Emerging techniques and impact of humanactivities in landslide risk management:
3D analysis and human induced landslides
Michel Jaboyedoff- Risk-group - Institute of Earth Sciences, University of Lausanne
GEOPOLIS – 3793 CH-1015 Lausanne – Switzerland(Michel.Jaboyedoff@unil.ch; +41 79 752 35 15; www.unil.ch/risk/)

Nowadays landslide risk management is facing two important challenges. The first is toimprove landslide predictability (hazard, early warning systems (EWS)). This is madepossible thanks to the recent progresses in geosciences enabled by the new affordable
technologies and computing power. The second challenge is to properly address the
problem of landslides caused by the increasing impact of human on environment,
especially in fast changing countries.
In landslide science, the emergence of high resolution earth surface 3D imaging has
played a major role by providing new viewpoints on the slope processes. This technique
has improved landslide mapping, enabling to better characterize landslide structures
and also to refine numerical modelling. Furthermore, the increasing number ofpermanent landslide monitoring systems (MS) provide new information about failure
mechanisms. These systems can include InSAR, LiDAR, GPS, pore water gauges, rain
gauges, inclinometers, etc. These integrated approaches have permitted to demonstratethat most of the mountainous areas are affected by landslides or large slope
deformations. As an example, several studies have shown that catastrophic landslides
had signs of precursory deformation visible on hi-resolution digital elevation model(HRDEM). Coupling MS and surface deformation analysis enables to characterize theprocesses that lead to failure, which is fundamental to improve hazard mapping and
EWS implementation. With this approach, the mechanisms of failure of several
rockslides have been refined (Eiger, Aknes, Randa, Turtle Mountain, etc.), but also of
small

rock

failures(Seychilienne,

Catalonia,etc.).

Moreover,non-permanent

deformations are now identified as potential sources of fatigue of the rock mass. For
instance, thermal effect on shallow rock failure starts to be well documented in−28−

Yosemite. Another example is the effect of groundwater table fluctuations on a large
rock wall in Norway inducing cycles of deformation of a few millimetres of amplitude(demonstrated by InSAR). Similar analyses are also applied successfully to landslides insoils such as earthflows, like Super Sauze in France. Finally, passive seismic monitoringis also a new tool to forecast the reactivation of earthflows by monitoring the decrease of
the surface wave velocity.
All these new techniques are promising in order to improve landslide risk management,
thanks to a better understanding of dynamic processes from landslide initiation(criterion for mapping) to failure forecast (basics for MS). But that understanding mustalso include the issues linked to landslides induced by human activities. This is criticalconsidering the fast changes in land uses. In several contexts, anthropic activities are
one of the major cause of landslides. Even if the causes are often bad engineering
practices, several cases have demonstrated that slope failures can be caused byenvironmental changes that are not considered to be systematically leading to themodification of the slope profile, water circulations, etc. This means that landslide
hazard assessment must clearly include the impact of past, present and futureanthropic changes. Based on the work of K. Terzaghi, seven questions must be answered
to tackle the potential impact of human on slope environment.
To conclude, the challenges that face the landslide risk management are, in my opinion,linked to the possibility to develop a deeper understanding of the processes permitting abetter detection of landslide prone areas and an improved forecast of failure or
reactivation of landslides. In addition, any operation in slopes must take into accountthe full impact on the neighbouring environment in order to prevent human induced
landslides.

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DOI 10.1007/s11069-010-9634-2
ORIGINAL PAPER

Use of LIDAR in landslide investigations: a review
Michel Jaboyedoff • Thierry Oppikofer • Antonio Abellán •Marc-Henri Derron • Alex Loye • Richard Metzger • Andrea Pedrazzini

Received: 12 February 2010 / Accepted: 24 September 2010 / Published online: 19 October 2010
 The Author(s) 2010. This article is published with open access at Springerlink.com

Abstract 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 LIDARderived 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.
Keywords Landslide  LIDAR  Laser scanner  High-resolution DEM  Topography
analysis  Structural analysis

1 Introduction
Remote sensing techniques for landslides investigations are undergoing rapid developments. The possibility of acquiring 3D information of the terrain with high accuracy andhigh spatial resolution is opening up new ways of investigating the landslide phenomena.
Recent advances in sensor electronics and data treatment make these techniques affordable.
The two major remote sensing techniques that are exponentially developing in landslides
investigation are interferometric synthetic aperture radar (InSAR) (Fruneau et al. 1996;
Colesanti et al. 2003; Squarzoni et al. 2003), and light detection and ranging (LIDAR)(Carter et al. 2001; Slob et al. 2002; Haugerud et al. 2003; Slob and Hack 2004). InSAR
techniques are usually ground-based (Stow 1996; Tarchi et al. 2003) or satellite-based
M. Jaboyedoff (&)  A. Abellán  M.-H. Derron  A. Loye  R. Metzger  A. Pedrazzini
Institute of Geomatics and Risk Analysis, University of Lausanne, 1015 Lausanne, Switzerlande-mail: michel.jaboyedoff@unil.ch
T. Oppikofer
Geological Survey of Norway, 7491 Trondheim, Norway

