Detailed information on the spatiotemporal snow depth distribution is a
crucial input for numerous applications in hydrology, climatology, ecology
and avalanche research. Today, snow depth distribution is usually estimated
by combining point measurements from weather stations or observers in the
field with spatial interpolation algorithms. However, even a dense
measurement network like the one in Switzerland, with more than one measurement
station per 10 km
Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, in most countries such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UASs), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Compared to manual measurements, such systems are relatively cost-effective and can be applied very flexibly to cover terrain not accessible from the ground. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) of less than 0.07 to 0.15 m on meadows and rocks and a RMSE of less than 0.30 m on sections covered by bushes or tall grass, compared to manual probe measurements. This new measurement technology opens the door for efficient, flexible, repeatable and cost-effective snow depth monitoring over areas of several hectares for various applications, if the national and regional regulations permit the application of UASs.
Information on the spatiotemporal distribution of snow depth (HS) is important for numerous applications, as it is a robust indicator for the amount of water stored as snow (snow water equivalent – SWE) (Jonas et al., 2009). It has a substantial impact on water supply and hydropower. The quality of hazard forecasting for floods and snow avalanches depends substantially on snow depth information (Bavay et al., 2009; McClung and Schaerer, 2006). The growth and habitat patterns of alpine flora and fauna are linked to the seasonal snow depth distribution (Bilodeau et al., 2013; Mysterud et al., 2001; Wipf et al., 2009). Annual changes in snow depth over the winter season have a strong impact on alpine tourism as more and more ski resorts depend on technical snow production.
Numerous studies report a very high spatial variability of snow depth within small distances, in particular in alpine terrain (Egli et al., 2011; Elder et al., 1998; Grünewald et al., 2010; Schweizer et al., 2008). Remote sensing is useful to monitor this spatial variability, because it can provide spatially continuous measurements at a high resolution of otherwise inaccessible areas. We define snow depth according to Fierz et al. (2009) as the vertical distance from the base to the snowpack surface at a specific location.
Terrestrial laser scanning (TLS) has been successfully applied in many case studies to measure HS distribution in small catchments with high vertical accuracies in the range of 0.10 m (Deems et al., 2013; Grünewald et al., 2010; Melvold and Skaugen, 2013; Mott et al., 2010; Prokop, 2008; Schaffhauser et al., 2008). A recent study by Deems et al. (2015) uses TLS to visualize the HS distribution in avalanche release zones for the education of ski resort staff and assesses the different error sources. However, TLS accuracies suffer from acute illumination angles, resulting in unfavorable laser footprints, in particular within flat areas. Furthermore, terrain sections behind convex landforms such as hills or moraines cannot be covered. Airborne laser scanning (ALS), on the other hand, is still very costly (e.g., Bühler et al., 2015a). Therefore, airborne digital photogrammetry is a promising and economic option for HS mapping in alpine terrain, in particular if it can be performed with cost-effective UASs.
First attempts to photogrammetrically map snow depth with analog imagery collected from manned aircrafts were already made decades ago (Cline, 1993, 1994; Smith et al., 1967). However the reported efficiency and the achieved accuracies of more than 1 m were insufficient for most applications. With the advent of digital photogrammetry, this changed fundamentally. Recent investigations report accuracies in the range of centimeters to decimeters, which allow a detailed analysis of the spatial variability of the mountain snow cover (Bühler et al., 2015a; Lee et al., 2008; Nolan et al., 2015) but still require a fully equipped manned aircraft and the corresponding maintenance logistics.
