Steep, hardly accessible cliffs of rhyolite tuff in NE Hungary are prone to
rockfalls, endangering visitors of a castle. Remote sensing techniques were
employed to obtain data on terrain morphology and to provide slope geometry
for assessing the stability of these rock walls. A RPAS (Remotely Piloted
Aircraft System) was used to collect images which were processed by Pix4D
mapper (structure from motion technology) to generate a point cloud and mesh.
The georeferencing was made by Global Navigation Satellite System (GNSS) with
the use of seven ground control points. The obtained digital surface model
(DSM) was processed (vegetation removal) and the derived digital terrain
model (DTM) allowed cross sections to be drawn and a joint system to be
detected. Joint and discontinuity system was also verified by field
measurements. On-site tests as well as laboratory tests provided additional
engineering geological data for slope modelling. Stability of cliffs was
assessed by 2-D FEM (finite element method). Global analyses of cross
sections show that weak intercalating tuff layers may serve as potential slip
surfaces. However, at present the greatest hazard is related to planar
failure along ENE–WSW joints and to wedge failure. The paper demonstrates
that RPAS is a rapid and useful tool for generating a reliable terrain model
of hardly accessible cliff faces. It also emphasizes the efficiency of RPAS
in rockfall hazard assessment in comparison with other remote sensing
techniques such as terrestrial laser scanning (TLS).
Location of studied cliff faces and an image of the rocky slope at
Sirok Castle, NE Hungary (a, b) and the geological map of the area
(redrawn after Balogh, 1964) (c). Legend for the geological map:
Miocene (1–7), Oligocene (8), Cretaceous (9), Triassic (10). 1: gravel and
conglomerate, 2: clay, 3: rhyolite tuff, 4: sand and sandstone, 5: siltstone,
6: rhyodacite tuff, 7: fine sand, 8: clay, 9: basalt, 10: radiolarite.
Introduction
In the past years, technological development of RPAS revolutionized the data
gathering of landslide-affected areas (Rau et al., 2011), recultivated mines
(Haas et al., 2016) and monitored coastal processes (Casella et al.,
2016) and levee breaches (Brauneck et al., 2016) or road cuts (Mateos
et al., 2016). RPAS has been increasingly used in engineering geology in
historical landslide mapping (Jovančević et al., 2016) and in slope
stability analyses (Niethammer et al., 2012; Fraštia et al., 2014) as
well as in other natural disasters such as earthquakes (Gerke and Kerle,
2011; Nex et al., 2014) or floods (Feng et al., 2015). RPAS can also be
combined with terrestrial laser scanning (TLS) since both remote sensing
tools provide high-precision terrain measurement (Fanti et al., 2013; Assali
et al., 2014; Francioni et al., 2014; Neugirg et al., 2016; Manconi and
Giordan, 2015). These tools can be used to validate height information
derived by other technologies. Rockfalls represent special landslide hazards
since their rapid movements and various trajectories make it difficult to
predict their hazard potential (Crosta and Agliardi, 2003; Manconi and
Giordan, 2014). Several methods have been suggested to assess cliff
stability,
ranging from physical prediction rockfall hazard index (Crosta and Agliardi,
2003) via the Rockfall Hazard Rating System (Budetta, 2004) to the modelling of
their trajectories (Crosta and Agliardi, 2002; Abbruzzese et al., 2009;
Copons et al., 2009; Samodra et al., 2016). These methods rely on
understanding failure mechanisms and on predicting displacement of rock
masses (Pappalardo et al., 2014; Stead and Wolter, 2015; Mateos et al.,
2016), or in some cases, on predicting the displacement of individual rock blocks
(Martino and Mazzanti, 2014). To gather data on the rockfall hazard of
existing cliff faces, a number of crucial data are needed on slope profiles,
material properties, block sizes (De Biagi et al., 2017) and possible
discontinuity surfaces that can contribute to slope instability. Slope
profiles can be obtained from point clouds, while material properties have to
be measured on-site (e.g. Uniaxial Compressive Strength by Schmidt hammer) or
under laboratory conditions (Margottini et al., 2015). For the detection and
mapping of joints and fractures it is possible to apply remote sensing
techniques (Fanti et al., 2013; Tannant, 2015; Salvini et al., 2017).
Most rockfall hazard publications deal with hard, well-cemented rocks, such
as limestone (Samodra et al., 2016) or various other types of sedimentary
rocks (Michoud et al., 2012), such as igneous or metamorphic rocks. In
contrast, very few previous studies have dealt with cliff face stability and rockfall hazard of low-strength rock such as volcanic tuffs (Fanti et al., 2013;
Margottini et al., 2015). Volcanic tuffs are very porous rocks and are prone
to weathering (Arikan et al., 2007). While the current paper deals with a low-strength pyroclastic rock, it has a slightly different approach to cliff
stability analysis. In this study, slope stability is assessed by using
a combination of remote sensing techniques, field measurements and
laboratory testing of tuffs with 2-D FEM (finite element method) analyses
of slopes. In contrast to other case studies, this study operates on
a smaller scale and studies the possibilities of wedge and planar failures.
More specifically, in our context, the cliff face is unstable as is
evidenced by falling blocks. Due to rockfall hazard, the small touristic
pathway was closed to avoid causalities. The current paper analyses the cliff
faces by condition assessment and stability calculations. Thus, this research
provides an assessment of how RPAS-based images and photogrammetric
processing can be used to derive a surface model at sites that are difficult
to access. The paper also demonstrates the combined use of photogrammetric,
surveying and engineering geological methods at difficult ground conditions
when assessing rock slope stability.
Study area
The study area is located on a medium-height mountain range in NE-Hungary, where a hardly
accessible jointed rhyolite tuff cliff face was studied. On the top of the
cliff, a touristic point, the Sirok Castle is located (Fig. 1). The
steep rhyolite tuff hill has an elevation of 298 m at the transition
area of two mountain ranges, Mátra and Bükk mountains. The tuff is
very porous and prone to weathering (Vásárhelyi, 2002; Kleb and
Vásárhelyi, 2003; Török et al., 2007).
Studied southern cliff faces: (a) image of the castle
obtained by RPAS with marked details, (b) distant view of the
eastern part of the cliff section, (c) steep cliffs dissected by
joints, (d) vertical to subvertical cliff face with steep joints and
traces of rockfall and (e) weathered rounded cliff with larger
tafoni.
