1. MONITORING GLACIAL VELOCITY VARIATION
IN THE RUSSIAN HIGH ARCTIC
USING REMOTE SENSING
A Thesis
Presented to the Faculty of the EAS Department
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Bachelor of Science with Honors
by
Adam J. Stewart
May 2014
3. ABSTRACT
Severnaya Zemlya, Novaya Zemlya, and Franz Josef Land, collectively known as the Russian
High Arctic, make up the largest ice field in all of Eurasia. Despite this, very little is known
about the glaciology of the region or its contribution to sea level rise due to its remote
location. For this reason, I use remote sensing to measure glacial velocity variations over
the course of the last 30 years. I use ASTER and Landsat satellite imagery to obtain
recent glacial velocities of Severnaya Zemlya and compare them to older velocities reported
by other glaciologists. I also make some of the first velocity measurements over southern
Severnaya Zemlya, as well as a complete velocity map of Novaya Zemlya. To do this, I
align my image pairs, utilize a Gaussian high-pass filter to accentuate the crevasses, and
use pixel-tracking to produce a snapshot of glacial surface velocities. By applying my own
noise removal script, I am able to remove almost all noise from these results and blend them
together into a single regional velocity map. Prior publications have hinted at an increase
in glacial velocities of the Academy of Sciences Ice Cap in northern Severnaya Zemlya, and
my research results corroborate this claim. Glacial velocities on this ice cap have more than
quintupled since 1995 and show signs of increasing throughout the Russian High Arctic. I
also provide qualitative observations that suggest glacial acceleration across Novaya Zemlya.
If these rates continue to increase, the contribution of the Russian High Arctic to sea level
rise may exceed previous expectations.
4. BIOGRAPHICAL SKETCH
I am a senior majoring in Science of Earth Systems, concentrating in Computational Geo-
physics, and graduating magna cum laude with honors. I spent last summer at the Andes
Field Camp and have spent previous summers working as the Ecology/Conservation Director
at a local Boy Scout camp. In addition to working as an undergraduate TA for several intro-
ductory physics and programming courses, I have spent the last year researching glaciers in
the Russian High Arctic through the use of remote sensing. I have also served as the Presi-
dent of the Science of Earth Systems Student Association and an executive board member of
the Cornell Ski and Snowboard Club. I plan on finding a job in software engineering to pay
off my student loans before returning to school for a master’s degree in Computer Science
and eventually a PhD in Geophysics.
iii
5. To Francie for keeping me sane and
to Tiffany for keeping me focused
iv
6. ACKNOWLEDGEMENTS
I would like to thank my advisor, Matt Pritchard, for giving me the incredible opportunity
to research the Russian High Arctic. He taught me glaciology, kept me on track to graduate,
and always managed to pull me out to the big picture when I got lost in the crevasses.
I would also like to thank the one and only, Andrew Melkonian, for being able to code
anything, anytime, anywhere. He not only taught me the pixel-tracking process, but also
taught me so much more about programming in general. And how can I forget Mike Willis,
aka “polar.mike,” whose databases and maps provided me with endless resources. Even
though he was far away geographically, he was always willing to help.
But most of all, I would like to thank the processor in Viedma, who did more work than
all of us combined. Hang in there buddy, it’s almost over! Lastly, I would like to thank the
Russian High Arctic for being such a beautiful place to work. I wouldn’t rather be in any
other place right now . . .
v
9. CHAPTER 1
INTRODUCTION
1.1 Geography
In the northernmost reaches of Russia just above the Arctic Circle lie several isolated island
archipelagos. These remote regions are inhabited by few and are locked in sea ice for most
of the year. Of particular importance to us are the following 3 archipelagos:
1.1.1 Novaya Zemlya
Novaya Zemlya (Russian: Н´овая Земл´я), which literally means New Land, is the largest of
the island archipelagos in the Russian High Arctic (RHA), covering an area of 82,600 km2
(Novaya Zemlya, 2014). It lies between the Barents Sea and Kara Sea and is composed of two
main islands, Severny (northern) and Yuzhny (southern), which are separated by a narrow
strait. It is the only permanently inhabited archipelago in the RHA, with a population of
2,429 as of 2010 (FSSS, 2011). Its primary use has been as a military base, and it was one of
two sites where the USSR tested nuclear weapons before the Nuclear Test Ban Treaty went
into effect in 1963. These tests included the largest nuclear weapon ever detonated — Tsar
Bomba (50 Mt) — in 1961 (Adamsky and Smirnov, 1994).
