Surface Roughness Scaling Trends K. Arrell, S. Carver School of Geography, University of Leeds, LS2 9JT Tel. (+44) 0113 3433343 Fax (+44) 0113 3433308 Email k.arrell@leeds.ac.uk 1. Introduction Greater accuracy and higher resolution terrain data from direct measurements (for example SAR/LiDAR/TLS) have created a wide range of opportunities for detailed landscape analyses previously hampered by a lack of suitable data. Further, the increasing size and volume of these datasets necessitate quantitative data generalisations and metadata that can inform process studies, for example drainage density and relative relief. A number of studies have attempted to extract geomorphically significant measures from digital elevation data (Pellegrini, 1995; Wood, 1996; Burrough, et al., 2000; Arrell et al., 2007), these have largely attempted to characterise landscape elements and thus infer geomorphic process. Attempts to characterise or classify landscapes holistically still remain under developed and would provide useful metrics for digital elevation data analysis and geomorphological applications for example landscape evolution modelling. This paper looks at the development of measures of surface roughness as a multi-scale index for characterising landscape types. We propose that the methods outlined here can provide landscape characterisations that reflect surface geomorphology, differentiating between surface types e.g. fluvial vs. glacial, erosional vs. depositional, soft vs. hard geology, when these landscape types exhibit different surface roughness scaling trends. We propose that scaling roughness trends will provide meaningful measures where local variability in surface properties governs the convergence and divergence of mass and energy which form critical controls on surface processes. 2. Study Area 2 m LiDAR data for the upper Wharfe Yorkshire, the Aire valley, and Cley-next-to- the-Sea and 5 m NEXTMap data for parts of the Cairngorms were used as test datasets. These varied landscapes were selected to assess the robustness of the outlined technique to differentiate between landscapes of differing characteristics and roughness. Further multi-resolution analyses were performed for the Wharfe using 2, 10, 50 and 75 m data. These data are all from proprietary sources, including both direct measurement and interpolated DEMs. There are summarised in Table 1. Resolution Type Source 2 m Direct measurement EA LiDAR 10 m Interpolation from1:10k map data Ordnance Survey Landform Profile™ 50 m Interpolation from 1:50k map data Ordnance Survey Landform Panorama™ 75 m Direct measurement NASA SRTM Table 1. DEM data sources. Proceedings of Geomorphometry 2009. Zurich, Switzerland, 31 August - 2 September, 2009 120