Validation of large-scale particle image velocimetry to acquire free-surface flow fields in vegetated rivers

Creëlle, Stéphan; Roldan, Rebeca; Herremans, Anke; Meire, Dieter; Buis, Kerst; Meire, Patrick; Oyen, T. Van; Mulder, Tom De; Troch, Peter

doi:10.1080/23249676.2016.1251856

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…Alternatively, LSPIV could be applicable in small yet highly seeded stream reaches. For instance, in case of sufficiently seeded surfaces, good LSPIV estimates have been obtained in vegetated environments by Creëlle et al ().…”

Section: Resultsmentioning

confidence: 99%

Streamflow Observations From Cameras: Large‐Scale Particle Image Velocimetry or Particle Tracking Velocimetry?

Tauro

Piscopia

Grimaldi

2017

Water Resources Research

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Image‐based methodologies, such as large scale particle image velocimetry (LSPIV) and particle tracking velocimetry (PTV), have increased our ability to noninvasively conduct streamflow measurements by affording spatially distributed observations at high temporal resolution. However, progress in optical methodologies has not been paralleled by the implementation of image‐based approaches in environmental monitoring practice. We attribute this fact to the sensitivity of LSPIV, by far the most frequently adopted algorithm, to visibility conditions and to the occurrence of visible surface features. In this work, we test both LSPIV and PTV on a data set of 12 videos captured in a natural stream wherein artificial floaters are homogeneously and continuously deployed. Further, we apply both algorithms to a video of a high flow event on the Tiber River, Rome, Italy. In our application, we propose a modified PTV approach that only takes into account realistic trajectories. Based on our findings, LSPIV largely underestimates surface velocities with respect to PTV in both favorable (12 videos in a natural stream) and adverse (high flow event in the Tiber River) conditions. On the other hand, PTV is in closer agreement than LSPIV with benchmark velocities in both experimental settings. In addition, the accuracy of PTV estimations can be directly related to the transit of physical objects in the field of view, thus providing tangible data for uncertainty evaluation.

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Section: Resultsmentioning

confidence: 99%

Streamflow Observations From Cameras: Large‐Scale Particle Image Velocimetry or Particle Tracking Velocimetry?

Tauro

Piscopia

Grimaldi

2017

Water Resources Research

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“…Shallow flows are also complex experimental settings that are typically difficult to survey with traditional measurement approaches and have been extensively studied with LSPIV. Additionally, LSPIV has been applied to characterise surface flows in vegetated rivers (Creëlle et al, 2018), small and irregular rivers (Gunawan et al, 2012), and mountain rivers (Stumpf et al, 2016). Advanced applications of LSPIV also entail the visualisation of particle motion in drip irrigation emitters to study and prevent clogging effects (Liu et al, 2016).…”

Section: Large-scale Particle Image Velocimetrymentioning

confidence: 99%

Optical sensing for stream flow observations: A review

Tauro

Petroselli

Grimaldi

2018

J Agricult Engineer

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Images are revolutionising the way we sense and characterise the environment by offering higher spatial and temporal coverage in ungauged environments at competitive costs. In this review, we illustrate major image-based approaches that have been lately adopted within the hydrological research community. Although many among such methodologies have been developed some decades ago, recent efforts have been devoted to their transition from laboratories to operational outdoor settings. Sample applications of image-based techniques include flow discharge estimation in riverine environments, clogging dynamics in irrigation systems, and flow diagnostics in engineering infrastructures. The potential of such image-based approaches towards fully remote observations is also illustrated through a simple experiment with an unmanned aerial vehicle.

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“…A perpendicular or nearly perpendicular orientation of the optical axes of these instruments in relation to the water surface (when the plane of view of the UAV's camera is parallel and flat to the XY plane), means that the ortho-rectification of images is not required as the drone x, y position is equal to the x, y position of the center of the image. [59][60][61]. The motorized gimbal on the UAS ensures that the camera is nearly orthogonal to the ground surface once the gimbal has been calibrated in reference to a flat surface.…”

Section: Introductionmentioning

confidence: 99%

River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar

Koutalakis

Zaimes

2022

Hydrology

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Water velocity and discharge are essential parameters for monitoring water resources sustainably. Datasets acquired from Unoccupied Aerial Systems (UAS) allow for river monitoring at high spatial and temporal resolution, and may be the only alternative in areas that are difficult to access. Image or video-based methods for river flow monitoring have become very popular since they are not time-consuming or expensive in contrast to traditional methods. This study presents a non-contact methodology to estimate streamflow based on data collected from UAS. Both surface velocity and river geometry are measured directly in field conditions via the UAS while streamflow is estimated with a new technique. Specifically, surface velocity is estimated by using image-based velocimetry software while river bathymetry is measured with a floating sonar, tethered like a pendulum to the UAV. Traditional field measurements were collected along the same cross-section of the Aggitis River in Greece in order to assess the accuracy of the remotely sensed velocities, depths, and discharges. Overall, the new technique is very promising for providing accurate UAV-based streamflow results compared to the field data.

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Validation of large-scale particle image velocimetry to acquire free-surface flow fields in vegetated rivers

Cited by 9 publications

References 36 publications

Streamflow Observations From Cameras: Large‐Scale Particle Image Velocimetry or Particle Tracking Velocimetry?

Streamflow Observations From Cameras: Large‐Scale Particle Image Velocimetry or Particle Tracking Velocimetry?

Optical sensing for stream flow observations: A review

River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar

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