In situ measurement of grain-scale fluvial morphology is important for studies on grain roughness, sediment transport and the interactions between animals and the geomorphology, topics relevant to many river practitioners. Close-range digital photogrammetry (CRDP) and terrestrial laser scanning (TLS) are the two most common techniques to obtain high-resolution digital elevation models (DEMs) from fluvial surfaces. However, field application of topography remote sensing at the grain scale is presently hindered mainly by the tedious workflow challenges that one needs to overcome to obtain high-accuracy elevation data. A recommended approach for CRDP to collect high-resolution and high-accuracy DEMs has been developed for gravel-bed flume studies. The present paper investigates the deployment of the laboratory technique on three exposed gravel bars in a natural river environment. In contrast to other approaches, having the calibration carried out in the laboratory removes the need for independently surveyed groundcontrol targets, and makes for an efficient and effective data collection in the field. Optimization of the gravel-bed imagery helps DEM collection, without being impacted by variable lighting conditions. The benefit of a light-weight three-dimensional printed gravel-bed model for DEM quality assessment is shown, and confirms the reliability of grain roughness data measured with CRDP. Imagery and DEM analysis evidences sedimentological contrasts between gravel bars within the reach. The analysis of the surface elevations shows the effect variable grain-size and sediment sorting have on the surface roughness. By plotting the twodimensional structure functions and surface slopes and aspects we identify different grain arrangements and surface structures. The calculation of the inclination index allows determining the surface-forming flow direction(s). We show that progress in topography remote sensing is important to extend our knowledge on fluvial morphology processes at the grain scale, and how a technique customized for use by fluvial geomorphologists in the field benefits this progress.

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In this two-part study, experiments are conducted to evaluate available topography measurement techniques for gravel-beds in a laboratory flume and to study their suitability for statistical roughness analysis. The available instruments for this study include, (i) an acoustic bed-profiler; (ii) a hand-held laser-scanner; and (iii) two digital consumer cameras forming a stereo-photogrammetric system, and are employed to obtain Digital Elevation Models (DEMs) of water-worked gravel-beds. In the first part of the study, the three measurement techniques are reviewed and their feasibilities for future grain-scale roughness work assessed, based on the obtained elevation datasets. Water-worked gravel-bed topographies are measured with all three available measurement techniques. The analysis of the DEMs concentrates on using Probability Distribution Functions (PDFs) and second-order structure functions of bed elevations. Roughness coefficients are determined and used as a benchmark for comparison of the three measurement techniques. Although, visually, differences in the DEMs obtained with different measurement techniques are observed, the results of the chosen statistical analysis do not disclose the visual differences to the same extent. It is shown that the used stereophotogrammetric system, although theoretically allowing a fast and high-resolution recording process, lacks behind in accuracy. Thus, the second part of the study identifies and presents steps to improve the quality of the obtained stereo-photogrammetric DEMs. A checklist is Page 2 provided, highlighting the improvements made in the follow-up study, in order to obtain a high-quality stereo-photogrammetric DEM. The overview will be useful for other researchers to make use of available low-cost and high-quality consumer camera equipment, to set-up their own, non-proprietary stereo-photogrammetric system.

There is a growing consensus that gravel‐bed roughness should be parameterized based on bed‐surface topography, not only sediment size. One benefit is the possible identification of various spatial scales of surface roughness and evaluation of their respective contributions to flow resistance (and also to bedload transport). The absence of relationships between roughness at the different scales is apparent in previous work, which currently limits roughness parameterization from topography and application in flow modeling. This study examines the use of moving‐window detrending on gravel‐bed digital elevation models (DEMs) for isolating roughness scales and their respective signatures. A large data set of 35 water‐worked gravel‐bed patches from both the laboratory and the field was used for the analysis. The measured bed topography was separated into two distinct DEMs: one representing grains, the other representing small bedforms. For all DEMs, bed‐elevation parameters measuring vertical roughness, imbrication, and spatial correlations were determined. Our results show distinct topographic signatures between grain and bedform DEMs. We show strong positive linear relationships between grain vertical roughness and the size of the bed‐surface material. Surface sediment arrangement also determined bedform shape, with groupings of coarse sediment forming humps on the surface, and finer sediment sheltered in hollows. Patch‐scale vertical roughness could not be estimated simply as the sum of grain and bedform vertical roughness. Instead, our results suggest weighted summation and the existence of universal weighting coefficients. Practical applications for studies on gravel‐bed roughness and flow modeling using DEMs are discussed.

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