Recently, UASs were applied to a wide range of mapping and monitoring studies in mountainous regions, especially with a focus on natural hazards. Fernández et al. (2015) provide an extensive overview of current surveys of landslides; Ryan et al. (2015) and Whitehead et al. (2013) published UAS applications on glaciers; Danzi et al. (2013) reported on rockfall, Dall'Asta et al. (2015) on rock glaciers and Tampubolon and Reinhardt (2015) on volcano mapping. Enßle et al. (2015) successfully tested UAS data acquisition in elevations up to 4200 m a.s.l., proving that UASs are capable of operating at very high altitudes. However, to this date, the number of studies dealing with UAS-based photogrammetry to map snow and avalanches is very limited. First, results have recently been published by de Michele et al. (2016), Eckerstorfer et al. (2016) and Vander Jagt et al. (2015). Additionally, Basnet et al. (2015), Prokop et al. (2015) and Thibert et al. (2015) reported on using ground-based photogrammetry for snow and avalanche detection. de Michele et al. (2016) conclude that UAS-based HS mapping holds great potential, but that further studies are required especially with regard to multi-temporal mapping, sensors capable of measuring in near-infrared wavelengths or the mapping of different snow cover conditions (new snow, wet snow, ice crusts etc.).
Technical specifications of the Falcon 8 UAS.
The UAS missions have been performed with an Ascending Technologies (AscTec) Falcon 8 octocopter equipped with a customized Sony NEX-7 camera. The Falcon 8 has been in serial production since 2009 and can be customized with different sensor systems. The system weighs 2.3 kg (incl. camera) and can be transported to remote locations fully assembled in a special backpack, a prerequisite for most alpine applications. A combination of on-board navigation sensors (Global Navigation Satellite System, GNSS, Inertial Measurement Unit, IMU, barometer and compass) and an adaptive control unit permit high positional accuracy of better than 2.5 m (Ascending Technologies, personal communication, 2015) and stable flight characteristics, even in challenging, alpine environmental conditions. The specifications of the Falcon 8 are listed in Table 1.
The Sony NEX-7 system camera features a 24 MP APS-C CMOS sensor and is
equipped with a small and lightweight Sony NEX 20 mm F/2.8 optical lens
(81 g). By removing the built-in short pass filter, the camera sensor is also
sensitive in the near-infrared spectrum. This allows us to mount the lens with
different filters for visible (RGB) and near-infrared (NIR) bands
(
The UAS missions are planned using the AscTec Navigator software on a tablet computer. Topographic maps are imported and the waypoint navigation is calculated based on camera specifications, desired ground sampling distance (GSD) and image overlap. At the location of a planned mission, the tablet computer is connected to the ground control station and last corrections to the flight plan, e.g., due to unexpected terrain variations, can be applied. During the flight mission, the UAS automatically moves from waypoint to waypoint; only the launch and final landing phase require manual interaction.
From our experience, portability of the UAS, a high image resolution and the ability to take off and land from an exposed site are key features for photogrammetric UAS missions within alpine, snow-covered terrain. The Falcon 8 offers a good compromise between flight endurance, payload and stability in most conditions. The radiometric and spatial resolution of the Sony NEX-7 camera enable the generation of highly accurate digital surface models (DSMs). The portability is excellent, as the UAS, radio modem and controlling computer fit to a daypack. The short flight time per battery charge, on the other hand, is a critical disadvantage of the octocopter technology. From our experience, longer flight times are the major advantage of fixed-wing UASs, like the eBee (sensefly). However, the available cameras have only low image resolution due to limited payload capacity, space and battery power. Larger fixed-wing drones, like the Sirius Pro from MAVinci, the UX5 from Trimble or the Q-200 from Quest UAS, suffer from quite bulky overall equipment and are therefore difficult to fly in high mountain areas. Feasible terrain (large flat areas) to safely land them does often not exist. For an extensive overview of currently available UASs, the reader is referred to Colomina and Molina (2014).
The regulations for flying UASs vary a lot from country to country or even
between different states or communities. If it is necessary to get flying
permits long before data acquisition, this limits the applicability and
flexibility of this technology considerably. The regulations in Switzerland
are quite user-friendly and are easy to fulfill. The UAS has to be within
line of sight and the pilot has to be able to interrupt the flight at any time.
Special permission is only necessary if crowds (more than 24 people within
short distance) are present within the overflown area or the area is close to
an airport (Swiss regulations:
Location of test sites Tschuggen and Brämabüel close to Davos, Switzerland; Pixmap© 2015 swisstopo (5 704 000 000), reproduced by permission of swisstopo (JA100118).