Although the first castle was first constructed in the 13th century AD, due
to war damages and reconstructions, the current structure encompasses wall
sections representing different construction periods. In these days, the
partially ruined walls have been restored, and the castle is open to
tourists, but the southern slopes are closed due to rockfall
hazard.
The hill represents a rhyolite tuff that was formed during the Miocene
volcanism (Badenian–Early Pannonian period). The cliff face was formed
due to late Miocene volcanic activity and is a part of the Inner
Carpathian volcanic chain. The geological map of the closer area clearly
reflects the dominance of pyroclastic rocks, with isolated occurrences of
Oligocene and Triassic rocks (Fig. 1). The cliffs are steep and display
several joints and discontinuity surfaces. The present study focuses on the
southern hillslope of the castle hill, where major rockfalls occurred in the
recent past (Fig. 2). The study area is divided into smaller units, where RPAS
and rockfall hazard assessment analyses were carried out (Fig. 3).
Materials and methods
The research uses two major methods: (i) RPAS and (ii) engineering
geology. The applied methods are summarized in a flow diagram displaying the
combination and links between the two methods (Fig. 4). The flow chart has
four major realms that have both RPAS and engineering geological units:
(i) preparation, (ii) field survey, (iii) data processing and calculation and
(iv) interpretation. The RPAS line is described in detail in the next
part of the paper, but is also linked to previous publications providing
overview of image acquisition, image processing and interpretation (Civera
et al., 2012; Westoby et al., 2012; Remondino et al., 2014). The engineering
geological part of the flow chart is also explained below and has
strong links to publications describing the application of RPAS to landslide
characterization and rock slope stability assessment (Niethammer et al.,
2012; Tannant, 2015).
Location of the illustrations in the paper and the rockfall-affected area. Red dotted and dashed line represents zones affected by rockfall.
Yellow dashed lines 1 to 5 mark the sections where slope stability was
calculated by using 2-D FEM model (Fig. 8). Dotted lines indicate the areas
shown in Figs. 5 and 9.
Flow chart showing the methods and obtained data set of this paper
indicating the interrelationship between RPAS and slope stability assessment
(see details in the text).
RPAS data acquisition and terrain modelling
The Remotely Piloted Aerial System (RPAS) was deployed on 21 February 2015,
when vegetation cover was limited. The remaining vegetation was manually
removed; luckily, the areas with the greatest hazard were barely covered. The
system is a modified commercial DJI Phantom 2 drone (DJI, 2015), where the
flying vehicle has been equipped with a synchronous image transfer
(first person view – FPV) option that also forwards the current flying
parameters (e.g. height, speed, tilt, power reserve). Due to the complexity
of the survey area, the flight was controlled manually; the required overlap
between images was ensured by the operator considering capture frequency. The
necessary overlap between images was controlled by the FPV option. For safety
reasons, the crew consisted of two persons: one to control the aircraft and the other one to continuously monitor the transferred video stream.
The camera control was done using a tablet.
The captured image positions around the reconstructed castle
hill (a) and the point clouds obtained by RPAS
technology (b) (see top view in Fig. 3.)
Image processing data.
Mean number of key points per image22 676Mean number of matched key points per image9546Mean reprojection error (pixel)0.176Time for SfM processing40 m:20 sTime for densification (point cloud)05 h:30 m:24 s
A GoPro Hero 3+ (GoPro, 2017) action camera was mounted onto a 2-DoF gimbal
of the unmanned aerial vehicle (UAV). The camera has a fixed 2.77 mm
focal length objective that is capable of capturing
4000 × 3000 pixel-sized .jpg images. The images were captured with
a sensitivity of ISO 100 and RGB colour space. The lens was used with a fixed
aperture of 2.8 and the shutter speed of the camera was able to be adequately adjusted.
Generally, the exposure time was set to 1/1400s and the images
were compressed at a rate of 4.5 bitspixel-1. There were three
imaging flights: two around noon and one at about 17:00. The
flying times were 13, 12 and 13 min at which 390, 365 and
419 images were captured. All 1174 images were involved in the
photogrammetric object reconstruction (Fig. 5). The photogrammetric
reconstruction has been done using Pix4Dmapper (Pix4D, 2017), which is based on
structure from motion (SfM) technology (Lowe, 2004; Westoby et al., 2012;
Danzi et al., 2013). SfM automatically identifies tie points considering
initial requirements (e.g. preliminary image centre positions, time stamps)
(Table 1). Camera calibration was executed during post-processing, and no
prior calibration was needed (Pix4D, 2017). After the image alignment, the
image projection centres and attitudes can be observed (Fig. 5). Then
19.3 millionpoints were obtained by the photogrammetric
reconstruction, which was appropriate for the engineering geological
application. The technology allows a higher resolution to be obtained, but
it was not necessary. The average point density is about
670 pointsm-3, but there are areas with double point density.
Colour digital surface model with 1 m contour line interval
of the study area. The solid black dots show the ground control points, while
the red dotted and dashed line represents zones affected by rockfall.
For georeferencing, particular tie objects were measured by the Global
Navigation Satellite System (GNSS). The used GNSS receiver was a Leica CS10
with a Gs08plus antenna (GS08, 2014; CS10, 2014). The measurement was done in
RTK mode supported by the Hungarian RTK network (RTKnet, 2013). There were
seven measured ground control points (Fig. 6) (GCPs); the mean 3-D
measurement accuracy was 4.9 cm (minimal value was 2 cm,
maximal value 9 cm). The RPAS technology has produced a considerable
number of data points (observations). Since this point cloud is difficult to
manage due to its size and heterogeneous point spacing, it
requires a sophisticated resampling
step, which was done using CloudCompare, and the spatial resolution of the
point cloud was set to 1 cm.
Differences between RPAS and TLS point clouds by CloudCompare shown
in metres (modus of differences is at about 0.01 m).
Rock mechanical tests and relevant standards.
Rock mechanical parameterNumber of specimensRelevant standardBulk density53EN 1936:2000Water absorption18EN 13755:2008Propagation speed of the ultrasonic wave53EN 14579:2005Uniaxial compressive strength31ISRM 2015Modulus of elasticity31ISRM 2015Tensile strength (Brasilian)23ISRM 2015
The RPAS data collection was validated by the use of terrestrial laser
scanning. The necessary data were captured by two scanners: a Faro Focus S
120 3D (Faro, 2016) and a Z + F Imager 5010C (Z + F, 2014). The
terrestrial laser scanning was executed on the same day as the RPAS mission.
