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New insight techniques to analyze rock-slope relief using DEM and 3D-imaging cloud points: COLTOP-3D software


Abstract and Figures

COLTOP-3D software performs structural analysis of a topography using a digital elevation model (DEM). A color is defined based on slope aspect and slope angle in order to obtain a unique color code for each spatial orientation. Thus continuous planar structures appear as a unique color. Several tools are included to create stereonet(s), to draw traces of discontinuities, or to compute automatically density stereonet. A new version has recently been developed to represent true 3D surfaces from point clouds. Examples are shown to demonstrate the efficiency of the method. High resolution DEMs acquired with Lidar techniques greatly improve topographic analyses.
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UNIL | University of Lausanne
Faculty of Geosciences and Environment
Institute of Geomatics and Risk Analysis
CH-1015 Lausanne
Faculty of Geosciences and Environment
Institute of Geomatics and Risk Analysis
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Jaboyedoff M., Metzger R., Oppikofer T., Couture R.,
Derron M.-H., Locat J. Turmel D. (2007): New insight
techniques to analyze rock-slope relief using DEM and
3D-imaging cloud points: COLTOP-3D software. In
Eberhardt, E., Stead, D and Morrison T. (Eds.): Rock
mechanics: Meeting Society’s Challenges and Demands
(Vol. 1), Taylor & Francis. pp. 61-68.
This version contains colour figures that were printed in
greyscale in the conference proceedings of the 1st
Canada-US Rock Mechanics Symposium, Vancouver,
Canada, 27-31 May 2007.
New insight techniques to analyze rock-slope relief using DEM and 3D-
imaging cloud points: COLTOP-3D software
M. Jaboyedoff, R. Metzger &T. Oppikofer
IGAR Institute of Geomatics and Risk Analysis, FGSE - University of Lausanne, Switzerland
R. Couture
Geological Survey of Canada, Ottawa, Ontario, Canada
M.-H. Derron
Geological Survey of Norway, International Center for Geohazards, Trondheim, Norway
J. Locat & D. Turmel
Université Laval, Québec, Canada
ABSTRACT: COLTOP-3D software performs structural analysis of a topography using a digital elevation
model (DEM). A color is defined based on slope aspect and slope angle in order to obtain a unique color code
for each spatial orientation. Thus continuous planar structures appear as a unique color. Several tools are in-
cluded to create stereonet(s), to draw traces of discontinuities, or to compute automatically density stereonet.
A new version has recently been developed to represent true 3D surfaces from point clouds. Examples are
shown to demonstrate the efficiency of the method. High resolution DEMs acquired with Lidar techniques
greatly improve topographic analyses.
Digital elevation models (DEMs) are used in
many hazard assessment methods, including land-
slides and rock instabilities. Slope angles are used
for stability estimation, e.g. infinite slope stability
models (Dietrich et al., 2001). Kinematic tests are
used to estimate the likelihood of failure in rock
slopes, such as slide, wedge and toppling failures
(Jaboyedoff et al., 2004; Gokceoglu et al., 2000;
Günther, 2000). DEMs are also used for modeling
rockfall trajectories.
Many GIS tools permit a mathematical analysis
of topography using slope, slope aspect, second de-
rivative, curvature, flow paths, etc. (Burrough and
McDonnell, 1998). But very few are dedicated to the
analysis of the relief structure. An attempt of merg-
ing slope angle and slope aspect in one document
was made by Brewer and Marlow (1993) to repre-
sent topography using colors dependent on both
slope angle and slope aspect, but the results were not
used for structural analysis. Using the dip and strike
direction of each cell, a DEM can be theoretically
represented with a map having a unique color for
each spatial orientation, allowing a very simple
slope analysis. This can also be applied to triangu-
lated surfaces made from 3D point clouds (i.e. x, y,
z, coordinates), should define, enabling for example,
the detection of planar structures within a cliff.
The increasing availability and accuracy of high
resolution DEMs by Lidar (LIght Detection And
Ranging) technologies allow for more detailed struc-
tural and morphological analyses and increase the
potential of DEM analyses. Although if the principle
of the proposed analysis is simple, the point cloud
management and surface creation is not straightfor-
In this paper we describe the principle of COL-
TOP -3D software, its use for DEM analysis and its
future evolution toward a true 3D analysis. This is
illustrated with some examples from rock cliffs in
Québec and in the Swiss Alps.