1.1.2 Severnaya Zemlya
Severnaya Zemlya (Russian: С´еверная Земл´я), which literally means Northern Land, is
the second largest archipelago in the RHA. It separates the Kara Sea and Laptev Sea and
is composed of several islands. The larger islands include October Revolution, Bolshevik,
Komsomolets, Pioneer, and Schmidt Islands. Severnaya Zemlya was not discovered until
1913 and was not mapped until the 1930s, making it the last discovered island archipelago
on Earth due in part to its remote location and the fact that it is locked in by ice for most of
1
10. the year (Barr, 1975). It is mostly uninhabited, with the exception of an Arctic base. The
archipelago is 48% glaciated and covers an area of 36,712 km2
(Severnaya Zemlya, 2014).
1.1.3 Franz Josef Land
Franz Josef Land (Russian: Земля Франца-Иосифа) sits farther north in the Arctic Ocean,
in between Novaya Zemlya, Severnaya Zemlya, and Svalbard. Covering a total area of
16,134 km2
, the 191 islands of the archipelago are uninhabited natural sanctuaries and are
∼85% glaciated (Franz Josef Land, 2014). The archipelago was discovered in an 1872–74
expedition and named after Franz Joseph I, the Emperor of Austria at the time.
My research has not yet focused on this region, and it is only included in the Introduction
for completeness. It will likely be the focus of future studies in the RHA.
Figure 1.1: Location of island archipelagos in the Russian High Arctic. Relief from Interna-
tional Bathymetric Chart of the Arctic Ocean. Ice from Atlas of the Cryosphere.
2
11. 1.2 Glaciology
With the rapid onset of anthropogenic climate change in the last century, it has become in-
creasingly important to make quantitative measurements of the cryosphere and to determine
the link between rising temperatures and glacial melt. More remote regions such as the RHA
are less understood than Greenland and Antarctica, which have been more heavily studied.
Covering an area of 55,600 km2
, the ∼2,000 glaciers and ice caps on the islands of the RHA
offer a perfect testing ground for remote sensing techniques (Dowdeswell and Hagen, 2004).
Despite their smaller size, these ice fields could undergo more rapid melting than their larger
counterparts in Greenland and Antarctica due to the abnormally strong warming measured
in the Arctic region (Walsh, 2009).
Since the 1960s alone, these ice fields have lost an estimated 100 km3
of ice, contributing
0.3 mm to global sea level rise (Govorukha et al., 1987). Gardner et al. (2013) calculated an
average mass budget for the RHA of −11 ± 4 Gt/yr from 2003–2009, showing an increase in
the rate of mass loss since the 1960s. If these rates continue to increase, the contribution to
sea level rise may approach 41.8 ± 5.5 mm, assuming complete melting (Huss and Farinotti,
2012). Of the 3 study regions, Novaya Zemlya appears to be melting at the fastest rate, with
∼35 Gt of mass loss from 2004–2010 (Figure 1.2). Therefore, I hypothesized that this region
would show the most acceleration over the last decade. Since it is farther north and more
isolated from the Gulf Stream, Severnaya Zemlya is undergoing less melting but has still lost
∼10 Gt. Franz Josef Land, which will be studied more heavily in future publications, is in
approximately net balance in terms of mass.
The Russian High Arctic has a higher percentage of tidewater glaciers than any other
place outside of Antarctica at 64.7%, making the region more prone to mass loss (Gardner
et al., 2013). As warming leads to more strain in the ice and more calving, the inrush of
warm ocean water can promote retreat in a positive feedback effect based on the tidewater
glacial cycle (Post et al., 2011). In this way, acceleration can be used as a proxy for moni-
toring the “health” of a glacier. By identifying glaciers and ice streams that show significant
3
12. acceleration, I can predict where mass loss is going to occur and which regions might show
catastrophic collapse in the near future. Although total glaciated area is important for cal-
culating the albedo of the region, and change in surface elevation of the ice field is important
for calculating mass loss, only acceleration can be used to predict the future state of these
glaciers. This makes the monitoring of glacial velocity variation crucial to assessing the fate
of this region with the warming expected.
