Stephane Bertin

Isolating roughness scales of gravel‐bed patches

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 dataset 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.

A Merging Solution for Close-Range DEMs to Optimize Surface Coverage and Measurement Resolution

The process of efficient and effective DEM merging is increasingly becoming more important. To allow DEM analysis for features of different scales, an increase in surface coverage cannot result in reduced measurement resolution. It is thus inevitable that merging individual high-resolution DEMs will become common practice for applications such as hydraulic roughness studies for fluvial surfaces. This paper presents an efficient and effective merging solution, whereby accurate co-registration of individual DEMs collected from consistent viewpoints and standard averaging for overlapping elevations ensure seamless merging. The presented method is suitable for DEMs collected using any measurement technology, as long as individual DEMs overlap and are arranged on regular grids. The merging solution is applied to the study of a laboratory gravel bed measured with vertical stereo photogrammetry at the grain scale (>106 points/m2). We show that the approach can be integrated into the DEM collection workflow at the design stage, which optimizes the measurement performance. We present how resampling before merging can be beneficial to keep data handling requirements practical, whilst ensuring accurate surface representation. Finally, the effect of scale variation is studied, showing that seamless merging applies to DEMs with variable resolution.

The development and internal assessment of a high-resolution, non-proprietary, stereo-photogrammetric setup for hydraulic experiments

Remote sensing of riverine gravel-beds has been shown to be fundamental to derive a theoretically driven definition of the hydraulic roughness and to understand the complex processes at the sediment-water interface. Commonly, 2D gravel-bed topography was recorded and analyzed, and only more recently technology allowed the measurement of high-resolution 3D Digital Elevation Models (DEMs). Equipment to do so is limited to Terrestrial Laser-Scanners (TLS) and proprietary stereo-photogrammetric systems and associated commercial software. Obtaining 3D DEMs of the gravel-bed allows the use of advanced statistical functions of riverbed elevations, like Probability Distribution Functions (PDFs) and structure functions, to characterize the spatial and temporal structural development of the riverbed surface. The promise of quick high-resolution data acquisitions, obtained with digital close-range stereo-photogrammetry, which can be employed at various locations, warrants detailed research into this area. In this paper we present the development of a high-resolution, non-proprietary stereo-photogrammetric setup, to be used for hydraulic experiments aimed at gravel-bed roughness characterization. Based on the quantitative assessment of the calibration process and stereo rectification of the images, means to evaluate the reliability of the system are described. It is shown that the extraction of the internal orientation of the two cameras and the external orientation of the stereo setup, as well as the rectification of the images to epipolar geometry, are crucial steps to successfully match corresponding pixels and obtain high-quality DEMs of a gravel-bed. Finally, surface plots of the measured gravel-bed topography are presented, showing how improvements are reflected in the quality of the DEMs.

The Ngongotaha river UDPS experiment: low-cost underwater dynamic stereo photogrammetry

We propose to integrate the newest developments in stereomatching theory, affordable parallel processing capabilities (using GPU e.g. PC gaming/graphic card) and statistical surface analysis to implement and test an in-situ Underwater Dynamic Stereo Photogrammetry (UDSP) system for civil engineering applications. The proposed UDPS system aims to provide underwater Digital Elevation Models (DEM), for applications such as a two-dimensional discrete matrix of data underwater elevations. Experiments on river bed stereophotogrammetry in the Ngongotaha Stream near Rotorua using consumer grade stereo cameras including Go-Pro and Fujifilm W3 are used in through-water and underwater calibration and stereo measurements of 32 pebbles on the river bed. Pebbles are measured and identified. Initial results highlight the need for specialised equipment for through-water and underwater photogrammetry experiments to limit blurring effects caused by the water-plastic-air interfaces. Despite poor optical quality of the images obtained, we were able to correlate pebble sizes from calibrated stereo depth maps and actual measurement.

Combining Structure from Motion and close-range stereo photogrammetry to obtain scaled gravel bar DEMs

ABSTRACT Digital elevation models (DEMs) have been increasingly applied in topographic studies in areas such as physical geography and hydraulic engineering. Several methods have been proposed to reconstruct DEMs, including classic close-range stereo photogrammetry and the more novel Structure from Motion (SfM) methodology. Past published studies tend to apply SfM to large-scale environmental processes, whilst classic close-range stereophotogrammetry is focusing on detailed small-scale applications. However, SfM requires multiple ground control points (GCPs) to allow for proper DEM scaling. The larger the study area, the more GCPs are required, resulting in increased operational complexity and time-consuming application of SfM. As the accuracy of the DEM depends on the equipment used to measure GCPs, this can also result in a cost-expensive operation. In the present study, we introduce a combined SfM and close-range stereo photogrammetry application, with the close-range stereo photogrammetry results serving as a control for providing scale, thus eliminating the need for traditional GCPs. To validate our methodology, we studied a 40 m long gravel bar. We used GoPro Hero 3 cameras for SfM measurements and replaced GCPs by DEMs obtained through close-range stereo photogrammetry with a Nikon D5100 camera pair in stereo. In addition to using photo-mode frames, we also studied the quality of DEMs obtained with GoPro Hero 3 video-mode frames, and show how the DEM quality is reduced due to the smaller image format, hence coarser point cloud spacing, which eventually results in a convex curvature when image overlap was increased. Our results show that it is possible to collect high-quality topographic surface data by only using cameras, and alleviate the need for GCPs. The proposed workflow reduces the complexity, time, and resource demands associated with deploying GCPs and necessary independent geo-referencing, ensuring that digital photogrammetry will continue to gain popularity for field surveying.