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Nat Hazards (2012) 61:5–28(Carnec et al. 1996; Singhroy 2009), and only rarely airborne. InSAR techniques aremainly dedicated to the detection and the quantiﬁcation of small displacements over large
areas. LIDAR (or laser scanning) provide high-resolution point clouds of the topography
and has several applications that range from mapping (Ardizzone et al. 2007; Jaboyedoff
et al. 2008a) to monitoring deformation (Gordon et al. 2001), landslides or rockfall displacements (Teza et al. 2007; Oppikofer et al. 2008; Abellan et al. 2010) to landslide insoils (Jaboyedoff et al. 2009a). The joint use of terrestrial laser scanning (TLS) and SAR
techniques may help to understand landslide phenomena, as is discussed in Teza et al.
(2008).
LIDAR is mainly used for landslide investigation to create accurate and precise highresolution digital elevation models (HRDEM) in raster grids or triangulated irregular
networks (TINs), which are 2.5D representations of the topography, or in true 3D point
clouds with a high density of information. This density mainly depends on the position of
the sensor: metric to decimetric resolution for airborne laser scanning (ALS) and centimetric to millimetric resolution for terrestrial laser scanning (TLS) (Shan and Toth 2008).
Helicopter-based ALS can give a higher resolution than aircraft-based ALS and especiallyallows orientating the scanner in all directions (Vallet and Skaloud 2004).
The number of publications discussing the use of LIDAR in landslide studies has grownconsiderably during the last decade (Derron and Jaboyedoff 2010). Main applications
range from mapping to monitoring and modelling slope mass movements. Two books wererecently published: ‘‘Topographic laser ranging and scanning’’, by Shan and Toth (2008)and ‘‘Laser Scanning for the Environmental Sciences’’, by Heritage and Large (2009). The
former provides a comprehensive compilation of the principles and fundamentals of
LIDAR technologies, reﬂecting the state-of-the-art of all the physical aspects of the sensor(components, calibration, waveform analysis, quality control of LIDAR data, ﬁltering andfeature extraction techniques, etc.). Although some of the chapters may be slightly focusedon the geomatics ﬁeld (and hence, out of the scope for the readers of this review), the ﬁrst
three chapters by Petrie and Toth (2008) are an excellent basis for the understanding of
instrumental issues: Chapter 1 describes basic (and not so basic) principles of laser proﬁling and scanning; Chapters 2 and 3 perform a detailed description of the main commercial and research systems currently available on the market for aerial and terrestrial
LIDAR, respectively. As regards the book edited by Heritage and Large (2009), it provides
numerous LIDAR applications to environmental sciences, being of great value forresearches in the ﬁeld of geomorphology, geostatistics, remote sensing and Geographic
Information Systems (GIS). The contributions can be mainly divided into mapping(ﬂooding areas, vegetation), geometrical aspects (study of geological outcrops and rivergravel bars) and monitoring (rocky and sandy coastal erosion, river dynamics, etc.).
Unfortunately, up to now there has been little effort to review the advances of LIDAR
applications on landslides. This paper tries to ﬁll this gap.

2 Laser scanning and/or LIDAR techniques
2.1 Short overview about LIDAR techniques and rangeﬁnder
The acronym LASER (or laser) stands for Light Ampliﬁcation by Stimulated Emission ofRadiation. A laser is a device that produces and emits a beam (or a pulse series) of highly
collimated, directional, coherent and in-phase electromagnetic radiation. As shown below,
laser systems can be used for the acquisition of large amounts of 3D information of the

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7Fig. 1 Principles of laser scanner data acquisition, showing the example of TLS

terrain at an extremely fast recording rate (Fig. 1). Although LIDAR is a term commonlyused in literature, laser scanner or laser range ﬁnder are preferred, which makes the linkwith past methods (laser ranging and laser proﬁling).
Laser scanning was developed in two ways, depending on the position of the sensor:
airborne-based for ALS and ground-based for TLS. Basic principles and processing of theformer are well-known issues since the end of the 1990s. A complete review of ALS
systems can be found in Wehr and Lohr (1999) and in Baltsavias (1999). An updated andclear reference is the already commented book of Shan and Toth (2008). Literature on TLS
has rapidly grown during the last 5 years (2005–2010). Petrie and Toth (2008) are a goodreference for understanding basic principles of TLS. Detailed guidelines that should rule
the use of the TLS in all the projects managed by the California Department of Transportation was written by Hiremagalur et al. (2007). Although an environmental scientist
may have found this lecture out of the scope of his ﬁeld of research, it provides useful
information on standards and speciﬁcations for the quality control of different TLS.
Finally, a brief review of LIDAR (and other conventional remote sensing techniques)applied to landslide monitoring is found in Prokop and Panholzer (2009) and in SafeLand
Deliverable 4.1 (2010). A combination of both techniques, ALS and TLS, is frequently
used to solve some acquisition problems for example the shadow areas when a scanned
surface is parallel to the line of sight (LOS) of one of the sensor.
2.1.1 Measurement principle
A laser scanner consists of a transmitter/receiver of laser beam and a scanning device. Two
different methods for range determination are possible (Wehr and Lohr 1999): phase andpulse method. The former allows more accurate range determination but suffers from alimited range (Petrie and Toth 2008). Alternatively, the latter allows a greater range, and istherefore implemented in most ALS and TLS used for Earth surface observations,
including landslide studies (Wehr and Lohr 1999; Baltsavias 1999).
Both airborne- and ground-based sensors send out laser pulses that get back-scattered byvarious objects (ground surface, vegetation, man-made constructions etc.) and record thereturning signal. Pulsed laser scanners use the measurement of the time of ﬂight (Dt) of thelaser pulse to compute the distance (2 9 d = c 9 Dt) and mirrors or mechanics to

