On the characterisation of open-flow seeding conditions for image-velocimetry techniques using UASs

2020 ◽  
Author(s):  
Silvano Fortunato Dal Sasso ◽  
Alonso Pizarro ◽  
Salvatore Manfreda
Keyword(s):  
River Discharge ◽  
Velocity Fields ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Seeding Density ◽  
Acquisition Time ◽  
Flow Conditions ◽  
Open Flow ◽  
Image Velocimetry

<p>In the last years, new technologies have been developed to monitor rivers in a real-time framework opening new opportunities and challenges for the research community and practitioners. Acquiring data in open flow conditions can be performed through the use of Unmanned Aerial System (UAS) to derive surface velocity fields and in consequence, river discharge. Significant work has been done to investigate the reliability of image-velocimetry techniques using numerical simulations and laboratory flume experiments, but, to date, the effects of environmental factors on velocity estimates are not addressed adequately. In this context, a critical variable is represented by the number of particles transiting on the water surface (defined as seeding density) during field surveys and their challenging dynamics along the cross-section, on both time and space. Seeding density has a significant effect on surface velocity estimation and river discharge accuracy. The goal of this study was, therefore, to evaluate the accuracy and feasibility of LSPIV and PTV techniques under different seeding and flow conditions using several footages acquired employing UASs. To this purpose, the seeding behaviour during the whole acquisition time was examined for each case study focusing on the quantification of essential variables such as seeding density, average tracers’ dimension, coefficient of variation of tracers’ area, and spatial dispersion of them in the field of view. For each case study, both image-velocimetry techniques have been applied considering several different sets of images to locally measure the accuracy of velocity estimations in challenging seeding conditions. Results show that the local seeding density, tracers’ dimension and their spatial distribution can strongly influence the reconstruction of velocity fields in natural stream reaches. Therefore, prior knowledge of seeding characteristics in the field can deal with the choice of the optimal image-velocimetry technique to use and the related setting parameters.</p>

Seeding metrics for image velocimetry performances in rivers

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

<p>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.</p><p> </p><p><strong>References </strong></p><p>Dal Sasso S.F., Pizarro A., Manfreda S., Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers. Remote Sensing, 12, 1789 (doi: 10.3390/rs12111789), 2020.</p><p>Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., …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 – 1559, 2020.</p><p>Pizarro A., Dal Sasso S.F., Manfreda S., Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.</p><p>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, 5173–5185, (10.5194/hess-24-5173-2020), 2020.</p>

scholarly journals Glacier Image Velocimetry: an open-source toolbox for easy and rapid calculation of high-resolution glacier velocity fields

2021 ◽  
Vol 15 (4) ◽  
pp. 2115-2132
Author(s):  
Maximillian Van Wyk de Vries ◽  
Andrew D. Wickert
Keyword(s):  
High Resolution ◽  
Open Source ◽  
Velocity Fields ◽  
Surface Velocity ◽  
Displacement Fields ◽  
Desktop Computer ◽  
Ice Cap ◽  
Image Velocimetry ◽  
Glacier Ice ◽  
Glacier Velocity

Abstract. We present Glacier Image Velocimetry (GIV), an open-source and easy-to-use software toolkit for rapidly calculating high-spatial-resolution glacier velocity fields. Glacier ice velocity fields reveal flow dynamics, ice-flux changes, and (with additional data and modelling) ice thickness. Obtaining glacier velocity measurements over wide areas with field techniques is labour intensive and often associated with safety risks. The recent increased availability of high-resolution, short-repeat-time optical imagery allows us to obtain ice displacement fields using “feature tracking” based on matching persistent irregularities on the ice surface between images and hence, surface velocity over time. GIV is fully parallelized and automatically detects, filters, and extracts velocities from large datasets of images. Through this coupled toolchain and an easy-to-use GUI, GIV can rapidly analyse hundreds to thousands of image pairs on a laptop or desktop computer. We present four example applications of the GIV toolkit in which we complement a glaciology field campaign (Glaciar Perito Moreno, Argentina) and calculate the velocity fields of small mid-latitude (Glacier d'Argentière, France) and tropical glaciers (Volcán Chimborazo, Ecuador), as well as very large glaciers (Vavilov Ice Cap, Russia). Fully commented MATLAB code and a stand-alone app for GIV are available from GitHub and Zenodo (see https://doi.org/10.5281/zenodo.4624831, Van Wyk de Vries, 2021a).

scholarly journals sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

2019 ◽  
Vol 11 (19) ◽  
pp. 2317 ◽  
Author(s):  
Paul Kinzel ◽  
Carl Legleiter
Keyword(s):  
Particle Image Velocimetry ◽  
River Discharge ◽  
Water Surface ◽  
Large Scale ◽  
Particle Image ◽  
Field Evaluation ◽  
Remotely Sensed ◽  
Surface Velocity ◽  
Unmanned Aerial Systems ◽  
Image Velocimetry

This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely ( R 2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections ( R 2 = 0.95), but the agreement was not as strong for the transect with greater depths ( R 2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and <1% at the second.

scholarly journals Spatial distribution of tracers for optical sensing of stream surface flow

2020 ◽  
Author(s):  
Alonso Pizarro ◽  
Silvano F. Dal Sasso ◽  
Matthew Perks ◽  
Salvatore Manfreda
Keyword(s):  
Large Scale ◽  
Optical Sensing ◽  
Surface Flow ◽  
Seeding Density ◽  
Stream Surface ◽  
Image Velocimetry ◽  
Level Of Aggregation ◽  
Scale Particle ◽  
The One

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.