2.1 Document types
Airborne Lidar DEMs have been available for more
than 10 years, either as point clouds or regular grids.
Up to now most of the acquisitions have been per-
formed with a nearly vertical laser beam, which
means that the cliffs are only poorly defined because
of a poor point density. The new techniques linked
to ground-based Lidar permit the creation of accu-
rate 3D images by merging several scans. By com-
bining the two technologies it is possible to obtain a
point cloud that has no preferential density direction
(Fig. 1), which means that the topographic surfaces
have similar point densities in all spatial directions.
The ground-based Lidar data often include vege-
tation, which must be removed manually. The inter-
est is also to create a routine to automatically re-
move the trees from scans.
2.2 Software principle
The first version of COLTOP-3D (Jaboyedoff and
Couture, 2003) displays a square DEM grid using
the Hue Saturation Intensity (HSI) wheel. The color
displayed is linked directly to direction of the nor-
mal (pole) of the DEM cells by representing the HSI
wheel, in a stereonet and showing the link between
pole orientation and colors (Fig. 2). The dip direc-
tion of the surface elements are represented by the
hue (H) of the wheel from 0 to 360° and the dip of
the pole using the saturation (S). The intensity (I)
can be changed for representation purposes. The link
with RGB value is performed following the relation-
ship proposed by Gonzalez et al. (2004).
Figure 1. Example of an airborne Lidar DEM on top, and the
merge of the ground-based and airborne Lidar DEM on the
bottom (DEM from Åknes project, Stranda Commune, Nor-
way; after Derron et al., 2005).
2.3 The 2D representation
Basically, COLTOP-3D was designed to use
square grids. Contrary to standard methods, the col-
ored pixels are created by using the normal of the
plane defined by 4 neighboring grid points and by
placing the HSI value corresponding to the normal
vector orientation at the center of these points (Fig.
2). If the grid's cell size is d and the four points’ alti-
tudes are z1, z2, z3 and z4, then the surface orienta-
tion can be defined by two following vectors:
)(½;0; 42311 zzzzdv
& (1)
)(½;;0 43212 zzzzdv
& (2)
They correspond to the line passing through the cen-
ter of the cell and linking the middle of the edges de-
fined by the segment linking the 4 grid points. The
pole is given by the cross product:
21 vvp
u (3)
Another solution has been used to represent sur-
faces created by TIN (triangulated irregular net-
work) techniques by simply applying the color re-
specting the above method to each triangle.
Figure 2. Illustration of the principle of the COLTOP-3D color
scheme. (A) The orientation is defined by four nearest
neighbors on a square grid or by three points of each triangle
of a TIN. (B) Relationship between Schmidt-Lambert projec-
tion and HSI wheel. (C) The HSI wheel plotted on a stereonet
that is afterward affected to the cells of A..
2.4 Other capabilities
COLTOP-3D possesses several others capabili-
ties besides the simple representation by means of
the HSI wheel (Fig. 3). The color scheme can be
also rotated in order to get a better visualization of
different planar features. Since the color is a direct
indicator of the orientation, one can select and click
on a colored pixel and instantaneously the dip angle
and dip direction is returned. By clicking on the im-
age, the standard sign of dip is plotted on the colored
DEM and the corresponding orientation is added to
a stereonet and listed in a text window.
Figure 3. Illustration of the capabilities of COLTOP-3D de-
signed for a square grid DEM (The image is normally in color,
see data repository).
By selecting an area of the DEM, it is possible to
calculate the histogram of the orientations (density
stereonet), for example to obtain the mean orienta-
tion of a planar surface. The DEM cells for which
the orientations corresponds to a chosen orientation
(defined by a dip angle, a dip direction and a toler-
ance (cone) around this orientation) can be mapped
in a single color. By choosing up to five different
orientations, this leads to an image of the relief dis-
playing only the selected orientations with user-
defined colors. Results can be exported in an ASCII
grid file that can be used in any common Geo-
graphic Information System (GIS) software.
Currently fault traces can be drawn in COLTOP-
3D by indicating one point and the orientation of the
fault or by clicking on 3 points. Further develop-
ments will implement least-square methods for the
determination of fault traces. The X-Y coordinates
of fault traces can be exported into a text file.
