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ICC 2009, Santiago de Chile Visualization of Glacier Surface Movement Samuel Wiesmann Institute of Cartography, ETH Zurich.

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Presentation on theme: "ICC 2009, Santiago de Chile Visualization of Glacier Surface Movement Samuel Wiesmann Institute of Cartography, ETH Zurich."— Presentation transcript:

1 ICC 2009, Santiago de Chile Visualization of Glacier Surface Movement Samuel Wiesmann Institute of Cartography, ETH Zurich

2 2 Outline  Introduction  Existing visualizations  Describing the data in geographic data cube  Shortcomings and problems  Approach  Outlook  Conclusions

3 3 Introduction  Visualization of glacier surface movement:  Ice flow: velocities  Changes in ice thickness  Changes in glacier length and ice covered area  Mass displacement  (change in shape of crevasses, movement of crevasses, …)

4 4 Existing Visualizations  Vector field … along with isotaches [Kääb 2005]

5 5 Existing Visualizations  Streamlines and trajectories [Kääb 2005][NASA SVS 2006/2009]

6 6 Existing Visualizations  Velocities: classified and stretched color ramp [Quincey et al. 2009] [Giles et al. 2009]

7 7 Existing Visualizations  Color coded velocities with overlain vectors [Bolch et al. 2008]

8 8 Existing Visualizations  Velocity vectors and color coded changes in elevation [Kääb 1997/2005]

9 9 Existing Visualizations  Dynamic arrows depict flow conditions [NASA SVS 2004/2009]

10 10 Existing Visualizations  Movie of 2.5D retreat simulation [Jouvet 2008]

11 11 Geographic Data Cube  The principle I Time Variable Space point in time (t 1 ) specific area, e.g. glacier surface variables from glacier surface (velocity, height, temperature, …) adopted from [Bahrenberg et al. 1990], [Maidment et al. 2002]

12 12  The principle II Geographic Data Cube e.g. velocity point in time (t 1 ) Time Variable Space

13 13  Situation in a glacier map Geographic Data Cube Time Variable Space velocity heights a.s.l. direction

14 14 Geographic Data Cube  Type 1: ca. 50% of analyzed visualizations (N=80)  fixed space, 1 point in time, 1 to 4 variables Time Variable Space [Kääb 2005]

15 15  The second type I Geographic Data Cube Time Variable Space velocity heights a.s.l. direction point in time (t 1 ) point in time (t 2 )

16 16  The second type II Geographic Data Cube Time Variable Space velocity heights a.s.l. direction point in time (t 1 ) point in time (t 2 )

17 17 Geographic Data Cube  Type 2: ca. 40% of analyzed visualizations (N=80)  fixed space, 2 (or more) points in time, 1 to 3 variables (whereof 1 at different times) [NASA SVS 2006/2009] Time Variable Space

18 18 Geographic Data Cube  Type 1: ca. 50% (N=80)  Type 2: ca. 40%  Type 3: ca. 10% fixed space, time animated, usually 1 variable Time Variable Space Time Variable Space Time Variable Space

19 19 Situation summarized  0% allowing for spatial navigation  0% allowing for thematic navigation  10% allowing for temporal navigation (usually start/stop)

20 20 Problems which arise  Overlaying symbols when comparing: 1 position (X/Y), 3 values [Kääb 1996]

21 21 Problems which arise  Overlaying symbols when comparing: e.g. feature tracking: 4 positions (X/Y), 4 values

22 22 Main problems  Problem of scale  Integration of time [Pritchard et al. 2005]

23 23 Approach  Intended system architecture Preprocessing User web-browser GIS-Server

24 24 Outlook I  Testing different visualization techniques  How to improve?  2D or 3D -- 2D and 3D?

25 25 Outlook II  A lot of data from many projects  Usually processed for only one publication   Bundle the data and re-use it!

26 26 Outlook III  Compare two glaciers at a certain date  Monitor a glacier over a specific time period  Compare two glaciers over this period of time  Calculate differences  Interpolation  Profiles on-the-fly

27 27 Outlook IV  Integration of glacier simulation models  Extract potentially dangerous areas  Resource when estimating potential natural hazards  … and many more …

28 28 Conclusions  Glaciology mostly uses “classic” cartography  Bundle the data!  GIS and cartography may provide the platform  Underlying technique exists and is ready to adapt  Improving the visualization and combining tools  More efficient gain of knowledge in glaciology

29 ICC 2009, Santiago de Chile Visualization of Glacier Surface Movement Samuel Wiesmann Thank you for your attention

30 30 Existing Visualizations  Partially dynamic and interactive visualization [Isakowski 2003]

31 31 Data Cube - Time  1 specific point in time  anywhere in space  any variable Time Variable Space

32 32 Data Cube - Space  1 specific location X/Y/Z  any point in time  any variable Time Variable Space

33 33 Data Cube - Variable  1 specific variable  any point in time  anywhere in space Time Variable Space


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