GIS5935 Module 2.1: Surfaces - TINs & DEMs

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Halfway through the course, we reach Module 2.1, which focuses on TINs & DEMs. While I mainly use DEMs in my work, this lab was a great opportunity to become more familiar with Triangulated Irregular Networks (TINs) and how they convey elevation. As a refresher, a "triangulated irregular network (TIN) is a data model commonly used to represent terrain heights" (Bolstad & Manson, 2022, p. 61). Our lab uses both TINs and DEMs to address elevation in California's Death Valley in a four-part series.

In Part A, we used the Death Valley TIN as an elevation source, then overlaid a radar image on it with some vertical exaggeration. This allowed us to get used to working with both 2D and 3D spaces.

Screenshot of Part A TIN and radar image overlay.

From there, we delved deeper into 3D applications in Part B, where we assessed ski run suitability using elevation, slope, and aspect rasters. We used a starting DEM to create the TIN for the 3D elevation surface, as well as the slope and aspect raster. From there, it was necessary to reclassify each raster DEM to the appropriate class values and combine them using a Weighted Overlay with weights of 25% for aspect, 40% for elevation, and 35% for slope. (Note to self: reclassifying takes patience and a fine eye -- don't rush through the data values, because ArcPro won't always keep up if you fly through the menus! I had to re-run Reclassify because of this.) Like in Part A, a 2.00 vertical exaggeration was also used to emphasize peaks and valleys.

Close-up screenshot of Part B on ski run suitability, with slight shadows.

Third, in Part C, we took an existing TIN and explored symbology settings based on slope, aspect, and triangular edges. It was fascinating to see each individual triangle's attributes upon clicking on one. We finished this section by creating our own symbology for the TIN alongside its contours.

Screenshot of Part C TIN symbology and contour lines that I have chosen.

Finally, Part D required us to create a TIN using the provided points shapefile and study area shapefile. Comparing the point distribution of the points shapefile to the terrain was eye-opening! Then we used the Spline tool, which takes those data points and makes a continuous raster DEM. This allowed us to generate contour lines using the post-Spline version and compare them with the TIN contours. There were quite a few similarities and differences between the two contours and their shapes. Overall, the greater the variation in elevation grade, the more contour lines there are; the flatter the area, the fewer there are. Depending on what kind of representation you are going for, contours from TINs are more appropriate for raw geometric accuracy, while DEM contours provide a cleaner, more readable perspective. Ultimately, there is a time and place for both data interpolation methods.

Screenshot of Part D TIN as the 3D elevation source, Spline DEM overlay, points shapefile, and TIN contours (100m). 


References:
Bolstad, P., & Manson, S. (2022). GIS fundamentals: A first text on geographic information systems (7th ed.). Eider Press.


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