3.1 3D point cloud data management
Ground-based laser scanners allow for capturing
dense 3-dimensional data sets (up to millions of
points) of the surface of an object, within a few min-
utes. However, the post-treatment and the standard
operating use of such large data sets may impair an
in-depth analysis for specific applications, such as
landslide and rockfall analysis. This is mainly due to
computer access time for the localization of data
points near a given location. To solve these prob-
lems, a structure based on octrees (an index based
on spatial portioning) is used, which allows for fast
localization of points within a given region, low
consumption of RAM, and hard drive access mini-
mization. First a region (root node) large enough to
enclose the entire point cloud is computed. Points
are added one by one until the root node (level 0)
contains a total number of point equals to a given
threshold. The node is then equally splitted into
eight sub regions (sub nodes) (level 1), and all
points of the root node are removed and added to
their corresponding sub nodes. This subdivision
process is repeated until the number of points in-
cluded in a sub node falls beneath a given threshold.
The value of the threshold must be large enough,
typically in the order of few hundreds of thousand,
for fast disk access, as all the points contained in a
sub node are automatically loaded into RAM and
unloaded from it as a whole. Figure 4 (top) shows an
example of a first order octree (only non-empty
nodes are shown) This first octree allow not only for
minimizing disk access, but also for minimizing
RAM consumption.
For solving hardware problems related to 3D point
topology, a second octree is built as described
above, but with a much smaller threshold: typically
it is in the order of a few hundreds to one thousand.
This leads to an octree with much more branches
(Fig. 4b).
The retrieval of neighborhood points of a given co-
ordinate (x,y,z) is then straightforward. First, the
node of the first octree holding (x,y,z) is retrieved,
and, if needed, the data points are loaded into RAM.
Secondly, the procedure is repeated for the second
octree. The few hundreds of points stored in this
node are inspected to obtain the nearest neighbors of
This structure is similar to the ones proposed by
Dey and al. (2001) and Schaffer and Garland (2005).
3.2 Normal estimation
Extensive work has pinpointed that eigenvalue
analysis of the covariance matrix of a local
neighborhood can be used to determine local surface
properties, and hence its normal vector (Pauly et al.,
2002). The covariance matrix C is defined over a lo-
cal neighborhood surrounding a point of interest as:
)( (4)
where the entries for a neighborhood containing k
points are defined as:
222 )(
)()()var( ¦
ix xx
iixy yyxx
with E(value) being the excepted value or the mean value
(E(x)=x), and var(x) and cov(x,y) denoting the variance of x
and the covariance between x and y respectively (Belton and
Lichti, 2002).
Since C is a symmetric and positive semi-
definite, its associated eigenvalues
i are greater
than or equal to zero. The local normal vector is
given by the associated eigenvector ei with the
smallest eigenvalue. The direction of the normal
vector is the same as the one found by least squares
plane fitting, since the two methods are equivalent
(Shakarji, 1998).
3.3 Removing of non-surface features
One of the main advantage of computing eigen-
values for normal estimation instead of least-squares
plane fitting, is that the eigenvectors correspond to
the principal components (directions and orienta-
tions) of the neighborhood and the eigenvalues will
represent the variance in each direction (Belton and
Lichti, 2002). Thus, it is possible to estimate the
change of geometric curvature, Mcurv, in the
neighbourhood of a single point, pi, with simple cal-
culation such as (Bae and Lichti, 2004):
icurv pM with 321
ddd (7)
Points lying on the surface will have a change of
curvature value close to zero. Belton and Lichti
(2002) use the values given by eq. 7 to classify the
points as surface (plane), edges or corners (their
field of application being terrestrial laser scans of
buildings). From a slope instability or rockfall haz-
ard analysis point of view, these features do not
have a particular meaning, but the change of curva-
ture can be used to automatically remove vegetation
from the data set, as it is excepted that such features
have a highly variable curvature. Depending on the
scanned area, it takes up to one day to manually re-
move trees from a single scan. Vegetation and foli-
age are automatically detected by specifying a
threshold for the values given by eq. 7. Points with
higher curvature than the threshold can be removed
from the dataset (Fig. 5). However, the procedure is
not as straightforward, as it can be seen from Figure
5. Some surfaces or ground points located on a frac-
ture or fault may also be deleted by blindly applying
the filter.
Figure 4: First order octree (A), second order oc-
tree (B) and original data set of the Randa rockfall
3.4 Surface reconstruction
Surface reconstruction is a topic of great interest
in the computer graphics field and there are numer-
ous works regarding surface reconstruction. Amenta
and Bern (1998) give a short review of the most
popular algorithms.
