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Drone-based UWB radar to measure snow layering in avalanche starting zones

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Slab avalanches release due to failure in a weak snow layer. Determining the spatial distribution and depth of weak layers in avalanche starting zones are high-risk tasks. Moreover, by manually digging snow pits, the occurrence of a weak layer can only be identified on a pit scale (meters). We therefore propose a technical solution to this problem by mounting an Ultra Wide Band (UWB) radar system onto a drone to obtain information about the occurrence, depth, and spatial distribution of weak layers over a larger area in order to improve safety for avalanche professionals. Here, we present the testing of an UWB radar system and show its capabilities of detecting snow stratigraphy. To simulate airborne operations, we have during the spring 2016 operated the radar system via a stationary rig 1 m above the snow, along 4.2 m long transects. For verification, we dug a full snow profile pit, identifying snow stratigraphy, liquid water content and snow density using traditional methodology as well as the Avatech SP2 and the Toikka SnowFork. Preliminary results were promising and showed the potential of an airborne UWB radar in detecting distinct snow layers. In the coming winter, the radar will be mounted on a drone to perform further airborne measurements. Future work will also include more comprehensive data analysis methods to improve snow layer identification and classification.
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DRONE-BASED UWB RADAR TO MEASURE SNOW LAYERING IN AVALANCHE STARTING ZONES
Rolf Ole Rydeng Jenssen*1,2, Markus Eckerstorfer1, Hannah Vickers1, Kjell-Arild Høgda1, Eirik Malnes1,
Svein Ketil Jacobsen2,1
1Norut, Northern Research Institute, Tromsø, Norway
2UiT The Arctic University of Norway, Department of Physics and Technology, Tromsø, Norway
ABSTRACT: Slab avalanches release due to failure in a weak snow layer. Determining the spatial
distribution and depth of weak layers in avalanche starting zones are high-risk tasks. Moreover, by
manually digging snow pits, the occurrence of a weak layer can only be identified on a pit scale (meters).
We therefore propose a technical solution to this problem by mounting an Ultra Wide Band (UWB) radar
system onto a drone to obtain information about the occurrence, depth, and spatial distribution of weak
layers over a larger area in order to improve safety for avalanche professionals. Here, we present the
testing of an UWB radar system and show its capabilities of detecting snow stratigraphy. To simulate
airborne operations, we have during the spring 2016 operated the radar system via a stationary rig 1 m
above the snow, along 4.2 m long transects. For verification, we dug a full snow profile pit, identifying
snow stratigraphy, liquid water content and snow density using traditional methodology as well as the
Avatech SP2 and the Toikka SnowFork. Preliminary results were promising and showed the potential of
an airborne UWB radar in detecting distinct snow layers. In the coming winter, the radar will be mounted
on a drone to perform further airborne measurements. Future work will also include more comprehensive
data analysis methods to improve snow layer identification and classification.
KEYWORDS: UWB radar, UAV, Snow stratigraphy, Avalanche risk assessment, Avatech.
1. INTRODUCTION
The release of a dry snow slab avalanche requires
the formation of failure within a so-called weak
layer buried below a cohesive, denser slab layer
(Gaume et al., 2016). To determine the
occurrence and spatial distribution of weak and
slab layers, pit scale observations are upscaled
using process thinking. This traditional field
methodology is both time consuming, risky, and
prone to observer bias. We therefore propose a
noninvasive method of obtaining spatially
upscaled information about snow stratigraphy in
avalanche starting zones, by using an UWB radar
mounted underneath an unmanned aerial vehicle
(UAV) or drone. This method has to some extent
been explored with semi-stationary, ground based
rigs before (Gogineni et al., 2013; Kanagaratnam
et al., 2007; Marshall et al., 2007). A similar
approach has also been tested from airplanes
over arctic sea ice (Kwok et al., 2011). Here we
present the first results obtained using a stationary
rig where the radar was mounted in order to
simulate airborne data acquisition.
2. METHODS
2.1 Ultra Wideband Snow Sensor (UWiBaSS)
The Ultra Wide Band Snow Sounder (UWiBaSS)
is a UIT developed radar system (Fig. 1)
specifically designed to detect snow stratigraphy.