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orientate the laser beam in a well-deﬁned direction. Knowing this line of sight (LOS)
direction and the attitude of the device (pitch, roll and yaw) allows determining theposition Dx, Dy, Dz of a reﬂective surface relatively to the device. The range is measured
without any artiﬁcial reﬂectors over an area equal to the laser’s spot dimension (beamwidth projected on the topography), which increases with distance due to the laser beam
divergence (Petrie and Toth 2008) (Fig. 1).
The absolute position of an ALS sensor is deﬁned by a Global Positioning System(GPS), and its attitude is recorded by an inertial system. Finally, the point cloud coordinates are estimated (x ? Dx, y ? Dy, z ? Dy) with an accuracy of around 15 cm (1r) byusing ground control points (Habib 2008). In practice, the position and orientation (attitude) of the TLS sensor are not deﬁned precisely in the ﬁeld, unless the TLS device is also
coupled with an inertial system and a GPS. This makes necessary a further georeferencingof the TLS point cloud. This process is carried out by aligning the TLS point cloud, either
using a set of ground control points or using another already georeferenced point cloud.
2.1.2 Analysis of back-scattered laser pulses
Most ALS and a few of the TLS systems use the full waveform of the laser signal. The fullwaveform of the back-scattered pulse includes some information on the properties of thereﬂexive objects within the beam area. This allows for instance removing part of thevegetation effect (Duong 2006; Harding 2008; Heritage and Large 2009). Alternatively,
range measurement using TLS can be undertaken without discriminating the full waveform, but recording several back-scattered pulses from one emitted pulse. This proceduremakes it possible to distinguish and to ﬁlter intermediate-range objects (vegetation, wires,birds and other undesired points). For landslides applications, the last returned pulse isusually the most relevant. Nevertheless, if the vegetation is too dense the laser beam will
not reach the ground, and direct visibility (LOS) between the TLS and the scanned surface
is of course necessary. However, these limitations are common with other remote sensing
techniques (surveying, radar and/or optical).
It must be said that one of the main issue in laser scanning is the removal of thevegetation (Harding 2008) either by automatic methods or manually. If vegetation is dense
sometimes the DEM created from ALS data is not better than a standard resolution DEM(approx. 25 m grid size) obtained from topographic maps.
2.1.3 Accuracy, resolution and point density
The typical accuracy of the laser instrument is ±1.5 cm, within maximum distances ofabout 800–1,000 m (Manetti and Steinmann 2007). Nevertheless, the instrumental accuracy is usually lower in practical applications due to unfavourable conditions such as:
poorly reﬂecting or very rough surfaces, parallel incident angles, bad weather conditions(rain or hot wind or fog), very bright ambient conditions, excessive range, etc.
Laser scanner’s resolution is a parameter that determines the level of detail that can beobserved in a point cloud. It is divided into range and angular (or spatial) resolution(Kamerman 1993; Lichti and Jamtsho 2006): (a) Range resolution is the ability of arangeﬁnder to resolve two objects on the same LOS; (b) Angular resolution is the ability toresolve two objects on adjacent LOS. This parameter is governed by two variables:
sampling interval (also called point spacing, which is user deﬁned) and laser beamwidth(which depends on the distance and the instrument). The estimation of the TLS resolution
is usually misunderstood and commonly interpreted as equal to the point spacing, obviating

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9

the inﬂuence of the laser beamwidth. Nevertheless, when sampling interval is lower thanthe laser beamwidth, ﬁne details are in fact blurred. A detailed explanation of this effectcan be found in Lichti and Jamtsho (2006). They proposed the use of the effective
instantaneous ﬁeld of view (EIFOV) as a measurement of the resolution of the TLS. The
lower sampling interval proposed by these authors is 86% of the laser beamwidth.
For ALS, the point density usually ranges from 0.5 to 100 pts/m2, while for TLS it
ranges usually from 50 to 10,000 pts/m2. In addition to the position, the intensity of thereﬂected signal is acquired. This parameter mainly depends on the beam wavelength, thetype of material (colours and roughness of the reﬂective surface), soil moisture and angle
of incidence (Ingensand 2006; Lichti 2007; Pesci et al. 2008; Voegtle et al. 2008).
The wavelength ranges between 500 and 1,700 nm (Wehr and Lohr 1999; Jaboyedoff
et al. 2009a). For TLS, the acquisition range increases usually with longer wavelengths,
from a few metres to 2,000 m if reﬂectivity of the surface is high (Jaboyedoff et al. 2009a).
2.1.4 Point cloud alignment
Alignment of the point cloud usually consists of a two-step process: a preliminary registration by a visual identiﬁcation of homologous points and an optimization of the alignment using an Iterative Closest Points (ICP) procedure (Besl and McKay 1992; Chen andMedioni 1992). Using this algorithm, the differences between points are iteratively reduced
by a minimization of a mean square cost function. Alternatively, the alignment overdiscrete parts of the slope can be carried out using the Roto-Translation techniquedescribed by different authors (Teza et al. 2007; Monserrat and Crosetto 2008; Oppikofer
et al. 2009).
2.2 Short historical overview
Early attempts to acquire Earth surface data sets by ALS were performed along proﬁles
using only an inertial system and other positioning technique for topographic mapping(Miller 1965; Krabill et al. 1984) with devices developed for water depth measurement(Hoge et al. 1980) or for altimetry purposes (Shepherd 1965). One of the ﬁrst applications
in geosciences of ALS (i.e., using inertial system coupled with GPS) has been to evaluate
the topographical changes of the Greenland ice sheet (Krabill et al. 1995, 1999).
TLS appeared later than ALS at the end of the 1990s (Large and Heritage 2009). This
instrument is an evolution of the electronic distance meter (EDM) and of total station,
which beneﬁts of the earlier ALS developments (many companies manufacturing ALS are
now also selling TLS). EDM was developed during the late 1950s and early 1960s(Dallaire 1974). These instruments allow for a very accurate distance measurement.
Nevertheless, these one-dimensional measurements were not suitable for the measurementof the 3D coordinates of single points (x, y, z). Furthermore, reﬂectors are usually required(Bromhead et al. 1988). By contrast, TLS measurements include real 3D information (x, y,z coordinates) plus intensity (I), and in some cases full waveform. In addition, TLS devices
are reﬂectorless (Petrie and Toth 2008) allowing for a broad coverage of the surveyed area.
The early tests for environmental application were made in the late 1990s and in the earlytwenty-ﬁrst century (Lichti et al. 2000).
A systematic bibliographic survey was carried out by Derron and Jaboyedoff (2010)
looking within the databases ISI Web of Knowledge and GEOREF. In this paper, thegrowth of the yearly production of publications and citations related to LIDAR and geosciences or landslide is analysed through logistic functions. The main outcome of this

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analysis is that the community of geoscientists working with laser scanning techniques ispresently building an innovative and useful knowledge and that this fast development has
the potential to continue for the next 5–10 years.