The surface reconstruction is very important for
landslide or rock instability studies, since it allows
firstly for the automatic delineation of faults, and
secondly for visibility culling purposes, as points in
the background may make the interpretation diffi-
Figure 5. Original point cloud (A), vegetation points high-
lighted in red (B) and picture of the scanned area (C) (Boule-
vard Champlain, Quebec City, Canada).
Most, if not all, of the surface reconstruction al-
gorithms imply that the surface to reconstruct is
smoothed and that the sampling density is fine
enough to capture all its features. However, this as-
sumption can often not be met on the scanned rock
surfaces, due to the intrinsic roughness of the study
site and/or the distance from the ground-based Lidar
device to the target (up to 1,000 m), which may lead
to an undersampling and the missing of small scale
features. Moreover, the collected data points may
easily reach values on the order of millions, impair-
ing most of the standard algorithms (Shaffer and
Garland, 2005). To overcome these problems, a lo-
cal reconstruction algorithm similar to the one pro-
posed by Linsen and Prautzsch (2001) is used. For
each point p, all the points within a user-defined, 3D
radius are retrieved (p1,…,pk). The k points are then
projected on the plane defined by p and its normal.
A local coordinate system transformation allows for
2D Delaunay triangulation (Delaunay, 1934). The n
triangles, which hold point p as a vertex, are inserted
in the global triangle surface list, the others being
dismissed. Our experience shows that n should be in
the range of 5 to 7, which is consistent with well dis-
tributed points. As stated by Linsen and Prautzsch
(2001), local reconstruction does not ensure the
topological correctness of the surface, but a post fil-
tering process can easily overcome this problem.
Starting from using the above triangulation method,
the surface can be represented with COLTOP-3D
color scheme (Fig. 6).
Figure 6. Surface reconstruction of a scattered points cloud of
the Randa rockfall. The colors correspond to the dip angle and
dip direction of the surfaces.
To illustrate an application of COLTOP-3D, we
present the analysis of a mountain peak in the Swiss
Alps, Grand Muveran summit (3051 m a.s.l.). This
peak is transected by long faults that are very diffi-
cult to measure directly on the field, because they
affect the relief at a small scale. Furthermore, such
summits are not easy to survey without perilous
climbing efforts (Figure 7A). As shown on Figure 8,
these large faults generate rock instabilities within
the cliffs.
The analysis performed with COLTOP-3D indi-
cates that the fault slopes have a mean dip direction
of 205° and a mean dip angle of 45°. The DEM
cells, whose orientation are within a tolerance of
±20° around this mean direction can be exported
into a GIS file. The results (Figure 9) show that the
west facing slope is clearly shaped by these discon-
tinuities (Figures 7 and 9).
Figure 7. View of the west face of the Grand Muveran summit
displaying sets of faults on picture (A) and on the 1 m resolu-
tion airborne laser-DEM represented by a 3D shaded relief in
(B) (Source: MNT-MO/MNS, (c) 2007 SIT).
This shows that it is very easy to obtain structural
data using aerial Lidar DEM. This example also
shows the need to acquire data with ground based
Lidar in order to study the instabilities within the
cliffs. For example, the instability shown in Figure 8
needs to be analyzed in detail by terrestrial Lidar
and the new COLTOP-3D version.
In the basement rock of the Swiss Alps, the
fracturing is well enough developed to shape up
to 50% of the slope orientations, or even more at
outcrop scale. Often the entire slope is controlled
by two or three main discontinuity sets
(Jaboyedoff et al., 2004).
Structural analysis of the scar of Frank Slide
(Canada) permitted to refine previous interpreta-
tions (Jaboyedoff et al., in press).
Recent works on the Eiger collapse in Switzer-
land clearly show the control of structures, and
that 3D point clouds are needed to understand the
mechanism of rock instabilities (Oppikofer et al.,
in prep.).
Figure 8. Example of rock slope instability scar (in yellow-
beige) controlled by the faulting system shown in Figure 7.
The efficiency of of the COLTOP color representa-
tion of the relief has also been illustrated by the fol-
lowing examples:
Figure 9. Application of COLTOP-3D to the Grand Muveran
summit. The faults shown in Figure 7 (mean dip direction and
dip angle is 205°/45°) are identified in grey.
3D point clouds from airborne or ground-based Li-
dar recordings permit a rapid structural analysis.
This is useful since joints and instabilities are often
in inaccessible zones.
