The Cryosphere, 9, 2201–2214, 2015
www.the-cryosphere.net/9/2201/2015/
doi:10.5194/tc-9-2201-2015
© Author(s) 2015. CC Attribution 3.0 License.
Revealing glacier flow and surge dynamics from animated satellite
image sequences: examples from the Karakoram
F. Paul
Department of Geography, University of Zurich, Zurich, Switzerland
Correspondence to: F. Paul (frank.paul@geo.uzh.ch)
Received: 20 February 2015 – Published in The Cryosphere Discuss.: 27 April 2015
Revised: 1 November 2015 – Accepted: 2 November 2015 – Published: 26 November 2015
Abstract. Although animated images are very popular on
the internet, they have so far found only limited use for
glaciological applications. With long time series of satellite
images becoming increasingly available and glaciers being
well recognized for their rapid changes and variable flow
dynamics, animated sequences of multiple satellite images
reveal glacier dynamics in a time-lapse mode, making the
otherwise slow changes of glacier movement visible and understandable
to the wider public. For this study, animated
image sequences were created for four regions in the central
Karakoram mountain range over a 25-year time period
(1990–2015) from freely available image quick-looks of orthorectified
Landsat scenes. The animations play automatically
in a web browser and reveal highly complex patterns of
glacier flow and surge dynamics that are difficult to obtain by
other methods. In contrast to other regions, surging glaciers
in the Karakoram are often small (10 km2
or less), steep,
debris-free, and advance for several years to decades at relatively
low annual rates (about 100 ma−1). These characteristics
overlap with those of non-surge-type glaciers, making a
clear identification difficult. However, as in other regions, the
surging glaciers in the central Karakoram also show sudden
increases of flow velocity and mass waves travelling down
glacier. The surges of individual glaciers are generally out of
phase, indicating a limited climatic control on their dynamics.
On the other hand, nearly all other glaciers in the region
are either stable or slightly advancing, indicating balanced or
even positive mass budgets over the past few decades.
1 Introduction
1.1 Visualizing glacier dynamics
Analysis of sequential satellite images has become a common
tool for deriving glacier changes through time in all
parts of the world. A “standard” way of documenting these
changes in scientific journals is the overlay of glacier outlines
from different points in time on one of the images used for
the analysis (e.g. Baumann et al., 2009; Bhambri et al., 2013;
Paul et al., 2004). In the case where multiple images are
available and changes take place mostly at the glacier terminus
(e.g. during an advance or retreat phase), terminus
positions are indicated by multiple lines with years either
attached to them (e.g. Jiskoot and Juhlin, 2009) or colourcoded
(McNabb and Hock, 2014; Quincey et al., 2011; Rankl
et al., 2014). When complex interactions take place between
two glaciers (e.g. a tributary is merging with another glacier),
phases of the changes are illustrated by showing sequential
images side by side (e.g. Belò et al., 2008; Bhambri et
al., 2013; Copland et al., 2011; Mukhopadhyay and Khan,
2014) or by two-dimensional drawings of changes in major
moraine patterns (e.g. Hewitt, 2007; Meier and Post, 1969;
Quincey et al., 2015).
Although these representations of changing glaciers are
scientifically sound and exact, they have some limitations in
demonstrating dynamic aspects. The key issue is related to
the limited ability of the human brain to recognize differences
between two (static) images when shown side by side
or to translate various outlines of terminus positions into the
correct sequence of changes, in particular when changes are
out of phase for a couple of glaciers. On the other hand, the
human brain recognizes movement well and tends to compensate
missing parts in a sequence of animated images due
Published by Copernicus Publications on behalf of the European Geosciences Union.
2202 F. Paul: Glacier flow and surge dynamics from animated satellite image sequences
to the slow processing of visual information, also known as
the “phi phenomenon” (e.g. MacGillivray, 2007). This helps
in translating time-lapse photography into continuous motion,
thus making the dynamic nature of otherwise slowly
moving objects or natural phenomena visible (e.g. cloud development,
aurora, tides). While cameras with an interval
timer were not common a decade ago and related footage
was rare, today’s widespread availability of webcams allows
pictures to be taken remotely and automatically each
day (or whatever period) at regular intervals. This can be
particularly interesting when glaciers are imaged, as their
movement is normally much too slow to be recognized (e.g.
www.chasingice.com).
At the satellite scale, the application of “flicker” images
(basically a rapid alternation of two images taken a few years
apart) for demonstrating glacier changes is common practice
and has been used to analyse glacier motion (Kääb et
al., 2003). In this way, coherent patterns of displacement of
the glacier surface have long been used to determine surface
flow velocities from feature tracking using cross-correlation
or other techniques (e.g. Kääb and Vollmer, 2000; Scambos
et al., 1992; Paul et al., 2015). With the now free availability
of long time series (starting in 1984) of orthorectified satellite
imagery from Landsat (e.g. Wulder et al., 2012), it is possible
to combine sequential satellite images into longer sequences
(> 20 years) and demonstrate landscape changes in a
time-lapse mode (e.g. world.time.com/timelapse2) including
glacier flow and dynamic changes over large regions. This
provides new insights and a more intuitive access to phenomena
such as the mutual interaction of different glaciers, fast
and slow flow of different glacier segments, advance and retreat
patterns, downwasting (i.e. surface lowering without retreat),
and the dynamics of supraglacial and proglacial lakes
and river streams. Depending on the time step between the
original images and the flow velocity of the glaciers, the impression
of more or less continuous flow can be obtained by
animating the individual images at high speed.
In this study animated sequences of orthorectified satellite
images covering a 25-year time period (1990–2015) are
used to demonstrate glacier dynamics and other landscape
changes in four regions of the central Karakoram. Though
this might be seen as a less quantitative approach than that
of studies determining the exact rates of glacier change, the
information obtained by looking at high-speed animations
of the individual images also provides insight into dynamic
glacier behaviour on a different level. There is also potential
for using such animations for educational purposes by
visualizing how glaciers flow and change through time. The
animations use the very old (> 25 years) image format GIF,
which has its drawbacks in terms of the number of colours
that can be used (only 256), but it is the only format that
allows a looping of high-frequency animations with screensize
images. The format has recently become increasingly
popular on the internet (e.g. giphy.com) and in mobile communication
(Isaac, 2015) for short repetitive animations due
to its easy use (no special software required) and relatively
small file size.