The raw point cloud measured by the Faro scanner contained
1.9 billionpoints, while the Z + F point cloud had
0.8 billionpoints. Both point clouds included X, Y and Z coordinates,
intensity and RGB colour values. RPAS- and TLS-based point clouds were
compared with CloudCompare software (CloudCompare, 2014) (Fig. 7).
As can be noted in Fig. 7, the largest difference between the two sources
is almost less than 10 cm and the majority of the differences are
about 1 cm. The point cloud was then imported into Geomagic Studio
2013 (GeomagicStudio, 2013) and meshed. The triangle side length was
5–7 cm. To support the engineering geological survey, several
horizontal and vertical sections were derived in Geomagic DesignX 2016
(GeomagicDesignX, 2016); these profiles were exported in CAD format.
The next step was to make cut-offs focusing only on the cliffs; this was done
by CloudCompare, followed by the points being exported in LAS format (LAS,
2012). The exported points could then be imported into SAGA GIS 2.1.2 (Conrad
et al., 2015), where the necessary DTMs were created using an inverse distance
weighting (IDW) algorithm (IDW, 2013). The derived DSM-grids have
5 cm spatial resolution, which is adequate for morphologic analyses
(Fig. 6) and suits to slope stability analysis. The morphology analysis has
concentrated on the catchment area (CA) (Costa-Cabral and Burges, 1994; Haas
et al., 2016) (Fig. 4), although several other morphological indices (e.g.
topographic wetness index, stream power index) were derived. These indices
express the potential relationship between surface geometry and geological
parameters.
Engineering geology and slope stability analysis
Geological data and written resources (Balogh, 1964; Haas, 2013, Lukács
et al., 2015) provided background information for the planning of engineering
geological field surveys (Fig. 4). Major lithotypes were identified and
described and geological profiles were recorded during the engineering
geological field surveys (Fig. 4). Rock joints, discontinuity surfaces and
fault systems were measured by using compass and structural geological
software applied in mobile phone. The structural geological data were analysed
by Dips software. Strength parameters were assessed on-site by using
a Schmidt hammer. Ten rebound values were measured on each surface and mean
values and SDs were also calculated. This method has been used previously to
gather rapid data on the rock strength of cliff faces (Margottini et al., 2015).
The data set was compared to rock mechanical laboratory tests.
Lithologic column of Sirok Várhegy showing the modelled topmost
10 m section of the hill (letters refer to lithologic units).
Samples for laboratory analyses were collected on-site (Fig. 4). Major rock
mechanical parameters were measured under laboratory conditions on
cylindrical specimens. These were drilled from blocks and cut into
appropriate sizes using cutting disc. The sizes of the tested specimen were
made according to EN (European Norm) on air-dried and water-saturated
samples.
The specimens were grouped according to their bulk density and the
propagation speed of the ultrasonic pulse wave. Strength parameters such as
uniaxial compressive strength and indirect tensile strength (Brasilian) were
measured according to relevant EN standards and
ISRM-suggested methods (International Association of Rock Mechanics). Modulus of elasticity was also calculated
(Table 2). The generalized Hoek–Brown failure criterion (Hoek et al., 2002)
was used to determine strength parameters of the rock mass. Altogether,
53 cylindrical test specimens were used for the tests.
The falling blocks can endanger tourists on the footpath below the castle on
the southern slopes; therefore the stability analysis of the rocky slopes was
focused on this part of the cliff (Fig. 3). First, the rock mass failure was
analysed with by the RocFall FEM software of the Rocscience (RS2). The
steepest sections were determined from the terrain model obtained from RPAS
data. The GSI (Geological Strength Index) values of the rock masses were determined according to Marinos
et al. (2005). The global stability of the hillslope of selected sections was
calculated with RS2 software. Since the rhyolite tuff is a weak rock with few
joints, the rock mass failure and the failure along discontinuities were also
analysed. This kinematic analysis had been done with a stereographic tool.
The orientations of main joint sets were obtained from DSM model-based
morphological analyses with the use of catchment area tool. It assumes that
major flow paths are related to joints; i.e. the fracture system controls the
drainage pattern (Costa-Cabaral et al., 1994). At accessible areas joints and
fractures were also measured on-site on the southern and south-eastern parts
of the hillslope. Additional control field measurements were also made in the
underground cellar system of the castle, where the tuff is also exposed. The
Dips software was used for the kinematic analysis. The direction of the
hillslopes and the direction of the discontinuities were compared to
determine the location of the potential hazardous failure zones on the
hillside. Stereographic plots were generated showing the possible failure
planes for all slope directions, and the safety factor of the possible planar
failure was calculated by RockPlane software. Wedge failure was modelled by
Swedge software. Toppling failure due to geological and geomorphological
conditions cannot occur. Slope stability calculations and stability
assessment formed the last part of the engineering geological analyses
(Fig. 4).
Results
The rhyolite tuff faces consist of moderately bedded ignimbritic horizons and also brecciated lapilli tuffs according to our field
observations (Fig. 6). The topmost 10 m of the cliff face, which was
modelled from slope stability, comprises three main horizons and can be modelled
as a “sandwich structure”. The lower and the upper parts are formed by thick
pumice containing lapilli tuffs. These beds enclose nearly 2 m of
well-bedded less welded fine tuff and brecciated horizons (Fig. 8).
Top view of the cliff (see the location in Fig. 3) obtained with
RPAS and the catchment area diagram obtained from DSM analysis by using a
catchment area module (Costa-Cabral et al., 1994). The latter was used
for joint pattern recognition. Numbers refer to major joint systems marked on
the catchment area map and on rose diagram of the field measurements and DSM data
set.
By combining and comparing all measured data of discontinuities and joints
using DSM and its derivative (Fig. 6.) and morphological index (Fig. 9),
the joint orientation was outlined. The filed survey validated the obtained
structural geological conditions, and six main joint sets (with dip angle/dip
directions of 85/156, 88/312, 79/110, 81/089, 82/064, 61/299)
were identified with prevailing a NE–SW direction.
Rock mechanical parameters of tuff used in the model: lapilli tuff
refers to upper and lower 4 m, less welded tuff refers to middle
stratigraphic unit.