3 Classiﬁcation of the main applications
TLS and ALS can be used for different types of investigation, which can be subdivided in 4
types (Table 1): (1) Landslide detection and characterization; (2) Hazard assessment andsusceptibility mapping; (3) Modelling; (4) Monitoring. This classiﬁcation may haveoverlaps, but allows us to classify the main objectives of the research. Our classiﬁcation isbased not only on the type of mass movements described by Cruden and Varnes (1996) but
also on the processes linked to these phenomena. The selected approach to study the mass
movement is strongly dependant on the scanned object: (1) we refer as ‘‘rockfalls’’ to all
the slope processes formed in rock slopes, from the detachment of the instability to the
propagation; (2) we refer as ‘‘landslides’’ to all the material that suffer large scale and slow
rate of deformation, in either soft, ﬁne materials (soils) and/or weathered and weak rocky
material, including deep-seated gravitational slope deformation (DSGSD) (Agliardi et al.
2009); ﬁnally, (3) we refer as ‘‘debris-ﬂows’’ to slope movements formed by debris type ofmaterial, moving down the slope as a ﬂow. This classiﬁcation makes sense in terms of
LIDAR analysis, because the analyses of the geomorphic features are not necessarily scaledependant. As an example, it can be impossible to distinguish a DSGSD from a classicallandslide only by inspecting a hillshade relief without knowing the scale.

Table 1 Classiﬁcation of the different applications of LIDAR technologies in landslide studies
Landslide

Rockfall

Debris-ﬂow(1) Landslide detection
and characterization(In Sect. 4)• Mapping of geomorphic
features• Rock face imaging
and characterization• Calculate
discontinuity
orientation• Detection ofmobilizable
volumes• Hydromorphic
characterization(2) Hazard assessment
and susceptibility
mapping (In Sect. 5)• Mainly as support for
mapping• Some attempts forsusceptibility andhazard mapping (notyet achieved)• Input for mapping
hazard based on
geomorphologic
approach(3) Modelling(In Sect. 5)• Classical modelling tools are • High-resolution DEM • Input for spreading
modelling
for trajectory
not able yet to handle hugemodelling
3D information density• HRDEM allow more accurate
landslide modelling byimproving their geometricalcharacterization(4) Monitoring(In Sect. 6)• Monitoring of surface
displacements• Volume budget

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• Monitoring of surface • Sediment budget• Monitoring ofdisplacement
morphologic• Detection of prechanges in channel
failure displacements(under development)• Quantiﬁcation of
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4 Detection and characterization of mass movements
4.1 Landslide
4.1.1 ALS
ALS permits an improvement in the landslide inventory mapping and more generally ofgeological mapping (Borlat et al. 2007), by increasing the resolution of the landslide
contours, permitting to identify scarps and displaced material. Nevertheless, the same
conceptual methods to detect landslides are still usually employed (Soeters and Van
Westen 1996; Keaton and DeGraff 1996). The morphological features of the landslides(e.g. scarps, mobilized material, foot) are easy to delineate based on hillshades of the
produced HRDEM (Carter et al. 2001; Haugerud et al. 2003; Ardizzone et al. 2007;
Jaboyedoff et al. 2008a; Corsini et al. 2009). This approach does not replace ﬁeld investigations, but it changes the ﬁeldwork methods, which become part of the validation
processes of a landslide inventory produced by HRDEM analysis (Fig. 2).
The detection can be performed either manually (e.g. expert criteria such as eye
inspection) or automatically (e.g. feature detection such as the curvature or roughness ofthe topography). McKean and Roering (2004) showed that spreading of topographic orientations within an earth-ﬂow is higher than that in undisturbed area. An extension of thismethod using Fourier analysis of the relief demonstrates that this method can classify more

Fig. 2 La Frasse landslide (Switzerland): a 3D view of the shaded relief of the main landslide extent. Three
sectors moving faster than the surrounding areas were identiﬁed at the toe of the landslide. b Cross-section
through the fast-moving area showing the modelled failure surfaces obtained using the SLBL method(Jaboyedoff et al. 2004b). c 3D view of the volume of the faster area 3 (Data from swisstopo)

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Fig. 3 Hillshade view of the upper part of Les Pics instability (Muraz, Switzerland). Gravitational features
as scarps, trenches and depressions forming the ‘‘Les Pics DSGSD’’ could be clearly identiﬁed. The real
instability extend could be better deﬁned using LIDAR-derived DEM than in aerial photographs or from
topographic maps (Data from swisstopo)

than 80% of total area that has already suffered a landslide (or not) in western US (Boothet al. 2009). Geomorphic criterion based on geostatistics (Glenn et al. 2006) succeeds in
differencing landslide activities. Nevertheless, such methods are not easy to apply due tofollowing reasons: (a) expert criteria are needed (interpretation of the results) and (b) the
presence of vegetation may degrade the DEM quality (Kasai et al. 2009). In addition, thedetailed morphology provided by HRDEM coupled with geologic information can allow
forecasting of the sensitive zones even if the triggering is human induced (Schulz 2007).
The interpretation of DSGSD is also improved by using HRDEM (Agliardi et al. 2009;
Oppikofer 2009; Corsini et al. 2009) especially because it permits a precise delineation of
the limits (Fig. 3).
4.1.2 TLS
Only few applications exist on landslide characterization (Rowlands et al. 2003), but inseveral cases TLS is a very useful tool to interpret the landslide mechanism, delineate
limits and estimate volumes (Corsini et al. 2009; Dunning et al. 2009). Jaboyedoff et al.
(2009a) shows a case study of circular landslides in silty sediments in river banks.
4.2 Rockfall
TLS and ALS techniques are responsible for a great improvement in rock slope characterization, mainly because the rock instabilities are principally controlled by structures thatare at least locally planar. The TLS applications characterizing rock mass are numerous