The colors obtained from grid DEMs using the
Hue Saturation Index in COLTOP-3D permit an
easy detection of the main features of a relief, such
as the main joint sets shaping rock faces. The col-
ored surfaces and their interactivity, allow for a de-
tailed structural analysis.
Unstructured clouds of 3D data points can serve
as a basis for surface reconstruction by triangulation.
M analysis tools will
point clouds open
a lot of new perspectives in relief interpretation as
he management of huge
oject (Stranda Commune,
Norway) and its leader Dr. L. Blikra for providing
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signed to each triangle. This color representation
forms a simple way to quickly obtain information
for slope analysis. COLTOP-3D has shown its effi-
ciency using square DEM grids. The difficulty to
implement a true 3D version comes from: (1) stor-
age and access of huge Lidar data sets; (2) octree
classification and triangulation of data points; (3)
extraction of 3D surface resulting from the triangu-
lation, and; (4) representation of the 3D surface ac-
cording to a Hue Saturation Index wheel using the
dip direction and dip angle.
The promising preliminary results presented here
indicate that these new DE
atly help structural geologists and rock mechan-
ics engineers by supplementing part of the classical
field work and permitting to contribute to more
quantitative field work analysis.
The use of both grid DEM and
gested in the above examples. The field work
will be greatly improved by the preliminary DEM
investigations in the office.
Since the power of computer will still increase in
the next years, permitting t
tasets, letting us think that the future is “cloudy”.
We thank the Åknes Pr
the DEM in Figure 1. D. Conforti and B. Ysseldyk
from Optech Corp. are thanked for their kind col-
laboration. We thank also the Canton de Vaud
(Switzerland) for providing DEM data of the Grand
Muveran region. We thank anonymous reviewers for
their help in improving this manuscript, especially
the GSC internal reviewer.
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... The advantage of the Hough transform for computing point normals is that millions of points can be processed, and the normal of a point on a sharp intersecting edge can be reconstructed [41]. COLTOP-3D is a software that can be intuitively used to visually identify discontinuities, faults, and cliffs in digital elevation models (DEMs) and point clouds [42,43]. The HSV-coloured point clouds facilitate visualization of the spatial distributions of set-based discontinuities [44,45]. ...
Full-text available
Airborne light detection and ranging (LiDAR) and unmanned aerial vehicle-structure from motion (UAV-SfM) provide point clouds with unprecedented resolution and accuracy that are well suited for the digital characterization of rock outcrops where direct contact measurements cannot be obtained due to terrain or safety constraints. Today, however, how to better apply these techniques to the practice of geostructural analysis is a topic of research that must be further explored. This study presents a processing procedure for extracting three-dimensional (3D) rock structure parameters directly from point clouds using open-source software and a three-dimensional distinct element code-assisted (3DEC) simulation of slope failure based on carbonate rock cliffs in the Jiuzhaigou Scenic Area. The procedure involves (1) processing point clouds obtained with different remote sensing techniques; (2) using the Hough transform to estimate normals for the hue, saturation, and value (HSV) rendering of unstructured point clouds; (3) automatically clustering and extracting the set-based point clouds; (4) estimating set-based geometric parameters; and (5) performing a subsequent stability analysis based on rock structure parameters. The results show that integrating different remote sensing techniques and rock structure computing can provide a quick way for slope engineers to assess the safety of blocky rock masses.
... In addition, simulations of the shear and fragmentation processes and the accompanying development of new discontinuities are still challenging because of the difficulties in obtaining and representing the information of complex discontinuities. Difficulties in obtaining pre-failure discontinuity information in the source area can be overcome through novel techniques, such as laser scanning (Jaboyedoff et al. 2007) or photogrammetry at the back scarp of the rock slope. Macciotta and Derek (2015) found a direct way to use the block size distribution at the source of a rock fall calculated through photogrammetric techniques to develop a discrete fracture network with the surveyed rock block sizes. ...
