Spatial distribution of tracers for optical sensing of stream surface flow

Abstract. River monitoring is of particular interest for our society that is facing increasing complexity in water management. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but also generating new challenges for the harmonised use of devices and algorithms. In this context, optical sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and level of aggregation. Therefore, a requirement is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of particle aggregation, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: i) Particle Tracking Velocimetry (PTV), and ii) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on density and aggregation) was introduced based on a newly developed metric π. This value can be approximated and used in practice as π = ν0.1 / (ρ / ρcν1) where ν, ρ, and ρcν1 are the aggregation level, the seeding density, and the converging seeding density at ν = 1, respectively. A reduction of image-velocimetry errors was systematically observed by decreasing the values of π; and therefore, the optimal frame window was defined as the one that minimises π. In addition to numerical analyses, the Basento field case study (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and an error reduction of about 15.9 and 16.1 % was calculated – using PTV and PIV, respectively – by employing the optimal frame window.

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Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5173-2020 ◽

2020 ◽

Vol 24 (11) ◽

pp. 5173-5185

Author(s):

Alonso Pizarro ◽

Silvano F. Dal Sasso ◽

Matthew T. Perks ◽

Salvatore Manfreda

Keyword(s):

Spatial Distribution ◽

Optical Sensing ◽

Spatial Clustering ◽

Surface Flow ◽

Seeding Density ◽

Stream Surface ◽

Image Velocimetry ◽

Distribution Index ◽

Research Goal ◽

The One

Abstract. River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical-sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: (i) particle-tracking velocimetry (PTV) and (ii) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI=ν0.1/ρρcν1, where ν, ρ, and ρcν1 are the spatial-clustering level, the seeding density, and the reference seeding density at ν=1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9 % and 16.1 % were calculated – using PTV and PIV, respectively – by employing the optimal frame window.

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Development and Testing of an Unmanned Aerial Vehicle for Large Scale Particle Image Velocimetry

Volume 3: Industrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy ◽

10.1115/dscc2014-5838 ◽

2014 ◽

Cited By ~ 4

Author(s):

Christopher Pagano ◽

Flavia Tauro ◽

Salvatore Grimaldi ◽

Maurizio Porfiri

Keyword(s):

Particle Image Velocimetry ◽

Large Scale ◽

Field Experiments ◽

Particle Image ◽

Low Cost ◽

Surface Flow ◽

Natural Environments ◽

Natural Stream ◽

Image Velocimetry ◽

Scale Particle

Large scale particle image velocimetry (LSPIV) is a nonintrusive environmental monitoring methodology that allows for continuous characterization of surface flows in natural catchments. Despite its promise, the implementation of LSPIV in natural environments is limited to areas accessible to human operators. In this work, we propose a novel experimental configuration that allows for unsupervised LSPIV over large water bodies. Specifically, we design, develop, and characterize a lightweight, low cost, and stable quadricopter hosting a digital acquisition system. An active gimbal maintains the camera lens orthogonal to the water surface, thus preventing severe image distortions. Field experiments are performed to characterize the vehicle and assess the feasibility of the approach. We demonstrate that the quadricopter can hover above an area of 1×1m2 for 4–5 minutes with a payload of 500g. Further, LSPIV measurements on a natural stream confirm that the methodology can be reliably used for surface flow studies.

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Seeding metrics for image velocimetry performances in rivers

10.5194/egusphere-egu21-9229 ◽

2021 ◽

Author(s):

Silvano Fortunato Dal Sasso ◽

Alonso Pizarro ◽

Sophie Pearce ◽

Ian Maddock ◽

Matthew T. Perks ◽

...

