I am working on a site selection projection where I will be unable to perform any in situ observations. A key component of the selection process is the ruggedness of the underlying terrain.

I plan on using GDALdem to create the derived ruggedness maps. I have read both the Wilson, et al. 2007 paper and the GDALdem documentation. While both offer insight into the algorithms used to generate the indices, only the Wilson paper offers an assessment of index suitability.

It is essential, because I cannot perform in situ observations, that the index selected over estimate the surface roughness.

In your experience, which index have you utilized, for what application, and why?

2 Answers 2


The paper "Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland" by Grohmann et al., 2011 describes the differences between a six methods of calculating surface roughness measurements from 2D digital topography. His paper was helpful since he provides a quantitative comparison of each method using a single test region at various spatial resolutions and window sizes. At the very end of his paper he states:

Standard deviation of slope remains the single most effective measure of surface roughness due to the simplicity of calculation, detection of fine scale/regional relief, and performance at a variety of scales.

He also recommends using vector dispersion and the standard deviation of profile curvature, based on their ability to depict terrain features. He disregards the method of area ratio since it "fails to distinguish between landforms in areas of low relief." The area ratio method is similar to the Rugosity index from Lundblad et al., but there may be some small difference in calculation (I have not looked at the code for the Rugosity index vs. the area ratio method used in Grohmann et al.).

I chose two methods: standard deviation of slope and vector dispersion; standard deviation of slope for simplicity/accuracy and vector dispersion since it is sensitive to local variations in elevation, which is suitable for my study areas.


I actually like using the Rugosity index from Lundblad et al. http://dusk.geo.orst.edu/esri04/p1208_cc.html http://proceedings.esri.com/library/userconf/proc04/docs/pap1208.pdf

I think this was only published as a paper presentation, but it is one of the most cited posters in oceanography.

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