VISUALIZATION OF GLACIER SURFACE MOVEMENTS

Samuel Wiesmann a, Andreas Kääb b, Lorenz Hurni a

a Institute of Cartography, ETH Zurich, 8093 Zurich, Switzerland wiesmann@karto.baug.eth.ch

hurni@karto.baug.ethz.ch

b Department of Geosciences, University of Oslo, 0316 Oslo, Norway, kaeaeb@geo.uio.no

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. References ANDRIENKO, N. AND ANDRIENKO, G. (2007): Multimodal Analytical Visualization of

Spatio-Temporal Data. In: Cartwright, W., Peterson, M.P., Gartner, G. (eds.): Multimedia Cartography, 2nd edition. Berlin: Springer, pp. 327–346.

BAUDER, A.C. (2001): Bestimmung der Massenbilanz von Gletschern mit Fernerkundungsmethoden und Fliessmodellierungen: eine Sensitivitätsstudie auf dem Unteraargletscher. Mitteilungen der Versuchsanstalt für Wasserbau, Hydrologie und Glaziologie (VAW) der ETH Zürich 169.

BUCHROITHNER, M.F. AND WALTHER, S. (2007): Mulitparametric Cartographic Visualisation of Glacier Rheology. The Cartographic Journal, Vol. 44 No. 4, pp. 304–312.

BUCKLEY, A., HURNI, L., KRIZ, K., PATTERSON, T. AND OLSENHOLLER, J. (2004): Cartography and visualization in mountain geomorphology. In: Bishop, M.P. and

Shroder Jr., J.F. (eds.): Geographic Information Science and Mountain Geomorphology. Springer, Praxis, Chichester, UK, pp. 253–287.

DYKES, J. (2005): Facilitating Interaction for Geovisualization. In: Dykes, J., MacEachren, A.M. and Kraak, M.J. (eds.): Exploring Geovisualization. On behalf of ICA by Elsevier, Amsterdam, pp. 265–291.

GILES, A.B., MASSOM, R.A. AND WARNER, R.C (2009): A method for sub-pixel scale feature-tracking using Radarsat images applied to the Mertz Glacier Tongue, East Antarctica. Remote Sensing of Environment 113, pp. 1691–1699.

HELBING, J. (2006): Glacier dynamics of Unteraargletscher: Verifying theoretical concepts through flow modeling. Mitteilungen der Versuchsanstalt für Wasserbau, Hydrologie und Glaziologie (VAW) der ETH Zürich, 196.

ISAKOWSKI, Y. (2003): Animation mit SVG als Visualisierungsmethode von räumlich-zeitlichen Prozessen in der Glaziologie. Versuch der Generierung dynamischer Gletscherdarstellung am Beispiel des Grubengletschers/Wallis. Diploma Thesis, FH Dresden and ETH Zurich.

JOUGHIN, I., HOWAT, I.M., FAHNESTOCK, M., SMITH, B., KRABILL, W., ALLEY, R.B., STERN, H. AND TRUFFER, M. (2008): Continued evolution of Jakobshavn Isbrae following its rapid speedup. Journal of Geophysical Research, Vol. 113.

JOUVET, G. (2008): Simulation of Rhonegletscher, Switzerland. Accessed 06 July 2009: http://iacs.epfl.ch/~jouvet/

KAUFMANN, V. (2004): Zur Topographie und Morphodynamik des Blockgletschers Hinteres Langtalkar, Hohe Tauern. Kartographische Nachrichten 6/2004, pp. 258–262.

KÄÄB, A. (1996): Photogrammetrische Analyse zur Früherkennung gletscher- und permafrostbedingter Naturgefahren im Hochgebirge. Mitteilungen der Versuchsanstalt für Wasserbau, Hydrologie und Glaziologie (VAW) der ETH Zürich, 145.

KÄÄB, A. AND VOLLMER, M. (2000): Surface Geometry, Thickness Changes and Flow Fields on Creeping Mountain Permafrost: Automatic Extraction by Digital Image Analysis. Permafrost and Periglacial Processes, 11, pp. 315–326.

KÄÄB, A. (2005): Remote sensing of mountain glaciers and permafrost creep. Schriftenreihe Physische Geographie, Glaziologie und Geomorphodynamik 48, Geographisches Institut der Universität Zürich.

KRAAK, M.J. (2008): Exploratory Visualization. In: Shekar S, Xiong H (eds.): Encyclopedia of GIS. Springer, Berlin Heidelberg New York, pp 301–307.

LANG, O., RABUS, B.T. AND DECH, S.W. (2004): Velocity map of the Thwaites Glacier catchment, West Antarctica. Journal of Glaciology 50/168, pp. 46–56.

NASA SVS (2009a): Jakobshavn Glacier Flow in the Year 2000. NASA Scientific Visualization Studio, SVS Image Server. Accessed 05 July 2009: http://svs.gsfc.nasa.gov/vis/a000000/a003300/a003374/index.html.

NASA SVS (2009b): Jakobshavn Glacier, Greenland Flow Field. NASA Scientific Visualization Studio, SVS Image Server. Accessed 05 July 2009: http://svs.gsfc.nasa.gov/vis/a000000/a003000/a003072/index.html.

PRITCHARD, H., MURRAY, T., LUCKMAN, A., STROZZI, T. AND BARR, S. (2005): Glacier surge dynamics of Sortebræ, east Greenland, from synthetic aperture radar feature tracking. Journal of Geophysical Research 110.

SHEPHERD, I.D.H. (2008): Travails in the third dimension: a critical evaluation of three-dimensional geographical visualization. In: Dodge, M., McDerby, M. and Turner, M.: Geographic Visualization: Concepts, Tools and Applications. John Wiley & Sons, Ltd, Chichester, pp. 199 – 222.

STROZZI, T., KOURAEV, A., WIESMANN, A., WEGMÜLLER, U., SHAROV, A. AND WERNER, C. (2008): Estimation of Arctic glacier motion with satellite L-band SAR data. Remote Sensing of Environment 112, pp. 636–645.