Keyword(s):

Spatial Distribution ◽

Optical Sensors ◽

Large Scale ◽

Surface Flow ◽

Surface Velocity ◽

Seeding Density ◽

Earth System ◽

Science Data ◽

Image Velocimetry ◽

Hydraulic Conditions

Optical sensors coupled with image velocimetry techniques are becoming popular for river monitoring applications. In this context, new opportunities and challenges are growing for the research community aimed to: i) define standardized practices and methodologies; and ii) overcome some recognized uncertainty at the field scale. At this regard, the accuracy of image velocimetry techniques strongly depends on the occurrence and distribution of visible features on the water surface in consecutive frames. In a natural environment, the amount, spatial distribution and visibility of natural features on river surface are continuously challenging because of environmental factors and hydraulic conditions. The dimensionless seeding distribution index (SDI), recently introduced by Pizarro et al., 2020a,b and Dal Sasso et al., 2020, represents a metric based on seeding density and spatial distribution of tracers for identifying the best frame window (FW) during video footage. In this work, a methodology based on the SDI index was applied to different study cases with the Large Scale Particle Image Velocimetry (LSPIV) technique. Videos adopted are taken from the repository recently created by the COST Action Harmonious, which includes 13 case study across Europe and beyond for image velocimetry applications (Perks et al., 2020). The optimal frame window selection is based on two criteria: i) the maximization of the number of frames and ii) the minimization of SDI index. This methodology allowed an error reduction between 20 and 39% respect to the entire video configuration. This novel idea appears suitable for performing image velocimetry in natural settings where environmental and hydraulic conditions are extremely challenging and particularly useful for real-time observations from fixed river-gauged stations where an extended number of frames are usually recorded and analyzed.&#160;References Dal Sasso S.F., Pizarro A., Manfreda S.,&#160;Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers.&#160;Remote Sensing,&#160;12, 1789 (doi: 10.3390/rs12111789), 2020.Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., &#8230;Manfreda S, Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, https://doi.org/10.5194/essd-12-1545-2020, 12(3), 1545 &#8211; 1559, 2020.Pizarro A., Dal Sasso S.F., Manfreda S.,&#160;Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation,&#160;Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.Pizarro A., Dal Sasso S.F., Perks M. and Manfreda S., Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173&#8211;5185, (10.5194/hess-24-5173-2020), 2020.

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Real-Time Measurement of Flash-Flood in a Wadi Area by LSPIV and STIV

Hydrology ◽

10.3390/hydrology6010027 ◽

2019 ◽

Vol 6 (1) ◽

pp. 27 ◽

Cited By ~ 5

Author(s):

Mahmood Al-mamari ◽

Sameh Kantoush ◽

Sohei Kobayashi ◽

Tetsuya Sumi ◽

Mohamed Saber

Keyword(s):

Large Scale ◽

Flash Flood ◽

Early Warning Systems ◽

Flash Floods ◽

Surface Flow ◽

Unidirectional Flow ◽

Real Time Measurement ◽

Positive Performance ◽

Image Velocimetry ◽

Scale Particle

Flash floods in wadi systems discharge large volumes of water to either the sea or the desert areas after high-intensity rainfall events. Recently, wadi flash floods have frequently occurred in arid regions and caused damage to roads, houses, and properties. Therefore, monitoring and quantifying these events by accurately measuring wadi discharge has become important for the installation of mitigation structures and early warning systems. In this study, image-based methods were used to measure surface flow velocities during a wadi flash flood in 2018 to test the usefulness of large-scale particle image velocimetry (LSPIV) and space–time image velocimetry (STIV) techniques for the estimation of wadi discharge. The results, which indicated the positive performance of the image-based methods, strengthened our hypothesis that the application of LSPIV and STIV techniques is appropriate for the analysis of wadi flash flood velocities. STIV is suitable for unidirectional flow velocity and LSPIV is reliable and stable for two-dimensional measurement along the wadi channel, the direction of flow pattern which varies with time.

