In-situ characterization of grain-scale fluvial morphology using Terrestrial Laser Scanning

The grain-scale morphology of fluvial sediments is an important control on the character and dynamics of river systems; however current understanding of its role is limited by the difficulties of robustly quantifying field surface morphology. Terrestrial Laser Scanning (TLS) offers a new methodology for the rapid acquisition of high-resolution and high-precision surface elevation data from in situ sediments. To date, most environmental and fluvial applications of TLS have focused on large-scale systems, capturing macroscale morphologies. Application of this new technology at scales necessary to characterize the complexity of grain-scale fluvial sediments therefore requires a robust assessment of the quality and sources of errors in close-range TLS data. This paper describes both laboratory and field experiments designed to evaluate close-range TLS for sedimentological applications and to develop protocols for data acquisition. In the former, controlled experiments comprising high-resolution scans of white, grey and black planes and a sphere were used to quantify the magnitude and source of three-dimensional (3D) point errors resulting from a combination of surface geometry, reflectivity effects and inherent instrument precision. Subsequently, a methodology for the collection and processing of grain-scale TLS data is described through an application to a coarse grained gravel system, the River Feshie (D50 32 to 63 mm). This stepwise strategy incorporates averaging repeat scans and filtering scan artefact and non-surface points using local 3D search algorithms. The sensitivity of the results to the filter parameter values are assessed by careful internal validation of Digital Terrain Models (DTMs) created from the resulting point cloud data. The transferability of this methodology is assessed through application to a second river, Bury Green Brook, dominated by finer gravel (D50 18 to 33 mm). The factor limiting the resolution of DTMs created from this second dataset was found to be the relative sizes of the laser footprint and smallest grains.