It consist of an Ilmsens m:explore1 sensor
connected to two Archimedean spiral antennas
(one transmit (TX), one receive (RX)) and a single
board computer running the Ilmsens developed
software to acquire and log the impulse
responses. The constructed antennas have
opposite polarization for RX and TX; where RX is
Right Hand Circularly Polarized (RHCP) and TX is
Left Hand Circularly Polarized (LHCP) (Fig 2).
These antennas have a measured frequency
range of approximately 950 MHz - 11 GHz (ca. 10
GHz bandwidth) (Tbl 1). However, the full range of
the antennas is not used since the radar sensor
operates at 0.1 - 6 GHz (5.9 GHz bandwidth) (Tbl
1). The antennas were placed in a housing with
absorbing material in the backing cavity to remove
the rear lobe of the antenna (Fig. 1). This causes a
1 http://ilmsens.com/index.php/en/m-explore
*
Corresponding author address:
Rolf Ole Rydeng
, Norut,
Pb 6434, Tromsø Science Park, 9294 Tromsø
;
tel:
+47 77 62 94 00;
email: rolfolejenssen@gmail.com
Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016
573
reduction of 0.5 (50 %) in antenna efficiency (i.e.3
dB gain loss), but the alternative is to use a
reflective backing, which is a very difficult task to
accomplish given the large bandwidth. With such a
large bandwidth, a reflector needs to facilitate for
all frequencies to avoid cancelling when returning
from the reflector. There are several suitable
antenna designs for snow measurement
applications (Mosy, 2009), however the
Archimedean spiral antenna have a impedance
stability over a very large bandwidth that is hard to
match with other antenna designs.
Fig 1: UWiBaSS system with antennas at the
bottom. UWB sensor and control PC are
mounted inside the box
Fig 2: Archimedean spiral antennas with opposite
polarization.
Tbl 1: UWiBaSS characteristics,
Sensor Value
Bandwidth 5.9 GHz
Range Resolution 2.252 cm
Sampling rate 13.312 GHz
Measurement rate 1 kHz
MLBS order 9 (511 points)
Antenna Value
Bandwidth 10 GHz
Efficiency 0.5
HPBW 70°
2.2
Data acquisition
rig
Before the radar is mounted on a UAV, we
simulated its intended elevated position above the
snow surface with a dolly rig (Fig. 3). To allow the
radar to move continuously and controlled in a
single direction a self-made camera dolly (mostly
used in film production to allow cameras to move
smoothly) was used, imposed on a rig using
aluminum ladders (Fig. 3). This proved to save
both time and funds, and yielded a smooth,
controlled, continuous data acquisition. Step
ladders where used as ”feet” for the rig, with some
extra clamps to hold the suspended ladder
between them. This assembly operated
satisfactory and was easily moved to different
locations if necessary. The total measurement
length became effectively the length of the ladder
chosen, which was approximately 4.3 m and
provided a measurement length of 4.2 m due to
the fastening at the end, and the length of the
dolly.
Fig 3: Experimental setup: Dolly rig with mounted
radar systems and PC to log data.
In Fig. 3 there are two mounted radar systems
visible. A Novelda radar2 and the UWiBaSS
radar, which were tested simultaneously for
comparison. The data from the Novelda radar
will not be presented in this paper. Some
experimentation with different speeds while
scanning was conducted to give some estimation
of the maximal speed one could use while still
acquiring a satisfactory image of the snowpack.
The maximum speed will depend on the desired
longitudinal resolution of the scan, but in our case
where centimeter resolution was desired the
2 https://www.xethru.com
Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016
574
maximum speed was found to be about 0.02 m/s.
There are many possibilities to how the scanning
might be performed from a UAV. For instance,
the UAV might be stationary during scanning and
move to selected locations in the avalanche
starting zone to perform data acquisition.
2.3 Data processing
Dry snowpacks have relatively low
electromagnetic attenuation and dielectric
constant. This allows the signal to penetrate with
ease and reach the bottom of the snowpack,
which is desirable. However, this also implies that
the reflected signal from each layer will be very
weak. Presently the processing of data is very
time consuming as even the smaller datasets
contained around 10 data points. We firstly
loaded the radar data and subtracted the
reference signal, before we modeled phase
response and compensated for non-linear phase.
Then we reformatted the data into an image and
performed image processing methods. These
included histogram equalization, wiener filtering
and sobel mask filtering. We then rectified the
pulse and adjusted for propagation speed through
the snow.