Very little work has been done to measure glacial velocities in Severnaya Zemlya. Moholdt
et al. (2012a) have done much of the preliminary work to measure glacial velocities using
Landsat and the European Remote-Sensing (ERS) satellites but have only focused on the
Academy of Sciences Ice Cap, the largest ice cap in the archipelago. They observed over a
fivefold increase in glacial velocities from 1995 to 2000–2002, but data from other years or
regions of Severnaya Zemlya have not been published up until now. By using ASTER and
Landsat, I am able to get a denser temporal coverage, allowing me to see variations in glacial
velocity on an annual scale. Using ASTER imagery, I find 2009–2012 glacial velocities to be
at or above the speeds that Moholdt et al. found for 2000–2002, making it likely that this
was not just a particularly warm year (see Results and Discussion chapters).
Further study of these regions will not only provide clear and immediate evidence of
climate change happening in our lifetimes, but will also help to narrow down ranges of
predictions for sea level rise under specific climate regimes.
4
13. Figure 1.2: Glacial mass anomalies in the Russian High Arctic (Moholdt et al., 2012b).
5
14. CHAPTER 2
DATA
2.1 Satellites
The data used in this research comes primarily from ASTER and Landsat satellite sensors.
2.1.1 ASTER
ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is an imaging
instrument on board the Terra (EOS AM-1) satellite, jointly run by NASA, Japan’s Ministry
of Economy, Trade and Industry (METI), and Japan Space Systems (NASA, 2014). It was
launched on December 18th
, 1999 and began collecting data on February 24th
, 2000. Running
continuously for the last 14 years, it captures visual images at a resolution of 15 m/pixel,
while creating a digital elevation model (DEM) at a 30 m/pixel resolution.
2.1.2 Landsat
The Landsat program is a series of missions run by the USGS, beginning in 1972 (USGS,
2014). To be specific, Landsat 5, 7, and 8 imagery are used for my study. Landsat 5 was
launched on March 1st
, 1984 and continued to work until it was decommissioned on June
5th
, 2013, making it the longest Earth-observing satellite mission in history. It has several
image bands, all of which are 30 m/pixel in resolution. Landsat 6 failed to reach orbit,
but Landsat 7 was successfully launched on April 15th
, 1999. It is still in operation today,
although it had a Scan Line Corrector (SLC) failure on May 31st
, 2003. Landsat 8 was
launched as a replacement for Landsat 7 on February 11th
, 2013. Both Landsat 7 and 8 have
several visible light bands with 30 m/pixel resolution, as well as a panchromatic band with
15 m/pixel resolution.
6
15. 2.2 Products
2.2.1 Product-14 vs. Product-L1B
The two basic image products I work with are Product-L1B and Product-14 images. When
a satellite takes an image, the sensor is usually at some angle with respect to a normal vector
of the Earth’s surface. This original image is known as a Product-L1B (Figure 2.1a) and
must be georeferenced and orthorectified onto a digital elevation model (DEM) in order for
it to be used. This orthorectified product is known as a Product-14 and includes both the
DEM itself (Figure 2.1b) and the orthorectified image (Figure 2.1c). Because errors can
occur in the orthorectification stage, especially with older satellites or poor-quality DEM’s,
it is usually best to work with the Product-L1B image and orthorectify it onto a better
DEM, as explained in the Methods chapter. Landsat downloads only come with a L1T/Gt
image, but ASTER downloads come with both Product-14 and Product-L1B imagery.
2.2.2 Digital Elevation Models
For ASTER images, the Product-14 comes with a DEM. This DEM is produced by a stereo-
graphic pair acquired by the nadir and backward looking sensors on board Terra. Since the
image that comes with the Product-14 was orthorectified onto this DEM, we can look at the
reliability of the elevations it shows to gauge the quality of the Product-14. Snow cover or
clouds, which are quite common in the Russian High Arctic, can create large errors in these
DEM’s, as seen in Figure 2.1b, so it is often beneficial to orthorectify spring and fall images
to the DEM of a summer image.
2.2.3 Metadata
Metadata was also downloaded for each image acquisition. This includes necessary parame-
ters for orthorectification and georeferencing, such as the altitude and geographic position of
7
16. the satellite at the time of image acquisition and the angles at which the sensor was pointing
relative to the normal vector of the Earth’s surface and relative to the path of the satellite.
