E0 ; and the transition from E1 to E0 provides a light with a precise wavelength λ = hc/(E1-E0), where h is Planck’s constant and c the speed of light. The result is a coherent light that is collimated by different techniques. LASER sources mainly include solid-state material, gas, or semiconductors. Notably, LASERs can be dangerous for eyes and skin because of the collimation, coherence and subsequent intensities. The higher the power, the greater the potential injury to eyes and skin for any wavelength of UV visible and infrared light. There are different classes of LASER, with class 1 being the least dangerous and generally safe. Various types of LASERs are used for LiDAR, from class 1 to class 4. The recommendation for the use of LASERs is that even with class 1, eye exposure should be avoided (Petrie and Toth, 2018b).

LiDAR devices

There are several types of LiDAR. All send LASER pulses or waves, which may have different shapes. The receiver receives the range R from the time of flight (TOF) of the pulse, or by solving R based on signal amplitude modulations inducing frequency modulations. The position relative to the LiDAR is provided by the orientation of the beam, guided by one or two rotating mirrors or/and by the device rotation or motion. The position is either relative to the LiDAR ( β and γ angles) or located (georeferenced) using differential GNSS (DGNSS) coupled with an IMU to obtain the full position and orientation (rotation in all axis) of the device (Figures 1 and 3). Some LiDAR devices are now only equipped with an IMU that provides no georeferenced PCs but allows for working underground or inside buildings.

TOF LiDAR

Most long-range LiDARs use TOF (Δt) measurements to obtain the distance (Beraldin et al., 2010):

R=c/n *∆t/2 

where c is the speed of the light and n is the index of refraction of the air; n depends on the air temperature, humidity and pressure (and is nearly equal to 1). The LASER sends a pulse (the shape depends on the manufacturer) with a duration of the order of nanoseconds, which is diffused and/or reflected depending on the type of material scanned. The return signal can include several echoes, i.e., TOFs, which are detected by various techniques, such as the maximum of each peak or the exceeding of a predefined threshold; it typically uses a constant fraction detection, i.e., it sets the echo threshold at a given ratio of its maximum amplitude (Beraldin et al., 2010). Most devices provide echoes extracted from the full wave signal without providing the full wave signal itself.

LiDAR using phase measurements

LiDAR instruments with modulation of signal amplitude are mostly used for short-range applications (< 100 m). They can be used in specific situations to monitor movements due to their high accuracy. The distances are provided by solving a set of the following equation (Petrie and Toth, 2018a):

R=0.5 (Mλ_c+ Δλ_c)

In the case of amplitude modulation, λ_c is the wavelength of the measured modulated wave (longer than that of the LASER, which is the carrier wave), M is the integer number of the wavelength, and Δλ_c is a fraction of the wavelength equivalent to the phase shift Φ (Δλ_c = λ_c Φ/2π). This can only be solved if several wavelengths are used (Petrie and Toth, 2018a). Some LASERs allow for frequency modulation, by using a long regular modulation of several milliseconds. The TOF is estimated from the shift of frequencies between the emitted and returned signals. (Brooker, 2009; Beraldin et al., 2010). Another possibility to obtain R from the above equation is to modulate two frequencies in order to reach the conditions for which Δ λ ₁ = Δ λ ₂= 0 (Brooker, 2009).

LASER scanning based on triangulation

This type of LiDAR can be highly precise, but the range of measured distance does not exceed 5 m. It is based on a triangulation method. The beam of backscattered light is collected by a detector in an aperture distant from the LASER source (Beraldin et al., 2010). The detector is located at a precise and constant distance behind the aperture, providing the angle of the returned beam entering in the aperture. This allows for the determination of the distance and position of the points scanned from the angle of the incident beam and the distance between the LASER source and the detector.