1.2 Surge-type glaciers
The Karakoram mountain range has been selected due to its
many surging glaciers that display a distinct dynamic behaviour
(e.g. Copland et al., 2011; Gardelle et al., 2013; Hewitt,
2007; Rankl et al., 2014). According to Jiskoot (2011), a
surge-type glacier oscillates between a period of slow or normal
flow (lasting tens to hundreds of years) named the quiescent
phase, and an active or surge phase during which flow
velocities increase by a factor of 10–1000 over a short period
(a few months to years), sometimes resulting in marked
frontal advances (kilometre scale). During a surge, a large
amount of ice is transported from a reservoir area to a receiving
area where it melts after a surge, predominantly by
downwasting. All three components (time periods for both
phases, velocities, terminus advance) reportedly vary over a
wide range (e.g. Sharp, 1988), resulting in an unclear separation
from non-surge-type glaciers (cf. Table 5 in Sevestre and
Benn, 2015) that might, for example, just advance over an extended
period of time (Meier and Post, 1969). A surge-type
glacier in its quiescent phase can often be identified from
distortions of the normally parallel-aligned medial and/or lateral
moraines (e.g. Grant et al., 2009; Kotlyakov et al., 2008).
Such distortions may result from the speed-up of either a specific
section of a glacier or the merging of a surging tributary
with the main glacier (e.g. Hewitt, 2007). In the latter case it
might be possible that the main glacier is – despite the surge
marks on its surface – not of surge type.
While the Karakoram region is well known for its many
surge-type glaciers (e.g. Copland et al., 2011; Hewitt, 2014),
counting them is challenging as the frequently used criteria
for their identification only partly apply. Many studies have
thus introduced a “surge index” to indicate the certainty that
a specific glacier is of surge type (cf. Sevestre and Benn,
2015). The evidence can be divided into geomorphological
and dynamic categories (e.g. Jiskoot, 2011). The former include
looped or distorted medial moraines, a glacier tongue
that is largely covered by crevasses and séracs during a surge,
a post-surge disconnection of the tongue well behind the terminus,
and rapid downwasting after the surge with the formation
of potholes and remaining stranded icebergs (e.g. Yde
and Knudsen, 2005). Dynamic criteria include (among others)
the terminus advance rate, the total advance over a given
period, the duration of the advance and retreat (or quiescent)
phase, the relative advance compared to the pre-advance
glacier length, absolute values of surface velocity, significant
velocity changes in specific regions of a glacier, surge periodicity
and inverse thickness changes in the ablation (mass
gain) and accumulation (mass loss) regions. For these dynamic
criteria, the values for surging glaciers can be 1 to 3
orders of magnitude higher than for non-surge-type glaciers
(e.g. Jiskoot, 2011). However, they can also lie within a simiThe
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F. Paul: Glacier flow and surge dynamics from animated satellite image sequences 2203
lar range, thus limiting the possibilities for a clear separation.
For this study a glacier is called “surging” based on its easily
identifiable strong and partly rapid advance or on the basis
of its classification in previous studies (Copland et al., 2011;
Gardelle et al., 2013; Rankl et al., 2014). In the event that
other criteria are used (e.g. large changes in flow velocity), a
different assignment might result.
2 Study region, data sets and methods
The study region is located in the central Karakoram mountain
range (Fig. 1) to the north of – and including – the large
and well-studied Baltoro Glacier (Quincey et al., 2009 and
references therein). Four regions are selected for the animations:
(1) Baltoro, (2) Panmah, (3) Skamri/Sarpo Laggo
and (4) Shaksgam. All regions are well known for their
many surge-type glaciers (cf. Copland et al., 2011; Rankl et
al., 2014), several of which have been studied in more detail
(Diolaiuti et al., 2003; Hewitt, 2007; Quincey et al., 2011;
Rankl et al., 2014). The region is characterized by very steep
and high terrain (often reaching more than 7000 m a.s.l.) with
numerous multi-basin valley glaciers, that often have further
tributary glaciers in their ablation zones (Iturrizaga, 2011).
The anomalous glacier behaviour in the study region (mass
gain and advancing glaciers over the past 2 decades), relative
to most other regions of the world has been named
the “Karakoram Anomaly” (e.g. Bolch et al., 2012; Hewitt,
2005). This behaviour might be attributable to an increase in
precipitation (e.g. Janes and Bush, 2012), but the large number
of actively surging glaciers in the region might also have
non-climatic causes (e.g. Hewitt, 2005; Jiskoot, 2011). A recent
study by Sevestre and Benn (2015) has suggested that
glaciers in this region are located in the climatically “correct”
zone for surge-type glaciers. Further details about the
topo-climatic characteristics of the region can be found in
Hewitt (2014).
The study region is completely covered by Landsat scene
148–35 (path–row) and partly by scene 149–35 (Fig. 1). Useful
Landsat scenes (sensors TM, ETM+ and OLI) acquired
near the end of the ablation period (summer) are available
for 18 individual years since 1990 and four further scenes for
the Panmah region from path–row 149–35 (see Table 1). For
the animations provided in the supplemental material, only a
selection of scenes have been used to limit file size and get
similar temporal differences. The full set of individual scenes
is provided on a separate web page (http://cryoportal.enveo.
at/glaciers.html). A Landsat MSS scene (path–row: 160–35)
from August 1977 was used to provide information on previous
glacier extents, but is not integrated into the animations.
Only the orthorectified quick-looks of all scenes were
downloaded from earthexplorer.usgs.gov and used for the animations.
