Terrain attribute selection in environmental studies

Author: Paulo van Breugel
Updated on: 2017-01-30

Exploring species-environment relationships is important for amongst others habitat mapping, biogeographical classification, conservation, and management. And it has become easier with (i) the advance of a wide range of tools, including many open source tools, and (ii) availability of more relevant data sources. For example, there are many tools with which it is relatively easy to create a wide range of derived terrain variables using digital elevation (DEM) or bathymetric (DBM) models. However, the ease of use of many of these tools, especially when used by non-experts, may lead to the selection of arbitrary or sub-optimal set of variables. In addition, derived variables will often be highly correlated (Lecours et al. 2017).

The paper by Lecours et al. (2017) focuses on terrain attributes and provides a framework for the selection of the best sub-sets of variables. In addition, the paper aims to explore the relationship between the importance of these groups and terrain complexity. To this end, they compare a large number of terrain attributes that can be derived from digital terrain models using a range of different commercial and open source tools.

Based on their analysis they come with a recommended set of attributes, which include the 1) relative difference to mean value, 2) local standard deviation, 3) easterness, 4) northerness, 5) local mean, and (6) slope. Together these variables were found to account for most of the main terrain properties and the variation in these properties.

It is thereby important to stress that for any real-life application, the selection of variables should foremost be based on their relevancy for the intended targets. For example, based on the ecology of a species, measures such as terrain wetness index could be more important for some species, while slope could be more important for others. Unfortunately, the lack of species specific information provides an impediment for an informed variable selection. In such cases, framework as proposed by Lecours et al. may help at least help to avoid covariation / multicollinearity and redundancy when selecting sets of explanatory variables (see also the article on stepwise VIF procedure for variable selection).

Different tools to compute terrain attributes

An additional objective of Lecours et al. (2017) was to explore existing GIS software to compute available local terrain attributes. They compared 11 different commercial and open-source software. The open source tools they included are Diva-GIS, SAGA GIS, uDig and QGIS. For QGIS I suppose the authors looked at the GDAL tools available (QGIS provides also access to e.g., GRASS GIS, SAGA GIS, and Orfeo toolbox, as well as a large number of addons). Missing from this list is GRASS GIS. GRASS GIS is a well established open source GIS tools, especially in the academic world, and it offers an interesting set of tools for the computation of the main topographic attributes. These include:

  • r.slope.aspect – slope, aspect, curvatures, first and second order partial derivatives
  • r.param.scale – elevation, slope, aspect, profile curvature, plan curvature, longitudinal curvature, cross-sectional curvature, maximum curvature, morphometric features
  • r.neighbors – average, median, mode, minimum, maximum, range, standard deviation, variance, diversity, interspersion)
  • r.topidx – topographic wetness index

In addition there are a number addons that compute different topographic or terrain attributes, such as:

In short, in GRASS GIS you can compute all main terrain attributes, and some more. And if there is no tool to compute your favourite terrain attribute. In that case, there is always the versatile r.mapcalc function. With its powerful syntax, including a neighbourhood modifier, it offers a very flexible tool to define your own functions and neighbourhood filters.


Lecours, V., Devillers, R., Simms, A. E., Lucieer, V. L. & Brown, C. J. Towards a framework for terrain attribute selection in environmental studies. Environmental Modelling & Software 89, 19–30 (2017).

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