(a) Product-L1B (b) DEM (c) Image
Figure 2.1: ASTER image types, taken over Novaya Zemlya, 27 July 2013. The DEM in
subfigure (b) is scaled from 0 to 1,000 m. The white region over the ocean in this DEM is
cloud-cover, at a height of ∼6,000 m.
2.3 Downloading Data
All ASTER images were downloaded from Reverb (http://reverb.echo.nasa.gov). I
searched for ASTER L1A Reconstructed Unprocessed Instrument Data V003, found the
best cloud-free images, and separately ordered GeoTIFF Product-L1B and Product-14DMO
images. From these, I only use the visible and near-infrared (VNIR) nadir-looking (V3N)
Band 3 (0.76–0.86 µm wavelength, 15 m/pixel resolution) images in my data processing.
All Landsat images were downloaded from Earth Explorer (http://earthexplorer.
usgs.gov). From the Landsat Archive, I searched for Landsat 8 OLI/TIRS, Landsat 7
ETM+ SLC-on, and Landsat 4-5 TM images. Landsat 8 images came from the Operational
Land Imager (OLI) and Thermal InfraRed Sensor (TIRS) instruments. Landsat 7 images
were only selected from the Enhanced Thematic Mapper Plus (ETM+) before the Scan Line
Corrector (SLC) failed. Landsat 5 images came from the Thematic Mapper instrument.
From each of these Product-14 images, only Band 4 (visible — red, 0.63–0.68 µm wavelength,
8
17. 30 m/pixel resolution) and Band 8 (panchromatic, 0.50–0.68 µm wavelength, 15 m/pixel
resolution) images are used in my processing.
Image quality download criteria was fairly lax, and all available images with at least
one glacier that was not obscured by clouds or fog were downloaded. Figure 2.2 shows the
number of relatively cloud-free images available over Novaya Zemlya for each satellite. As
seen in the figure, very few images are available over the 1990s, but the late 1980s and 2000s
are well-imaged.
Figure 2.2: Histogram of the number of relatively cloud-free images available over Novaya
Zemlya for each satellite.
9
18. CHAPTER 3
METHODS
The first step of the pixel-tracking procedure is to choose a pair of overlapping satellite
images that show the same ice-covered areas at different times. Glaciers with visible crevasses
are ideal and allow the software to track motion effectively. I generally choose images with a
time separation of between 1 week and 2 months, ensuring that crevasses are clearly visible.
I also exclude images dominated by fresh snowfall, clouds, fog, or excessive shadows.
Once a pair of images is selected, the images need to be warped to a common UTM zone.
I use zone 47 for Severnaya Zemlya and zone 40 for Novaya Zemlya, as most of the images
are already in or close to these UTM zones. This minimizes image distortion due to warping
from one UTM zone to another.
In order for pixel-tracking to detect offsets on the scale of meters, the images need to
be well coregistered and orthorectified (adjusted to account for elevation). Coregistration is
performed on all image pairs by the Automated Registration and Orthorectification Package
(AROP, Gao et al., 2009). AROP locates stationary tie points between images - usually
bedrock - and warps one image to the other. Problems with this step occur in images with
excessive snow cover since this obscures bedrock and other stationary tie points.
Coregistration and orthorectification are applied to image pairs where raw data is avail-
able for both satellites (ASTER-ASTER). I orthorectify the raw L1B of one image (with
moderate cloud cover or a poor quality DEM) using the DEM of another image (with a
better DEM).
Registration is performed on already orthorectified imagery (Product-14 for ASTER,
L1T/GT for Landsat) for image pairs where raw data is not available (Landsat-Landsat,
Landsat-ASTER, ASTER-Landsat). Image pairs are then manually viewed using ENviron-
ment for Visualizing Images (ENVI) software to determine whether processing improved their
alignment. Image pairs for which the initial images are poorly aligned and the AROP results
are poorly aligned are discarded. The original images are used if the initial alignment is ad-
10
19. equate and AROP does not improve alignment. The AROP results are used where AROP
produces adequate alignment and improves on the initial alignment. Processing with AROP
improves alignment for 60% of ASTER and Landsat 7/8 image pairs, but only improves
about 30% of Landsat 5 image pairs due to poor initial georeferencing.
Next, a Gaussian high-pass filter is applied to the image pairs. This consists of convolu-
tion with a kernel that accentuates high-frequency features such as crevasses, which produce
the best pixel-tracking results.