Mechanical propertyUpper and lower unitMiddle unit(marked by A in Fig. 10)(marked by B–D in Fig. 10)(lapilli tuff)(less welded tuff)Bulk density (ρ)(kgm-3)18151635Uniaxial compressive strength (σc)(MPa)8.020.35Tensile strength (σt)(MPa)0.830.04Modulus of elasticity (E)(GPa)0.970.05
The laboratory tests of tuffs provided the input data for stability analysis
for the two main lithologies: upper and lower unit of lapilli tuff and middle
unit of less welded tuff (Table 3). In the model calculations GSI = 50
was used.
The results of the global stability analysis of the slopes
(sections 3 and 4 in Fig. 3). Total displacements are marked from blue to red
(lithology is indicated by letters A–D; note the weak zone marked by B–D;
description of lithologies is given in Fig. 8.)
The results of RS2 FEM analyses suggest that the global factor of safety is
SRF = 1.27–1.71 in the studied sections (some of the selected sections
are shown in Fig. 3). The aim of the analysis is to determine the critical
strength reduction factor (SRF) that can be considered the safety factor of
the slope (Fig. 10). The SRF factor is influenced by the weak tuff layer
(marked by B–D in Fig. 8), which has very low shear strength compared to the
lapilli tuff. Colours in Fig. 10. represent the total displacements as
a result of the shear strength reduction analysis. Thus these are not real
displacements of the hillslope. The figure demonstrates only how the failure
of the slope would occur with reduced shear strength parameters. Our failure
analyses have proved that the bottom of the slip surface would be in this
weaker layer and could lead to a larger mass movement.
Kinematic analysis of planar failure by RockPlane (main joint sets
are marked by red circles 1–6 m).
Other failure modes that were studied are planar failure and wedge failure,
which are often controlled by joints and discontinuities. According to data
obtained from remote sensing and according to field measurements there was no
uniform spacing of the discontinuities. Stereographic plots with possible
failure planes for all slope directions (Fig. 11) indicate that the most
hazardous part of the slope is the one where the plane orientation is
75/75. The calculated factor of safety (FS = 1.15) implies a high
probability of planar failure.
Examples for the kinematic analysis of wedge failures (main joint
sets are marked by red circles 1–6 m).
Three possible wedge failure modes were identified as being the most
hazardous according to our calculations by Swedge software (Fig. 12). In
these three cases the factor of safety was in the range of 1.38–1.94,
representing the hazards of rockfalls along wedges delineated by different
joint systems.
Discussion
There are three critical sets of input data in the modelling of rocky slopes:
(i) terrain model and slope geometry, (ii) joint systems and (iii) strengths
of rock mass.
To obtain the first, the slope geometry, RPAS-based surveying technique was
used similarly to previous studies (Giordan et al., 2015). In many previous
studies RPAS mission was performed following a flight plan (Eisenbeiss, 2008;
Lindner et al., 2016). In our case no flight plan was made prior to the
mission, and the windy conditions were also not favourable for a
pre-programmed flight route. The flight was manually controlled and the
skilled personnel with a first person view tool controlled the image
acquisition. The crucial points were the necessary overlaps of images and the
matching. The overlaps were ensured by three consequent flights around the
study area that provided a dense network of image acquisition locations
(Fig. 5). The obtained 1174 images covered the entire study area with
appropriate overlap. The number of images is reasonable, since in previous
studies 400 images were taken for a smaller translational rockslide by
a GoPro Hero 3 Black camera (Tannant et al., 2017) or approx. 400–900 images
with a higher resolution camera (18 MP) for a landslide area that is
approximately five times larger than this one (Lindner et al., 2016). The
Pix4Dmapper software (SfM) was used to identify key points. Nearly 10 000
key points were found on each image (Table 1), which is considered
a sufficient number for appropriate matching (Remondino et al., 2014). A
camera self-calibration tool and rolling shutter effect correction were the
other key features of this software that allowed the images of GoPro
Hero 3+ camera to be processed. The obliquity of images (Rupnik et al.,
2014) and the density of obtained data (Remondino et al., 2011, 2014; Rupnik
et al., 2015) are crucial in the applicability and accuracy of these images.
These were also managed by Pix4Dmapper. The GNNS system and the ground
control points (Fig. 6) allowed the rocky slope to be georeferenced. Our
results show that the mean 3-D accuracy was 4.9 cm. It is comparable
with the ground resolution of 1–3.5 cmpixel-1 of an Italian
rockslide survey (Tannant et al., 2017) or 3.3–4.1 cm (Neugirg
et al., 2016). This resolution was appropriate for creating a reliable
digital surface model.
RPAS-based data were also validated with TLS measurements. The co-use of these
remote sensing tools has been previously well documented for other
applications such as soil roughness (Milenkovich et al., 2016) in erosion
detection (Neugirg et al., 2016) or in cultural heritage (Eisenbeiss and
Zhang, 2006). The RPAS-obtained data validation was performed by comparing
the two point clouds obtained by RPAS and TLS. The surfaces were resampled in
order to homogenize the spatial resolution. The point densities have been
tested in CloudCompare, as a unit sphere of volume of 1 m3 was
defined where the points can be counted and then the sphere could be moved
along the whole surface. The differences in point clouds are less than
10 cm (Fig. 7), which is considered a reasonably good match in
terrain modelling (Neugirg et al., 2016). This computation proved that the
average point density in both point clouds are practically the same, although
the RPAS densities are more homogeneous, while the TLS has denser point
clouds close to the scanning stations, as it was expected on the basis of
previous works (Naumann et al., 2013).
Another aspect causing some differences between the two data sets is that the
image-based reconstruction is performed by interest operators, usually
SIFT (scale-invariant feature transform) or similar computer vision operators
(Lowe, 2004). These operators are generally sensitive to intensity jumps,
points or corners, and textural changes in the input images. If the image
resolution is not adequate or the object is locally “smooth”, these
operators do not return with surface points and the output of the
reconstruction has some filtered effect. Fortunately, the surface
reconstruction quality in RPAS processing resulted in minor, ignorable smoothing
effects. Comparing the two data sets, it is clearly proven that the geometric
resolution of the RPAS-based digital surface model corresponds to the TLS
one, offering very similar quality data (Fig. 7). It is necessary to note
that vegetation can hamper TLS measurements (Prokop and Panholzer, 2009;
Scaioni et al., 2014; Tannant, 2015) and thus limit the comparison of RPAS-
and TLS-obtained data (Milenkovic et al., 2016). In our case most of the
study area was bare and if vegetation occurred it was manually removed.