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13(Slob and Hack 2004; Jaboyedoff et al. 2007; Oppikofer et al. 2009; Sturzenegger andStead 2009; Armesto et al. 2009; Lato et al. 2009), but still quite rare for ALS-DEM.
4.2.1 ALS
Structural analysis at regional scale are proposed using ALS-derived HRDEM using theprinciple of COLTOP (Jaboyedoff et al. 2004a, 2007, 2009b), which allows to attribute a
unique colour for each topographic orientation. This permits also to create stereonets on
the basis of plane orientations measured directly on the point cloud (Fig. 4). The main setsof structural features have been identiﬁed in several rockslides in Norway (Derron et al.
2005). This approach also allowed a fast structural characterization of the hardly accessible
Mount Steele rockslide in Yukon, Canada (Brideau et al. 2009) and the reinterpretation offormer rockslide such as Frank slide (Sturzenegger et al. 2007a; Froese et al. 2009). At
regional scale, the fjord valley of Tafjord in Norway has been investigated in order tocharacterize the present and former rock slope instabilities, such as their volume andmechanisms (Oppikofer 2009).

Fig. 4 ALS-DEM from Yosemite National Park (by courtesy of G. Stock). a COLTOP coloured DEM.
b Identiﬁed discontinuity sets. c Discontinuities D1 and D2 forming a wedge that promoted instabilities.
d Relative density of daylighting D1-D2 wedges the topography

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4.2.2 TLS
TLS characterization of rock slopes are among the ﬁrst applications using TLS for slope
mass movement, mainly to obtain accurate slope proﬁle and discontinuity sets orientations(Bornaz et al. 2002; Slob et al. 2002). A technical report of the US Department ofTransportation (Kemeny and Turner 2008) provides a brief introduction of the maincommercial hardware and softwares and best practices for ﬁeldwork campaigns.
Several studies are proposing different ways to characterize discontinuity sets, which
can be mainly classiﬁed as (a) using ﬁtting planes (Fernández 2005; Abellan et al. 2006;
Oppikofer et al. 2008; Sturzenegger and Stead 2009), (b) using TIN surfaces as indicatorsof the plane orientations (Feng et al. 2001; Slob et al. 2002). A description of the methodology employed for rockmass characterization using the commercial software Split-FX
is provided in Kemeny and Post (2003) and Kemeny and Turner (2008). Several authors(Lato et al. 2009; Abellán et al. accepted) have shown that there is an optimal number ofpoints for the point cloud to get reliable results depending on the precision of the device: asparse TIN suffers from smoothening over corners and edges, creating inaccuracies inplane representation. On the other hand, a dense TIN is affected by data noise. (c) Other
techniques allow for the automatic delimitation of a set of neighbourhood points characterized by the same normal vector. As a result, the calculation of the orientation of the
discontinuity planes is obtained. The COLTOP technique (Jaboyedoff et al. 2004a, 2007,2009b; Derron et al. 2005) permits to visualize the orientation of each discontinuity set bya unique colour, which makes the approach very similar to a ﬁeld data acquisition(COLTOP-3D, www.coltop3d.ch). The extracted data permit to analyse the rock instability
mechanisms (Janeras et al. 2004; Jaboyedoff et al. 2008b; Oppikofer et al. 2009).
Different authors have pointed out the existence of two types of biases in the determinationof the orientation of discontinuities (e.g. Sturzenegger and Stead 2009; Lato et al. 2009): (a)scale bias, when discontinuity sets are smaller than the spatial resolution (point spacing) and(b) orientation bias, when the incidence angle of a given data set inﬂuences the spatial
resolution, and hence, the number of poles in stereonet when doing automatic procedures.
Another area of application that is merging in rock slope characterization is theroughness determination (Fardin et al. 2004; Haneberg 2007; Tatone and Grasselli 2009)
aimed to better quantify the well-known methods for rock mass rating systems (Romana
1993). Nevertheless, certain limitations are linked to the achievement of this aim, such as
the intrinsic instrumental accuracy, resolution and the inﬂuence of different scanner
positions on the ﬁnal results (Sturzenegger et al. 2007b). Although these limitations, further studies are necessary due to the great potential of such methods assuming selfsimilarity hypothesis (Fardin et al. 2004).
4.3 Debris-ﬂow
ALS-derived DEM gives an accurate topography and permits to identify morphologies thatare signiﬁcant in order to characterize the past activity. To this end, morphometric indicators such as roughness, and curvature of the slope are used (Staley et al. 2006; Cavalli
and Marchi 2008). This approach gives quite good results in discriminating the past debrisﬂow activity (Cavalli and Marchi 2008) but presents some limitations (Staley et al. 2006).
The variograms inspection gives promising results for characterization of the landforms(Trevisani et al. 2009).
TLS has also been used to assess limited sections of channels, allowing very detailed
mapping and section proﬁles evolution (Oppikofer 2009; Theules et al. 2009).