They are provided as false colour composites at
the original 30 m resolution, showing glaciers in light blue
to cyan, clouds in white, water in dark blue, vegetation in
Figure 1. Landsat scene of the study region from 2004 showing
footprints of the four subregions depicted in Figs. 2–5. The black
square in the inset shows the location of the study region in the
Karakoram mountain range (map taken from Google Earth). The
image centre is at 36◦ N, and 76.3◦ E.
green and bare terrain in brown. All scenes are processed in
a standardized processing line at USGS (with colours balanced)
and are provided with extra files that include projection
information and geolocation for easy import into GIS
software.
The animations are created by displaying all images in a
GIS (e.g. QGIS, ArcMap), exporting the maps to a 24-bit
image file, converting all images to GIF format and by creating
the animated GIF image with a delay of 1/10 s using
convert from ImageMagick. Annotated versions of the four
subregions are shown in Figs. 2–5 for orientation and as a
reference (using the scene from 2004). In general, the temporal
difference between two images in the animation is 1 or
2 years (see Table 1), but sometimes it is also 3 or even more
(e.g. 5 from 2004 to 2009).
3 Observations in the study region
A wide variety of dynamic changes are visible in the animations.
They range from the variable extent of seasonal
snow cover, to clouds (with their shadows) that pop up
on individual frames, to the steady flow of large glaciers
with traceable surface characteristics (moraines, lakes), to
glacier fronts advancing and retreating at different rates,
mass waves travelling through individual glaciers, downwasting
of debris-covered glacier sections and short-term velocity
pulses within a glacier. As all of the above are happening
at the same time, it is easy to lose focus. Following
the evolution of specific changes thus requires focussing the
view on a specific region and ignoring everything else. In
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2204 F. Paul: Glacier flow and surge dynamics from animated satellite image sequences
Table 1. Overview of the 21 Landsat scenes used to create the animations in the Supplement for the four subregions shown in Figs. 2–5.
The MSS scene is only added for completeness. Abbreviations are as follows. From the “sensor” column, MSS: MultiSpectral Scanner, TM:
Thematic Mapper, ETM+: Enhanced Thematic Mapper + and OLI: Operational Land Imager. “Date” shows day.month, “day” signifies
the day number within a year and “P–R” shows path–row. Letters in columns 7–11 denote the following subregions: A and A: Baltoro, B:
Panmah, C: Skamri, D: Shaksgam. The number in each column, A to D, gives the order in the sequence. GLS: Global Land Survey. Note
that for the animation in the Supplement, only the subset A is used; an extended time-series A can be created from the individual images
available from http://cryoportal.enveo.at/glaciers.html.
No. Sensor Date Year Day P–R A A B C D Remarks
0 MSS 02.08 1977 214 160–035 – – – – – not used for the animations
1 TM 07.08 1990 219 149–035 – – 1 – – path 149
2 TM 02.07 1991 183 148–035 1 1 – – –
3 TM 19.08 1991 231 148–035 – – 2 1 1
4 TM 07.07 1993 188 148–035 2 2 3 2 2 has much snow
5 TM 17.07 1994 198 149–035 – – 4 – – path 149
6 TM 01.09 1996 245 148–035 – – – 3 – has fresh snow
7 TM 18.07 1997 199 148–035 3 3 5 4 3
8 TM 07.09 1998 250 148–035 4 – 6 5 –
9 TM 27.08 2000 240 148–035 5 4 – – –
10 ETM+ 21.07 2001 202 148–035 6 – 7 6 4
11 ETM+ 09.08 2002 221 148–035 7 – 8 – –
12 ETM+ 14.08 2004 227 148–035 8 5 9 7 5 GLS2005, striping removed
13 ETM+ 26.07 2006 207 149–035 – – 10 – – path 149, striping removed
14 TM 11.08 2009 223 149–035 – – 11 – – path 149
15 ETM+ 12.08 2009 224 148–035 9 6 – 8 6 GLS2010, striping removed
16 TM 23.08 2010 235 148–035 – – 12 9 7
17 TM 10.08 2011 222 148–035 10 – – – 8 has clouds
18 OLI 14.07 2013 195 148–035 11 7 13 10 9
19 OLI 24.07 2014 205 149–035 – – 14 – – path 149
20 OLI 04.07 2015 185 148–035 – – 15 11 – has much snow
21 OLI 21.08 2015 233 148–035 – – – – 10
the following, some prominent observations are described for
each subregion.
3.1 Observable glacier flow and terminus fluctuations
3.1.1 Subregion (1): Baltoro
In subregion (1) Baltoro Glacier and its numerous tributaries
dominate the scene (Fig. 2). With few exceptions, all glaciers
show continuous and near-steady flow. Despite the easily recognizable
high flow velocity of the main glacier, its terminus
remains in about the same position. Supraglacial lakes
across its surface appear and disappear but are easily traceable
markers for the surface flow. Towards the upper parts of
the glacier (to the right of the image), flow velocity seems
to increase, because tracing features becomes increasingly
difficult. In the accumulation region, flow dynamics are difficult
to follow due to a lack of traceable features and the
high variability of snow extent. However, in the lower right
of the image, steep snow-covered glacier headwalls (facing
north) and some deep crevasses reveal very high flow velocities.
The flow speed here is high enough that the 1–3-year
time step between images fails to provide the impression of
continuous flow; instead it more looks like a nervous shak-
ing.
As the animations are particularly useful for recognizing
changes, the advancing fronts of several rather small glaciers
with narrow and/or heavily debris-covered tongues are also
easily visible. In most cases it would have been nearly impossible
to spot these changes from comparison of image pairs
alone. This is also the case for several advancing glaciers in
the lower part of the image. Two surge-type glaciers (Trango
and Muztagh in Fig. 2) with distorted moraines and a clearly
visible variability in surface elevation can be seen to the north
of Baltoro. To the south of them is the surge-type Liligo
Glacier, which has been studied in detail before (e.g. Belò et
al., 2008). This glacier reached its maximum extent around
1998 and was in its downwasting or quiescent phase afterwards.