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Image-velocimetry techniques under particle aggregation for streamflow monitoring: a numerical approach

10.5194/egusphere-egu2020-15525 ◽

2020 ◽

Author(s):

Alonso Pizarro ◽

Silvano Fortunato Dal Sasso ◽

Salvatore Manfreda

Keyword(s):

Large Scale ◽

Ad Hoc ◽

Numerical Approach ◽

Surface Flow ◽

Particle Aggregation ◽

Seeding Density ◽

Stream Flows ◽

Flow Monitoring ◽

Time Flow ◽

Image Velocimetry

Monitoring extreme events with high accuracy and consistency is still a challenge, even by using up-to-date approaches. On the one side, field campaigns are in general expensive and time-consuming, requiring the presence of high-qualified personnel and forward planning. On the other side, non-contact approaches (such as image velocimetry, radars, and microwave systems) have had promising signs of progress in recent years, making now possible real-time flow monitoring. This work focuses on the estimation of surface flow velocities for streamflow monitoring under particle aggregation, in which tracers are not necessarily uniformly distributed across the entire field of view. This issue is extremely relevant for the computing stream flows since velocity errors are transmitted to river discharge estimations. Ad-hoc numerical simulations were performed to consider different levels of particle aggregation, particle colour and shapes, seeding density, and background noise. Particle Tracking Velocimetry (PTV) and Large-Scale Particle Image Velocimetry (LSPIV) were used for image velocimetry estimations due to their widely used worldwide. Comparisons between the theoretical and computed velocities were carried out to determine the associated uncertainty and optimal experimental setup that minimises those errors.

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Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers

Remote Sensing ◽

10.3390/rs12111789 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1789 ◽

Cited By ~ 2

Author(s):

Silvano Fortunato Dal Sasso ◽

Alonso Pizarro ◽

Salvatore Manfreda

Keyword(s):

Water Resource Management ◽

Large Scale ◽

River Flow ◽

Low Cost ◽

A Priori ◽

Surface Flow ◽

Seeding Density ◽

Unmanned Aerial Systems ◽

Flow Monitoring ◽

Image Velocimetry

River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing.

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Case Study: Utilizing Large Scale Particle Image Velocimetry to Monitor Rural Streams during High Flow Events

World Environmental and Water Resources Congress 2011 ◽

10.1061/41173(414)249 ◽

2011 ◽

Author(s):

Brian Wardman ◽

Bryce Cruey ◽

Joey Howard

Keyword(s):

Particle Image Velocimetry ◽

Large Scale ◽

Particle Image ◽

High Flow ◽

Image Velocimetry ◽

Scale Particle

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Large Scale Infrared-Based Remote Sensing of Turbulence Metrics in Surface Waters: Going Beyond Mean Flow

14th International Symposium on Particle Image Velocimetry ◽

10.18409/ispiv.v1i1.53 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Seth Avram Schweitzer ◽

Edwin Alfred Cowen

Keyword(s):

Large Scale ◽

Surface Velocity ◽

Seeding Density ◽

Mean Flow ◽

Tracer Particles ◽

Image Velocimetry ◽

Temporal Averaging ◽

Spatio Temporal ◽

Scale Particle ◽

Image Collection

In recent years field-scale applications of image-based velocimetry methods, often referred to as large scale particle image velocimetry (LSPIV), have been increasingly deployed. These velocimetry measurements have several advantages—they allow high resolution, non-contact measurement of surface velocity over a large two dimensional area, from which the bulk flow can be inferred. However, visiblelight LSPIV methods can have significant limitations. The water surface often lacks natural features that can be tracked in the visible and generally requires seeding with tracer particles, which creates concerns regarding the fidelity with which tracer particles track the flow, and introduces challenges in achieving sufficient and uniform seeding density, in particular in regions with appreciable velocity accelerations such as turbulence. In LSPIV, image collection is generally limited to daylight hours, and can suffer from non-uniformity of illumination across the camera’s field of view. Due to these issues LSPIV often requires spatio-temporal averaging, and as a result is generally able to extracting the mean, but not the instantaneous, velocity field, and hence is often not a suitable tool for calculating turbulence metrics of the flow.

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