DEM visualisation techniques: Openness

Several DEM visualisation techniques are based on some sort of simulated terrain illumination: Shaded Relief simulates directional illumination from a point light source, Sky-View Factor simulates diffuse illumination from a homogeneously bright hemisphere.

Openness is a visualisation technique which is similar to Sky-View Factor. However, contrary to considering a homogeneously bright hemisphere centered above each pixel, the computation of Openness considers a full sphere centered on each pixel. The Openness algorithm looks at a surrounding area with a specified radius and assesses whether or not there are terrain points which would obstruct illumination from that direction. In practice this is achieved by finding (along each radial line) the smallest angle between terrain points and the zenith angle. These angles are then aggregated for n radial lines (usually 8 or 16) by computing their average. As a result, higher/lower Openness values are assigned to more/less exposed terrain points.

Charcoal burning platforms in the southern Black Forest. Positive Openness visualisation.

Charcoal burning platforms in the southern Black Forest. Positive Openness visualisation.

When you think about this principle for a while you might wonder what would happen if you calculated Openness as the average of the smallest nadir angles (instead of zenith angles). And yes, this can be used as a visualisation technique as well. To distinguish between the two approaches, the one based on zenith angles is called Positive Openness while the one based on nadir angles is called Negative Openness. Negative Openness has high/low values for strongly/weakly incised terrain points.

It is worth noting that Negative Openness is not simply Positive Openness with a ‘-‘ sign but is based on a different computation resulting in different values for the same point. Negative Openness calculated as described above has a positive range of values. However, to make the resulting visualisations more intuitively readable, Negative Openness values calculated by LiVT are multiplied with -1. As a result, positive terrain features are characterised by higher values of both Positive and Negative Openness then negative terrain features. Positive and Negative Openness visualisations can be combinedby computing a (weighted) average of th respective greyscale images.

Charcoal burning platforms in the southern Black Forest. Negative Openness visualisation.

Charcoal burning platforms in the southern Black Forest. Negative Openness visualisation.

Charcoal burning platforms in the southern Black Forest. Grayscale average of Positive and Negative Openness.

Charcoal burning platforms in the southern Black Forest. Grayscale average of Positive and Negative Openness.

Openness visualisations can be vey useful for interpreting lidar-based DEMs as they clearly show small-scale relief features. At first sight, they appear somewhat similar to Sky-View Factor visualisations; however, the visual impression of the overall landscape forms is lost. An advantage is that small-scale features are visulised equally well on flat terrain and on slopes.

References

Yokoyama, R., Shirasawa, M., Pike R.J., 2002. Visualizing topography by openness: a new application of image processing to digital elevation models. Photogrammetric Engineering & Remote Sensing 68(3), 257-265.

Doneus, M., 2013. Openness as visualization technique for interpretative mapping of airborne lidar derived digital terrain models. Remote Sensing 5(12), 6427-6442. [open access]

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Working with LiVT: file size limits and performance

It’s been a bit more than half a year since I first published LiVT on Sourceforge. Since then, I have been able to add a few more algorithms, but there are still a few bugs waiting to be fixed.

All in all, feedback so far has been positive, but I am still hoping that someone will offer help to improve the project. One thing that has been mentioned repeadedly is the need to know the limits of the software regarding maximum file sizes.

Another relevant issue is the performance of LiVT, i.e. the time needed per unit area. This differs greatly from algorithm to algorithm. Furthermore, different settings in each algorithm will strongly influence processing times. Therefore, I have run all tests using the default settings with the exception of Cumulative Visibility where I used an angular resolution of 10° (instead of the 1° default). When changing processing parameters, processing times can change proportionally (e.g. for maximum radius or no. of direction in the radial Sky-View Factor algorithm), quadratic (e.g. for filter radius in the filter algorithms) or even faster (e.g. for the number of scales in Exaggerated Relief or Multi-Scale Integral Invariants). The test data set had a resolution of 1 m. Note that for the same total area, file size and processing times quadruple when resolution is doubled.

These are the results of the tests I have run:

Algorithm

maximum DTM file size

[million pixels]

performance (Intel Xeon 3.2 GHz, 64 bit)

[km2/min]

Filter (Laplacian of Gaussian)

132

30

Shaded Relief

30

15

Exaggerated Relief

30

 0.48

Sky-View Factor

131

0.96

Trend Removal

132

5.22

Local Relief Model

56

0.09

Local Dominance

90

2.22

Cumulative Visibility

90

0.25

Accessibility

132

1.45

Multi-Scale Integral Invariants

144

0.57

Openness

132

1.92

These tests were run on an 64 bit Intel Xeon at 3.2 GHz under Windows Vista. As a single instance of LiVT uses only one processor core anyway, the number of processors and cores does not play a role. Running the performance tests on other computers showed that 64 bit has some advantage over a 32 bit system: On a slightly faster clocked 32 bit AMD Phenom at 3.4 GHz (also under Windows Vista), performance was on average 87% of that on the 64 bit computer. Finally, just for fun I also tested a 32 bit Intel Atom processor (on a four or five year old EeePC) at 1.6 GHz under Windows XP. On that computer, performance was on average 18% of that on the 64 bit machine.

Lidar visualisation and interpretation workshop 2014 in Esslingen, Germany

Registration is now open for the Lidar visualisation and interpretation workshop 2014 in Esslingen, Germany. The workshop will be a four-day event (including one field day) for students, graduate students and young professionals who are looking for theoretical background as well as hands-on experience with visualisation techniques for high-resolution digital elevation models (mainly but not exclusively based on airborne lidar) in the field of archaeology.

Date: 08-11 July 2014

Location: Esslingen, Germany

Contact: ralf.hesse@rps.bwl.de

The aims of the workshop are to bring together students and young professionals to learn about lidar visualisation techniques and archaeological interpretation. The programme will include presentations, visualisation and mapping exercises and a field day.

The maximum number of participants will be 20; please register early. Participation fee will be 50 €. A limited number of Archaeolandscapes travel grants will be available.

Tentative programme

• Monday, July 7:
o arrival
• Tuesday, July 8:
o morning: introduction to workshop; presentations
o afternoon: visualisation and mapping exercises
• Wednesday, July 9:
o full day: field trip
• Thursday, July 10:
o morning: visualisation and mapping exercises
o afternoon: visualisation and mapping exercises
• Friday, July 11:
o morning: combining lidar and aerial photography
o departure

Venue

The workshop will take at the State Office for Cultural Heritage Baden-Württemberg (Landesamt für Denkmalpflege) in Esslingen, Germany. Esslingen is located 15 minutes by train from the city of Stuttgart and 30 minutes by bus from Stuttgart airport. The State Office for Cultural Heritage is located a short walk from the train station and bus terminal as well as from Esslingen’s historic city centre.

Important deadlines

• 28 October 2013: registration for workshop begins
• 28 February 2014: registration for workshop ends
• 04 April 2014: application deadline for Archaeolandscapes travel grants

Registration

If you are interested in taking part in the workshop, please send an e-mail to ralf.hesse@rps.bwl.de.

The e-mail should contain the following information:

• name, surname
• status (student / graduate student, post-doc…)
• institution, country
• reason for wishing to take part in the workshop
• previous experience with interpretation of airborne lidar (if any)