As the animations cover the surge and post-surge periods,
the rapid advance of the front during the surge and the
following thinning and disconnection of the lower tongue can
be traced very well. Three of the four rather small glaciers in
the southwest corner of the image (marked with an “x” in
Fig. 2) show a behaviour that is similar to Liligo, in particular
the one to the very left. At the same time, the terminus
of the glacier to its right goes slightly back and forth several
times.
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F. Paul: Glacier flow and surge dynamics from animated satellite image sequences 2205
Figure 2. Subregion (1) (Baltoro) shows the tongue of Baltoro Glacier and its surrounding tributaries. SG (orange): actively surging glacier,
SG (white): surge-type glacier (only on Figs. 4 and 5), A: advancing glacier. The Landsat scene 148-035 is from 14 August 2004.
3.1.2 Subregion (2): Panmah
In the Panmah region (Fig. 3, subregion (2)), the variability
in late summer snow extent, and the differences between
the behaviour of the steady-flowing and actively surging
glaciers are easily recognizable. While the large tongues of
Biafo, Choktoi and Nobande Sobonde (NS) glaciers show
the steady flow typical of non-surge-type glaciers, several
(partly tributary) glaciers show strong advances (First Feriole,
Shingchukpi), sudden onset of fast flow (Drenmang),
or mass waves travelling down glacier (Chiring), and most
of them finally collide with other glaciers (e.g. Maedan,
Chiring) and create the well-known distorted and looped
moraines (Hewitt, 2007). Also apparent is the asynchronous
nature of the advance/retreat (or downwasting) phases. While
Chiring Glacier finished its surge before 1998 (cf. Hewitt,
1998), other glaciers either started to surge around 2000
(First Feriole), were already in full surge mode then (e.g.
Shingchukpi), or began to surge later (e.g. in 2006 for Drenmang).
The surge of First Feriole glacier started while the ice
masses from the previous surge were still down and backwasting
in the valley floor. This gives the impression of
one retreating and one advancing terminus at the same time.
As in the Baltoro region, the termini of many of the much
smaller surrounding glaciers are either stationary or advancing
slowly. Some of the advancing glaciers have a terminus
width of only 1 or 2 pixels but their changes can be easily
followed in the animation.
3.1.3 Subregion (3): Skamri
The variability described above for subregion (2) is also apparent
in subregion (3), as depicted in Fig. 4. Many of the
surge-type glaciers are actively surging during the period of
observation and only a few are in their quiescent phase. The
smaller non-surge-type glaciers are either stagnant or slightly
advancing, while the large debris-covered glaciers are stagnant
and downwasting. A wide range of terminus advance
rates is apparent as well. While one glacier (North Chongtar)
advances very slowly (and might not be identified as being
surge-type from its advance rate), others advance rapidly and
strongly. They partly merge for some time with a larger main
glacier (Sarpo Laggo and Skamri). As some of these glaciers
(marked with an “x” in Fig. 4) retreat back to their former
positions over the remainder of the time period, the looping
of the animations creates the impression that they are pulsating.
This is different for South Chongtar Glacier, which
has a downwasting and retreating ice mass from a previous
surge in front of its more or less stagnant terminus. However,
on closer inspection, one can see a surge front, a somewhat
wider and advancing region up-glacier that is travelling down
towards the stagnant terminus, likely indicating a forthcoming
surge. If this is already termed a surge, then this glacier
is in its quiescent and surge phase at the same time (demonwww.the-cryosphere.net/9/2201/2015/
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2206 F. Paul: Glacier flow and surge dynamics from animated satellite image sequences
Figure 3. Subregion (2) (Panmah) shows the region around Panmah and Choktoi glaciers with surrounding tributaries; annotations and
Landsat scene as in Fig. 2.
Figure 4. Subregion (3) (Skamri) shows the region between Skamri and Sarpo Laggo glaciers; annotations and Landsat scene as in Fig. 2.
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F. Paul: Glacier flow and surge dynamics from animated satellite image sequences 2207
strating the difficulties for using the terminology correctly at
a given time).
The small glacier on the opposite side of the valley is also
advancing and might again (as in 1977) surge down to the
tongue of Sarpo Laggo Glacier. Moni Glacier (Fig. 7) shows
some interesting interaction with Sarpo Laggo. It seems that
the lobate tongue of Moni (resulting from a previous surge)
blocked the flow from Sarpo Laggo for some time and a fast
flow event within the blocked ice mass advected the lobate
structure from Moni Glacier quickly down valley for some
years, as has also been observed for several surging glaciers
in the Pamir (Kotlyakov et al., 2008). During and towards the
end of this event, a substantial thinning of Sarpo Laggo can
be observed upstream. From the moraine deformation visible
in the last images, it also seems that Moni Glacier was again
pushing into Sarpo Laggo, maybe starting its next surge. A
similar interaction might also happen for South Skamri and
Skamri glaciers. The animations reveal the increasing blocking
of the flow of the main glacier by the surge of its tributary,
South Skamri Glacier. So far, Skamri Glacier has not reacted
to this blocking, but a sudden push of the distorted moraine
in the upper left is already visible (see also Fig. 3c in Quincey
et al., 2015). It remains to be seen if this push will catch up
with the still ongoing surge of South Skamri.
3.1.4 Subregion (4): Shaksgam
In subregion (4) such a short-lived high-speed push event can
be seen on Crown Glacier. The most massive surge can be
seen for North Crown Glacier that has also reactivated the
downwasting ice masses from a previous surge of a similarsized
neighbouring glacier in its own surge (cf. Fig. 8 in
Quincey et al., 2015). This glacier also shows a mass wave
that is travelling down to the front creating the strong advance.
Several other larger glaciers in the region are also
advancing or even surging, depending on the definition of
a surge. The ongoing surge of Tatulu Guo Glacier, shown in
the upper right of Fig. 5, has also been described by Quincey
et al. (2011). Several smaller glaciers show either stationary
or slowly advancing fronts.