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5 Hazard assessment, susceptibility mapping and modelling
5.1 Landslide
5.1.1 ALS
One of the earliest applications of ALS-DEM in landslide susceptibility mapping andmodelling is the work of Dietrich et al. (2001) (see also Montgomery et al. 2000). They
showed that a 1 m resolution ALS-DEM improved greatly the results of the model
SHALSTAB, an inﬁnite slope stability model. One of the most achieved study of landslide
hazard assessment using ALS-DEM is the one made by the USGS on the Puget Sound area
(Washington state; Baum et al. 2005). In this study, ﬁeld investigation is coupled with
HRDEM analysis and shallow landslide modelling using TRIGRS (Savage et al. 2003), a
sophisticated inﬁnite slope model including transient inﬁltration. This study demonstrates
improvements provided by ALS-DEM for hazard mapping. Along the same line, Haneberg
et al. (2009) have shown convincing results for shallow landsliding, coupling an inﬁnite
slope model with geomorphic characterizations using an ALS-DEM.
Of course, the traditional hazard mapping by ﬁeld investigation is improved by ALSDEM, especially for inventories but also for characterizing the susceptibility using morphometric indicators (Chigira et al. 2004; Haneberg et al. 2005; Jaboyedoff et al. 2008a).
We can also expect that it will soon possible to infer hazard by comparing several ALSDEM of different epochs. This was already proposed by Chigira et al. (2004) who
emphasized the possibility to use two inventories to assess hazard, but in their application
the ﬁrst inventory was still produced by ﬁeld work only.
The ALS-DEM allows a detailed delimitation of the landslides on the topographicalsurface (van den Eeckhaut et al. 2007; Corsini et al. 2009). Using these limits and theDEM, it is possible to infer the failure surface in 3D. For instance, the sloping base level(SLBL) concept (Jaboyedoff et al. 2004b, 2009b) makes it possible to obtain very quickly aplanar or curved quadratic surface by using simple geometrical rules. For example, Fig. 2
shows La Frasse landslide in the Swiss Alps and the expected failure surface of a fastmoving part.
5.1.2 TLS
TLS is mostly used for characterization or monitoring of landslides and not really for
hazard mapping. Nevertheless, TLS can greatly improve the site-speciﬁc modelling,
especially when the topography is changing and if only a poor DEM is available. For
example, a detailed slope proﬁle extracted from a TLS-DEM has permitted to identify thedestabilized zone of an earth-ﬂow using a standard Bishop analysis (Jaboyedoff et al.
2009c).
5.2 Rockfall
5.2.1 ALS
Regional rockfall hazard or susceptibility assessment from source area to rockfall propagation using ALS-DEM is not yet implemented as a routine, although all the pieces of the
puzzle are present. The ﬁrst issue is to locate the source areas. This is often performed at

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Fig. 5 Example of the slope angle distribution (SAD) computed on a ALS-DEM: a SAD of an undisturbed
scree slope (outlined in white) showing that the slope angle values follow a normal distribution around an
average value close to 35. b SAD of an alpine valley decomposed into 5, Gaussian distributions; ‘‘A’’
indicates the threshold angle above which the slope belongs dominantly to the cliffs and is therefore
considered as potential rockfall source area; ‘‘B’’ indicates the mode of GDMU steep slopes. The two cliffs
Gaussian distributions represent two families of bare rock cliff faces lithologically distinct (modiﬁed after
Loye et al. 2009)

regional scale using simple slope angle thresholds (Guzzetti et al. 2003; Frattini et al.
2008). But, as shown by Loye et al. (2009), more information can be potentially extracted
from the slope angle distribution. By decomposing the histogram of slope angles of an area
into Gaussian distributions, it is possible to identify the threshold above which the relief isdominantly composed of cliffs (Fig. 5). This threshold depends on the type of bedrock, thepresence or absence of a soil cover and the DEM resolution (Loye et al. 2009). Thismethod makes it possible to discriminate efﬁciently the true cliffs from the one drawn on
topographic maps. In addition, as proposed by Günther (2003) and Jaboyedoff et al.
(2004c), structural analysis performed on ALS-DEM (Fig. 4) can be the basis for kinematic test. As discussed in Janeras et al. (2004), the accuracy of the results is greatly
improved by using a HRDEM.
Hazard assessment requires often performing trajectographic modelling in order todelineate the propagation area. The modelling has been greatly improved by the ALS-DEM(Agliardi and Crosta 2003; Lan et al. 2007) by introducing more dispersion in the propagation (Fig. 6), as demonstrated by Agliardi and Crosta (2003). In addition, the kinetic
energy proﬁle is greatly modiﬁed when the resolution of the DEM is increased. Bothparameters are of primary importance for hazard mapping and dimensioning of mitigation
measures.

Fig. 6 Inﬂuence of the DEM mesh size in the scattering of the rockfall trajectories (after Agliardi and
Crosta 2003)

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5.2.2 TLS
A great improvement in the rockfall hazard assessment is the monitoring of fallen blocks
from a cliff between two epochs or more (Rosser et al. 2005; Lim et al. 2006; Lato et al.
2009; Abellan et al. 2010; Pedrazzini et al. 2010). Indeed, such approaches allow for thequantiﬁcation of the magnitude and activity of rockfalls in a cliff. Rosser et al. (2007)
quantiﬁed the increase in rockfall activity some months previous to the occurrence oflarger events. Similarly, when this activity decreases, it may be inferred that the potential
instability becomes stabilized during a given period (Pedrazzini et al. 2010). In addition,
new methods of mobile terrestrial LIDAR along road cuts already produce detailed 3Dmodels that provide data for structural analysis. If such acquisitions are repeated over time,they can be used to detect the most active areas (Lato et al. 2009).
5.3 Debris-ﬂow
As suggested by different authors (e.g. Jakob 2005; Horton et al. 2008), process-basedmodelling of debris-ﬂows is a difﬁcult task because of the complexity of the phenomenon
and the variability of controlling factors. Detailed topography (slope angle of channels aswell as the channel proﬁle) together with the available channel material can be criticalparameters (Hungr 2005).
These are the major input from both ALS- and TLS-DEM, and they are critical to assess
properly the volumes that can be mobilized. Based on the topography curvature of ALSDEM and other properties, it is also possible to extract potential source areas for debrisﬂows (Horton et al. 2008). The second improvement provided by ALS-DEM is thepossibility to improve the modelling of propagation, despite the fact that computing time isvery high if a full resolution ALS-DEM is used to compute the models on large areas.
Similarly to rockfall modelling, Stolz and Huggel (2008) have shown that the modelling ofdebris-ﬂows can be really improved by using HRDEM. Nevertheless, it must be emphasized that as a channel is a dynamic body and breakouts are always possible, a full
resolution approach can also be a pitfall for models. For instance road embankments on the
HRDEM are diverting ﬂow, while in reality road bridges often exist to let the ﬂow pass.
Nevertheless, ALS-derived HRDEM with reduced resolution (10 9 10 m) permits toobtain very good results for propagation calculations at regional scale (Horton et al. 2008).