Rock avalanche is one of the most spectacular and catastrophic type of natural hazard phenomena. Those events typically start with a giant rock block or multiple blocks becoming detached from the rock slope, progressively fragmenting and transforming into rapidly moving cohesionless rock debris. Discontinuities are widely distributed in rock masses. Although research on rock avalanche phenomena is extensive, the role of discontinuities in different phases of rock avalanches, including the susceptibility, development, and runout phases, has not been systematically and comprehensively addressed, which has aroused a long-standing controversial issue. In this paper, the effects of discontinuities on the three phases of rock avalanches are systematically reviewed and discussed. The preexisting discontinuities influence not only the detachment of rock masses in the failure process but also their disintegration and propagation during runout. As a precursory factor, discontinuities control the kinematic feasibility of rock slope failure and the rock mass strength and thus control the susceptibility of the rock slope to failure as well as the size and spatial distribution of potential rock slope failure areas. During the development phase, the existing discontinuities will propagate and coalesce, increasing the slope fragmentation and decreasing the resistance to failure, and the kinematics of detachment evolve. It is worth noting that the evolution and failure phase would not happen, or just in moments in an earthquake-triggered events(s) or similar events. During runout, the control of discontinuities on rock avalanches is primarily reflected by shear and progressive fragmentation accompanied by heterogeneous distributions of stress and grain size, efficient energy transfer, and characteristic deposits. Nevertheless, dynamics of rock avalanches is complex, and controversial disputes remain; there is no straightforward conclusion. The inherent geology might play a dominant role in determining their strengthening or weakening effect in the various stages of rock avalanches. Several perspectives on future research are discussed, and approaches for focusing on the challenging research required to better our understanding of the role of discontinuities are suggested.
... This technology has been widely applied to geohazard recognition and inventory mapping, emergency investigation, and risk assessment by many countries, including Italy, Austria, Japan, New Zealand, and so on (Comert et al., 2019;Petschko et al., 2016;Chigira et al., 2004). With the development of the LiDAR technology and continuous reduction of its cost, the coverage and volume of the country-wide airborne LiDAR data further expand and grow (Ardizzone et al., 2007;Chen et al., 2006), which provides huge resources for geohazard recognition in dense-vegetation areas and leads to new insights into the evolution of various geomorphological landscapes (Bell et al., 2012;Jaboyedoff et al., 2012Jaboyedoff et al., , 2007. ...
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Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging (LiDAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the LiDAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne LiDAR DEM derivatives. Quasi-3D relief image maps are generated based on the sky-view factor (SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km2 study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of LiDAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m2. Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of LiDAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.
Collecting quantitative data to support geological analysis and modelling is nowadays a fundamental requirement in all geology disciplines, including structural geology, stratigraphy, and geomorphology, on the Earth and on planetary bodies of the Solar System. In many cases the answer to this need is a Digital Outcrop Model (DOM), a Digital Elevation Model (DEM), or a Shape Model (SM): this can be a digital representation of an outcrop or topographic surface, or of a whole small body (asteroid or comet nucleus) for an SM, generally combined with imagery, that can be quantitatively visualized and studied in 3D, with the goal of obtaining quantitative measurements. 3D datasets and models for geological purposes include different complementary products: DEMs, DOMs, SMs, and subsurface models. The main differences among these different products are: (i) their nature, since DEMs, DOMs, and SMs represent relief surfaces showing outcropping geological structures that are completely accessible to characterization (up to some precision/resolution), while subsurface models reproduce inaccessible subsurface geological structures with some unavoidable level of uncertainty (hence they are models ); and (ii) their topology/dimensionality, as DEMs are actually 2.5D surfaces, generally covering large areas, DOMs are truly 3D surfaces, including multivalued reliefs (e.g. complex or overhanging reliefs, cliffs, caves, etc.), but are generally limited to smaller‐scale outcrops, and SMs are closed surfaces covering a whole small body, where subsurface models are essentially volumetric. In this volume we collect various examples of methods and techniques used to collect, analyze, and model 3D datasets, based on one or more supports (DEM, DOM, SM, subsurface model), and on different software tools, remote sensing, and modelling techniques. Reading the chapters authored by experts in different fields, it will become apparent that (i) the fundamental techniques allowing the production of DEMs, DOMs, and SMs through photogrammetry, laser scanning devices, and radar interferometry are well consolidated, and are almost seamlessly shared between the community of scientists working on the Earth and on planetary bodies of the Solar System; (ii) the particular way these techniques are applied in specific geological environments may change and, for instance, acquisition schemes in photogrammetry still represent a potentially critical issue; (iii) DOM, DEM, and SM processing, elaboration and analysis, including the analysis of image data associated with these surfaces, are active fields of research that are subject to continuous improvements; and (iv) the production of subsurface geological models based (also) on surface data is still not very common, particularly in planetary geology contexts. One of the aims of this volume is to disclose the numerous points that geological disciplines have in common in applications on the Earth and on planetary bodies of the Solar System, and to favor the communication and collaboration between different scientific communities.