3.2 Surface elevation changes
As mentioned above, changes in surface elevation can be followed
in the animations, in particular where there are stable
lateral moraines. Changes observed include the thickening/thinning
pattern typical of surging glaciers that occurs
as a surge front propagates down glacier (as for Chiring,
Drenmang and North Crown glaciers), as well as the
downwasting of the stagnant tongues of several of the large
debris-covered glaciers, in particular in subregion (3) (Sarpo
Laggo, Skamri). There are also glaciers where no elevation
changes can be detected, such as Baltoro and Choktoi
glaciers, where flow appears to be stable. Rather interesting
is also the surface lowering of Panmah Glacier (the lower part
of Nobande Sobonde) in subregion (2), which occurs despite
additional mass input from surging tributaries (Chiring and
Shingchukpi). More subtle are the elevation increases and
decreases that result from flow blocking such as that which
characterizes the interaction between Moni and Sarpo Laggo
or Drenmang and NS glaciers. In particular the latter can also
be seen in the study by Gardelle et al. (2013), who determined
elevation changes over the 2000–2008 period using
differencing of two digital elevation models (DEMs). The animations
reveal how these changes took place and how they
were related to other changes associated with a glacier surge,
such as frontal advance/retreat and velocity changes.
3.3 Lakes and debris cover
Another form of variability can be seen in the numerous
(hundreds) supraglacial lakes and ponds covering the lower
parts of Baltoro Glacier (subregion (1)), Panmah (subregion
(2)) and some other glaciers. These lakes seem to be rather
short-lived (about 2–3 years), limiting their use for determining
flow velocities by feature tracking to a 1-year period.
Most of the lakes are about the same size but their
shape varies rather strongly from scene to scene. For Baltoro
Glacier, it is apparent that supraglacial lakes often form
in zones of compressive flow (where larger tributaries join),
indicating that surface meltwater is not efficiently drained.
However, it also has to be considered that the images are
taken at different dates each year and that the extent and level
of the lakes varies over a year in response to changing melt
conditions and rainfall. Stationary lakes outside the lateral
moraines also change size over time.
Supraglacial debris occurs in form of the typical flowparallel
moraines (where they first appear) that often spread
out to create a complete debris cover near the terminus, in
the form of distorted (wave-like) bands where glaciers with
unsteady flow interact, or in the form of local debris accumulations
from rock fall events. One glacier (Mundu) in subregion
(1) has regular and similar-sized patches of debris on its
surface, indicating periodic rock fall activity.
3.4 Apparent movement of stable terrain
Finally, subregion (2) shows local movement of terrain that
is actually stable, mostly along mountain peaks and ridges.
This is likely the result of the use of different DEMs to orthorectify
the satellite images. As this apparent movement is
concentrated on regions outside the glaciers (i.e. on “stable”
terrain), an algorithm for calculating flow velocity would obtain
a considerable surface displacement in these regions,
which has to be removed manually before assessment of the
accuracy of glacier velocities over stable terrain can be performed.
The animated images clearly reveal such regions,
helping to determine the quality of the orthorectification for
an entire time series and identify the obviously unstable rewww.the-cryosphere.net/9/2201/2015/
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2208 F. Paul: Glacier flow and surge dynamics from animated satellite image sequences
Figure 5. Subregion (4) (Shaksgam) shows the region to the north of Skamri Glacier to both sides of the Shaksgam Valley; annotations and
Landsat scene as in Fig. 2.
gions for the post-processing of velocity data (e.g. Kääb,
2005).
4 Discussion
4.1 Creating animations from image time series
The animations presented here would not have been possible
without the accurate and consistent orthorectification of
all satellite scenes by USGS. Indeed, errors in the DEM used
for orthorectification translate into incorrect pixel positions
that change when a different DEM is used for orthorectification,
especially in high relief and steep terrain (see Sect. 3.4).
However, for relatively flat glacier tongues, this effect is
small and does not obscure the much larger changes due to
glacier flow. It must also be mentioned that the animations
are created from image quick-looks that are freely provided
(earthexplorer.usgs.gov) (a) in full resolution, (b) geocoded,
(c) with a small file size (about 10 MB), (d) in false colour
(revealing glaciers) and (e) with colours and brightness values
well balanced among all scenes. The latter property is
an invaluable asset for viewing the animations. If brightness
values or colours were more variable, it would be impossible
to look at the animations due to a stroboscope effect. Hence,
the consistency of the processing line is responsible for the
clarity with which the glacier dynamics can be seen.
With the processing line being established, scenes must
have been acquired over a sufficiently long time period (at
best each year) and be available in the USGS archive. The
former point is not necessarily the case for the first years
of Landsat operation and during its commercial phase (see
Goward et al., 2006). The latter point (transfer of scenes from
non-USGS archives to USGS) is an ongoing process that
will continuously expand the possibilities for other regions.
Data availability is also restricted after 2003, when the scanline
corrector of ETM+ failed and intense striping degraded
the usefulness of the scenes. It comes with some luck that
three of the post-2003 scenes from ETM+ used here were
in the Global Land Survey (GLS) 2005 and 2010 catalogues
and had the striping more or less removed (i.e. filled with
information from other scenes). Surprisingly, this filling of
missing lines had no noticeable effect on the boundaries or
surface features of the quickly changing glaciers, e.g. due
to clouds or different snow conditions. Apart from some remaining
artefacts, the former regions with missing lines can
only be identified by carefully analysing the individual images.
This implies that great care has been taken to correct
the striping and/or that the replacement scenes were acquired
close to the date of the corrected scenes.
On the more practical side, it is important that appropriate
scenes are available. Key requirements are that all scenes are
taken in each year near the end of the ablation period (with as
much glacier ice exposed as possible) at about the same day
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F. Paul: Glacier flow and surge dynamics from animated satellite image sequences 2209
(to avoid strong changes due to shadow) and without clouds.
While it was not possible to have identical extents of snow
cover on all images used in this study (resulting in a rather
nervous flickering when animated), the variability of shadow
extent is rather limited due to the high solar elevation. Clouds
were an issue in some years and regions (e.g. in 2008), but
the large data gap between 2004 and 2009 is mostly due to
missing stripe-filled quick-looks as suitable ETM+ scenes
are available for 2005, 2006 and 2007. For the Panmah region
in the east, it was possible to include additional scenes
from the neighbouring path (149–035) in 1990, 1994 and
2006 (see Table 1), resulting in smoother transitions. Splitting
the region into subregions thus also provided a higher
flexibility for selection of good images.