6 Monitoring
6.1 Landslide
Before the existence of remote sensing techniques such as LIDAR, RADAR and photogrammetry, landslide monitoring was based on single-point measurements, using either
GPS or total stations. But obtaining the complete ﬁeld of displacements for the wholelandslide is of great help to understand landslide kinematics and failure mechanism(Fig. 7). Laser scanning is nowadays a common tool for displacement monitoring even if
few published papers exists. The basic principle is the same for both ALS- and TLS-DEM,
requiring at least two epochs of HRDEM acquisition. Nevertheless, as monitoring requires
both high resolution and high precision data sets, most of the works have been done up to
now using TLS-derived HRDEMs. The results are either vectors between two points (orcommon areas) or distances between two data sets (point to surface comparison) either in a

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Fig. 7 Example of interpretation of landslide mechanism by using two successive TLS-DEMs performedon the 16 March and 12 April 2006 along the Sorge river (Switzerland). a Altitude difference between thescans and b interpretation of the cross-section showing a rotational landslide (modiﬁed after Jaboyedoff
et al. 2009a)

user-deﬁned direction or as shortest distance (Hausdorff distance) between the two surfaces. This difference calculation allows for the computation of volume differences, as is
discussed by different authors (Bitelli et al. 2004; Chen et al. 2006; Corsini et al. 2007;
Teza et al. 2007; Dewitte et al. 2008; Jaboyedoff et al. 2009a; Prokop and Panholzer 2009;
Mueller and Loew 2009; Avian et al. 2009; Baldo et al. 2009).
The standard deviation of the measurements between two epochs can be quite high,depending on multiple factors, such as the quality of the TLS data sets, the density ofpoints, the existence of vegetation, the roughness of the relief, the quality of the alignment
between the scans, the relative or absolute positions of the TLS and the variation of thesurface of the terrain between the two epochs. For instance, these values were not less than
5 cm at 100 m for Prokop and Panholzer (2009), or 3 cm for a 300 m range for Teza et al.
(2008). In our recent experiences, it is possible to compare TLS data sets acquired consecutively and reach values of the standard deviation of around 1 cm for close ranges (lessthan 100 m) and high point densities. This is in agreement with the results obtained on the‘‘fast’’ moving landslide of Super-Sauze (France) where it was possible to measure
velocities of 3.2 cm/day (Travelletti et al. 2008).
The low number of monitored landslides using ALS-derived HRDEM is due to thelower precision of ALS compared to TLS (*15 cm vs. *1.5 cm). As a result, comparisonof such HRDEM requires a longer period between two acquisitions to obtain reliable
results. Nevertheless, using classical photogrammetry on ancient aerial photos, it is possible to obtain displacement maps simply comparing objects locations on the pictures andon the ALS-DEM (Roering et al. 2009; Baldo et al. 2009).
6.2 Rockfall
Monitoring surface displacements in rock slopes is, in a way, simpler than in soil slopes
because the displacements can be considered as rigid body transformations (Teza et al.
2007; Monserrat and Crosetto 2008; Abellan et al. 2009; Oppikofer et al. 2009). As aconsequence, it is easier to identify and decompose the movements as a combination oftranslations and rotations of different parts of the slope. A good example for monitoring
surface displacements is the huge slope displacements that occurred at the eastern ﬂank of
the Eiger (Switzerland), which was moving by more than 70 cm/day during the paroxysmal activity (Oppikofer et al. 2008). The detailed displacement analysis using the rigid
body transformations of the upper part of the Åknes rockslide in Norway combined with

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detailed structural analysis allowed to derive the possible landslide mechanism (Oppikofer
et al. 2009). Recent developments improved this methodology by introducing Euler angles
to describe the rotational movement of the Cinque Torri rock towers in Italy (Viero et al.
2010).
Together with these monitoring results, the possibility to link spatial and temporal
prediction of rockfalls constitutes a great challenge for landslide monitoring. Indeed, twodifferent precursory indicators are currently being investigated: (1) the increase in rockfall
activity before the ﬁnal collapse, shown by Rosser et al. (2007) for a hard rock cliff at thecoast of Staithes (UK); (2) the detection of precursory displacements is investigated forlarge rockfalls (e.g. 125 cm/day preceding a 170,000 m3 partial collapse of the Eiger
rockslide, Oppikofer et al. 2008) and fragmental rockfalls (e.g. centimetric displacements
preceding rockfalls with few to hundred cubic metres, Abellan et al. 2009, 2010) (Fig. 8).
Although in certain cases precursory movements can be of the same order of magnitude
than instrumental errors, different authors have observed that errors can be considerably
reduced by taking into account the information of the neighbouring points, i.e. noise
reduction by ﬁltering or averaging (Lindenbergh and Pfeifer 2005; Monserrat and Crosetto
2008; Abellan et al. 2009). As an example, it was shown that it is possible to detect
millimetric surface displacements in an outdoor experiment, even if single points had ahigher standard deviation (Abellan et al. 2009).
6.3 Debris-ﬂow
As we already mentioned, it is possible to monitor the channel changes using TLS(Oppikofer 2009; Theules et al. 2009) (Fig. 9). Recently, Scheidl et al. (2008) were able tocompare two deposition areas of debris-ﬂows that occurred during the heavy rainfall ofAugust 2005 in Switzerland using two ALS-DEM acquisitions. They obtained good results
but still with high errors that can reach more than 50% for eroded volumes, which isprobably overestimated because the errors are computed pixel by pixels.