The documentation of joint system and discontinuities are crucial for rock
wall stability assessment (Tannant et al., 2017). A field survey can only
provide reliable data on joint orientation of accessible areas (Margottini
et al., 2015); however, the joint system that was found on inaccessible
cliffs was not detectable. To overcome this problem, RPAS-generated images
were used; the frequency of joints was observed based on these images like in
previous studies by Assali et al. (2014), Martino and Mazzanti (2014) and
Margottini et al. (2015). The required resolution for joint frequency is of
the order of 10 cm, rarely 1 cm (Tannant, 2015; Tannant
et al., 2017). The RPAS technique allows for plane surface geometries;
however, many joints are not plane surfaces and there are sets in shadows
that are difficult to visualize. Thus, RPAS can be used to outline the strike
of major joints, but it might cause problems when it comes to the
determination of dip and the displacement along the fault planes (e.g.
slickensides).
Our field tests indicate that the application of a Schmidt hammer in rock
strength analysis is limited when it comes to the analysis of low-strength
rocks, such as volcanic tuff (Aydan and Ulusay, 2003). As a consequence,
laboratory analyses of samples were also required to obtain reliable strength
parameters. To measure the strength and to understand the weathering
characteristics, samples were taken representing different stratigraphic
positions. Our lab test data (Table 3) clearly indicate that a low-strength
unit is found in the studied sections (unit marked by B–D in Fig. 8).
Whether the low strength of this zone is related to differential weathering
(Török et al., 2007) or if it is associated with inherited weakness
(micro-fabric) is not clear. This layer is a potential failure zone as
was shown by slope stability calculations. A similar intercalation of
pumice-rich layered deposits was modelled by Damiano et al. (2017). They
found that a pumice-reach weak zone is prone to rainfall-induced landslides.
Our results are in good correlation with these findings since the studied
rhyolitic volcanic tuff was also proved to be very prone to weathering.
A loss in tensile strength of 60 % was measured under simulated
laboratory conditions (Stück et al., 2008). Weathering processes have
long been known to induce landslides and cause slope stability problems in
various lithologies and especially in pyroclastic rocks (Chigra, 2002; Fanti
et al., 2013). At the studied rhyolite tuff cliff face, it was shown that
a joint system is responsible for slope instabilities: planar and wedge
failures were found (Figs. 11 and 12). These failure modes are common in hard
jointed cliff faces (porphyry in Agliardi et al., 2013; mica-schist in Tannant
et al., 2017; limestone in Feng et al., 2014). Our study demonstrates that joint
systems have significant control on slope stability, not only in hard rock
lithologies but also in weak tuffs. It is in line with Fanti
et al. (2013) and Margottini (2015), since in Italy and in Giorgia rock walls
of volcanic tuffs suffered landslides. The kinematic analysis of tuff rock
walls of Tuscany (Fanti et al., 2013) also demonstrated that wedge failure
and planar failure are the most common failure mechanisms of tuff cliff
faces.
Conclusions
The manually controlled flights of RPAS provided excellent information on
slope geometry of highly dissected and inaccessible slopes. The necessary
overlap between images was ensured by three flights over the small area and
by skilled personnel using a first person view system with a synchronous
image transfer. The obtained data were managed by Pix4Dmapper (SfM) software
allowing the identification of nearly 10 000 key points per image. The
TLS-based point clouds proved to be good tools for validating the accuracy of
images and data sets of manually controlled RPAS. In our study the maximum
difference between the two point clouds was less than 10 cm but was
mostly around 1 cm. RPAS collected images and the point cloud-based
digital surface model and the catchment area method especially allows the
detection of joint system (mainly strikes and partly dips but not
slickensides) but field validation and field measurements of accessible
joints and faults are recommended to justify joint orientation. The obtained
digital surface model was accurate enough to allow cross sections for rock
wall stability calculations. The lithology and physical parameters of the
studied steep cliffs are not uniform and intercalations of weak layers of
vitric tuff and volcanoclastic breccia were found. According to 2-D FEM
modelling the intercalating low-strength layer is the one at which potential
slip surface can develop, causing larger-scale mass movements, but at present
it has low probability. Joint systems have a crucial role in the stability of
the studied rhyolite tuff cliff faces. The greatest hazard is related to
planar failure along ENE–WSW joints and to wedge failure.
Data availability
The data can be requested from the corresponding author. It
will not be publicly available until the project
terminates.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “The use of remotely
piloted aircraft systems (RPAS) in monitoring applications and management of
natural hazards”. It is a result of the EGU General Assembly 2016, Vienna,
Austria, 17–22 April 2016.
Acknowledgements
The help of Balázs Czinder, Béla Kleb, Zoltán Koppányi,
Bence Molnár, Bálint Pálinkás and
Balázs Vásárhelyi is acknowledged. The research was supported by
the National Research Development and Innovation Fund (NKFI) (ref. no.
K 116532). The constructive comments of the anonymous reviewers are
appreciated. Edited by: Daniele
Giordan Reviewed by: three anonymous referees
ReferencesAbbruzzese, J. M., Sauthier, C., and Labiouse, V.: Considerations on Swiss
methodologies for rock fall hazard mapping based on trajectory modelling,
Nat. Hazards Earth Syst. Sci., 9, 1095–1109,
10.5194/nhess-9-1095-2009, 2009.Agliardi, F., Crosta, G. B., Meloni, F., Valle, C., and Rivolta, C.:
Structurally-controlled instability, damage and slope failure in a porphyry
rock mass, Tectonophysics, 605, 34–47, 10.1016/j.tecto.2013.05.033,
2013.Arikan, F., Ulusay, R., and Aydin, N.: Characterization of weathered acidic
volcanic rocks and a weathering classification based on a rating system,
B. Eng. Geol. Environ., 66, 415–430, 2007, 10.1007/s10064-007-0087-0,
2007.Assali, P., Grussenmeyer, P., Villemin, T., Pollet, N., and Viguier, F.:
Surveying and modelling of rock discontinuities by terrestrial laser scanning
and photogrammetry: semi-automatic approaches for linear outcrop inspection,
J. Struct. Geol., 66, 102–114, 10.1016/j.jsg.2014.05.014, 2014.Aydan, Ö. and Ulusay, R.: Geotechnical and environmental characteristics
of man-made underground structures in Cappadocia, Turkey, Eng. Geol., 69,
245–272, 10.1016/S0013-7952(02)00285-5, 2003.