The final step in creating the images was the selection of
the most useful scenes for each subregion. This was a compromise
between (a) using as many largely cloud-free images
as possible, (b) having a more or less constant time
step of 1–3 years between each image, (c) avoiding strong
changes in reflectivity due to seasonal snow, and (d) capturing
the relevant processes that sometimes occur only in
a specific year. As Table 1 reveals, it was not really possible
to satisfy point (b) for any of the regions without compromising
the other points. This results in a non-equidistant
temporal difference between the images (from 1 to 5 years)
with potential effects on the perception of flow velocities
(e.g. flow seems faster with 3-year than with 1-year time
steps). Obvious changes in flow velocity have thus to be interpreted
with great care. As a compromise, for some regions,
more individual images are provided on the separate server
(http://cryoportal.enveo.at/glaciers.html) than were actually
used for the animations. Experimenting with these additional
images is encouraged (see Sect. 4.3).
Overall, the animations presented here provide an
overview on glacier and landscape dynamics over a period
of 25 years that cannot be created in all regions of the world.
Apart from the above criteria related to the availability and
selection of images, glaciers also have to be large and structured
enough to reveal flow dynamics. Otherwise, it will be
mostly the changes in extent that can be followed, but this
might be of interest as well.
4.2 Advantages of the animations compared to other
methods
As described in the introduction, several possibilities exist to
visualize glacier changes (e.g. front variations) or dynamics
(e.g. flow fields and their changes over a given time period)
in a quantitative way (e.g. overlay of colour-coded and/or annotated
outlines or maps, side by side comparisons). The animations
on the other hand, provide information and insights
that are complementary to the above classic (quantitative)
ways. Apart from the fact that the animations reveal the temporal
evolution of otherwise very slow processes in a much
faster (time-lapse) mode and provide a true dynamic feeling,
they also give a holistic view of changes taking place at
the same time (e.g. terminus fluctuations, elevation changes,
flow velocities) that is nearly impossible to obtain from static
maps. Examples are the stagnant downwasting lower tongues
of Skamri and Sarpo Laggo glaciers, and the mass wave that
is travelling down Chiring Glacier at high velocity, resulting
in a fast and strong advance of the terminus. This easily
visible combination of effects might ultimately help to establish
a more comprehensive definition of surge-type or surging
glaciers that seemingly needs to be expanded to correctly
capture the full variability of possible glacier dynamics.
When looking at the benefits of animations more systematically,
they can be roughly grouped into three categories:
(I) more subtle insights facilitating process understanding,
(II) new scientific information, and (III) technical advantages.
Insights of category (I) are
a. how a glacier flows in general and how a main stream
of ice is fed by its tributaries;
b. the succession or timing of events for individual glaciers
and the entire region;
c. understanding of glacier dynamics for the wider public
that is not used to interpret colour-coded velocity fields
or line graphs.
Insights of category (II) include
a. identification of surging glaciers using a range of criteria
that are all visible at the same time (e.g. advance
rates of the terminus, internal velocity changes, travelling
of mass waves through the glacier, typical downwasting
pattern after a surge);
b. for long time series, information retrieval is maximized
when full surge cycles are captured;
c. a holistic and at the same time very detailed view of
decadal glacier variability and dynamics in a large re-
gion;
d. the gradual downwasting of mostly large, stationary and
debris-covered glaciers can be followed through time;
e. the very high flow velocities of the ice in steep parts
of accumulation regions that are not captured by traditional
methods such as offset tracking from optical
(contrast issue, stationary crevasses) or microwave data
(radar shadow, decorrelation);
f. the rapid changes (formation and decay) of supraglacial
lakes;
g. the identification of very tiny (1–2 pixel) debris-covered
glacier fronts (that are hard to recognize in individual
images) and their changes through time;
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2210 F. Paul: Glacier flow and surge dynamics from animated satellite image sequences
h. movement of terrain that should be stable but is not, due
to different DEMs being used for orthorectification (important
for accuracy assessment of velocity products).
In principle, most of the above points can also be detected
if individual images are uploaded in an image browser by
scrolling through them back and forth. So what are the technical
advantages (category III) of using the automated ani-
mations?
a. On the practical side, the manual scrolling through the
time series can only be performed a few dozen times
before either the finger becomes tired or the scroll wheel
stops working;
b. similarly, an automated animation allows the mouse to
be used to point out specific details;
c. more technically, for large data sets (e.g. more than six
images) it might not be possible to include all images in
the series within a single scroll;
d. scientifically, the constant repetition rate of the individual
images cannot be achieved by manual scrolling (important
to recognize changes in flow velocity);
e. visually, the back and forth movement of the ice is disruptive
and does not provide the same category (I) insights
(e.g. of continuous flow);
f. finally, the repetition rates can be adjusted (lowered) to
study specific changes in detail.
Of course, animated flicker images provide similar advantages,
but they are normally restricted to two images and do
thus not show the full temporal development over a longer
time. They also suffer from the back and forth effect (IIIe)
that hinders the impression of continuous flow. However,
they can be very practical for rapid change detection when
image conditions are about the same.
4.3 Educational use of the animations
As mentioned above, there is certainly potential for creating
or using animations for educational purposes. While the visualization
of glacier flow dynamics itself might be of interest
for teaching, public communication or exhibitions, classroom
experiments might look at using other images for each
time series (fewer/more), changing the repetition rate, analyse
effects of the looping, and adding annotations such as a
time bar. Remote-sensing-related questions might focus on
the spectral properties of ice and snow and the false colour
composites used, spatial resolution and visibility of details,
or the value of long-term time series and free data availability.