Beam characteristics

A LASER beam is not fully collimated; the minimum divergence ω is given by Wehr (2018):

ω=2.44 λ/D

where D is the aperture diameter. The value is usually provided by the manufacturer based on the shape of the beam. The diameter d of the footprint or illumination spot, perpendicular to the beam, is given by Wehr (2018):

d≈D+ωR or d≈ ωR

which ranges from 30 cm to 2.7 m, assuming R = 1000 m for usual values of w for LiDAR : 0.3 – 2.7 mrad (Wehr, 2018). The power collected P_r by the receiver of area A can be approximately calculated assuming a beam illuminates a Lambertian surface larger than the beam footprint and is perpendicular to it. If ρ  is the reflectivity, M_a is the atmospheric transmission, and the transmitted power is P_T, then P_r is given by (Baltsavias, 1999):

P_r≈ ρ (M² A_r)/(π R² ) P_T

For example, assume M = 0.8, ρ = 0.5, R = 1000 m, A_r = π 0.1² = 0.00785 m² , and P_T = 2000 W; then P_r = 8.0 10^-10 and P_T = 1.60 10^-6 W. This can vary depending on the hypothesis about the reflection type. For more details about beam geometry and LiDAR acquisition techniques, please refer to Baltsvias (1999), Wehr and Lohr (1999) and Wehr (2018). In addition, if the illuminated surface is not perpendicular to the beam, then P_r must be replaced by:

P’_r ≈ P_r  cos ∝

This occurs in the case of a Lambertian diffused reflector where a is the angle between the beam and the normal to the surface, showing that the transmitted power decreases with increasing α (Carrea et al., 2016). Furthermore, an additional geometric factor must be taken into account; when a beam illuminates a surface S obliquely, the size of the footprint increases proportionally to:

S ~ ωR/cos α

This implies that an echo integrates several distances and creates an asymmetry in the returned pulse (Lichti and Skaloud, 2010).

Issues

The above brief theoretical background aids in understanding the limitations of the LiDAR techniques. The first problem is working with low-reflectivity surfaces, which drastically decrease the maximum distance that can be detected. This can depend on the surface color and roughness, as dusty surfaces have very low reflectivity. Depending on the wavelength, moisture and ice can also absorb the beam. In our experience, the color of the illuminated area can also marginally affect distance measurements (max. a few mm). The footprint has an impact because the return signal can contain a mix of several contributions, if there are different surfaces with different orientations within the illuminated area or slightly different distances from the LiDAR. If these surfaces are separated by enough distance, the LiDAR will detect several echoes. And if α is sufficiently large; the echo is asymmetrical and influences the intensity and the X, Y, Z point position.

Some less frequent issues can be linked to n the refraction index of the atmosphere. It  can change with temperature; which can be linked, for instance, to hot wind ascending along cliffs. Air moisture can also impact n. These variations in n modify the light velocity and can induce beam distortions leading to unprecise X, Y, Z point locations and coordinates. The device itself can also generate problems due to misalignments, moisture in the device, dirty optics, or a decrease in LASER power.

Different LiDAR configurations

Currently, the evolution is so fast in terms of LASER scanning that it is almost impossible to provide a complete up-to-date overview. The acquisition frequency is from 10 kHz to 1 MHz and is not an issue for most applications. The accuracy can be improved to reach millimeter accuracy for static acquisitions by TLS. When using GNSS and IMU for in-motion acquisition (ALS, Drone, Mobile), the results can vary but one can expect a data accuracy of a few centimeters. The choice of tool primarily depends on the acquisition needs, and the environment. For instance, a TLS device can be equipped to function as a mobile LiDAR, which is sufficient for many applications. No recommendations can be made because of the versatility of the systems. Drone –LiDAR and handheld portable LiDAR (Figure 2) will be increasingly used in the near future.

It is important to consider the influence of the acquisition viewpoints used during scanning. (1) Parts of the surface can be hidden by occlusion (Sturzenegger and Stead, 2009; Lato el al., 2010). With a static LiDAR, several scans can be acquired and merged to overcome this issue but drone – LiDAR may solve most of this problem because of its versatility, nearly 360° view and flexibility in the altitude of flight. (2) The point density can also vary greatly depending on the angle of view α. This can bias analyses of the surface orientation such as discontinuities in rock (Sturzenegger and Stead, 2009; Lato el al., 2010). To image in 3D, it is best to be closer to the perpendicular of the surface. As ALS can produce low-density PCs along nearly vertical rock walls.