Balogh, K.: Geological Formations of Bükk Mountains, Annual Report of the
Hungarian Geological Survey, 48, 245–719, 1964 (in Hungarian).Brauneck, J., Pohl, R., and Juepner, R.: Experiences of using UAVs for
monitoring levee breaches, IOP C. Ser. Earth Env., IOP Publishing, 46,
012046, 10.1088/1755-1315/46/1/012046, 2016.Budetta, P.: Assessment of rockfall risk along roads, Nat. Hazards Earth
Syst. Sci., 4, 71–81, 10.5194/nhess-4-71-2004, 2004.Casella, E., Rovere, A., Pedroncini, A., Stark, C. P., Casella, M.,
Ferrari, M., and Firpo, M.: Drones as tools for monitoring beach topography
changes in the Ligurian Sea (NW Mediterranean), Geo-Mar. Lett., 36, 151–163,
10.1007/s00367-016-0435-9, 2016.Chigira, M.: Geologic factors contributing to landslide generation in
a pyroclastic area: August 1998 Nishigo Village, Japan, Geomorphology, 46,
117–128, 10.1016/S0169-555X(02)00058-2, 2002.
Civera, J., Davison, A. J., and Martínez Montiel, J. M.: Structure from
Motion Using the Extended Kalman Filter, Springer Tracts in Advanced
Robotics, ISBN 978-3-642-24834-4, 1–172, 2012.Cloud Compare point cloud processing software (CC): available at:
http://www.cloudcompare.org/ (last access: 1 February 2017), 2014.Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L.,
Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated
Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007,
10.5194/gmd-8-1991-2015, 2015.Copons, R., Vilaplana, J. M., and Linares, R.: Rockfall travel distance
analysis by using empirical models (Solà d'Andorra la Vella, Central
Pyrenees), Nat. Hazards Earth Syst. Sci., 9, 2107–2118,
10.5194/nhess-9-2107-2009, 2009.Costa-Cabral, M. C. and Burges, S. J.: Digital Elevation Model Networks
(DEMON): a model of flow over hillslopes for computation of contributing and
dispersal areas, Water Resour. Res., 30, 1681–1692, 10.1029/93WR03512,
1994.Crosta, G. and Agliardi, F.: How to obtain alert velocity thresholds for
large rockslides, Phys. Chem. Earth. Pt. A B C, 27, 1557–1565,
10.1016/S1474-7065(02)00177-8, 2002.Crosta, G. B. and Agliardi, F.: A methodology for physically based rockfall
hazard assessment, Nat. Hazards Earth Syst. Sci., 3, 407–422,
10.5194/nhess-3-407-2003, 2003.Damiano, E., Greco, R., Guida, A., Olivares, L., and Picarelli, L.:
Investigation on rainwater infiltration into layered shallow covers in
pyroclastic soils and its effect on slope stability, Eng. Geol., 220,
208–218, 10.1016/j.enggeo.2017.02.006, 2017.
Danzi, M., Di Crescenzo, G., Ramondini, M., and Santo, A.: Use of unmanned
aerial vehicles (UAVs) for photogra mmetric surveys in rockfall instability
studies, Società Geologica Italiana, Roma, 2013.De Biagi, V., Napoli, M. L., Barbero, M., and Peila, D.: Estimation of the
return period of rockfall blocks according to their size, Nat. Hazards Earth
Syst. Sci., 17, 103–113, 2017, 10.5194/nhess-17-103-2017, 2017.DJI phantom 2 quadrocopter (DJI): available at:
http://www.dji.com/phantom-2 (last access: 1 February 2017), 2015.
Eisenbeiss, H.: The autonomous mini-helicopter: a powerful platform for
mobile mapping, Int. Arch. Photogramm., 37, 977–983, 2008.
Eisenbeiss, H. and Zhang, L.: Comparison of DSMs generated from mini UAV
imagery and terrestrial laser scanner in a cultural heritage application,
Int. Arch. Photogramm., XXXV, 90–96, 2006.Fanti, R., Gigli, G., Lombardi, L., Tapete, D., and Canuti, P.: Terrestrial
laser scanning for rockfall stability analysis in the cultural heritage site
of Pitigliano (Italy), Landslides, 10, 409–420,
10.1007/s10346-012-0329-5, 2013.Faro terrestrial laser scanner (Faro): available at:
http://www.faro.com/en-us/products/3d-surveying/faro-focus3d/overview
(last access: 1 February 2017), 2016.Feng, Q., Liu, J., and Gong, J.: Urban flood mapping based on unmanned aerial
vehicle remote sensing and random forest classifier – a case of Yuyao,
China, Water, 7, 1437–1455, 10.3390/w7041437, 2015.Feng, Z., Li, B., Yin, Y. P., and He, K.: Rockslides on limestone cliffs with
subhorizontal bedding in the southwestern calcareous area of China, Nat.
Hazards Earth Syst. Sci., 14, 2627–2635, 10.5194/nhess-14-2627-2014,
2014.Francioni, M., Salvini, R., Stead, D., and Litrico, S.: A case study
integrating remote sensing and distinct element analysis to quarry slope
stability assessment in the Monte Altissimo area, Italy, Eng. Geol., 183,
290–302, 10.1016/j.enggeo.2014.09.003, 2014.Fraštia, M., Marčiš, M., Kopecký, M., Liščák, P.,
and Žilka, A.: Complex geodetic and photogrammetric monitoring of the
Kral'ovany rock slide, J. Sustain. Min., 13, 12–16, 10.7424/jsm140403,
2014.Geomagic Design X 3-D modelling software (GeomagicDesignX): available at:
http://www.geomagic.com/en/products-landing-pages/designx (last access:
1 February 2017), 2016.Geomagic Studio 3-D modelling software (GeomagicStudio): available at:
http://www.geomagic.com/en/ (last access: 1 February 2017), 2013.Gerke, M. and Kerle, N.: Automatic structural seismic damage assessment with
airborne oblique pictometry imagery, Photogramm. Eng. Rem. S., 77, 885–898,
10.14358/PERS.77.9.885, 2011.Giordan, D., Manconi, A., Allasia, P., and Bertolo, D.: Brief Communication:
On the rapid and efficient monitoring results dissemination in landslide
emergency scenarios: the Mont de La Saxe case study, Nat. Hazards Earth Syst.
