E3S Web of Conferences 415, 03026 (2023)
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https://doi.org/10.1051/e3sconf/202341503026

Photogrammetrically UAV based terrain data generation andautomatic extraction of torrential properties
Gregor Rafael Schmucki1,2,4*, Perry Bartelt1,2, Yves Bühler1,2, Andrin Caviezel1,2, Christoph Graf3, Mauro Marty3,Andreas Stoffel1,2 and Christian Huggel4
1 WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, CH 7260
2 Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, CH 7260
3 Swiss Federal Institute for Forest Snow and Landscape Research WSL, Birmensdorf, Zürich, CH 8903
4 University of Zurich, Department of Geography, Zurich, CH 8057

Abstract. Debris flows are a severe hazard in mountainous regions. However, cost-effective long-term
studies of debris flows are seldom, which leads to substantial uncertainties in hazard mitigation methods.
This paper investigates whether cost-effective remote sensing techniques can be applied to assess the hazards
of mountain torrents and to gather accurate long-term information on the development of the watershed.
Torrents that are prone to debris flows are often devoid of vegetation and can thus be well surveyed using
photogrammetric methods based on uncrewed aerial vehicle (UAV) surveys. The possibility of extracting
automatically torrent parameters from high-resolution terrain models, such as cross-sectional area or slope,is explored. The presented methodology yields continuous and automatically derived parameters along thetorrent, which is a major advantage over pointwise field surveys. Cross-validation with field measurements
reveals a strong agreement. These parameters are very accurate along highly incised sections, while they areseverely limited along sections with steep adjacent hillslopes and/or dense vegetation. We show that thesekinds of assessments greatly gain from UAV data followed by automatic parameter extraction. The extracted
parameters provide insights so that key sections and weak points can be identified and accurately assessedin the field. We find that UAV data can contribute to a comprehensive, reproducible and objective
assessment of torrent processes and predispositions. However, ground-based fieldwork is still essential andfurther research on remote sensing-based hazard assessment of torrents prone to debris flows is crucial.

1 Introduction
The way debris-flow hazard analyses are performed
vary
greatly[1].
In
Switzerland,
somegeomorphological assessment approaches are atdisposal to assess debris-flow prone catchments [2-5].
So far, none of these methods have become anestablished standard in engineering practice. The
amount of available time and money for field
investigations are important factors for practitioners. As
a result, often only a limited amount of field work canbe conducted. Nevertheless, accurate, up-to-dateand in-depth information about a study site is ofvery great importance and crucial for the quality of
the hazard assessment. The use of UAV may open up
new opportunities for more objective and time-efficienthazard assessments. We address the question of how
cost-effective remote sensing techniques can be used in
torrent hazard assessment. In particular, whether groundclassification algorithms applied on photogrammetrically derived point clouds allow for the derivationof high resolution digital terrain models which aid in an
automatic extraction of continuous data along thetorrent. The advantages of UAV derived terrain data canbe found in its high spatial resolution and a
representation of the current state of the torrent
morphology. These days photogrammetric derived point
cloud can be produced to significant lower costs
compared to LiDAR UAV surveys. The acquisition
costs of a LiDAR UAV are many times higher thanthose of a photogrammetric UAV. Here, we investigated
the ground classification of photogrammetric point

clouds, knowing that LiDAR based products would
provide highly more accurate results.
A detailed comparison of LiDAR andphotogrammetric derived terrain models and an in-depth
analysis of how (photogrammetric) UAV data may be
used within the hazard assessment of torrents can befound in [6].