Filtering

For landslide research, in most of the cases, the last echo is used to generate the point cloud or a DTM. Ocasionally in locations where dense vegetation completely hides the ground, the last pulse is not the ground. For most cases, it is necessary to remove the vegetation echoes from the PC by hand or using automatic or semiautomatic methods. If there are RGB values or infrared attributes for each point, they can be used to discriminate vegetation (Lichti, 2005). Brodu and Lague (2012) have proposed analyses of Eigenvalues of neighboring points to discriminate the vegetation from the more structured points using multiscale criterion. This is implemented in CloudCompare free software (Girardeau-Montaut, 2006) and several other methods exist as well.

Georeferencing and coregistration

Usually, point clouds or DEMs are transformed to obtain an absolute position in a known geographic coordinate system such as Universal Transverse Mercator (UTM). This is performed in several ways. Classically, it was performed using ground control points (GCP), which were used to adjust the position based on least square methods. Since the advent of DGNSS and IMU, GCPs are not necessarily required for most ALS, drone and mobile LiDAR (El-Sheimy, 2018).

A single PC can be georeferenced using GCP acquired using DGNSS. In landslide studies, the absolute position is often not necessary and approximate orientations and horizontality are sufficient because many applications examine the differences between two or more PCs or require only a precision of 1° in orientation. Thus, the second PC is aligned on the first one using fixed references, i.e., parts of the topography that have not changed (i.e., were unaffected by landslide deformation) using the iterative closest point method (ICP), which minimizes the Euclidian distance between the PCs of selected areas (Besl and McKay, 1992; Chen and Medioni, 1992). This procedure is called coregistration. This method is often used to georeference a TLS point cloud on a preexisting DEM even if it has a rather low resolution (25 m).

Coregistration is also frequently used  to merge several cloud points to obtain a full view of a topography from different points of view. The point clouds must overlap by at least 20 to 30% to obtain a good result and avoid alignment errors. Georeferencing or coregistration must be checked to compare successive acquisitions or merged scans, where they are overlapped, in order to detect progressive drifts or shifts in the data.

The first tools used are often represented using a hillshade DTM (Boucheny, 2009; Burrough et al., 2015) or slope angle map. Other types of shading such as Eye Dome Lighting (EDL) (Girardeau-Montaut, 2006) or ambient occlusion (Tarini et al., 2006) may improve the visualization of PCs in 3D or DEMs in 2D. Note that some of these tools are implemented in the free software CloudCompare (2018) (Noël et al, 2018). The literature about landslides and LiDAR is now so vast that this chapter only provides a partial view of  numerous valuable studies.

Mapping

Major contributions of LiDAR techniques for landslide studies were rapidly recognized (Carter et al., 2001; Haugerud et al., 2003; Chigira et al., 2004; Ardizzone et al., 2007). Landside mapping using LiDAR DTM (LDTM) is so widespread that it is impossible to conduct an exhaustive review. However, some points can be emphasized. Ardizzone et al. (2007) compared the results of an inventory based on field visits and aerial photos with an inventory based on LDTM. They showed that the LDTM greatly improved the limits of landslides. Attempts to characterize landslides based on LDTM using indexes such as roughness or the second derivative of the LDTM allowed for discriminating the landslide area (McKean and Roering, 2004), but this method may require too much tuning to be efficient.

Visual inspection of LDTM provides an excellent tool to map landslides. As there is no fixed methodology for such an approach, without a field visit, some geological structures may mimic landslide structures. The most important aspect is the ability to detect the incipient movement of landslides before catastrophic failures. By comparing pre- and post-LDTM, Chigira et al. (2013) showed that the large landslides that occurred during Typhoon Talas in Japan (September 2011) – inducing several causalities – were detectable on the pre-event LDTM. Scarps and depressions were visible before the event (Figure 4). This demonstrates that it is possible to identify the existence of landslide initiation that can be reactivated catastrophically because of significant triggering factors such as earthquakes or heavy rainfall.

Rock structure characterization and rockfall sources

In most cases, rockfall initiations are induced by the presence of unfavorable discontinuity orientations and operate by sliding, toppling or other mechanisms (Hungr et al., 2014). That is why structural analysis of rock faces are of primary importance.

