Measurement of Gravel-Bed Topography: Evaluation Study Applying Statistical Roughness Analysis

Abstract

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.

Roughness of a subglacial conduit under Hansbreen, Svalbard

“…According to previous research in natural rivers (Nikora and others, 1998;Bertin and Freidrich, 2014), the PDFs are close to the Gaussian distribution, and thus, we assume the 2nd-order (p = 2) moment of the roughness height provides the regional scale information.…”

Bridging the lab‐field interface in fluvial morphology at patch‐scale: Using close‐range photogrammetry to assess surface replication and vegetation influence

“…Close‐range photogrammetry uses two consumer grade (i.e., inexpensive) Nikon D5100 cameras with 20‐mm Nikkor lenses installed in stereo above the surface (Bertin, ; Bertin & Friedrich, ; Bertin, Friedrich, Delmas, & Chan, ; Bertin, Friedrich, Delmas, Chan, & Gimel'farb, ) to capture an area of 350 mm (transverse) by 500 mm (downstream). Cameras were installed on a moving frame (laboratory) and tripods (field) (Bertin & Friedrich, ; Groom et al, ) to facilitate the collection of three sets of stereo images (30% overlap) merged together for a larger areal coverage (Bertin, Friedrich, & Delmas, ).…”

Quantifying flow resistance in mountain streams using computational fluid dynamics modeling over structure‐from‐motion photogrammetry‐derived microtopography

“…Because bed elevations in natural rivers typically follow a Gaussian distribution (Kundu et al, 2012;Bertin and Friedrich, 2014), the most important information of streambed topography can be characterized by the second-order structure function D G2 , which is similar to semivariogram (Bachmaier and Backes, 2011). Because bed elevations in natural rivers typically follow a Gaussian distribution (Kundu et al, 2012;Bertin and Friedrich, 2014), the most important information of streambed topography can be characterized by the second-order structure function D G2 , which is similar to semivariogram (Bachmaier and Backes, 2011).…”

“…Flow resistance tends to scale with the roughness of streambed microtopography, which is characterized either in terms of the statistics of the surface grain size distribution (Hey, 1979;Bathurst, 1985;Ferguson, 2007) or through measurements with photogrammetric or laser scanning methods (Carbonneau et al, 2003;Heritage and Milan, 2009;Hodge et al, 2009;Bertin and Friedrich, 2014). Flow resistance tends to scale with the roughness of streambed microtopography, which is characterized either in terms of the statistics of the surface grain size distribution (Hey, 1979;Bathurst, 1985;Ferguson, 2007) or through measurements with photogrammetric or laser scanning methods (Carbonneau et al, 2003;Heritage and Milan, 2009;Hodge et al, 2009;Bertin and Friedrich, 2014).…”