The latter might be further explored in hands-on lectures
or summer schools (e.g. Manakos et al., 2007) by creating
such animations for other regions with sufficient temporal
coverage. The animations might also help understanding of
natural variability over timescales that are not available from
any other source. Finally, the time-lapse mode compresses a
25-year development into 1 s, i.e. compared to reality, glacier
flow is shown about 800 million times faster. This fact might
be explored on a more philosophical level.
4.4 Flow velocities in accumulation regions
Due to the lack of contrast and traceable features, optical
sensors usually fail to provide information on flow velocities
in the accumulation regions of glaciers (e.g. Quincey
et al., 2009; Dehecq et al., 2015). The same is true for microwave
sensors in steep terrain due to radar shadow and
layover effects (e.g. Rott, 2009). However, high-resolution
missions such as TerraSAR-X have improved the situation
to some extent (Rankl et al., 2014) and show localized regions
of fast flow, even at the highest elevations of glaciers.
The animations reveal that the regions of fast flow are rather
widespread in steep accumulation regions and not adequately
captured by current velocity maps.
4.5 Identification of surge-type glaciers
As mentioned in the study by Sevestre and Benn (2015) and
several previous investigations (e.g. Copland et al., 2011; Hewitt,
2007; Quincey et al., 2011; Rankl et al., 2014), the central
Karakoram has a high abundance of surge-type glaciers
of which many have actively surged in the past 25 years.
As “normal” glacier advances are basically a consequence of
changed climatic conditions, while surges largely result from
internal mechanisms (e.g. Jiskoot, 2011; Meier and Post,
1969; Raymond, 1987; Sharp, 1988), it is important to distinguish
the two glacier types. However, the animations reveal
a large heterogeneity of the surging glaciers in terms of
size, hypsometry, exposure, advance rates, surge durations,
etc. that clearly overlap with the characteristics of non-surgetype
glaciers.
While the frontal advance rate, duration or distance are
only three of many criteria used to identify glaciers as surging
(see overview in Sevestre and Benn, 2015), the above
examples reveal that there is actually a continuum of advance
rates that allows no clear separation between surging
and advancing glaciers. The same is true for advance durations
that vary from short pulses (1–2 years) of rapid advance
(Drenmang) to slow advances taking more than 10 or
even 25 years (First Feriole, North Chongtar). Similar advance
rates and durations can also be found for non-surgetype
glaciers. Moreover, glaciers of nearly any size seem to
surge, from small (< 1 km2
) and steep, to large (> 10 km2
)
and flat. Hence not all the advancing glaciers in the animations
need be surging glaciers. The related annotations in
Figs. 2–5 (A for advancing, SG for surge-type glacier) are
thus subjective to some degree and can change when other
criteria are applied.
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F. Paul: Glacier flow and surge dynamics from animated satellite image sequences 2211
As previous studies have shown (e.g. Raymond, 1987),
some glacier surges involve considerable changes in flow
velocity, surface elevation and extent. Such quasi-parallel
changes are recognizable in the animations for some glaciers
in subregion (2) (Chiring, Drenmang). However, in several
cases, only some of these changes occur and for this study the
characterization is based on easily recognizable advances,
consistent with previous literature (Copland et al., 2011;
Rankl et al., 2014; Quincey et al., 2014) and historic satellite
images (MSS scene from 1977). However, glacier 14 in
Fig. 6 of the study by Rankl et al. (2014) is only marked as
advancing in this study, rather than surging, and this is certainly
subjective. On the other hand, glacier 15 in their study
(North Chongtar) is identified as surge-type but has actually
been advancing slowly since the 1970s, i.e. for more than
40 years. This gives rise to the question of how slow and prolonged
an advance can be for it still to be characterized as a
surge.
Previous studies that have characterized surge-type
glaciers according to their topographic characteristics (e.g.
area, length, slope, debris cover) have found a tendency
for surge-type glaciers to be longer, less steep, with more
branches, and being more fully debris-covered than nonsurge-type
glaciers of similar size (e.g. Clarke et al., 1986;
Barrand and Murray, 2006; Rankl et al., 2014; Sevestre and
Benn, 2015). In contrast, many of the surge-type or surging
glaciers in the study region are comparatively small
(2–20 km2
range) and steep, debris-free (apart from medial
moraines), and they have single- or dual-basin accumulation
regions. It is assumed that this difference is also a result of
a failure to clearly distinguish between surging tributaries
and non-surging trunk glaciers in previous studies. Moreover,
these steep, small and largely debris-free glaciers could have
been missed in previous assessments, as it is very difficult to
identify them as surge-type when they are in their quiescent
phase.
Another possibility for separating surging from normal advancing/retreating
glaciers is related to their specific postsurge
behaviour (i.e. the quiescent phase). As the animations
reveal (for glaciers that do not flow into another glacier from
the outset), the way the extended tongue down-wastes and
disintegrates after a surge is rather specific. It seems (e.g.
for Liligo in Fig. 2 or Shingchukpi in Fig. 3) that the entire
surged ice mass is transformed into dead ice after a surge
and decays by downwasting, similar to the ice resulting from
a dry calving event. After some years, this downwasting separates
the lower part of the surged ice mass from an upper
part at about 1/4 to 1/3 of its length (when measured from
the terminus). This points to thicker ice near the terminus
relative to the rest of the tongue, as ablation would normally
be higher at the lower elevations of the terminus (assuming
clean ice). This specific pattern of dead-ice downwasting after
a surge is rather unique for surge-type glaciers and might
be a good criterion for identifying them.
Figure 6. The still-advancing (surging) tongue of First Feriole
Glacier in the Panmah subregion. The image is a screenshot from
Google Maps and was acquired on 6 June 2014.
Figure 7. A surging tributary of Sarpo Laggo Glacier can be seen to
the right of the centre of the image, as viewed from Moni Glacier in
the foreground (see Sect. 3.1.3 for details). To the left of the centre
of the image, another unnamed surging glacier is visible. The photo
was taken in 2006 by Michael Beck (www.himalaya-info.org).
