Multi-scale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Abstract

Surface roughness is an important geomorphological variable which has been used in the earth and planetary sciences to infer material properties, current/past processes and the time elapsed since formation. No single definition exists, however within the context of geomorphometry we use surface roughness as a expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter's behaviour at different spatial scales and dataset resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenisation of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography tends to highlight local features and doesn't detect regional relief. Standard deviation of elevation correctly identified breaks-of-slope and was good at detecting regional relief. Standard deviation of slope (SDslope) also correctly identified smooth sloping areas and breaks-of-slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks-of-slope, although not as strongly as SDslope and it is very sensitive to noise and spurious data. In general, SDslope offered good performance at a variety of scales, whilst the simplicity of calculation is perhaps its single greatest benefit.

Using a Lidar-Based Height Variability Method for Recognizing and Analyzing Fault Displacement and Related Fossil Mass Movement in the Vipava Valley, SW Slovenia

“…It has been shown that the degree of surface roughness is most strongly influenced by various sedimentation processes, in addition to the characteristics of the sedimentary material. Similarly, Grohmann et al [49,50] determined that different surface roughness values are attributed to landslide formation processes (recent and fossil) and time elapsed since the surface formation, in addition to material characteristics.…”

A framework for quantifying geodiversity at the local scale: a case study from the Rokua UNESCO Global Geopark

“…The approximate elevation accuracy of the DEM was 0.3 m and the point density was at least 0.5 points per square metre [53]. To illustrate the topographical variation of the study sites, the standard deviation of elevation, slope angle [54] and topographical wetness index (TWI; [55]) was calculated for each study circle. These topographical variables correlated well with georichness in a landscape scale study [27].…”

Exploring Terrain Indices and Slope Morphometry for Trail and Campsite Planning on Mount Irau, Malaysia

“…The implementation of automated techniques for landform classification has become quite common in scientific society (Tagil & Jenness, 2008;Grohmann et al, 2009;Ilia et al, 2013;McVicar & Körner, 2013;Rigolsanchez et al, 2015;Skentos & Ourania, 2017). Consequently, landform elements such as mountains, hills, valleys, canyons, plateaus, plains, and other landform classifications have reduced the complexity of terrain units that can be easily described; this also provides more understandable information paths of land categories.…”