Collecting quantitative and extensive datasets in the field is fundamental in structural geology, stratigraphy, and sedimentology, rock mechanics, and in other fields of the Earth and planetary sciences. Digital Outcrop Models (DOMs) provide a 3D framework for collecting these large datasets and can be obtained from laser scanning or photogrammetric surveys, carried out either with an avionic platform (airplane, helicopter, drone) or with terrestrial methods. In this chapter we review best‐practice methods for collecting DOMs, focusing particularly on terrestrial and drone photogrammetric surveys and on critical issues that determine their efficiency, reliability, and accuracy. Then we compare the two main formats for DOMs: point clouds (PC‐DOMs) and textured surfaces (TS‐DOMs). Finally, we outline typical goals and workflows for the geological interpretation of DOMs on PC‐ and TS‐DOMs, either from laser scanning or photogrammetric surveys.
Terrestrial lidar scanning (TLS) has become a widely accepted expert tool for monitoring geohazards on bare or sparsely vegetated slopes through change detection. While trees can be an important indicator of landslide activity at a slope, vegetation is often removed or ignored when monitoring landslides with TLS. This paper explores the use of multi-temporal terrestrial lidar scanning at a slope in the Peace River valley of British Columbia to test the author’s hypothesis that tree stems in TLS data can be used to track landslide displacement and provide insight into the landslide mechanism. Six TLS datasets, each collected approximately 6 months apart, are used, and roto-translation methods are employed to determine the azimuth, plunge, and toppling angle of trees between each TLS scan. The tree displacement patterns are compared to TLS change detection results on bare-ground, and to single-point tracking techniques for extracting displacement vectors. Considerations for future applications are discussed.
A novel method is presented that is capable of more accurately extracting rock surface features based on the geometrical characteristics of a 3D point cloud obtained from a survey. The core feature of the algorithm is a newly established method that can provide a robust estimation of point normals, while excluding statistical outliers from the calculation. A resilient multivariate mean and covariance estimator, termed Deterministic Multivariate Mean (DetMM) estimator, is used, which is capable of removing outlier effectively when calculating normals. To ensure the efficient operation of the estimator, a hybrid method is proposed where a simple and efficient method to estimate normal vectors is implemented first to filter the data before the robust DetMM method is applied for more accurate estimation of difficult cases. By using this strategy, the efficiency is significantly improved while the same level of accuracy is maintained. In addition, a novel segmentation algorithm, a region growing method, is introduced to handle complex geological features, which is capable of accurately detecting very small rock surfaces. The robustness and reliability of the developed method are compared with those of the well-known normal estimation method using principal component analysis (PCA). Finally, the method is validated on two case studies where the 3D point datasets were gathered from scanning two very different rock faces. The results have demonstrated that the proposed method outperforms significantly the conventional techniques in terms of accuracy with an acceptable increase in computation cost.
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Quantitative characterization of discontinuities is fundamental to define the mechanical behavior of discontinuous rock masses. Several techniques for the semi-automatic and automatic extraction of discontinuities and their properties from raw or processed point clouds have been introduced in the literature to overcome the limits of conventional field surveys and improve data accuracy. However, most of these techniques do not allow characterizing flat or subvertical outcrops because planar surfaces are difficult to detect within point clouds in these circumstances, with the drawback of undersampling the data and providing inappropriate results. In this case, 2D analysis on the fracture traces are more appropriate. Nevertheless, to our knowledge, few methods to perform quantitative analyses on discontinuities from orthorectified photos are publicly available and do not provide a complete characterization. We implemented scanline and window sampling methods in a digital environment to characterize rock masses affected by discontinuities perpendicular to the bedding from trace maps, thus exploiting the potentiality of remote sensing techniques for subvertical and low-relief outcrops. The routine, named QDC-2D (Quantitative Discontinuity Characterization, 2D) was compiled in MATLAB by testing a synthetic dataset and a real case study, from which a high-resolution orthophoto was obtained by means of Structure from Motion technique. Starting from a trace map, the routine semi-automatically classifies the discontinuity sets and calculates their mean spacing, frequency, trace length, and persistence. The fracture network is characterized by means of trace length, intensity, and density estimators. The block volume and shape are also estimated by adding information on the third dimension. The results of the 2D analysis agree with the input used to produce the synthetic dataset and with the data collected in the field by means of conventional geostructural and geomechanical techniques, ensuring the procedure’s reliability. The outcomes of the analysis were implemented in a Discrete Fracture Network model to evaluate their applicability for geomechanical modeling.
