IEEE Geoscience and Remote Sensing Magazine - March 2023 - 80

posing a serious threat to Antarctic research teams [19]. When
snowcats and measuring instruments are employed to conduct
survey missions, they typically use existing satellite remote
sensing data to design a safe driving route in the study
region to avoid cracks and distribution areas and guarantee
the safety of workers and devices. However, existing remote
sensing data with insufficient spatiotemporal resolution
and a scarcity of in situ data cannot provide accurate topographic
information [15]. UAV-based high-resolution aerial
images have a high temporal resolution with a spatial resolution
up to the centimeter-scale and can temporarily map
the terrain of a study area before surveying. Such data have
been widely used to identify and analyze dynamic changes
in rift valleys and crevasses [15], [64]. UAVs have a flexible
flight time, and their high-resolution aerial images and videos
can be used to understand the evolution of large cracks on
the ice surface in a short period of time [50]. Several studies
exist on the identification and evolution of rifts and crevasses
in the area between the Russian Progress Station and
Vostok Station sledge airfield in Antarctica [15], [19], [26],
[76]. Highly accurate DOMs and DEMs were generated from
the Geoscan 201 UAV images of the 62nd Russian Antarctic
Expedition through the geomorphological measurement of
glacier DEMs in 25-cm, 50-cm, and 1-m resolutions and a
model containing 16 morphological variables, including
horizontal and vertical curvature, and a ~1.5-m-wide and
1.2-m-thick snow-covered rift was identified and studied.
Horizontal, vertical, minimum, and maximum curvatures
can be used to identify long cracks, while short cracks are
more obvious in the horizontal, minimum, and maximum
curvatures. The generated model also considered the environmental
requirements for UAV operations in Antarctica, where
such surveys should be carried out under abundant sunshine
and less cloud cover. Low-contrast and nearly white aerial
images can be easily obtained in the presence of low-lying
clouds and diffused reflection; however, the microfeatures of
ice are not visible, and the images are difficult or impossible
to utilize in further data processing. UAV surveys cannot be
conducted in the presence of low-lying clouds (100-200 m
above ground level) since the ice, sky, and horizon are indistinguishable
in this scenario [15], [19], [26], [76]. To date,
these are the most comprehensive UAV-based studies on the
identification and evolution of rifts and crevasses on the ice
surface. Furthermore, the Chinese Antarctic expedition team
used a UAV platform composed of a DJI Phantom 4 and DRTK
mobile station to model surface microtopography features,
such as blue ice, rifts, crevasses, dolines, and melting
ponds, around the Zhongshan Station in the EAIS. This further
proves that the surface microtopography characteristics
that are not visible in satellite images can be identified in
UAV images, which can be used to correlate glacial surface
and subsurface processes [64]. Figure 6 shows an example
of crevasse identification (the red polygon). High-resolution
UAV images create a centimeter-scale orthomosaic, where finer
surface microtopographic features are clearly visible compared
to Sentinel and Landsat images that have a meter-scale
80
resolution [Figure 6(a)]. The surface elevation of the red polygon,
as determined by the UAV, ranges from ~86 to 111 m.
Crevasses can be located using an elevation map, but their
contours cannot be determined accurately [Figure 6(b)]. Figure
6(c) provides the curvature of the red polygon region created
from the UAV-derived surface elevation product. A positive
curvature indicates that the surface at that cell is upward
convex, while a negative curvature indicates that the surface is
upward concave, and a value of zero indicates that the surface
is flat. The distribution of crevasses can be derived by binarizing
processing (expansion and contraction) the data.
UNMANNED AERIAL VEHICLES IN THE STUDY OF
SUBSIDENCE AND MELTING PONDS
Studies on the unusually fast and profound sinking of glaciers
are extremely rare. This type of subsidence can be caused
by hydrological activity on the surface of, within, and beneath
the ice sheet. Ice dolines are topographic features that emerge
following a collapse [24], [25], [77]. Ice dolines and melting
ponds not only indicate changes in the ice sheet but also pose
a threat to glacier surveys and other operations; thus, their formation
and development must be investigated thoroughly.
The Dålk Glacier collapsed catastrophically in January 2017
[24]. UAV-based orthomosaics and DEMs of the Dålk Glacier
were acquired 10 days before the collapse, 1 h after the collapse,
and 10 days after the collapse, using terrain modeling
technologies [24], [77]. The obtained information combined
with data acquired in the 35th Chinese National Antarctic Research
Expedition (CHINARE) were used to study the changes
in the catastrophic subsidence of the Dålk Glacier, East
Antarctica, and the surrounding area in 2017 (Figure 7). The
images showed that in the image 10 days before the subsidence,
there was no meltwater pooling to form a melting pool,
only a melting pool on the west side of the imminent subsidence
region [Figure 7(a) and (e)]. The ice doline formed on
30 January 2017, and the corresponding image showed that
the melting pool on the west side had connected with the
ice doline area, and the water stored in the melting pool had
drained into the doline [Figure 7(b)]. However, in the images
captured one day and 10 days after the subsidence, the meltwater
in the ice doline had also drained farther downward
[Figure 7(c), (f), and (g)]. The sinking process was envisaged
and studied using 2D and 3D analyses [24]. Previous research
has revealed the existence of a thin-topped cave within the
glacier. Meltwater from the glacier surface collected at its top,
causing the cave to collapse. Ice surface hydrological mechanisms
can induce large-scale rapid subsidence without requiring
outbursts of subglacial lakes and subglacial geothermal
and volcanic activity [24]. UAV-based data acquired 10 days
before and one day after the collapse also showed that the glacier
topography and distribution of melting ponds changed
before and after the collapse, indicating a critical relationship
among the formation of ice dolines, subglacial runoff, and
supraglacial meltwater processes [77]. The data acquired from
the 35th CHINARE also showed that meltwater reappeared in
the ice doline [Figure 7(d) and (h)]. UAV photogrammetry
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MARCH 2023
IEEE Geoscience and Remote Sensing Magazine - March 2023

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