The images are processed with Agisoft PhotoScan Pro v1.1.6, to generate
georeferenced DSMs and orthophotos using dense point cloud generation with
the default parameters. PhotoScan is based on a
structure-from-motion (SfM) algorithm (Koenderink and van Doorn, 1991; Verhoeven, 2011) and provides a
complete photogrammetric workflow with special emphasis on multi-view
reconstruction of UAS-based images. The tie point matching in PhotoScan
allows the estimation of the internal and external camera orientation
parameters and is followed by adding georeference information (coordinate
system and reference points). The resulting model is linearly converted using
a seven-parameter Helmert transformation, and therefore only compensates
linear misalignment. Nonlinear deformations from the model are removed by
optimizing the estimated point cloud and camera parameters using four radial and
four tangential distortion coefficients (Agisoft PhotoScan User Manual,
“Quality” defines the desired reconstruction detail level. Higher
quality settings can be used to obtain more detailed and more accurate
geometry, but can result in much longer processing time. “Depth filtering” allows outliers from the point cloud, which
are caused by poor texture of the scene, noisy or blurry images, to be removed.
Depending on the complexity of the scene geometry, different depth filtering
modes can be applied. The accuracy of the exported product needs to be
analyzed to estimate the complexity of the model and thus select an
appropriate depth filtering mode.
To test the feasibility of UAS-based HS change mapping, two easily accessible test sites in the region of Davos, Switzerland, have been chosen that represent typical terrain characteristics of high alpine environments (Fig. 1).
Data acquisition parameters for Tschuggen.
Orthophotos of the four different data acquisitions at Tschuggen depicting the change in snow coverage overlaid by the locations of the manual HS measurements and the applied reference points.
The test site Tschuggen is at the bottom of the Flüela valley at an
elevation of 1940 m a.s.l. very close to the timberline. This spot is well
accessible even during the winter season, because the Flüela pass road
is regularly cleared until this point. The high alpine valley bottom
features both flat alpine meadows and hilly alpine terrain. The main
land cover is a mixture of bushes (mainly alpine rose, juniper and erica)
containing steep rocky outcrops and sparse larch and pine trees
(Fig. 10). Only moderate HS variability can be
expected at this site in an average winter season because it is not usually
exposed to high winds. The mean slope angle of the test site is
19
Data acquisition parameters for the Brämabühl test site.
A total of 252 images were acquired at this 400 m
For the absolute orientation, selected reference points (RPs)
were applied, which were required to be clearly visible in the base
imagery on all four acquisition dates. The RPs, bright quartz marks on
rocks and center lines of the road, were measured with a Leica TPS 1200
differential GNSS with an expected accuracy of better than 0.03 m. The
achieved average accuracy of the orientation process is 0.038 m
(
Simultaneously to the UAS data acquisition, HS reference measurements were
acquired with a marked avalanche probe (Fig. 2). An investigation by Prokop
et al. (2008) as well as our own experience show that such measurements are
also affected by errors in the range of 0.05–0.10 m. At every reference
plot, five manual plumb vertical measurements within 1 m
The test site Brämabühl is located at the top of the ski area
Jakobshorn in Davos, Switzerland, at an elevation of 2500 m a.s.l. and is
approximately 5.5 km linear distance from the test site Tschuggen (Fig. 1).
At this test site, we expect a much higher variability of HS and in particular
higher maximum HS values compared to the test site Tschuggen. The high wind
exposure around the top of a crest at high elevation is expected to lead to a
large amount of windblown snow. Additionally, the ski runs present within the
area are typically areas for snow grooming and artificial snow production.
The top of Brämabühl is mainly covered by high alpine meadow and
small bushes (Fig. 10). No trees or larger bushes grow at this elevation and
local climate. The mean slope angle of the test site is 30
Near-infrared orthophotos snow-covered (left panel) and snow-free (right panel), acquired over the Brämabühl test site including the applied reference points and reference HS measurements.
For this test site, near-infrared imagery was selected, which is expected
to have higher contrast and lower reflection on snow-covered areas
(Bühler et al., 2015b). Table 3 shows the data acquisition information
and Fig. 3 the resulting orthophotos, with a spatial resolution of 0.025 m.