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Rock slope failures in urban areas may represent a serious hazard for human life, as well as private and public property, even on the occasion of sporadic episodes. Prevention and mitigation measures indispensably require a proper rock mass characterization, which is often achieved by means of time-consuming, costly and dangerous field surveys. In the last decades, remote sensing devices such as high-resolution digital cameras, laser scanners and drones have been widely used as supplementary techniques for rock slope analysis and monitoring, especially in poorly accessible areas, or in sites of large extension. Although several methods for rock mass characterization by means of remote sensing techniques have been reported in specific studies, there are very few contributions that focused on comparing the different methods in an attempt to establish their advantages and limitations. With this study, we performed digital photogrammetry, Terrestrial Laser Scanning and Unmanned Aerial Vehicle surveys on a cliff located in a popular tourist attraction site, characterized by complex geological and geomorphological settings, as well as by disturbance elements such as vegetation and human activities. For each point cloud, we applied geostructural analysis by means of semi-automatic methods, and then compared multi-temporal acquisitions for cliff monitoring. By quantitative comparison of the results and validation by means of conventional geostructural field surveys, the pros and cons of each method were outlined in attempt to depict the conditions and goals the different techniques seem to be more suitable for.
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Rockfall events consist one of the most hazardous geological phenomena in mountainous landscapes, with the potential to turn catastrophic if they occur near an anthropogenic environment. Rockfall hazard and risk assessments are recognized as some of the most challenging surveys among the geoengineering society, due to the urgent need for accurate foresight of likely rockfall areas, together with their magnitude and impact. In recent decades, with the introduction of remote sensing technologies, such as Unmanned Aerial Vehicles, the construction of qualitative and quantitative analyses for rockfall events became more precise. This study primarily aims to take advantage of the UAV’s capabilities, in order to produce a detailed hazard and risk assessment via the proposition of a new semi-quantitative rating system. The area of application is located in the cultural heritage area of Kipinas Monastery in Epirus, Greece, which is characterized by the absence of pre-existing data regarding previous rockfall events. As an outcome, it was shown that the suggested methodology, with the combination of innovative remote sensing technologies with traditional engineering geological field surveys, can lead to the extraction of all the necessary quantitative data input for the proposed rating system for any natural slope.
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We give a simple combinatorial algorithm that computes a piecewise-linear approximation of a smooth surface from a finite set of sample points. The algorithm uses Voronoi vertices to remove triangles from the Delaunay triangulation. We prove the algorithm correct by showing that for densely sampled surfaces, where density depends on a local feature size function, the output is topologically valid and convergent (both pointwise and in surface normals) to the original surface. We briefly describe an implementation of the algorithm and show example outputs.
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We present a new external memory multiresolution surface representation for massive polygonal meshes. Previous methods for building such data structures have relied on resampled surface data or employed memory intensive construction algorithms that do not scale well. Our proposed representation combines efficient access to sampled surface data with access to the original surface. The construction algorithm for the surface representation exhibits memory requirements that are insensitive to the size of the input mesh, allowing it to process meshes containing hundreds of millions of polygons. The multiresolution nature of the surface representation has allowed us to develop efficient algorithms for view-dependent rendering, approximate collision detection, and adaptive simplification of massive meshes. The empirical performance of these algorithms demonstrates that the underlying data structure is a powerful and flexible tool for operating on massive geometric data.
Accurate prediction of rockfalls is a major need in mountain areas, both for hazard assessment and the design of countermeasures. In this paper, the performance of an original simulation code, initially developed for regional-scale analysis, is tested at the local scale by using high-resolution input data, in order to show its application to site-specific problems. The code is based on a kinematic algorithm and allows to run detailed, spatially distributed simulations of rockfall on a three-dimensional topography described by a Digital Elevation Model. Two examples from the Central Italian Alps, both characterised by the occurrence of frequent historical events, valuable elements at risk (urban areas, corridors) and countermeasures (barriers and retaining walls) are presented. The suggested approach proves to effectively account for rockfall dynamics when used with high-resolution data. Model calibration issues are discussed and model results are compared to available experimental data. The scale dependency of the results is also discussed.