High-resolution topographic surveying is traditionally associated with high capital and logistical costs, so that data acquisition is often passed on to specialist third party organizations. The high costs of data collection are, for many applications in the earth sciences, exacerbated by the remoteness and inaccessibility of many field sites, rendering cheaper, more portable surveying platforms (i.e. terrestrial laser scanning or GPS) impractical. This paper outlines a revolutionary, low-cost, userfriendly photogrammetric technique for obtaining high-resolution datasets at a range of scales, termed 'Structure-from-Motion' (SfM). Traditional softcopy photogrammetric methods require the 3-D location and pose of the camera(s), or the 3-D location of ground control points to be known to facilitate scene triangulation and reconstruction. In contrast, the SfM method solves the camera pose and scene geometry simultaneously and automatically, using a highly redundant bundle adjustment based on matching features in multiple overlapping, offset images. A comprehensive introduction to the technique is presented, followed by an outline of the methods used to create high-resolution Digital Elevation Models (DEMs) from extensive photosets obtained using a consumer-grade digital camera. As an initial appraisal of the technique, an SfM-derived DEM is compared directly with a similar model obtained using Terrestrial Laser Scanning. This intercomparison reveals that decimetre-scale vertical accuracy can be achieved using SfM even for sites with complex topography and a range of land-covers. Example applications of SfM are presented for three contrasting landforms across a range of scales including; an exposed rocky coastal cliff; a breached moraine-dam complex; and a glaciallysculpted bedrock ridge. The SfM technique represents a major advancement in the field of photogrammetry for geoscience applications. Our results and experiences indicate SfM is an inexpensive, effective, and flexible approach to capturing complex topography.

Repeat topographic surveys are increasingly becoming more affordable, and possible at higher spatial resolutions and over greater spatial extents. Digital elevation models (DEMs) built from such surveys can be used to produce DEM of Difference (DoD) maps and estimate the net change in storage terms for morphological sediment budgets. While these products are extremely useful for monitoring and geomorphic interpretation, data and model uncertainties render them prone to misinterpretation. Two new methods are presented, which allow for more robust and spatially variable estimation of DEM uncertainties and propagate these forward to evaluate the consequences for estimates of geomorphic change. The fi rst relies on a fuzzy inference system to estimate the spatial variability of elevation uncertainty in individual DEMs while the second approach modifi es this estimate on the basis of the spatial coherence of erosion and deposition units. Both techniques allow for probabilistic representation of uncertainty on a cell-by-cell basis and thresholding of the sediment budget at a user-specifi ed confi dence interval. The application of these new techniques is illustrated with 5 years of high resolution survey data from a 1 km long braided reach of the River Feshie in the Highlands of Scotland. The reach was found to be consistently degradational, with between 570 and 1970 m 3 of net erosion per annum, despite the fact that spatially, deposition covered more surface area than erosion. In the two wetter periods with extensive braid-plain inundation, the uncertainty analysis thresholded at a 95% confi dence interval resulted in a larger percentage (57% for 2004-2005 and 59% for 2006-2007) of volumetric change being excluded from the budget than the drier years (24% for 2003-2004 and 31% for 2005-2006). For these data, the new uncertainty analysis is generally more conservative volumetrically than a standard spatially-uniform minimum level of detection analysis, but also produces more plausible and physically meaningful results. The tools are packaged in a wizard-driven Matlab software application available for download with this paper, and can be calibrated and extended for application to any topographic point cloud (x,y,z).

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[1] Recent advances in technology have revolutionized the acquisition of topographic data, offering new perspectives on the structure and morphology of the Earth's surface. These developments have had a profound impact on the practice of river science, creating a step change in the dimensionality, resolution, and precision of fluvial terrain models. The emergence of ''hyperscale'' survey methods, including structure from motion photogrammetry and terrestrial laser scanning (TLS), now presents the opportunity to acquire 3-D point cloud data that capture grain-scale detail over reach-scale extents. Translating these data into geomorphologically relevant products is, however, not straightforward. Unlike traditional survey methods, TLS acquires observations rapidly and automatically, but unselectively. This results in considerable ''noise'' associated with backscatter from vegetation and other artifacts. Moreover, the large data volumes are difficult to visualize; require very high capacity storage; and are not incorporated readily into GIS and simulation models. In this paper we analyze the geomorphological integrity of multiscale terrain models rendered from a TLS survey of the braided River Feshie, Scotland. These raster terrain models are generated using a new, computationally efficient geospatial toolkit: the topographic point cloud analysis toolkit (ToPCAT). This performs an intelligent decimation of point cloud data into a set of 2.5-D terrain models that retain information on the high-frequency subgrid topography, as the moments of the locally detrended elevation distribution. The results quantify the degree of terrain generalization inherent in conventional fluvial DEMs and illustrate how subgrid topographic statistics can be used to map the spatial pattern of particle size, grain roughness, and sedimentary facies at the reach scale.Citation: Brasington, J., D. Vericat, and I. Rychkov (2012), Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning, Water Resour. Res., 48, W11519,