2 Point Cloud Classification
We classified photogrammetric point clouds in highalpine terrain. As conventional standard classification
routines perform poorly, we develop dedicated routines
which succeed in such complex alpine terrain.
Photogrammetrical approaches which make use of RGB
information seem to provide the best results [7].
Followingly, we highlight two methods based on theproprietary software LAStools and Terrasolid (seeFigure 1).
2.1 Terrasolid based Approach
TerraScan is a powerful software solution for processing
LiDAR point clouds [8]. With the Terrasolid software,
the aim was to create a classification routine bygenerating a smooth surface that can then be classified
while disregarding residual noise. From the original
point cloud, less reliable, noisy and isolated points areremoved. In a subsequent step, a smoothed surface is© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 03026 (2023)
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Fig. 1. Two point cloud classification approaches based on the dedicated software tools Terrasolid and LAStools after [8].

generated and a vegetation index is calculated for theensuing ground classification.
The Terrasolid-basedgroundclassification
algorithm is limited in distinguishing between grass
vegetation and higher vegetation such as shrubs andtrees. Along the channel bed, this limitation has littlesignificance. However, when the derived terrain modelis used for numerical simulations, the results require agreat deal of caution when analysing them.

possible ground points are then classified using a highlycoarse step size. Within a subsequent iterative
triangulated irregular network (TIN) densification withdifferent step sizes, the remaining noise points areremoved. However, sharp edges such as artificial
constructions are cut off in the final DTM through thisprocess. A further limitation is that the methodology
relies on a coarse reference height model to distinguishbetween high vegetation and grass vegetation. In regionsof the world where there is no such data available thisapproach will unfortunately not be applicable.

2.2 LAStools based Approach

Both approaches presented have limitations thatneed to be carefully considered. Depending on theterrain, the more powerful algorithm should be chosen.
If the torrent is densely covered with vegetation, aphotogrammetric UAV flight during the leaf-free season
or a LiDAR-based flight may improve the results
implying higher costs, obviously.

LAStools is a another dedicated tool for post-processingLiDAR point clouds, which allows the workflow to beadapted to the specific requirements [9]. Here we aimed
for a ground classification that considers all potential
ground points, even below the top vegetation layer,
which is applicable in steep, densely vegetated terrain.
In a first step, the potential ground points (lowvegetation and bare ground) are selected based on thecalculation of a difference to a reference height modeland the computation of a vegetation index. The resulting

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Fig. 2. Automatically extracted torrential properties within a GIS and Object-based Image Analysis (OBIA) based workflow.

The embankment extraction was performed using
OBIA in eCognition. A multi-resolution segmentation
was performed based on a calculated distance map,slope and orientation. The objects representing the slope
were selected based on different conditions. This
segmentation works well in terrain with a highlyvariable inclination between the adjacent slope and theembankment. Along areas with a steep adjacent slope,
automatic extraction of the torrent bed is severelylimited. Another main limitation was found along
densely vegetated areas where the derived
photogrammetrical terrain models are stronglytriangulated.
As can be seen in Figure 3, the method allows for acontinuous computation of the torrent properties along
the torrent. Since the fully automatic OBIA-based
extraction was severely limited in complex terrain, weadditionally drew manual polygons representing theslopes. The cross-section area derived from GIS inFigure 3 shows the calculated cross-section based on themanually drawn polygon. There are some strong
variations in the upper catchment. However, the main
patterns along the torrent can be found in both versions.

3 Torrential Properties Extraction
Field observations are crucial when assessing thepotential magnitude of debris-flow prone torrent. Many
assessment approaches aim to estimate the geometricfeatures of the torrent in order to obtain an estimate ofpotential depth and embankment erosion. As these fieldmeasurements are very time-consuming, discrete andbased on subjective expert judgement, the goal is toautomatically extract the required parameters through a
GIS and Object-based Image Analysis (OBIA)
workflow (see Figure 2).
Based on a hydrological analysis, we extracted apolyline representing the torrent. Along this line,transect lines were created perpendicular to the torrent.
The elevation attribute of the transect point wastransferred to the transect lines and gridded with this
attribute to create a continuous surface representing theelevation of the torrent bed. Subtracting the elevation of
the torrent bed from the elevation of the DTM provides
a representation of the relative elevation above thetorrent. A suitable threshold is chosen to extract thebottom of the torrent.