The same image overlap of 70 % along-track and cross-track, like at the
Tschuggen test site, was used. For the second field campaign, data
acquisition was performed with the updated Falcon 8, explaining the much
higher number of images and ground coverage in Table 3. To cover the 500
HS maps (top panels) and corresponding orthophotos (middle panels) of the area around the chapel in the center of the test site. At the bottom the orthophoto of the snow-free reference (bottom left panel) and the spatial distribution of melt rates as percentage of remaining snow compared to the peak of winter (11 March 2015) are depicted. Black areas depict a lack of data values.
Statistical evaluation of the HS measurements (left panel) and the standard
deviations
The image processing scheme from the Tschuggen experiment was repeated, but
due to the smoother terrain with only a few clearly identifiable reference
points, 10 artificial RPs (white plastic sheets with a symmetric black
cross in the middle) were distributed and were measured with a Trimble
GeoXH differential GNSS, with an expected accuracy of better than 0.10 m.
This approach allows a very accurate identification of the RPs in the
imagery. However, the distribution of the artificial RPs is time-consuming
and a meaningful distribution over the test site is often not possible due
to e.g., avalanche danger. In addition the applied Trimble GeoXH has a lower
positioning accuracy than the Leica TPS 1200 GNSS used at Tschuggen. Using
10 RPs, the achieved reference accuracies of 0.019 m in
The snow-covered imagery was referenced by taking natural RPs, which
are clearly visible in the snow-free and snow-covered imagery (Fig. 3). The corresponding
Simultaneously to the winter UAS data acquisition, HS was measured with a marked avalanche probe at 22 plots as reference data, locating the center points of the plots using the Trimble Geo XH GNSS.
To produce the high spatial resolution (0.10 m) HS maps, the snow-free DSM
(29 September 2015) has been subtracted from the snow-covered DSMs
(11 March, 24 April and 12 May 2015). These maps reveal the high spatial variability
of HS already present at sheltered locations in alpine terrain
(Fig. 4, top panels). Particularly in the southeastern
part of the test site, areas with complex topography exist. Patches with
nearly no snow in wind-facing areas (luv) and pockets filled by windblown
snow with HS up to 2 m in the wind-sheltered areas (lee) are located
within less than 1 m distance. For the area depicted in
Fig. 4, the mean HS
Overall HS map of the Brämabühl test site (top left panel) and close-up of the central part (top right panel). The locations of the reference plots are displayed as red circles. 3-D view of the HS draped over the hillshade of the snow-free DSM facing from north to south (bottom panel).
Statistical evaluation of the mean HS (bars) and its standard deviation (line), classified by the exposition (left panel) and exposition map (right panel).
The locations of the probe measurements are depicted in
Fig. 2. We compare the mean
The HS root mean square error (RMSE) over all 50 reference plots is 0.25 m
and there is an average underestimation of HS by 0.2 m. For a more detailed
analysis we divide the reference measurements in two classes based on the
manual analysis of the 0.025 m spatial resolution snow-free orthophoto
acquired on 28 September 2015: (a) short grass/rocks, where no high
vegetation is present and (b) bushes/high grass, where the surface
of the dense vegetation is more than 0.10 m higher than the bare ground. In
the second class, the snow-free DSM is higher than the terrain without
vegetation. Because the snow presses the grass and bushes to the ground
in winter, the difference between the snow-covered and snow-free DSM results
in a systematic underestimation of HS. For the class of short
grass/rocks, the RMSE is 0.07 m and there is a mean shift of only 0.05 m
for all three flight dates. For the class of bushes/high grass, on the
other hand, the RMSE is 0.30 m and there is a bias of 0.29 m, corresponding
to the mean height of bushes and tall grass within the investigation area.
For snow hydrological applications, it is also important to gain information
on the standard deviation
To assess the repeatability of the UAS HS mapping, we analyze the altitude deviation of the different DSM at 10 550 grid cells on the snow-free road. The calculated RMSE values compared to the summer DSM (28 September) are 0.093 m (11 March), 0.052 m (24 April) and 0.045 m (12 May 2015). This indicates that the error of the method is smaller than 0.1 m.