2.4 Snow pit observations
The snow profile measurements consisted of
identifying snow stratigraphy and taking a
hardness profile with the Avatech SP2 snow
probe, as well as measuring the density and liquid
water content with the Toikka Snow fork. The
density measurements with the Snow fork were
also correlated with weight measurements. During
both campaigns in February and March 2016,
snow conditions were dry.
3. RESULTS & DISCUSSION
3.1 Comparison between snow pit observations
and radar measurements
In the following, we present preliminary results that
will give a first impression on how capable the
system is in resolving snow stratigraphy. In Fig 4
we show the results obtained during the first field
campaign on 25 February 2016, compared to a
stratigraphy profile obtained with the AvaTech
SP2. Due to recent accumulation of low-density
snow on the surface, we placed a Perfect
Electrical Conductor (PEC) plate at the snow
surface to mark the snow surface. This is seen as
a large red area on the right side. Low-density
snow is difficult to detect with this radar due to the
low relative permittivity of snow, which decreases
significantly with lower densities.
The liquid water content of snow also greatly
affects the relative permittivity (Tiuri et al., 1984).
In our test snow profiles, the snow layers had no
significant amount of liquid water. The density of
the snow ranged from about 0.1 g/cm3 to 0.35
g/cm3, which was the largest contributor to
horizontal reflections in the radar image. In
general, however, these first results are very
satisfying as several thin layers of both low and
high strength and density were resolved in the
radar image. In Fig 5, we present a comparison
between an obtained radar image on 11 March
2016 and a density profile obtained by the Toikka
Snow Fork. Note the response from the aluminum
ladder in the upper part of the radar images. This
is due to the wide radiation pattern of the
Archimedean spiral antennas used. This needs to
be taken in to consideration when mounting on a
UAV. Again, correlation between measured snow
stratigraphy and radar response is visible. Clearly,
with increasing density, the radar reflectance
increased as well.
3.2 Future work
The Ilmsens system will as a next step be
mounted underneath an octocopter drone and
flown over snow. Based on the experiences from
the experimental setup we anticipate some
challenges with imaging snow depth and
stratigraphy from a UAV. Especially the distance
to the target will have to increase from 1 m to 3-10
m.10m for safe flying. According to the radar
equation the need for more radiated power
increases with the fourth power for increasing
distance to the target (Taylor, 2012). A possible
improvement is to mount a more directional
antenna in the UWiBaSS system (e.g. UWB
Vivaldi antennas). This will limit clutter from the
frame of the UAV and increase the power directed
down to the snowpack. Future work will also
include finding better methods for extracting the
interesting part of the data. Some focus is directed
at the Hough transform as it should provide a good
measure for horizontal lines. This will hopefully
create improved result in terms of resolving the
snowpack stratigraphy. Additionally, more work
should be done regarding the in situ
measurements. The work done so far shows
acceptable correlation with the radar
measurements, but several more factors need to
be examined e.g. grain size, snow temperature
and detailed density for each layer.
Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016
575
Fig 4: Comparison between AvaTech SP2 hardness profile, and radar image from 11.03.16
Fig 5: Comparison between AvaTech SP2 hardness profile, density profile derived from the Toikka Snow
Fork and radar image from 25.02.16
4. CONCLUSION
In this study we have investigated the possibility of
detecting snow stratigraphy with a radar operating
at some distance above the snow surface. In
preliminary tests using a dolly rig, our developed
UWiBaSS system seems to perform well.
Prominent snow layers are visible in the radar
images and with more sophisticated processing
methods, it should become possible to distinguish
snow layers for inexperienced users as well. In the
coming months, we will work on getting the radar
airborne with a drone, in order to be able to fly it to
avalanche starting zones, where it should seek for
weak layer slab layer combinations.
Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016
576
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Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016
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Snow fracture in relation to slab avalanche release: critical state for the onset of crack propagation. The Cryosphere
  • J Gaume
  • A Van Herwijnen
  • G Chambon
  • J Schweizer
Gaume, J., van Herwijnen, A., Chambon, G. and Schweizer, J., 2016. Snow fracture in relation to slab avalanche release: critical state for the onset of crack propagation. The Cryosphere.doi:10.5194/tc-2016-64, 2016