Pixel-tracking is performed by “ampcor”, a normalized amplitude cross-correlation pro-
gram available in the Repeat Orbit Interferometry PACkage (ROI_PAC, Rosen et al., 2004).
Ampcor produces offsets by first setting up a box in the reference image for a given location,
then moving a same-sized box within a specified area of the search image surrounding an
initial guess of the corresponding position in the reference image. The x, y offset that pro-
duces the highest cross-correlation coefficient between the box in the reference image and the
same-sized box in the search image is recorded as the offset between the two for the given
location.
The offset results are further refined by removing an affine fit and assigning the appro-
priate geographic coordinates to each offset. Dividing the offsets by the time separation of
the image pair yields glacier surface velocities.
I wrote a noise removal script, which I apply to each pair of north-south/east-west velocity
files. It reads these files and first removes all speeds greater than the maximum expected
glacial velocities. Then, for each pixel in the image, it creates a 3 × 3 kernel around it. If
there are not a user-specified number of pixels in this kernel with speeds within a certain
percentage of the maximum expected velocity in both north-south and east-west images,
it removes that pixel. This script is run on all noisy results and requires manual tweaking
of input parameters (maximum velocity, number of similar pixels required, and similarity
tolerance) to provide the best results.
Lastly, I inspect maps of the velocities and discard any north-south or east-west motion
11
20. that is not consistent with the geometry of the basin. By repeating this process numerous
times over each archipelago, I produce hundreds of successful pairs that I then blend into a
regional velocity map. The entire process is outlined in Figure 3.1 below.
Figure 3.1: Flowchart of methods. Uses ASTER imagery over Ice-Stream B on the Academy
of Sciences Ice Cap, Severnaya Zemlya.
12
21. CHAPTER 4
RESULTS
The extreme weather of the region makes pixel-tracking difficult because only a few
months of the year are snow-free. Pixel-tracking relies on the presence of crevasses or other
trackable features, which are easily obscured by fresh snowfall or clouds. Of the images
available for download, over 90% are too cloudy or foggy to be of any use. The images I used
mostly come from late spring to summer, when the least amount of snow covers the region.
Image pairs that have low angles of sunlight, differing levels of snow cover, or different levels
of ice melt can not be correlated.
Based on trial and error, images with a time separation of between 1 week and 2 months
generally provide the best results. This is highly dependent on the nature of the glaciers
themselves, however. Fast-moving glaciers (over 2 m/day) often have so much strain oc-
curring that pixel-tracking results in decorrelation of the images if they are separated by
more than 3 weeks. This is especially prominent near the terminus of the glacier, where
the highest velocities occur. Slow-moving glaciers (less than 0.5 m/day) often have fewer
crevasses present since they undergo less strain than their faster-moving counterparts. If
trackable features are present on these slow-moving glaciers, they require a time separation
of image pairs of between 1 and 2 months.
When visually analyzing pixel-tracking results, there are several criteria I use to discern
whether or not the results are reliable. The first thing I look for is coherence. If results
are decoherent or display random noise, they are obviously not useful. This happens very
regularly near the tops of ice caps, where snow obscures any trackable features, and over
the ocean. If results are coherent, then I look for a few other cues. First, the bedrock must
remain stationary. If AROP failed to perfectly align the two images, there may be motion in
the bedrock, which suggests that there may also be glacial motion that is an artifact of this.
Since glacial velocities can be variable, it is hard to set a threshold for believable velocities,
but glaciers over Severnaya Zemlya do not seem to get much faster than 3.5 m/day, and
13
22. glaciers over Novaya Zemlya do not seem to get much faster than 10 m/day.
Consistency is also important. If a glacier is observed to move very slowly 9 times out of
10 but shows a fast velocity in one pair, that result is questionable. One must also take into
account seasonal variability, which can double or even triple velocities from the winter to the
summer. Most glaciers show geographically consistent velocities, at least locally. The last
thing to look for is whether or not the velocities seem reasonable based on the geometry of
the glacial basin. By looking at the DEM of the area, I can usually predict the direction of
flow by the gradient of the slope. If a glacier should be flowing southward, but pixel-tracking
shows east-west movement, the results are dubious. Occasionally, velocities strongly correlate
with elevation, and motion elevation correction has to be performed. This involves applying
a linear regression between elevation and bedrock motion and then removing the best-fit
parameters from the ice. Another problem that rarely occurs is the appearance of banding
in the velocities perpendicular to the direction of satellite motion, particularly on Landsat 5.