7 Discussions and perspectives
The real use for landslide studies of HRDEM from ALS or TLS started in 2000, althoughsome developments started in the 1960s. For ALS this comes from the fact that GPS has

Fig. 8 Spatial prediction of rockfalls through the detection of pre-failure deformation using TLS. a Study
area and analysed area; b comparison of TLS data sets during a time span of 303 days. Colour scalefrom -0.1 up to 0.1 m (after Abellan et al. 2010)

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Fig. 9 Sediment budget calculated by the subtraction of two TLS data sets of the bed of the Manival torrent(France) (modiﬁed after Theules et al. 2009)

become available for public application during the mid-1990s. The TLS device market
developed later because of the lack of software for 3D data processing and problems forhandling huge amount of data (point clouds with several millions of points).
Main advantages of the technique are the acquisition of real 3D information, the fast
data acquisition, easy set up and portability and its high resolution, at least for the groundbased instrument. Reversely, the main limitations of the ground-based technique are theexistence of shadow areas caused by rugged topography, the huge quantity of acquired
information and the needed post-processing techniques for ﬁltering and alignment of thedata sets, i.e. when a large area is scanned, several data sets must be merged and due toerror propagation the alignment started to be more complicated and time-consuming toobtain reliable results.
As we have seen the number of applications is increasing very quickly. This isunderlined by the fact that the recent syntheses about landslides risk include laser scanning

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and HRDEM as important tools (Van Westen et al. 2006; Fell et al. 2008; Derron andJaboyedoff 2010). Figures including LIDAR images are presently published in papers
without any other information, which means that it is now considered as a routine method
demonstrative enough to be self-explanatory (Jibson 2006).
All the presented applications indicate that research has not yet well deﬁned the limit of
the LIDAR techniques, without taking into account the future advances, especiallyfor monitoring purposes. It is clear that HRDEM is an inestimable support not only forlandslide mapping, but also for hazard assessment. The use of the full data quality formodelling purposes is still not achieved.
LIDAR intensity provides some information on the material type, but it remains a
difﬁcult task to discriminate different materials (e.g. lithologies) based on the laser
reﬂectivity because of the various causes of intensity variations. This has been applied withgood results to the classiﬁcation of river bed at different epochs by identifying water–landboundary (Höﬂe et al. 2009). Some research groups are now looking at further reﬁnementof LIDAR imaging coupled with other remote sensing techniques. LIDAR data setsmerged with RGB colours obtained from optical camera were tested on the Vesuvius crater
providing encouraging results but is still far from an operative one (Pesci et al. 2007,2008). Coupled with LIDAR data sets, the hyperspectral camera permits to get additional
information on rock characteristics (Kurz et al. 2008).
One of the most promising advances is probably to analyse simultaneously InSAR
results and HRDEM. The problem is to obtain coherent results between LOS distancechanges from InSAR data and the 3D displacement vector extracted from LIDAR data. In
any case, the ﬁrst results are already consistent for TLS (Teza et al. 2008) and for ALS(Roering et al. 2009).

8 Conclusions
Above synthesis and considerations shows that HRDEM is increasingly being used in thelandslide community, becoming an essential tool for landslide risk management s.l. In a
few years, LIDAR sensors will probably be a standard tool for landslide analysis. Following the current trend, full coverage by ALS-DEM in most of the rich countries will bereached within the next 10 years. As the technique is also progressing, more accurate andprecise ALS and TLS devices will appear, allowing for the generation of more accurate
DEM. In addition, the vertical cliffs will certainly be also better imaged by new equipments, such as helicopter-based LIDAR sensors. Nevertheless, the huge amount of data
will remain a problem as the computers will need to be more powerful with increasing dataacquisition capacity as it is already the case with mobile LIDAR systems that have an
acquisition rate of up to 200 kHz.
The real challenge is to develop new methods to better take beneﬁt from HRDEM.
Indeed a lot of new information can be extracted from such DEM that we not yet
contrived to do. Although great advances have been developed in geometrical aspects,
most of the conceptual models remain tied to the past. Could we combine these accurate
HRDEM with a better modelling of landslide mechanisms (including rockfalls anddebris-ﬂows)? Is it possible to integrate surface measurements with early warning systems? The challenge for HRDEM exploitation is not only linked to landslide but also for
instance to the analysis of fracturing at the surface using HRDEM and its extrapolation
to underground.

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Acknowledgments We are grateful to the National Swiss Foundation for Science project Number 200021118105 and the EU project SafeLand (Grant Agreement No: 226479). We thank Dr. Greg Stock, geologist at
Yosemite National Park, for providing us LIDAR data and J. Theules from CEMAGREF (Grenoble, France)
who provided us one ﬁgure for illustrating TLS and debris-ﬂows. In addition we are grateful to Marie
Charrière and Florian Humair for editing some parts of the texts. The three anonymous reviewers are
acknowledged for their valuable comments that helped us to improve the manuscript.
Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium,provided the original author(s) and source are credited.

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