E3S Web of Conferences 415, 01001 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341501001

Debris-flow monitoring with high-frequency LiDAR scanners: anew method to infer the internal dynamics of debris flows
Jordan Aaron1,2*, Raffaele Spielmann1,2, Brian W. McArdell1, and Christoph Graf1
1 Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland
2 Geological Institute, ETH Zürich, 8902 Zürich, Switzerland

Abstract. In-situ measurements of debris-flow properties are crucial for understanding their movement
mechanisms and quantifying their impact. Here we present the first results of a field monitoring campaign,at Illgraben, Switzerland, to measure debris-flow parameters using high temporal (10 Hz) and spatial
resolution LiDAR sensors at several locations along the channel. The point cloud data is projected onto
video images to enhance visualization and aid in the interpretation of the measurements. We process thedata using machine vision and deep learning based algorithms, and show that this system can accurately
measure front and flow surface velocity, flow depth and bed elevation change, as well as the size, style ofmotion (e.g. rotating or floating without rotation) and trajectories of individual particles. This system thus
provides a promising new method for inferring the internal dynamics of debris flows.

1 Introduction
Improved measurements of in-situ debris-flow
properties are critical for better process understanding
and risk quantification [1,2]. In particular, hightemporal and spatial resolution measurements of criticalparameters, including front and surface velocity, flow
depth, channel bed elevation change, and individual
particle motion are crucial for understandingfundamental debris-flow mechanisms, which include
excess pore pressure generation and dissipation, basal
and bank erosion, and longitudinal sorting [1-6]. Here
we use time-lapse LiDAR scanners, originallydeveloped for autonomous vehicles, combined withimage and deep-learning based processing algorithms,
to make in-situ measurements of these criticalparameters.
Debris-flow properties have been measured in-situ atmany locations [1]. These studies have revealed awealth of information regarding basal forces and porepressures [3,6], velocity profiles [3], accumulation anddepletion volumes between events [7], front and surface
velocities [1], as well as flow hydrographs and volumes[1].
LiDAR scanners provide a means to directly
measure many in-situ parameters of debris flows [8].
However, this data is difficult to process, and there is aneed for algorithms that can be used to derive criticalparameters from LiDAR data. Further, high-frameratecameras are capable of collecting a wealth of
information about the surface of moving debris flows,
but suffer from camera distortion. LiDAR-Camera
fusion presents an opportunity to derive 3D informationfrom these videos, although this has yet to be tested infull scale debris flows.
In the current work we present and analyse a uniquedataset of timelapse point clouds of moving debris
flows, collected at the Illgraben in Valais, Switzerland.
We fuse the point clouds to video data collected at thesame site in order to derive high spatial and temporal
resolution measurements of critical debris-flow
parameters. The results have significant implications
for understanding debris-flow motion.

2 Study site and event description
Timelapse point clouds of a moving debris flow
were recorded at the Illgraben catchment, located in theSwiss Canton of Valais. The Illgraben has a long history
of in-situ monitoring of debris flows [1,6], and a longterm monitoring system was established in the year
2000. Presently, this system includes depth sensors at 5locations, a force plate that measures basal normal andshear stresses, as well as the LiDAR scanner and video
cameras used in the present work.
The Illgraben spans approximately five kilometersfrom the upper catchment, where debris flows initiate,
until the Rhone river, where they deposit. The data usedhere was collected at a monitoring station approximately
2.5 km from the upper catchment, on a platform
suspended over the channel using two cables. Thestation is located above a concrete check dam, andmonitors 60 m upstream and 70 m downstream.
A debris-flow event occurred on September 19,
2021, and was captured by the monitoring system (Fig.
1). This event lasted for approximately 30 minutes, and* Corresponding author: jordan.aaron@erdw.ethz.ch© 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, 01001 (2023)
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4 Results and discussion

featured a bouldery front followed by a finer grained
slurry. We analysed this event in the present work.

We captured a continuous time series of point clouds
throughout the event, as shown on Fig. 1. This dataclearly show the front arrival, the trajectories ofboulders and woody debris within the flow, as well as
the change in the height of the surface of the debris flow.
Manual mapping and the two PIV based velocity
estimate methods show that the front velocity varies
between 0.8 and 1.75 m/s, and surface velocities during
the event reach up to about 3 m/s [13].

