In-situ measurement of river-bed sediment porosity using Structure-from-Motion image analysis

Tabesh, Mina; Hoffmann, Thomas; Vollmer, Stefan; Schüttrumpf, Holger; Frings, Roy M.

doi:10.1016/j.geomorph.2019.04.011

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…Secondly, NDG requires a close contact between the device's bottom and the sediment surface; therefore, NDG can only be applied to even sediment surfaces, such as the subsurface sediments investigated in this study. If information is needed about the porosity of armour layers, which are notoriously uneven, the photogrammetry method described in Tabesh et al (2019) is recommended.…”

Section: Discussionmentioning

confidence: 99%

“…If information is needed about the porosity of armour layers, which are notoriously uneven, the photogrammetry method described in Tabesh et al . (2019) is recommended.…”

Section: Discussionmentioning

confidence: 99%

“…Thirdly, the porosity values measured with the NDG were compared with porosity values measured with yet another technique, namely the photogrammetry method presented by Tabesh et al . (2019). This was done for 10 of the 103 NDG positions.…”

Section: Methodsmentioning

confidence: 99%

“…In this way, three‐dimensional point clouds and DEMs (digital elevation models) of the sediment surfaces before and after excavation were constructed. The difference between the two DEMs provided the total volume of the sediment sample (Tabesh et al ., 2019). This total volume was then combined with the measured solid fraction volume to calculate porosity.…”

Section: Methodsmentioning

confidence: 99%

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Spatial variability in river bed porosity determined by nuclear density gauging: A case study from a French gravel‐bed river

et al. 2021

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Porosity is one of the key properties of fluvial sediments. It is defined as the ratio of pore volume to total volume. In river science, porosity is often assumed to be spatially constant, which might be a gross simplification of reality. Ignoring the spatial variations in porosity can cause errors in morphological, ecological, hydrological, hydrogeological and sedimentological applications. Although detailed information about spatial porosity variations can be obtained from porosity measurements at field sites, such information has never been collected where these variations might be important. In this study, field porosity measurements were carried out to quantify the magnitude of the spatial porosity variation for four different sedimentological environments of a braided river: a confluence, a tributary delta, a braid bar and a secondary channel. A nuclear density gauge was used for the measurement of porosity. The nuclear density gauge proved to be a time-saving and labour-saving technique that produces accurate porosity values with a root mean square error of 0.03. The four sedimentological environments showed significant differences in porosity, with mean porosity being lower for confluence and bar than for delta and secondary channel. Semi-variogram analysis showed the absence of any spatial correlation in porosity for distances beyond 4 m. This shows that distance cannot be used as a parameter for porosity extrapolation in a fluvial system unless the extrapolation distance is less than 4 m. At least eight measurements of porosity are required to obtain a reliable estimate of mean porosity in a sedimentary environment, i.e. with uncertainty <0.03. Although grain size characteristics were found to have a significant impact on porosity, the relationships between these parameters and porosity were not very strong in this study. The unique porosity dataset, presented in this article, provides a valuable source of information for researchers and river managers.

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Section: Discussionmentioning

confidence: 99%

“…If information is needed about the porosity of armour layers, which are notoriously uneven, the photogrammetry method described in Tabesh et al . (2019) is recommended.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Spatial variability in river bed porosity determined by nuclear density gauging: A case study from a French gravel‐bed river

et al. 2021

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“…The progress in structure and motion estimation (a.k.a. structure-from-motion) research has been hectic, stimulated by recent breakthroughs in computer vision, the advent of digital photography and the augmented reality [1][2][3][4][5][6]. This progress has the potential to substantially increase the use of the structure from motion technique for a variety of applications, for example the growing application of unmanned aerial vehicles for remote surveying for a numerous of ecological domain [7].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional structure from motion recovery of a moving object with noisy measurement

Choi

2020

IJECE

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In this paper, a Nonlinear Unknown Input Observer (NLUIO) based approach is proposed for three-dimensional (3-D) structure from motion identification. Unlike the previous studies that require prior knowledge of either the motion parameters or scene geometry, the proposed approach assumes that the object motion is imperfectly known and considered as an unknown input to the perspective dynamical system. The reconstruction of the 3-D structure of the moving objects can be achieved using just two-dimensional (2-D) images of a monocular vision system. The proposed scheme is illustrated with a numerical example in the presence of measurement noise for both static and dynamic scenes. Those results are used to clearly demonstrate the advantages of the proposed NLUIO.

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Effect of sediment form and form distribution on porosity: a simulation study based on the discrete element method

et al. 2022

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Porosity is one of the key properties of dense particle packings like sediment deposits and is influenced by a multitude of grain characteristics such as their size distribution and shape. In the present work, we focus on the form, a specific aspect of the overall shape, of sedimentary grains in order to investigate and quantify its effect on porosity, ultimately deriving novel porosity-prediction models. To this end, we develop a robust and accurate simulation tool based on the discrete element method which we validate against laboratory experiments. Utilizing digital representations of actual sediment from the Rhine river, we first study packings that are composed of particles with a single form. There, porosity is found to be mainly determined by the inverse equancy, i.e., the ratio of the longest to the smallest form-defining axis. Only for small ratios, additional shape-related properties become relevant, as revealed by a direct comparison to packings of form-equivalent but smooth ellipsoids. Since sediment naturally features form mixtures, we extend our simulation tool to study sediment packings with normally-distributed forms. In agreement with our single form studies, porosity is found to depend primarily on the inverse of the mean equancy. By supplying additional information about a second form factor and the standard deviations, we derive an accurate model for porosity prediction. Due to its simplicity, it can be readily applied to sediment packings for which some measurements of flatness and elongation, the two most common form factors, are available. Graphical abstract

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In-situ measurement of river-bed sediment porosity using Structure-from-Motion image analysis

Cited by 9 publications

References 20 publications

Spatial variability in river bed porosity determined by nuclear density gauging: A case study from a French gravel‐bed river

Spatial variability in river bed porosity determined by nuclear density gauging: A case study from a French gravel‐bed river

Three-dimensional structure from motion recovery of a moving object with noisy measurement

Effect of sediment form and form distribution on porosity: a simulation study based on the discrete element method

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