The HS map with a spatial resolution of 0.1 m shows different
characteristics compared to the Tschuggen test site. The expected higher HS
values of up to 5 m are clearly visible in Fig. 6. The
close-up of the central part reveals interesting details such as the
linear feature of buried hiking paths in the northwest or the
snow grooming on the ski tracks. Over the entire area we calculate a mean HS
The mean HS distribution classified by the terrain expositions confirms the visual impression that the south-facing slopes have much lower HS values than the north-facing slopes (Fig. 7). Additionally, the standard deviation of the mean HS shows a tendency to be smaller at southern expositions (SE, S and SW). This slope aspect analysis was performed on the snow-free DSM, which was resampled to 1 m to filter out small exposition changes. Such statistical evaluation enables a more detailed analysis of mountain HS distribution on local to regional scale.
The comparison of the photogrammetric HS with manual HS measurements results
in a RMSE of 0.15 m and a very high correlation coefficient of
Statistical evaluation of the HS measurements (left panel) and the standard
deviations
Based on the experience gained at the two presented test sites, the following key points require a more detailed discussion because they are crucial for the application of UASs in high alpine terrain.
Steep terrain, high altitudes, low temperatures and often wind speeds of more
than 10 m s
For a long time, photogrammetry on snow-covered terrain was considered unfeasible, due to low contrast, a limitation only recently overcome as highlighted in current studies (Bühler et al., 2015a; Lee et al., 2008; Nolan et al., 2015). The smoother the snow surface, the harder it gets for the structure-from-motion software to identify meaningful matching points. This becomes obvious if we look at homogenous areas within the shaded DSM at shadowed and at well-illuminated snow-covered locations (Fig. 9). In shadowed areas (e.g., shadow of the chapel tower) the clearly visible noise introduced to the DSM shows amplitudes of up to 0.40 m. In the bright, very homogenous areas, the noise shows amplitudes of up to 0.15 m. This indicates that a fresh snow surface is less suitable than an older, weathered surface; but due to strong winds and large differences in radiation, alpine snow surfaces develop detectable features such as sastrugi or wind ripples already during or very shortly after snowfall. Very homogenous snow surfaces occur only within very small parts of our test sites.
Additional problems occur if reflections of the sun on the snow saturate the camera sensor. Therefore it is recommended that the camera exposure time is properly set and the imagery is stored in raw format using the full bit depth of the sensor, typically 10 to 14 bits. Standard JPEG image compression, which is the default storage setting for most cameras, is limited to 8 bits, storing only 256 grayscale values per band. However, further investigations are required to quantify the benefit of 12 bit image storage over the 8 bit JPEG compression on snow-covered areas.
As snow absorbs more energy in the near-infrared (NIR) part (
Winter orthophoto of the area close to the chapel within the test site
Tschuggen
Exact relative georeferencing (co-registration) between the two DSMs is
essential for correct HS calculation (snow-covered DSM minus snow-free DSM).
Even small shifts in absolute referencing with artificial RPs measured with differential GNSS; relative referencing with natural RPs that are well visible in the
snow-free and the snow-covered imagery; absolute referencing of one DSM with differential GNSS and then relative
referencing of the second DSM by identifying points in the second DSM that are well visible.
A major drawback of method (a) is that all reference points have to be
manually deployed and measured with differential GNSS devices to achieve
accuracy in the range of centimeters to a decimeter. They should be
distributed equally over the entire area of interest and all elevation
bands. In high alpine terrain this is often not possible, for example due to
avalanche danger. The methods (b) and (c) exclude the possibility of a
potential GNSS shift but are only applicable if areas with distinct terrain
features exist that are not covered by snow. This was the case at our test
sites but might not be feasible in winters with exceptionally high amounts
of snow. The referencing strategy has to be evaluated carefully prior to a
UAS HS mapping campaign. A direct matching of the snow-covered to the snow-free
point cloud (Gruen and Akca, 2005) is not feasible as the
terrain shows large differences over most parts due to the snow cover.
