VISUALIZATION OF GLACIER SURFACE MOVEMENTS
Samuel Wiesmann a, Andreas Kääb b, Lorenz Hurni a
a
b
Institute of Cartography, ETH Zurich, 8093 Zurich, Switzerland
[email protected]
[email protected]
Department of Geosciences, University of Oslo, 0316 Oslo, Norway,
[email protected]
Abstract
Currently, many glaciers are retreating very fast. Even though the reason seems to be
clear, the dynamic processes of a glacier are very complex and sub-processes are not
clearly understood yet. Visualizing the glacier surface movements may help to
understand the behavior of the system. This paper presents the most common
visualization methods of glacier surface movement and shows the difficulties to fulfill
the demands of a visualization of such a highly dynamic system in space and time. A
concept for an interactive approach is presented, which also considers the dimension of
time.
1.
Introduction
Flow of glaciers is an important process in cold mountains and polar regions. Glaciers
move downwards, showing various velocity patterns within the flow system, and
shaping the terrain. Glacier flow is influenced by gravitational force, tectonic
conditions, climate and climate change, weathering, water cycle, etc. The glacier’s
surface and all other glacier conditions therefore change in space and time. The
quantification and visualization of the glacier surface movements is important for the
understanding and modeling of the dynamic processes involved in ice flow. It also
supports estimating the system response to modified environmental conditions.
2.
Objectives
The multidimensional characteristics of glacier surfaces, glacier flow, and other glacier
changes are best shown through dynamic visualization. The methods used so far have
all their specific problems, mainly because of their static approach. The aim of this
study is to find a set of methods that better visualize glacier surface movements and
help to better understand and explore measured or modeled glacier flow. Our approach
considers the dimension of time and allows the expert to analyze the visualized data
interactively.
3.
Current research
3.1
Cartographic visualization
KRAAK (2008) emphasizes the importance that maps nowadays act as flexible interfaces
to geospatial data. The user should not only see the result of the visualized data, but also
receive access to the geospatial database. By clicking on an object, for example, the
information stored behind the graphic may be revealed. DYKES (2005) also mentions a
trend in geovisualization to offer increasing flexibility and efficiencies of interaction.
He states an important role to the future collaboration between different fields of
science to benefit and learn from other disciplines. Also according to ANDRIENKO AND
ANDRIENKO (2007), highly interactive maps play an important role in data analysis and
hypothesis generation. Especially when dealing with non-trivial datasets, it is an
advantage if the data can be viewed from diverse perspectives. Multiple displays of
various types, which may be coordinated dynamically, could facilitate new knowledge
creation. ANDRIENKO AND ANDRIENKO (2007) see an important and very complex
research problem in ensuring the usability of interactive tools. They notice that users,
when brought face to face with new techniques, are often incapable or unwilling to
apply the tools.
3.2
Methods determining the velocity field
In several former projects, the velocity fields of the selected glacier surfaces were
measured, modeled, or estimated using different methods (e.g. JOUGHIN ET AL. 2008,
KAUFMANN 2004, LANG ET AL. 2004, BAUDER 2001, KÄÄB 1996). Based on repeated
images from a glacier surface, for example, the displacement rates within a time period
can be measured at individual points. This is done by selecting objects (or image
patterns) in the first, older picture. If these objects or patterns can be recognized in the
second, newer picture, the displacement between the point at time of picture 1 to the
point at time of picture 2 can be calculated (e.g. KÄÄB AND VOLLMER 2000). Knowing
the displacement and the time period, the average velocity is defined. The more points
can be analyzed, the more complete is the velocity field of the surface determined.
Several other methods for determining the velocity field of glacier surfaces (e.g.
differential SAR interferometry, DInSAR) are well-established, but not discussed here.
For a clear overview see e.g. KÄÄB (2005), chapter 4.
3.3 Velocity field visualization
In most cases, the results of the measurements are visualized by static vectors,
representing the amount of displacement (and speed respectively) for each chosen point.
The vectors have their starting point on the place of the object in the first picture and
point in the direction of the corresponding object in the second picture. The length of
the vector is proportional to the calculated velocity. In its simplest form, the velocity
field is visualized with these vectors only, without any additional symbolization or
information (e.g. HELBING 2006, p.34).
For a better orientation, this visualization is often combined with terrain information as
for instance contour lines, relief shading, and/or orthoimages (BUCKLEY ET AL. 2004).
Additionally to the vector field, isotaches can be superimposed to support the overview
of the flow conditions (Fig. 1). In other visualizations, solely isotaches (without vectors)
are used to provide an overview of the speed conditions on the glacier surface
(KAUFMANN 2004). In the latter case, the information about the flow directions can only
be guessed.
Figure 1. Vector field along with isotaches (white, [m a-1]) represent
glacier surface velocities. Combined with orthoimage and
contour lines (black) for orientation. (KÄÄB 2005)
The isotaches supply absolute speed values directly in the map, whereas vectors
facilitate recognizing relative velocity differences in the flow field. A legend containing
a declaration of values for vector lengths can likewise deliver the absolute velocities
(e.g. KÄÄB 2005, p.149). Others design a legend with a number of vectors not only
differing in length, but also add a color code for differing speeds: e.g. red, short vectors
for slow, and blue, long vectors for fast movement (e.g. BAUDER 2001, p.91).
Flow fields are also visualized with streamlines. Streamlines represent the hypothetical
path of selected particles and demonstrate that way the general flow conditions.
Absolute speed values cannot be displayed, whereas information about the relative
velocities can be assumed (Fig. 2, left). However, this uncertainty may be removed
when visualizing the path lines with so called trajectories: single symbols along the path
lines, for example, can be used for each time step to provide the missing information
(Fig. 2, right).
