1. Mapping from Airborne Laser Scanners: Applied Techniques and Visualizations
Thomas J. Pingel, Northern Illinois University
December 6, 2013
Geography and Earth Science Department
University of Wisconsin – La Crosse

2. Geography can make a real difference.

3. You can make a difference locally.

4. One of the most exciting opportunities is in Light Detection and Ranging (LiDAR) technology.
6 inches tall by 2 inches in diameter. 700,000 points per second. Weighs less than 2 kg.

5. Central Research Question:
How can we, in an automatable way, produce an visualizations and images that assist in the interpretation, analysis, and understanding of local events?
The fact that we’re focusing on events also implies a time-aware GIS or database on the backend. We are looking at modeling either real-time events or at least very recent events, but either requires a dynamic database.

6. Outline Project overview Terrain generation from LiDAR
Perceptually shaded slope maps
Application to archaeology

7. Terrain generation LiDAR Assumption of little available geodata
Relatively inexpensive
Highly accurate
Portable
But needs processing
Assumption of little available geodata
Ground cues can be very valuable in street network ID

8. This is a very simple visualization of the data that LiDAR provides.

9. Terrain Extraction is Important
Davidson Library sits approximately 6 meters above the ground due to a terrain layer error.

10. Terrain Extraction: The Simple Morphological Filter (SMRF)
Emphasizes reducing Earth-as-Object error
Still very good at reducing Object-as-Earth error
Lowest total error rate of any published algorithm tested against ISPRS dataset
tpingel.org/code

11. Cross Section View of Image Opening

12. A sample progression of SMRF
When windowSize = [ ], slope = 15% and elevationThreshold = .5

13. Sometimes, projects have unexpected applications
Sometimes, projects have unexpected applications. This is the Ma’adim Vallis extraction, based on SMRF.
700 km long, 20 km wide, 2 km deep

14. Visualizing Terrain
Hans Gyger’s 17th century shaded relief maps of areas of Switzerland
Eduard Imhof (1895 – 1986) led the development of the modern method
Digital DEMs and hillshade algorithms (Horn, 1981) led to widespread use
reliefShading.com
shadedRelief.com
1965/1982/2007 (ESRI Press)
Definitive text: Cartographic Relief Presentation
reliefshading.com; shadedrelief.com

15. Jenny and Hurni (2006) recently described a method to computationally receate Imhoff’s color shaded relief maps.

16. Kennelly (2008) discusses many techniques to augment the traditional shaded relief, including multiple sources of illumination and the use of curvature.

17. However, there are significant issues with shaded relief images
However, there are significant issues with shaded relief images. First, more realistic images, like hillshades, do not always perform better than prepared cartographic products.
They also do not translate to urban areas very well, where relief is typically low, and aspect is highly regular.

18. Kennelly and Stewart (2006) offer some methods that bring out immense detail in a DEM-derived LIDAR visualization.

19. But we know that realism is not always best for visualizations.
We also need a straightforward way for GIS users to create visualizations with this data that are useful for a variety of purposes, not just more aesthetically pleasing.

20. This is a LIDAR-derived Digital Surface Model of
UC Santa Barbara, represented in shaded relief.

21. This is a PSSM of the same data.

22. (Pingel 2010, following Proffitt et al. 1995)
PSSMs are based on the idea of “cognitive slope.” People exaggerate the vertical component of slope by a factor of 2.3x.
(Pingel 2010, following Proffitt et al. 1995)

23. Perceptually Shaded Slope Maps (PSSMs)
Slope is exaggerated, then mapped to graytone
Resulting appearance looks hand-drawn, which speaks to its efficacy as a visualization
No spatial displacement errors common with orthophotos
Offers a higher contrast image than hillshade, with better affordance for color overlay
Most appropriate for mixed / urban environments
How do we assess performance?

24. Mental Rotation

25. PSSMs are slightly faster, and more accurate.

26. Profile Estimation

27. Profile Estimation

28. PSSMs are much faster, and more accurate.

29. Last year, we successfully used PSSMs in combination with our LIDAR filter SMRF to visually explore the forest floor of the ancient Maya site, El Pilar.

30. Test Case: El Pilar Ancient Maya City on Belize-Guatemala border
El Pilar – “Watering Basin”
Hundreds of buildings in dozens of plazas
Data generously provided by Dr. Anabel Ford

34. Hillshaded image of DSM

35. SMRF + PSSMs at El Pilar, Guatemala

37. Why GIS? Virtually no one starts in GIS Why did I stay?
Creativity and puzzle solving
Chance to merge geography with computer science and psychology
People connect with maps
It’s easy to make a big difference