K. Arrell, S. Carver
School of Geography, University of Leeds, LS2 9JT
Tel. (+44) 0113 3433343 Fax (+44) 0113 3433308
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.