RPs would not be necessary if a very accurate (better than 0.05 m) GNSS/IMU system were available directly on the UAS. First, UAS products with such high-accuracy GNSS sensors are already available on the market. However, an initial investigation by Harder et al. (2016) indicates that the achieved orientation accuracy is not sufficient for snow depth mapping without ground reference measurements.
Within the accuracy range of the HS maps of 0.05–0.15 m, the vegetation at the base of the snow cover has a strong influence on the results. At the test site Tschuggen small bushes, mainly alpine rose, juniper and erica, rise up to 0.50 m above ground in summer (Fig. 10a). In winter they are pressed down to the ground by the snowpack but form a snow-free layer at the bottom of the snowpack which can have a depth of a few centimeters to decimeters (Feistl et al., 2014). This leads to a systematic underestimation of HS mapped with photogrammetry (snow-free DSM is too high) as well as a systematic overestimation of HS measured manually with the avalanche probe because the probe penetrates the snow-free bottom layer and sometimes even the first layers of the ground. The “real” HS is most probably a value between the manual probe and the photogrammetric measurements. High grass, on the other hand, is usually pressed down to the ground completely, only leaving a snow-free layer of less than a few centimeters (Fig. 10b). This makes the probe measurements more reliable but can falsify the photogrammetric measurements if the grass is high during the snow-free data acquisition. Alpine meadows should therefore be surveyed right after mowing or late in autumn while the grass is low. From our experience, it is very difficult to correct the photogrammetric HS based on underlying vegetation because the elevation differences vary very much within short distances. A possibility might be to apply a vegetation classification based on the orthophotos to correct the underestimation of HS in areas with many bushes. But there is a high risk to introduce new errors and this possibility has to be investigated in more detail in the future. Photogrammetric HS mapping is difficult above, below and around trees, as trees are nearly always moved by wind and the resulting ambiguous tree top positions interfere with image matching. Additionally, areas below trees are not visible in the nadir imagery. Therefore laser scanning, measuring first and last returns or even full wave form signals, is still the best choice for investigations in forested areas (Moeser et al., 2015).
UAS-based digital photogrammetry is able to map the spatial variability of alpine HS with accuracies of 0.07 to 0.15 m RMSE compared to traditional manual measurements with avalanche probes. These accuracies are in the same range as HS measurements acquired by terrestrial laser scanning (Deems et al., 2013) and reported in the manned-airplane-based study by Nolan et al. (2015) and the UAS-based studies by Vander Jagt et al. (2015), de Michele et al. (2016) and Harder et al. (2016). It is clearly better than the RMSE of 0.30 m reported by Bühler et al. (2015a), using an ADS80 survey camera mounted on a manned airplane, but can only cover considerably smaller areas. Fixed-wing UASs, flying at high altitudes above ground, would be able to cover larger areas of several square kilometers. Future investigations have to clarify how accurate the results from such platforms can be as the spatial resolution of the input imagery is worse and the results might get much more affected by wind.
UASs enable fast, flexible, repeatable and detailed analysis of the spatial distribution of the mountain snow cover over several hectare areas. We successfully applied a complete photogrammetric workflow at a sheltered test site at the valley bottom (Tschuggen) and at an exposed test site at a mountain top (Brämabühl), mapping extreme HS variability of up to 5 m within less than 3 m distance, confirming the important role of wind and terrain features on HS distribution in alpine regions (Mott et al., 2010).
A key to robust photogrammetric HS measurements is the accurate
co-registration of the snow-free and the snow-covered digital surface models (DSMs).
Even small shifts in
We expect that UASs will get more and more important for environmental mapping and that they have the potential to change the frequency and quality of geodata acquisition fundamentally.
The underlying data sets (original UAS imagery and reference measurements) as well as the 3-D snow depth animation can be accessed by contacting the corresponding author Yves Bühler (buehler@slf.ch) directly.
Parts of this work were supported by the Austrian Academy of Sciences (ÖAW) under the project RPAS4SNOW. Edited by: E. Berthier