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Visualization of Glacier Surface Movement

Visualization of Glacier Surface Movement. Samuel Wiesmann Institute of Cartography, ETH Zurich. Outline. Introduction Existing visualizations Describing the data in geographic data cube Shortcomings and problems Approach Outlook Conclusions. Introduction.

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Visualization of Glacier Surface Movement

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  1. Visualization of Glacier Surface Movement Samuel Wiesmann Institute of Cartography, ETH Zurich

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

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

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

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

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

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

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

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

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

  11. Time specific area, e.g. glacier surface point in time (t1) variables from glacier surface (velocity, height, temperature, …) Space Variable Geographic Data Cube • The principle I adopted from [Bahrenberg et al. 1990], [Maidment et al. 2002]

  12. Time point in time (t1) e.g. velocity Space Variable Geographic Data Cube • The principle II

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

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

  15. point in time (t1) point in time (t2) velocity direction heights a.s.l. Geographic Data Cube • The second type I Time Space Variable

  16. point in time (t1) point in time (t2) velocity direction heights a.s.l. Geographic Data Cube • The second type II Time Space Variable

  17. Time Space Variable [NASA SVS 2006/2009] 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)

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

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

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

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

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

  23. Preprocessing Userweb-browser GIS-Server Approach • Intended system architecture

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

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

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

  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. Thank you for your attention Visualization of Glacier Surface Movement Samuel Wiesmann swiesmann@ethz.ch

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

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

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

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

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