This has been attributed to "scanning pattern variations due to scan mechanism instability
and jitter" (Storey et al., 2008).
4.1 Severnaya Zemlya
Although the smaller glaciers on Rusanov, Karpinsky, and Leningradskiy Ice Caps do not
provide clear results, the larger or faster-moving glaciers on the Academy of Sciences Ice Cap
and the ones flowing into the Matusevich Ice Shelf and Marata Fjord provide great results.
On the Academy of Sciences Ice Cap, 6 distinct glaciers are observed: Ice-Streams A-D
(see Moholdt et al., 2012a) as well as Glaciers #13 and #18 (see Sharov, 2009). As seen in
Figure 4.1, Ice-Streams B-D (labeled as IS-B, etc.) show faster velocities than Ice-Stream A
or Glacier #18. Due to the lack of visible crevasses, it is difficult to find any better results
for the latter two glaciers. The results decorrelate near the terminus of Ice-Streams C-D and
Glacier #13 as a result of excessive strain.
14
23. Figure 4.1: Academy of Sciences Ice Cap glacial velocities (2009–2012). Velocities are derived
from a blend of 9 ASTER image pairs collected during April through August. Background
was made from DEM of Severnaya Zemlya on a grayscale from -80 to 800 m. DEM was
downloaded from http://www.viewfinderpanoramas.org/
Beautiful results are also obtained over eastern Karpinsky and University Ice Caps, par-
ticularly near the Marata Fjord. Glacier #56 and #59 are relatively slow-moving and much
harder to obtain results for. The results shown in Figure 4.2 for these two glaciers comes
from a single early Spring pair, so they could likely reach faster velocities during the summer.
Other than the aforementioned glaciers on the Academy of Sciences Ice Cap, the glaciers
on the eastern side of University Ice Cap are the only other ones in Severnaya Zemlya that
show clear crevasses. This explains why such nice results are found.
15
24. Figure 4.2: East Karpinsky and University Ice Caps glacial velocities (2010–2012). Velocities
are derived from a blend of 5 ASTER image pairs collected during March through August.
Background was made from DEM of Severnaya Zemlya on a grayscale from -80 to 1,000 m.
DEM was downloaded from http://www.viewfinderpanoramas.org/
16
25. 4.2 Novaya Zemlya
One of the most striking things I noticed about Novaya Zemlya was the dichotomy between
its glaciers on the northwest side and the southeast side of the islands. On the northwest
side, most of the glaciers are valley glaciers and are channeled down narrow outlets. They
are more likely to form complex branching forms and are generally faster-moving, with clear
visible crevasses. On the southeast side, however, most of the glaciers form as Piedmont
glaciers. They are significantly wider and generally slower-moving, although some have a
more concentrated higher velocity stream within them.
A relatively complete velocity map is shown for Novaya Zemlya in Figure 4.3. This
map is composed from Landsat-Landsat, Landsat-ASTER, ASTER-Landsat, and ASTER-
ASTER pairs, mostly from the Spring to early Summer of 2013. The largest and fastest
moving glacier near the upper-left corner is Inostrantseva, which reaches a maximum velocity
near the terminus of 6 m/day, although it can reach up to 10 m/day later in the summer.
The velocity colorbar on the figure was scaled to 3 m/day to highlight other slower-moving
glaciers.
17
26. Figure 4.3: Novaya Zemlya glacial velocities (2013). Velocities are derived from blend of
22 ASTER/Landsat image pairs collected during March through August. Background was
made from DEM of Novaya Zemlya on a grayscale from -80 to 1,000 m. DEM was made
from digitized Russian cartographic maps.
18
27. CHAPTER 5
DISCUSSION
5.1 Severnaya Zemlya
Since Moholdt et al. (2012a) is the only paper that summarizes velocities over Severnaya
Zemlya, it is the only other data against which I can currently compare my own findings.
Moholdt observed a more than fivefold increase in glacial velocities over the Academy of
Sciences Ice Cap, as seen in Figures 5.1 and 5.2. My results also follow this observed trend,
and I have measured maximum glacial velocities over 2009–2012 as being at or above what
Moholdt measured in 2000–2002 (5.3).
Figure 5.1: Academy of Sciences Ice Cap glacial velocities (1995) (Moholdt et al., 2012a).