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Fig. 3. Continuous illustration of the GIS and OBIA based torrential parameters of the Arelen Catchment near Davos CH.

The derived measures can be used for assessing weakpoints, bedload capacity thresholds, dividing the torrent
into homogenous sections or intercompare it withtemporally spaced surveys.

assessed on the basis of accurate fieldwork. UAV dataand its derivatives have the potential to provide a
stepping stone towards reproducible and objective
hazard assessment. We urge to combine proper
fieldwork and automatic extraction of torrent
dimensions to be implemented in modern torrent hazard
assessment procedures.

4 Conclusions and Outlook
We introduce approaches for the automatic extraction of
torrent properties based on photogrammetrically derived
UAV data. We show how hazard assessments may beenriched and complemented by objective, fast andspatially complete key parameters. The proposed
approaches particularly lead to a faster, morereproducible and objective torrent analysis. The
presented methodology provides continuous geometry
data along the torrent, which is a great advantage over
the often locally limited field measurements. Crossvalidation with field measurements reveals goodagreement. However, a large-scale independent study
with accurate independent field measurements would
need to be conducted by practitioners to prove thevalidity of these results. Problems with dense vegetation
along the embankment could be solved by applying thealgorithm to LiDAR-UAV terrain data. Difficulties
caused by steep adjacent slopes remain.
At this stage, the cost of LiDAR-based surveys ismultiple times higher than photogrammetric surveys.
We therefore recommend that the data be acquired
photogrammetrically and processed using the proposedclassification routines. Along densely vegetated areas of
the catchment, additional LiDAR flights may beconsidered to further improve the results.
In summary, in-depth hazard assessment remains achallenge. Further interdisciplinary research combining
remote sensing techniques and hazard assessment
methods for a wider variety of study areas is crucial.
Fieldwork will always remain a compulsory part of theassessment process. At this stage, the available
sediment, erosion capacity, slope processes and thecondition of mitigation structures can only be properly

References
1.

2.

3.

4.

5.

6.

4

M. Jakob, Debris-flow Hazards and Related
Phenomena, chap. Debris-flow hazard analysis,411–443, Praxis Publishing, (2005)
E. Gertsch, C. Lehmann, M. Spreafico, Methods
for the Estimation of Erosion, Sediment Transportand Deposition in Steep Mountain Catchments,
International Commission for the Hydrology of theRhine Basin (2012)
E. Gertsch, Geschiebelieferung alpiner
Wildbachsysteme bei Grossereignissen Ereignisanalysen und Entwicklung eines
Abschätzverfahrens, Ph.D. thesis, Universität Bern
(2009)
E. Frick, H. Kienholz, H. Roth, SEDEX - eine
Methodik zur gut dokumentierten Abschätzung derFeststofflieferung in Wildbächen, Wasser Energie
Luft, 2 (2008)
M. Spreafico, C. Lehmann, O. Naef, Empfehlung
zu Abschätzung von Feststofffrachten in
Wildbächen, 4, Arbeitsgruppe für operationelle
Hydrologie, Bern (1996)
G. R. Schmucki, P. Bartelt, Y. Bühler, A.
Caviezel, Ch. Graf, M. Marty, A. Stoffel, Ch.
Huggel, Towards an automated acquisition andparametrization of debris-flow prone torrent
channel properties based on photogrammetricderived UAV data. Earth Surface Processes andLandforms 48, 9 (2023)

E3S Web of Conferences 415, 03026 (2023)
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7.
8.
9.

https://doi.org/10.1051/e3sconf/202341503026

N. Anders, J. Valente, R. Masselink, S. Keesstra,
Drones 3, 61 (2019)
Terrasolid, TerraScan User Guide, Terrasolid Ltd,(2021)
LAStools, Efficient LiDAR Processing Software,
rapidlasso GmbH, (2022)5