The PC allows workers to visualize the discontinuity sets and identify them using many different approaches. They can be selected manually or by fitting planes automatically (Oppikofer et al., 2009; Sturzenegger and Stead, 2009; Gigli and Casagli 2011; Riquelme et al. 2014). Manual identification can be supported by the COLTOP scheme, which provides a unique color per orientation (Jaboyedoff et al., 2007: Assali et al., 2014). LDTMs have also improved understanding of the impact of structures on large rockslides (Jaboyedoff et al., 2009; Chigira et al., 2013).

Several attempts have been made to automatically compute discontinuity sets (Ferrero et al. 2009; Garcia-Selles et al. 2011; Gigli and Casagli 2011; Assali et al., 2014; Riquelme et al. 2014; Dewez et al. 2016). These methods follow different strategies, and each of them has pros and cons: they work rather well on well-defined structures, can be used for a first approach, and require a verification that the chosen set of parameters provide reliable results.

The data extracted from the structural analysis allows for detecting potential zones of instability or evaluating rockfall susceptibility using kinematic tests on PCs (Matasci et al., 2018) or on LDTM (Günther, 2003; Oppikofer et al., 2009; Jaboyedoff et al., 2012; Loye et al., 2012). A simple way to use PC or LDTM is to determine slope angle thresholds based on expert knowledge (Guzzetti et al., 2003; Frattini et al., 2008). More systematic approaches have been developed by Loye et al. (2009), by decomposing the slope angle histogram, extracted from an LDTM, using one or several Gaussian distributions to obtain the morphological elements. It is typically easy to identify rock walls and find the slope angle threshold above which they represent the majority of the morphology.

Volume estimation

Landslide volume estimation is mainly performed using the difference between two LDTMs within the perimeter of the landslide. The LDTMs must first be coregistered. This is often performed for debris flows when pre- and post-event LDEMs are available (Bull et al., 2010; Bremer and Saas, 2012). If only one LDTM exists, the landslide volume depends on the construction method of the failure surface or the reconstruction of the topography before the event. One method is to use the sloping local base level, which allows for reconstructing a quadratic surface mimicking the topography or the failure surface. This surface is built by an iterative computation within the limits of a landslide (Jaboyedoff et al., 2019).

Rockfall volume estimation is usually performed using two successive PCs that are coregistered to determine the differences. This can be performed in several ways. Grids can be created in the direction perpendicular to the rock face around the failure zone, and the differences in these oblique DEMs can be determined by summing the DEM of difference (DoD) within a given perimeter, or using a threshold value for the difference (Rosser el a. 2007; Loye et al., 2016). Other solutions are to triangulate meshes of PCs, eventually smoothed, and calculate the difference between surfaces (Guerin et al., 2017). Another method uses two PCs and, within a given perimeter, creates a PC of the block. The space in between the selected points can be filled completely by tetrahedrons to obtain the block volume (Carrea et al., 2014).

Several studies describe rockfall activity. The results typically show a power law distribution for the volume (Rosser et al., 2007; Royan et al., 2014; Loye et al., 2016), which is very important for rockfall hazard assessment (Hantz et al., 2003). The errors linked to such evaluations have been detailed in Lane et al. (2003) and Schürch et al. (2011).

Landslide monitoring includes several different strategies that lead to a temporal evolution of maps, distances, and other information. They include simple comparisons of periodic LiDAR acquisitions or real-time monitoring. The simplest approach is the detection of surface changes, which can be performed by a simple closest point method (Girardeau-Montaut, 2006). The real-time monitoring is based on frequent acquisition and fast analysis of distance changes in the LiDAR.

Surface changes

The DoD allows to estimate the activity of landslides via movements between two different LDTM acquisitions (Burns et al., 2010). This widely used method is fast and allows to rapidly check if acceleration has occurred. It also provides information to understand the deformation and behavior of a landslide.

The activity of the landslide of Pont Bourquin initiated in 2007 near Les Diablerets in the Swiss Prelaps. It is monitored by periodic TLS acquisitions since 2007 and up to now. The comparison of two PCs demonstrates that material is accumulating at its front (Bièvre et al., 2018) (Figure 5). This indicates a mass transfer in the middle of the landslide and an accumulation zone at the bottom.