Sci., 15, 2009–2017, 2015, 10.5194/nhess-15-2009-2015, 2015.GoPro action cam (GoPro): available at: https://gopro.com/ (last
access: 1 February 2017), 2017.Haas, F., Hilger, L., Neugirg, F., Umstädter, K., Breitung, C.,
Fischer, P., Hilger, P., Heckmann, T., Dusik, J., Kaiser, A., Schmidt, J.,
Della Seta, M., Rosenkranz, R., and Becht, M.: Quantification and analysis of
geomorphic processes on a recultivated iron ore mine on the Italian island of
Elba using long-term ground-based lidar and photogrammetric SfM data by
a UAV, Nat. Hazards Earth Syst. Sci., 16, 1269–1288,
10.5194/nhess-16-1269-2016, 2016.Haas, J.: Geology of Hungary, Springer, Berlin, 1–246,
10.1007/978-3-642-21910-8, 2013.
Hoek, E., Carranza-Torres, C., and Corkum, B.: Hoek–Brown failure
criterion – 2002 Edition, Proc. NARMS-TAC Conference, Toronto, 1, 267–273,
2002.Inverse distance weighting interpolation (IDW): available at:
http://gisgeography.com/inverse-distance-weighting-idw-interpolation/
(last access: 1 February 2017), 2013.Jovančević, S. D., Peranić, J., Ružić, I., and
Arbanas, Ž.: Analysis of a historical landslide in the Rječina River
Valley, Croatia, Geoenvironmental Disasters, 3, 26,
10.1186/s40677-016-0061-x, 2016.Kleb, B. and Vásárhelyi, B.: Test results and empirical formulas of
rock mechanical parameters of rhiolitic tuff samples from Eger's cellars,
Acta Geologica Hungarica, 46, 301–312, 10.1556/AGeol.46.2003.3.5,
2003.LAS laser scanner point cloud datatype specification (LAS): available at:
https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html
(last access: 1 February 2017), 2012.Leica CS10 controller (CS10): available at:
http://leica-geosystems.com/products/gnss-systems/controllers/leica-viva-cs15-and-cs10
(last access: 1 February 2017), 2014.Leica Cyclone point cloud processing software (LeicaCyclone): available at:
http://leica-geosystems.com/products/laser-scanners/software/leica-cyclone
(last access: 1 February 2017), 2016.Leica GNSS receiver (GS08): available at:
http://leica-geosystems.com/products/gnss-systems/smart-antennas/leica-viva-gs08plus
(last access: 1 February 2017), 2014.Lindner, G., Schraml, K., Mansberger, R., and Hübl J.: UAV monitoring and
documentation of a large landslide, Appl. Geomat., 8, 1–11,
10.1007/s12518-015-0165-0, 2016.Lowe, D. G.: Distinctive image features from scale-invariant keypoints, Int.
J. Comput. Vision, 60, 91–110, 10.1023/B:VISI.0000029664.99615.94,
2004.Lukács, R., Harangi, S., Bachmann, O., Guillong, M.,
Danišík, M., Buret, Y., von Quadt, A., Dunkl, I., Fodor, L.,
Sliwinski, J., Soós, I., and Szepesi, J.: Zircon geochronology and
geochemistry to constrain the youngest eruption events and magma evolution of
the Mid-Miocene ignimbrite flare-up in the Pannonian Basin, eastern central
Europe, Contrib. Mineral. Petr., 170, 1–26, 10.1007/s00410-015-1206-8,
2015.Manconi, A. and Giordan, D.: Landslide failure forecast in near-real-time,
Geomat. Nat. Haz. Risk., 7, 639–648, 10.1080/19475705.2014.942388,
2014.Manconi, A. and Giordan, D.: Landslide early warning based on failure
forecast models: the example of the Mt. de La Saxe rockslide, northern Italy,
Nat. Hazards Earth Syst. Sci., 15, 1639–1644,
10.5194/nhess-15-1639-2015, 2015.Margottini, C., Antidze, N., Corominas, J., Crosta, G. B., Frattini, P.,
Gigli, G., Giordan, D., Iwasaky, I., Lollino, G., Manconi, A., Marinos, P.,
Scavia, C., Sonnessa, A., Spizzichino, D., and Vacheishvili, N.: Landslide
hazard, monitoring and conservation strategy for the safeguard of Vardzia
Byzantine monastery complex, Georgia, Landslides, 12, 193–204,
10.1007/s10346-014-0548-z, 2015.Marinos, V., Marinos, P., and Hoek, E.: The geological strength index:
applications and limitations, B. Eng. Geol. Environ., 64, 55–65,
10.1007/s10064-004-0270-5, 2005.Martino, S. and Mazzanti, P.: Integrating geomechanical surveys and remote
sensing for sea cliff slope stability analysis: the Mt. Pucci case study
(Italy), Nat. Hazards Earth Syst. Sci., 14, 831–848,
10.5194/nhess-14-831-2014, 2014.Mateos, R. M., García-Moreno, I., Reichenbach, P., Herrera, G.,
Sarro, R., Rius, J., Aguiló, R., and Fiorucci, F.: Calibration and
validation of rockfall modeling at regional scale: application along
a roadway in Mallorca (Spain) and organization of its management, Landslides,
13, 751–763, 10.1007/s10346-015-0602-5, 2016.Matlab mathematical environment (Matlab): available at:
https://www.mathworks.com/products/matlab.html (last access: 1 February
2017), 2017.Michoud, C., Derron, M.-H., Horton, P., Jaboyedoff, M., Baillifard, F.-J.,
Loye, A., Nicolet, P., Pedrazzini, A., and Queyrel, A.: Rockfall hazard and
risk assessments along roads at a regional scale: example in Swiss Alps, Nat.