4.6 Repeat surges
Many of the glaciers in the study region have reportedly
surged during the past century (cf. Copland et al., 2011), and
historic satellite imagery (e.g. the MSS scene from 1977)
reveals different extents of the surge-type glaciers analysed
here. For example, First Feriole Glacier was in contact with
Panmah Glacier in 1977 and the latest high-resolution satellite
image (from 6 June 2014) available in Google Earth
(Fig. 6) reveals that the glacier is still in full surge mode
and might re-establish contact with Panmah Glacier in 2 or
3 years, resulting in a ca. 40-year surge cycle. A tributary of
Sarpo Laggo in subregion (3) (no. 45 in Copland et al., 2011;
no. 16 in Rankl et al., 2014) was in contact with the main
glacier back in 1977, 1991 and again in 2007, indicating a
ca. 15-year cycle. An image of its surge front from July 2006
is shown in Fig. 7. This is about 1.5 years before the glacier
came into contact with Sarpo Laggo Glacier. Such a periodic
repetition of surges is also a good indicator of surge-type be-
haviour.
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2212 F. Paul: Glacier flow and surge dynamics from animated satellite image sequences
4.7 Mass budgets
As glacier surges might have non-climatic controls, the
frontal changes of the non-surge-type glaciers can be better
interpreted in climatic terms. In all four subregions, these
glaciers have had either stable or advancing termini over
the 25-year period (see Sect. 3.1). This implies that past
mass budgets have generally been close to zero or even positive
(e.g. Janes and Bush, 2012). Unfortunately, there is
only indirect evidence for this as these glaciers are in general
too small to obtain reliable geodetic mass budgets from
DEM differencing (Gardelle et al., 2013). On the other hand,
the characteristic mass loss at higher elevations and gain in
the ablation region is easily visible for glaciers that surged
after 2000 in the elevation difference grids presented by
Gardelle et al. (2013). The resulting near-zero mass budgets
of these glaciers are, however, only half the story. On a longer
timescale, the surged ice masses also melt down and result in
an overall mass loss of the glacier. For several glaciers the
downwasting and mass loss after a surge can be traced in the
animations. Downwasting also occurs in the ablation regions
of some larger, debris-covered glaciers that are influenced
by surges of tributaries (Panmah, Sarpo Laggo or Skamri).
In this case it might be possible that the tributary surges
move the ice of the main glacier to lower elevations where
the debris cover is insufficient to protect the ice from melting.
When considering these complex dynamic interactions,
it seems appropriate to exclude surge-type glaciers from climate
change impact studies that are related to timescales
shorter than a full surge cycle.
5 Conclusions
This study discussed and presented (in the Supplement) animated
satellite image sequences from four regions in the central
Karakoram mountain range, covering a 25-year time period.
The high repetition rate of 1/10 s per frame gives the
impression of continuous glacier flow and reveals changes
in morphology and dynamics with a clarity that cannot be
achieved with side by side comparison of static images or
colour-coded glacier outlines from different points in time.
Though changes are not determined in a quantitative way,
the time-lapse mode of the animations provides insights that
are difficult to obtain otherwise. Apart from really seeing
how glaciers flow and interact, the particular advantage is the
holistic view of glacier changes, showing advance/retreat, elevation
changes and surface flow of multiple glaciers in a
region all at the same time. This is particularly useful for
identification of surging glaciers, which show all or several
of these changes in parallel.
The animations also reveal variations that have not been
reported before (e.g. the high flow velocities in steep accumulation
regions) and that would be difficult to detect
using other methods (e.g. advances of very small debriscovered
glaciers). The automated looping through all images
has a number of technical advantages over the routinely used
flicker images (e.g. full time series) and manual browsing
(e.g. constant repetition rate) that allow a more in-depth analysis
of specific changes. There is great potential for using the
animations for educational purposes and further experimenting
with the combination of individual images (provided separately)
and the frame interval. Generating such animations
for other glacierized regions would likely provide additional
insights, but it has to be considered that data availability (e.g.
due to clouds, snow cover, or unfilled ETM+ stripes) might
limit the possibility of creating such dense time series.
As previous studies have shown, the study region is
characterized by abundant surge-type glaciers, of which several
were actively surging during the observation period
1990–2015. However, the animations reveal a wide spectrum
of surge types from short-lived velocity pulses (without
frontal advance), to mass waves travelling down-glacier
(with strong frontal advance), to highly variable advance
rates, distances and surge durations, all indicating that no
single definition can be used to identify them as surge-type.
It seems that several relatively small, steep and debris-free
glaciers surge as well. As they lack the typical morphological
evidence for surging, such as looped or distorted medial
moraines, their surges can only be recognized from timeseries
analysis. Overall, the surges are generally out of phase
with one another and some glaciers seem to surge periodically
with repeat cycles of a few decades. The considerable
overlap between the characteristics of surge-type and nonsurge-type,
but advancing, glaciers causes problems for clas-
sification.
The animations reveal that large glaciers with steady flow
(Baltoro, Choktoi) have stationary terminus positions and
stable surface elevations, while those influenced by surging
tributaries (Panmah, Sarpo Laggo, Skamri) have stationary
fronts but show considerable downwasting. In the latter case
the ice of the main glacier might have been pushed downstream
by a tributary surge. Considering such complex interactions
and the possible non-climatic control of glacier
surges, it seems advisable to exclude surge-type glaciers
from the sample when climate change impacts are investigated
on a timescale shorter than the surge cycle.
The Supplement related to this article is available online
at doi:10.5194/tc-9-2201-2015-supplement.
Acknowledgements. This study has been performed in the framework
of the ESA project Glaciers_cci (4000109873/14/I-NB). All
Landsat data were obtained from USGS. I would like to thank
the scientific editor M. Sharp for his valuable comments and the
careful review of the paper. Thanks are extended to B. Marzeion,
D. Quincey and one anonymous referee for providing constructive
reviews. S. Allen and P. Rastner gave helpful feedback to an
earlier version of the paper.
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F. Paul: Glacier flow and surge dynamics from animated satellite image sequences 2213
Edited by: M. Sharp
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