Multitemporal PCs enable estimations of the rate of cliff retreat from landslides and/or rockfalls along coasts (Rosser et al., 2007; Collins and Sitar, 2008; Lim et al., 2010; Michoud et al., 2015). It is possible to acquire PCs along transportation networks from a vehicle moving faster than 60 km/h to monitor rockfall activity and structures (Lato et al., 2009; Jaboyedoff et al., 2018).

Detailed analysis of rock mass displacements or sequential failures provides information about the failure mechanisms. The monitoring of rock columns in the Åknes rockslide (Norway) by comparison of two PCs demonstrated very small toppling components by extracting the roto-translation matrix (Oppikofer et al., 2009). Similar behavior has been demonstrated in the Dolomite (Italy) (Viero et al., 2010). Abellan et al. (2009) showed that the precision of displacement measurements can be far more accurate than  a centimeter and, in our experience, the precision can reach millimeters. Careful monitoring of a rockfall sequence in Yosemite National Park showed that exfoliation joints can propagate by redistribution of stress in a sequence of successive step-by-step failures (Stock et al., 2012). These studies showed how PCs can open new avenues to understand the mechanisms of slope failure (Prokop and Panholzer, 2009).

Potential methods for real-time monitoring

The methods presented in this section can be applied in support of real time monitoring; by recognizing shapes in successive PCs, displacement time series can be generated. It is possible to obtain correspondence between small domains of the PCs and determine the vectors of displacement (Teza et al., 2007). This has also been performed based on the recognition of a target represented by cubes on vegetated landslides (Franz et al., 2016). This has the advantage of providing monitoring that replaces total stations without requiring on-site prism targets. Other monitoring strategies consist of coupling PCs and image pattern recognition methods on associated hillshades (Travelletti et al., 2014; Fey et al., 2015). This is performed using the correspondence between the 2D image and the PC.

Time series of PCs are efficient in quantifying the deformation path leading to rock mass failure based on filtering PCs and measuring the distance between successive acquisitions (Abellan et al., 2010; Royan et al., 2014). Frequent acquisitions and analysis of displacement allowed for forecasting rockfall during the collapse of the eastern flank of the Eiger (Switzerland) (Oppikofer et al., 2008).

Real-time LiDAR monitoring has been developed for mining companies. Using 4D filtering techniques and averaging PCs. Kromer et al. (2017) used this approach to monitor and detect slope movements. Monitoring of the Sechilienne (France) rockslide allowed for following the prefailure displacement of a rock mass (Figure 6). Unfortunately, LiDAR is very sensitive to bad weather, which can restrict the use of such technique.

Monitoring a cliff of shales, sandstones and carbonates in the UK every hour for 10 months showed that the contribution of small very frequent detachments of less than 0.1 m³ contributed 98%, instead of 67%, if the interval between acquisitions decreased to less than <1 h instead of 30 days (Williams et al., 2018). This demonstrates that some rockfall events are the result of several smaller events and, if the acquisitions are separated by a longer time lag, the apparent number of rockfalls decreases. This is demonstrated by the steeper slope of the power law distribution of the volumes for frequent acquisitions (Figure 7).

High-precision LDTM has resulted in improvements to landslide models. The first improvement was linked to observations that can be made using PCs or LDTM, which provide details to create complex models of stability (Brideau et al., 2011). The highly detailed topography allows for refining shallow, simple landslide modeling (Dietrich et al., 2001; Baum et al., 2005). The area of high susceptibility surface is reduced compared to coarser DTM because of the high resolution, improving the slope maps and flow accumulation resolution. Using PCs, the susceptibility of rockfall failures can be assessed based on kinematic tests associated with simple geomechanical models. On a rock wall in Yosemite National Park, Matasci et al. (2018) showed that the most active source areas correspond to the high susceptibility zones detected independently on PCs.

The most demonstrative example of improvements is from rockfall modeling. Agliardi and Crosta (2003) showed that the terrain roughness-induced dispersion of the rockfall radically changes rockfall hazard maps. The energy of falling debris is also modified significantly. Recent models take advantage of the fine resolution of the PC instead of the LDTM. The point cloud allows to identify rockfall sources with more precision than the LDTM. This is because the point cloud, unlike the LDTM, can represent vertical or even overhanging faces (Fig. 8). It also has the advantage of including real 3D roughness induced by boulders (Noël et al., 2016). This implies that the rockfall models are closer to “reality”.