Hazards Earth Syst. Sci., 12, 615–629, 10.5194/nhess-12-615-2012,
2012.Milenkovic, M., Karel, W., Ressl, C., and Pfeifer, N.: A comparison of UAV
and TLS data for soil roughness assessment, ISPRS Annals of the
Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume
III-5, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic,
145–152, 10.5194/isprsannals-III-5-145-2016, 2016.Naumann, M., Geist, M., Bill, R., Niemeyer, F., and Grenzdörffer, G.:
Accuracy comparison of digital surface models created by unmanned aerial
systems imagery and terrestrial laser scanner, Int. Arch. Photogramm.,
XL-1/W2, 281–286, 10.5194/isprsarchives-XL-1-W2-281-2013, 2013.Neugirg, F., Stark, M., Kaiser, A., Vlacilova, M., Della Seta, M.,
Vergari, F., Schmidt, J., Becht, M., and Haas, F.: Erosion processes in
calanchi in the Upper Orcia Valley, southern Tuscany, Italy based on
multitemporal high-resolution terrestrial LiDAR and UAV surveys,
Geomorphology, 269, 8–22, 10.1016/j.geomorph.2016.06.027, 2016.Nex, F., Rupnik, E., Toschi, I., and Remondino, F.: Automated processing of
high resolution airborne images for earthquake damage assessment, Int. Arch.
Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 315–321,
10.5194/isprsarchives-XL-1-315-2014, 2014.Niethammer, U., James, M. R., Rothmund, S., Travelletti, J., and Joswig, M.:
UAV-based remote sensing of the Super-Sauze landslide: evaluation and
results, Eng. Geol., 128, 2–11, 10.1016/j.enggeo.2011.03.012, 2012.Pappalardo, G., Mineo, S., and Rapisarda, F.: Rockfall hazard assessment
along a road on the Peloritani Mountains (northeastern Sicily, Italy), Nat.
Hazards Earth Syst. Sci., 14, 2735–2748, 10.5194/nhess-14-2735-2014,
2014.Pix4D photogrammetry software (Pix4D): available at:
https://pix4d.com/product/pix4dmapper-pro/ (last access: 1 February
2017), 2017.Prokop, A. and Panholzer, H.: Assessing the capability of terrestrial laser
scanning for monitoring slow moving landslides, Nat. Hazards Earth Syst.
Sci., 9, 1921–1928, 10.5194/nhess-9-1921-2009, 2009.Rau, J. Y., Jhan, J. P., Lo, C. F., and Lin, Y. S.: LANDSLIDE MAPPING USING
IMAGERY ACQUIRED BY A FIXED-WING UAV, Int. Arch. Photogramm. Remote Sens.
Spatial Inf. Sci., XXXVIII-1/C22, 195–200,
10.5194/isprsarchives-XXXVIII-1-C22-195-2011, 2011Real time kinematic net service (RTKnet): available at:
https://www.gnssnet.hu (last access: 1 February 2017), 2013.Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., and Sarazzi, D.: UAV
PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING – CURRENT STATUS AND FUTURE
PERSPECTIVES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,
XXXVIII-1/C22, 25–31, 10.5194/isprsarchives-XXXVIII-1-C22-25-2011,
2011.Remondino, F., Spera, M. G. Nocerino, E., Menna, F., and Nex, F.: State of
the art in high density image matching, Photogramm. Rec., 29, 144–166,
10.1111/phor.12063, 2014.Rupnik, E., Nex, F., Toschi, I., and Remondino, F.: Aerial multi camera
systems: accuracy and block triangulation issues, ISPRS J. Photogramm., 101,
233–246, 10.1016/j.isprsjprs.2014.12.020, 2015.Salvini, R., Mastrorocco, G., Seddaiu, M., Rossi, D., and Vanneschi, C.: The
use of an unmanned aerial vehicle for fracture mapping within a marble quarry
(Carrara, Italy): photogrammetry and discrete fracture network modelling,
Geomat. Nat. Haz. Risk., 8, 34–52, 10.1080/19475705.2016.1199053,
2017.Samodra, G., Chen, G., Sartohadi, J., Hadmoko, D. S., Kasama, K., and
Setiawan, M. A.: Rockfall susceptibility zoning based on back analysis of
rockfall deposit inventory in Gunung Kelir, Java, Landslides, 13, 805–819,
10.1007/s10346-016-0713-7, 2016.Scaioni, M., Longoni, L., Melillo, V., and Papini, M.: Remote sensing for
landslide investigations: an overview of recent achievements and
perspectives, Remote Sens.-Basel, 6, 9600–9652, 10.3390/rs6109600,
2014.Stead, D. and Wolter, A.: A critical review of rock slope failure mechanisms:
the importance of structural geology, J. Struct. Geol., 74, 1–23,
10.1016/j.jsg.2015.02.002, 2015.Stück, H., Forgó, L. Z., Rüdrich, J., Siegesmund, S., and
Török, Á.: The behaviour of consolidated volcanic tuffs:
weathering mechanisms under simulated laboratory conditions, Environ. Geol.,
56, 699–713, 10.1007/s00254-008-1337-6, 2008.Tannant, D. D.: Review of photogrammetry-based techniques for
characterization and hazard assessment of rock faces, Int. J. Geohazards
Environ., 1, 76–87, 10.15273/ijge.2015.02.009, 2015.Tannant, D. D., Giordan, D., and Morgenroth, J.: Characterization and
analysis of a translational rockslide on a stepped-planar slip surface, Eng.
Geol., 220, 144–151, 10.1016/j.enggeo.2017.02.004, 2017.Török, Á., Forgó, L. Z. Vogt, T., Löbens, S.,
Siegesmund, S., and Weiss, T.: The influence of lithology and pore-size
distribution on the durability of acid volcanic tuffs, Hungary, in: Building
Stone Decay: From Diagnosis to Conservation, edited by: Prykril, R. and
Smith, J. B., Geological Society, London, Special Publications, 271,
251–260, 10.1144/GSL.SP.2007.271.01.24, 2007.
Vásárhelyi, B.: Influence of the water saturation for the strength of
volcanic tuffs, in: Workshop on Volcanic Rocks, edited by: Dinis da Gama, C.
and Ribeiro e Sousa, L., Proccedings, Lisboa, 89–96, 2002Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., and
Reynolds, J. M.: Structure-from-Motion' photogrammetry: a low-cost, effective
tool for geoscience applications, Geomorphology, 179, 300–314,
10.1016/j.geomorph.2012.08.021, 2012.
Z + F terrestrial laser scanner (Z + F): available at:
http://www.zf-laser.com/Z-F-IMAGER-R-5010~C.3d_laserscanner.0.html?&L=1
(last access: 1 February 2017), 2014.