Geomorphometry in Landscape Ecology:
Issues of Scale,
Physiography, and Application
Kirsten Erin Ironside1,*, David J. Mattson1
, Terence Arundel1 , Tad Theimer2
, Brandon Holton3 , Michael Peters4,
Thomas C. Edwards, Jr. 5 , Jered Hansen1
1 U.S. Geological Survey, Southwest Biological Science Center, United States
2 Biological Sciences Department, Northern Arizona University, United States
3 National Park Service, Grand Canyon National Park, Science and Resource Center, United States
4 Pterylae Systems, Arizona, United States
5 U.S. Geological Survey, Utah Cooperative Fish and Wildlife Research Unit, Department of Wildland Resources, Utah State University, United States
Environment and Ecology Research 6(5): 397-412, 2018
Abstract
Topographic measures are frequently used in a variety of landscape ecology applications, in their simplest form as elevation, slope, and aspect, but increasingly more complex measures are being employed. We explore terrain metric similarity with changes in scale, both grain and extent, and examine how selecting the best measures is sensitive to changes in application. There are three types of topographic measures: 1) those that relate to orientation for approximating solar input, 2) those that capture variability in terrain configuration, and 3) those that provide metrics about landform features. Many biodiversity hotspots and predators have been found to coincide with areas of complexity, yet most complexity measures cannot differentiate between terrain steepness and uneven and broken terrain. Currently characterizing terrain in landscape-level analyses can be challenging, especially at coarser spatial resolutions but developing methods that improve landscape-level assessments include multivariate approaches and the use of neighborhood statistics. Some measures are sensitive to the spatial grain of calculation, the physiography of the landscape, and the scale of application. We show which measures have the potential to be multi-collinear, and illustrate with a case study how the selection of the best measures can change depending on the question at hand using mountain lion (Puma concolor) occurrence data. The case study showed a combination of infrequently employed metrics, such as view-shed analysis and focal statistics, outperform more commonly employed singular metrics. The use of focal statistics as a measure of topographic complexity shows promise for improving how mountain lions use terrain features. Keywords Habitat, Topography, Terrain, Ruggedness, Mountain Lion, Puma concolor
4. Conclusions
Digital elevation models can provide critical information on how organisms relate to terrestrial landscapes. However, the multitude of metrics available and the unknown sensitivities to specification can be overwhelming in applied landscape scale studies. In general, the coarser the spatial grain, the more similar metrics can become, but the degree that terrain measures were affected varied for several metrics. Similarly, changes in extent resulted in some metrics being more sensitive than others in how correlated they were with other metrics, but highly correlated metrics appear to be rather robust to change in physiography. We caution that some measures of topographic complexity are not able to distinguish steepness of slope from uneven or rugged terrain. For mountain lions, we found rarely employed metrics to be the best ranked in model comparisons – first, a metric calculating variation in topographic form, next, another complexity metric, fractal dimensions, and, lastly, one that relies on the view-shed concept of openness. Many wildlife resource selection studies have focused on terrain measures capturing particular features of landscapes such as steepness or a particular landform, but analysis of mountain lions showed configuration of terrain patches and how topography influences visibility to be the most important. How view-sheds are used by animals needs further exploration but could provide meaningful insight into predator-prey dynamics. Metric ranking was influenced by detection correction measures and is another consideration in relating occurrence with terrain. No single metric outperformed a multivariate approach, suggesting landscape ecologists may not want to rely on a single metric of topography. Quantifying how organisms orient to topography is not resolved and many aspects of how terrain is characterized need further exploration in terms of analysis window affects. The use of focal statistics of continuous measures is an area of landscape quantification that shows promise for improving our understanding of how animals respond to terrain.
Figure 1. Digital Elevation Models (DEMS) are used for a variety of landscape metrics, sometimes just as raw 3-dimensional coordinates, and often as slope and aspect. Several other metrics have been developed for quantifying the effects of solar radiation, including several transformations of aspect, indices of relative solar inputs, and complex models of solar insolation. Measures of orientation have strong influences on vegetation, evapotranspiration, and snow accumulation. DEMs have also been used to quantify topographic complexity and landforms. These metrics can serve as proxies, such as slope, which is often used to describe escape terrain for bighorn sheep, and mountain lions have been described as selecting for rugged terrain. Photographs courtesy of the U.S. Geological Survey.
Figure 2. Study area hillshade (A.) shown with all GPS locations collected from mountain lions in northern Arizona. The black line shows the boundary where random background points were drawn. Color points are from selected mountains whose home ranges represent different physiographic regions (B.-F.).
Figure 3. Heat map Pearson correlation coefficient matrix for metrics calculated at 1 km resolution (A.) and 30-m resolution (B.) with shades of blue showing strong positive correlations and shades of red strong negative correlations. The difference in correlation coefficients between scales is shown in part C.
Figure. 4. Heat map of mean Pearson correlation coefficient matrix for metrics calculated at a 30-m resolution across five physiographic extents (A.), the associated standard deviation around the mean (B.), and the coefficient of variation (C.).
Figure 5. Raster maps of a valley shown in 3D from a view point (left panels) and 2D bird’s eye view (right panels), where the left side of the valley is steep and relatively flat faced, while the right side of the valley is steep but undulating and complex faced. Many measures meant to capture complexity, such as TRI, do not distinguish between steepness and ruggedness (Top Panels). Curvature, a metric of form, captures ridges and draws (Middle Panels). Calculating the variability in form (standard deviation of curvature) for an analysis window differentiates between flat faced slopes and those with undulating or broken features (Bottom Panel).
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