As demonstrated earlier, LiDAR has significantly impacted landslide research and practitioners. Although LiDAR acquisition is more expensive than photogrammetric methods, such as structure from motion (SfM), LiDAR has major advantages that often justify its use. It is especially efficient for vegetated areas (Figure 9) and, because LiDAR systems include the georeferencing in most configurations, these methods can be complementary. The applications listed above depend on the distance of acquisition and georeferencing methods, so the level of accuracy and precision can be very high, from millimeters to a few centimeters, and no improvements are needed for standard landslide and geomorphic studies. Notably, the monitoring of landslide movements will be enhanced by improvements in accuracy and precision of point cloud data to understand processes and improve early warning systems (EWS).

The applications shown here may be used by landslide researchers and practitioners as soon as LiDAR PCs or LDTMs are available. This adds incredible value to landslide studies, but such data is best  used in combination with field investigations. LiDAR data provide guidance for field investigations and valuable information that cannot be extracted from the field; that is why it is recommended to combine approaches. As shown earlier, LiDAR has improved rockfall, debris flow, landslide, and rockslide characterization, which are important for hazard assessment and risk management.

From a more general perspective, the integration of data from many origins is often the base of any study, for which LiDAR-derived products are strong basic data, particularly for geographic information systems (GIS). The combination  of LiDAR and SfM PCs can be an excellent compromise to complete TLS data with SfM acquired by drones or ALS data with SfM data from vertical rock faces. In addition, such mixed documents are very important for communication (Figure 9), which is becoming increasingly important for risk management.

There is potential for development using the information contained in PCs of landslides. For rock mass characterization, there is a wide scope for new and innovativeresearch. For instance, automated characterization of discontinuities is not completely explored (Riquelme et al., 2015). In addition, there are theoretical issues to resolve such as: how to define discontinuity spacing and trace lengths using a PC coupled with images? For landslides, the extraction of potential failure surfaces based on the geomorphic characteristics must be investigated. Based on monitoring using multi-temporal PCs or LDTMs, there is room to develop automatic 3D shape tracking for landslide deformations; a few methods are available but are not necessarily applicable to any type of movement.

Characterization of landslides using LiDAR, DTMs, and PCs remains challenging because, at present, there are gaps in our  understanding of how to compute the strain at the surface of landslides, although some studies are available (Teza et al., 2008; Travelletti et al., 2014). Landslide modeling must exploit the full resolution of PCs or LDTMs, with the exception of rockfall modeling, which has already taken advantage of the high resolution.

There will likely be improvements in LiDAR accuracy, precision and acquisition duration in the upcoming years. There are now several types of LiDAR such as TLS, ALS, Mobile LiDAR, LiDAR mounted on drones (Figure 10) and handheld systems. The link with GNSS, IMU and RGB images will be improved and generalized. These advances depend on the development of LiDAR for autonomous cars (Bimbraw, 2015), as the market is so large. This indicates that development and prices will decrease drastically but may not affect long-range devices. The last improvement will come from satellites, as there is no LDTM over the whole earth. A possible first step is the recently launched ICESat-2 satellite (September 2018), designed to monitor the Greenland icecap and provide data for other regions (https://icesat-2.gsfc.nasa.gov/), which will be valuable for regions that lack good DEMs.

We thank the risk group of the Institute of Earth Sciences of the University of Lausanne for the discussion and are especially grateful to Martin Franz, François Noël and Jérémie Voumard who provided us new versions and modified figures. We also thank the RIEGL Company for providing drone LiDAR images. The English language of this manuscript was edited by American Journal Experts, we thank them for their support. We are thankful to Professor Simon Mudd for his fruitful comments on this chapter.

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Further reading

Zwally, H. J., Schutz, B., Abdalati, W., Abshire, J., Bentley, C., Brenner, A., Bufton, J. et al., 2002. ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. J. Geodyn., 34, 405–445.