As I am now coming down to the last two months of my MPhil research, and desperately trying to finish on time (!!), i thought i’d most a small snippet from my literature review regarding the use of surface roughness as a tool for distinguishing and characterising morphological features. Enjoy………!

Surface morphology is one of the most commonly used criteria to characterise and distinguish alluvial surfaces of different ages (Wells et al., 1987, Bull, 1991, Ritter et al., 1993, Frankel and Dolan, 2007). Previous studies suggest that alluvial surfaces become smoother with increasing age before reaching a threshold (~70ka) where, once crossed, roughness increases with age (Bull, 1991, Matmon et al., 2006, Frankel and Dolan, 2007). This tool is critical for active tectonic investigations as based on the estimated ages of surfaces and their measured displacement, fault slip rates, recurrence intervals and bracketing ages of deformation events can be determined. However, quantifying surface roughness in the past has been difficult due to the available aerial photographs and low-resolution remotely sensed data, which lacked the spatial resolution necessary to make a quantitative comparison between alluvial surfaces (Farr and Chadwick, 1996). Past field techniques included clast size counts and topographic surveying however these methods were labour intensive and generally limited to a small spatial extent, thus providing data only on a small area of each individual surface (Frankel and Dolan, 2007).

The growing use of LiDAR (Light Detetction and Ranging data) has renewed interest in the quantitative characterisation of alluvial landforms and is now providing new opportunities to study landforms in unprecedented detail. The high-resolution of LiDAR topographic data and its associated highly accurate DEMs enables surface characteristics to be readily and easily extracted, in particular surface roughness. Previous investigations employing the use of LiDAR DEMs, together with measuring surface roughness to differentiate and characterise landscape morphology, have included mapping the spatial and temporal characteristics of a landslide in New Zealand (McKean and Roering, 2004), as well as an investigation into debris flow fan patterns in central Death Valley, California (Staley et al., 2006). More recently Frankel and Dolan (2007) demonstrated that alluvial fan surfaces of differing ages can be successfully distinguished through quantifying surface roughness, derived from high-resolution LiDAR data. These past studies confidently suggest that the second objective to analyse surface roughness derived from high resolution LiDAR data can be used to efficiently differentiate and map alluvial landforms.