19
28. Figure 5.2: Academy of Sciences Ice Cap glacial velocities (2000–2002) (Moholdt et al.,
2012a).
Although no one else has measured any glacial velocities over the rest of Severnaya
Zemlya, we now have ASTER velocities from 2000–2012, which will serve as benchmarks
to compare future velocities to. Particularly, data gathered over the Matusevich Ice Shelf
may prove useful. The Matusevich Ice Shelf, situated between the Rusanov and Karpinsky
Ice Caps, was the largest ice shelf in all of Eurasia prior to its collapse during August and
September, 2012. It is expected that the release of this buffer zone will result in significant
increases in glacial velocity and calving rates. By comparing my results from 2010–2012 to
future results post-breakup, we will soon be able to see what the effect of this collapse will
be.
20
29. Figure 5.3: Velocity variation in glaciers on the Academy of Sciences Ice Cap. Velocities
from 1995 and 2000–2002 are maximum speeds reported in Moholdt et al. (2012a).
5.2 Novaya Zemlya
Likewise, my 2013 Novaya Zemlya velocity map is one of the first regional velocity maps ever
produced. By continuing my work in the region with other years of data, future researchers
can make a time-lapse photo detailing the acceleration that is occurring here.
In addition to my quantitative measurements, I have also made several qualitative ob-
servations that suggest an increase in average glacial velocity. When looking at Landsat 5
images over the southeast coast of Novaya Zemlya in the 1980s, I noticed that there were
no crevasses visible whatsoever. This suggests that the ice is not undergoing very much
strain and therefore must not be moving very quickly, similar to Ice-Stream A in Severnaya
21
30. Zemlya. However, when looking at images over the same glaciers in 2013, there are abun-
dant crevasses on at least part of every glacier on the southeast coast. This suggests a rapid
change in velocity over the last few decades.
On the northeast shore of Novaya Zemlya, I noticed obvious glacial retreat by comparing
the terminus of several glaciers over the years. As seen in Figure 5.4, the glacier terminus
of Vil’kitskogo Sev. is retreating at a rate of 170 m/year. This calving rate is well beyond
what is expected for tidewater glaciers and suggests imminent collapse.
Figure 5.4: Glacial retreat of Vil’kitskogo Sev. Glacier in northern Novaya Zemlya, taken
from Landsat images. Base image was taken by Landsat 8 on 1 August 2013.
22
31. CHAPTER 6
CONCLUSION
My preliminary results agree with trends noted in the literature, which suggest accelera-
tion of Russian High Arctic glaciers. In particular, glaciers on the Academy of Sciences Ice
Cap in Severnaya Zemlya and glaciers all over Novaya Zemlya are significantly increasing in
velocity. Although I cannot prove cause-and-effect, this change is likely due to the additional
stress of climate change in the region.
The effect of this stress can be seen in the massive destabilization of the Matusevich Ice
Shelf, as well as the marked increase in the presence of crevasses and the glacial retreat seen
in Novaya Zemlya. If these trends continue, Russian High Arctic melting could accelerate
predictions for sea level rise.
Further research needs to be done in the Russian High Arctic if we are ever going to
understand the dynamic processes that are occurring there. To determine whether or not
acceleration correlates with mass loss, we will need to see if glaciers experiencing rapid
acceleration also exhibit mass loss, via dh/dt analysis of DEM’s. If a direct relationship
exists, velocity variation can be used as a proxy for mass loss.
With the addition of Landsat imagery over Severnaya Zemlya, it will be possible to push
our knowledge of glacial velocities back to the 1980s, giving us a better idea of the long-term
trend occurring. With the use of Interferometric Synthetic Aperture Radar (InSAR), specif-
ically from the European Remote-Sensing Satellite, we can also measure winter velocities
and gain a better understanding of the seasonal cycle. This is due to the active radar sensor
used, which does not rely on sunlight like optical passive sensors. Since the Arctic Circle
does not receive almost any sunlight during the winter, ASTER and Landsat images are
useless. High-resolution imagery can also provide clearer results than ASTER or Landsat
due to the ease with which crevasses can be correlated.
My work is far from complete, with decades of Landsat imagery over Novaya Zemlya
and Franz Josef land still in the works. In the end, we hope to have composite velocity
23
32. maps for every year over every region, allowing us to see the gradual evolution of ice streams
throughout these Russian archipelagos.
24
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