3 Methods
The debris flow was captured using an Ouster OS1-64(Gen-1) LiDAR scanner that has a 33.2 degree field ofview, and 64 scan rings that each collect 2048 points at
a frequency of 10 Hz. The scanner is positioned on aplatform attached to two cables in the center of thechannel. Alongside the LiDAR scanner, two cameras
captured the debris flow at a resolution of 1920x1080
pixels, and 25 frames per second. The monitoring
station is triggered by an upstream geophone, which
sends a signal to a custom logger program which records
the LiDAR data.
To take full advantage of the complementary
sensors, we adapted the method provided in [9] toestimate the rigid body transformation (translation androtation) between the two cameras and the LiDAR
sensor. The results of this, shown on Fig. 2, allow us toobtain 3D coordinates of pixels in the image, and tocolour the point cloud in locations where the field ofview overlaps. As described below, this transformation
is used to estimate debris-flow front and surface
velocities, as well as to track individual particles within
the debris flow.
Further, we estimated thetransformation between the LiDAR data and pre- andpost- event DTM’s derived from UAV data [7].
We use four methods to estimate debris-flow
velocities:
1. Manual mapping of boulders and woody debris in
the point cloud data, as described in [13].
2. Particle image velocimetry (PIV) applied to thevideo frames, with subsequent projection of pixel
displacements into the LiDAR point clouds [14].
3. PIV applied to hillshade projections of the LiDAR
data [14].
4. Automatic detection and localization of boulders
and woody debris using a convolutional neuralnetwork [14].
For methods 2 and 3, the Matlab toolbox PIVLab[10] was used.
For method 4, we trained an off-the-shelf
implementation of YoloV5 [11] to automatically detectlarge boulders and woody debris in the video images[14]. Subsequently, we use the ‘Sort’ algorithm [12] toassociate detections in subsequent video frames into
tracks of individual particles. Finally, we projected thetrack detections into the LiDAR point clouds to obtain
their 3D position.
We obtained instantaneous flow depths bydifferencing each frame obtained during the debris-flow
event with the pre-event scan, assuming that thechannel-bed elevation remains constant. A similar
procedure was used to obtain the height difference
between pre- and post- event scans, in order to highlightareas of erosion and deposition. There are some
substantial uncertainties in this flow-depth estimation
procedure, because the true base of the flow is unknown
due to erosion and deposition through time during theevent.

Fig. 1: Overview of sensor setup, arrival of the front andovertopping of the check dam (lower panel). LiDAR data isoverlain on a UAV derived DTM of the site (data provided
courtesy of T. de Haas using methods described in [7]). Axis
ticks are in m, and referenced to a local coordinate system.

Our object detector achieved a test-set precision of~0.8, and a recall of ~0.6 [14]. The sort algorithm
identified about 15,800 tracks, 5,513 of which were
present for more than 1 s. The difference between thesetwo values indicates that the same track is likely
detected multiple times by the algorithm. A more robust
association of detections into tracks is a subject for
future work. Example detections from the network areshown Fig. 2, as are the corresponding point clouds forselect features. Velocities of certain features are shown2

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on Fig. 3. As can be seen on the right of Fig. 2, the twodetections near the camera are precise, however image
distortion further from the camera results in theextraction of points behind the target feature. Further
processing of the extracted features should overcome
this limitation.

Fig. 2: Object detector output for a frame corresponding to the front arrival. The blue rectangle show the neural network detections,the black dots show the LiDAR point cloud projected onto the image, and the red boxes show three features whose corresponding point
cloud was extracted, and are shown on the right side of the Figure. The features were extracted following interpolation of the point
cloud, and the tree is colored based on the camera image. The units of the ticks on the inset are in meters, and are referenced to a local
coordinate system.

Fig. 3: Surface velocity through time, estimated using the methods described in [14] and in the text.

We find that surface velocities of the event varystrongly (Fig. 3), likely due to changing composition of
the flow through time. We note a distinct increase invelocity at approximately 7 minutes (Fig. 3).
Interestingly, our two PIV-based automatic velocity
estimation techniques provide different velocity
estimates following this jump (see also [14]). Manualfeature mapping indicates that different objects havedifferent velocities (Fig 3). The two automatic methods

appear to be sensitive to different features of the flow
[14].
Finally, the vertical difference between the pre- andpost- event scans (Fig. 4) show net deposition above thecheck dam (y-coordinate > 0), and some erosion
downstream of the check dam where the channel goesaround a bend. We note that the LiDAR scan does notspan the width of the channel at all locations, so this3E3S Web of Conferences 415, 01001 (2023)
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The authors would like to thank Stefan Boss for his excellent
support with the installation and maintenance of the fieldinstallation. We also thank Sebastian Harder and Melissa
Kundert for their diligent work in creating the test set used for
CNN training, and Tjalling de Haas, Utrecht University, for
supplying DEM data. Funding for this project was provided by
SNSF Ambizione Grant (PN 193081), as well as internal funds
from the Chair of Engineering Geology, ETH Zürich.

accumulation/depletion estimate is only valid for asubset of this particular location.

6 References
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Fig. 4: Erosion and deposition areas obtained fromdifferencing the pre- and post- event scans. The coordinatesystem is an unreferenced local coordinate system, with tickvalues in meters.

5 Conclusion and outlook
Timelapse LiDAR scanners, fused with high frameratecamera data, provides a means for collecting a wealth ofdata from moving debris flows. Here we have shownthat this information can be used to automatically derive
dense surface velocity, flow depth, bed elevation change
and feature trajectory information. These parameters
are crucial for understanding debris-flow mechanisms,
and ultimately managing their hazard.
The results presented herein were taken from thefirst event captured with the system at Illgraben. Overthe next years, further events that occur in the catchment
will enable an analysis of a variety of relevant debrisflow phenomena, such as the temporal and spatialvariation in the size of large features, superelevation,and surge velocity, which will help to elucidate some ofthe processes described here. Further, the influence ofdebris-flow velocity and feature size will be explored infuture events. It is expected that careful analysis of thisdata will reveal surprising features of debris-flow
motion, and will provide a foundation for a newunderstanding of these destructive flows.

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