All the visualization methods mentioned so far allow a combination with additional
information, which could be symbolized using area-wide color transitions. For example,
this could be changes in height, ice temperature, etc.
Figure 2. Left: Streamlines represent the hypothetical path (NASA SVS 2009a).
Right: Trajectories depict the actual particle path (KÄÄB 2005).
Another common alternative is visualizing the velocity amplitude (speed) with a color
code. A classified or stretched color ramp allocates the velocities to a color. The
analyzed moving surface is then for example area-wide colored by interpolation
between the calculated locations. GILES ET AL. (2009) choose a visualization using one
color value for each class (Fig. 3, left), whereas STROZZI ET AL. (2008) or
BUCHROITHNER AND WALTHER (2007) use a stretched color ramp (Fig. 3, right).
Especially in the latter example of visualization, it is difficult to allocate velocities and
therefore difficult to analyze the data in detail. Nevertheless, this type of visualization
may provide a good overview of the overall flow conditions in small scale maps.
Figure 3. Velocities visualized with classified color ramp (left, GILES ET AL. 2009)
and stretched color ramp (right, STROZZI ET AL. 2008).
Figure 4. Color-coded velocities with overlain vectors (left, PRITCHARD ET AL. 2005)
and overlain trajectories (right, LANG ET AL. 2004).
Other authors use a combination of the color-based and the vector-type visualization of
velocities. PRITCHARD ET AL. (2005) for example document the flow conditions of a
glacier surge with stretched colors and overlaying vectors (Fig. 4, left). JOUGHIN ET AL.
(2008) widen this method and add isotaches, but without labeling. LANG ET AL. (2004)
choose a similar visualization method, but use trajectories instead of vectors (Fig. 4,
right).
All of these visualization methods provide a good overview of the overall flow
conditions, but they also have clear limitations. The static approaches only allow the
illustration of one specific state of the velocity field. Comparing two states from
different dates is difficult because the information (vectors or colors) will overlap. Also,
the illustration of continuous processes is difficult. Therefore, the static approach is of
limited use when analyzing complex spatio-temporal information.
The dynamic approaches available online mostly are movies, which are rendered in
advance and simulate the movement (generally past and future retreat) of the surface
(e.g. JOUVET 2008, Fig. 5). JOUVET (2008) combines this visualization with a 2D map,
which represents and changes the glacier outline simultaneously (not shown in fig.5).
The user starts, stops, or pauses the movie, but has no options to interact with the data.
Figure 5. Movie of 2.5D retreat simulation. (JOUVET 2008)
Dynamic arrows may be used for visualizations focusing on the flow field conditions
(Fig. 6). The arrows follow the streamlines and the speed of each arrowhead is
proportional to the calculated surface velocities. Watching the animation moving the
progress controller back and forth conveys a very illustrative impression of the flow
conditions. Nevertheless, it is not possible to receive information about the underlying
data.
Figure 6. Movie with dynamic arrows depicts the flow conditions. Speed of arrowheads
is proportional to calculated surface velocity. (NASA SVS 2009b)
Figure 7. Dynamic and interactive visualization, which allows the user to query
underlying data. (ISAKOWSKI 2003)
ISAKOWSKI (2003) not only animates visualizations of moving surface processes, but
also enables access to a part of the underlying data. The user selects a point of interest
(in fact: a location on the surface where measured data is available) and receives the
data in the form of a diagram next to the animated map (Fig. 7). Together with
controlling the time, the user may analyze the data on a more detailed level. Concerning
the analyzing options, this interactive approach clearly shows the right direction.
Combined with further methods, an additional value could be gained.
4.
Expected results by applying new visualization methods
In order to take the surface’s changes in space and time into account, dynamic
visualization is able to overcome some of the problems sketched above. This will lead
to a multidimensional method, based on a 4D database of a glacier. Once the data of the
glacier surface and the movement velocities is stored in a spatial database, diverse
visualization methods may be derived from.
The analyzing method described in chapter 3.2 results in point clouds representing the
glacier surface at different times, as well as calculated displacements and velocities
respectively. Therefore, it is foreseen to visualize the glacier surface using a 2.5D grid
model as a starting point. Rotation of the object or moving the viewpoint with respect to
the scene should be enabled to avoid occlusion problems (SHEPHERD 2008). A slider
enables switching between the different points in time where analyzed data is available.
Using grid interpolation, a smooth transition between scene 1 and scene 2 may be
achieved.
The images, which are at the basis of the displacement measurement, will be included
and serve as additional information when visualizing the moving objects. However,
orthophotos overlain on a DTM show distortions, especially on steep slopes. In case of
using morphing techniques, it is doubtful that a starting picture already containing
severe distortions is still of big value. Therefore, an additional alternative of a synthetic
texture is targeted.
It will be an important part of the study to find the best suited methods to visualize the
calculated displacements and velocities, changes in heights, mass movement, or mass
loss in combination with the 2.5D grid.
Object-based visualization on a computer screen will also allow a combination of the
moving objects with additional quantitative information or object-related information on
a more detailed level (e.g. for selected objects only). That way, it simplifies the
exploration of the data, which is derived from the analysis of the repeated images.
5.
Conclusions
The study will demonstrate the feasible spectrum of suitable visualization methods.
However, the work is still in progress. If an ingenious dynamic visualization method
succeeds, it can significantly improve the understanding and monitoring of glacier mass
movements. This again may help to understand climate-change-related processes, as
well as hazards resulting from such glacial processes. Especially in times of very fast
changing glacier conditions, it is of crucial importance to gain as much knowledge
about the behavior of glaciers as possible.
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