Novel strategies for complex foothills seismic imaging — Part 1: Mega-near-surface velocity estimation

2020 ◽  
Vol 8 (3) ◽  
pp. T651-T665
Author(s):  
Yalin Li ◽  
Xianhuai Zhu ◽  
Gengxin Peng ◽  
Liansheng Liu ◽  
Wensheng Duan
Keyword(s):  
Seismic Imaging ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Innovative Approach ◽  
Surface Velocity ◽  
Near Surface ◽  
Depth Migration ◽  
Static Corrections ◽  
Over Time

Seismic imaging in foothills areas is challenging because of the complexity of the near-surface and subsurface structures. Single seismic surveys often are not adequate in a foothill-exploration area, and multiple phases with different acquisition designs within the same block are required over time to get desired sampling in space and azimuths for optimizing noise attenuation, velocity estimation, and migration. This is partly because of economic concerns, and it is partly because technology is progressing over time, creating the need for unified criteria in processing workflows and parameters at different blocks in a study area. Each block is defined as a function of not only location but also the acquisition and processing phase. An innovative idea for complex foothills seismic imaging is presented to solve a matrix of blocks and tasks. For each task, such as near-surface velocity estimation and static corrections, signal processing, prestack time migration, velocity-model building, and prestack depth migration, one or two best service companies are selected to work on all blocks. We have implemented streamlined processing efficiently so that Task-1 to Task-n progressed with good coordination. Application of this innovative approach to a mega-project containing 16 3D surveys covering more than [Formula: see text] in the Kelasu foothills, northwestern China, has demonstrated that this innovative approach is a current best practice in complex foothills imaging. To date, this is the largest foothills imaging project in the world. The case study in Kelasu successfully has delivered near-surface velocity models using first arrivals picked up to 3500 m offset for static corrections and 9000 m offset for prestack depth migration from topography. Most importantly, the present megaproject is a merge of several 3D surveys, with the merge performed in a coordinated, systematic fashion in contrast to most land megaprojects. The benefits of this approach and the strategies used in processing data from the various subsurveys are significant. The main achievement from the case study is that the depth images, after the application of the near-surface velocity model estimated from the megasurveys, are more continuous and geologically plausible, leading to more accurate seismic interpretation.

scholarly journals Efficiency of a Digital Particle Image Velocimetry (DPIV) Method for Monitoring the Surface Velocity of Hyper-Concentrated Flows

Geosciences ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 383 ◽  
Author(s):  
Donatella Termini ◽  
Alice Di Leonardo
Keyword(s):  
Particle Image Velocimetry ◽  
Digital Image ◽  
Particle Image ◽  
Estimation Error ◽  
Digital Particle Image Velocimetry ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Image Velocimetry ◽  
Image Technique ◽  
Interrogation Area

Digital particle image velocimetry records high resolution images and allows the identification of the position of points in different time instants. This paper explores the efficiency of the digital image-technique for remote monitoring of surface velocity and discharge measurement in hyper-concentrated flow by the way of laboratory experiment. One of the challenges in the application of the image-technique is the evaluation of the error in estimating surface velocity. The error quantification is complex because it depends on many factors characterizing either the experimental conditions or/and the processing algorithm. In the present work, attention is devoted to the estimation error due either to the acquisition time or to the size of the sub-images (interrogation areas) to be correlated. The analysis is conducted with the aid of data collected in a scale laboratory flume constructed at the Hydraulic laboratory of the Department of Civil, Environmental, Aerospace and of Materials Engineering (DICAM)—University of Palermo (Italy) and the image processing is carried out by the help of the PivLab algorithm in Matlab. The obtained results confirm that the number of frames used in processing procedure strongly affects the values of surface velocity; the estimation error decreases as the number of frames increases. The size of the interrogation area also exerts an important role in the flow velocity estimation. For the examined case, a reduction of the size of the interrogation area of one half compared to its original size has allowed us to obtain low values of the velocity estimation error. Results also demonstrate the ability of the digital image-technique to estimate the discharge at given cross-sections. The values of the discharge estimated by applying the digital image-technique downstream of the inflow sections by using the aforementioned size of the interrogation area compares well with those measured.

scholarly journals An evaluation of image velocimetry techniques under low flow conditions and high seeding densities using Unmanned Aerial Systems

2020 ◽  
Author(s):  
Sophie Pearce ◽  
Robert Ljubicic ◽  
Salvador Pena-Haro ◽  
Matthew Perks ◽  
Flavia Tauro ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Surface Flow ◽  
Extraction Rate ◽  
Particle Identification ◽  
Surface Velocity ◽  
Low Flow ◽  
Unmanned Aerial Systems ◽  
Flow Conditions ◽  
Image Velocimetry

&lt;p&gt;Image velocimetry (IV) is a remote technique which calculates surface flow velocities of rivers (or fluids) via a range of cross-correlation and tracking algorithms. IV can be implemented via a range of camera sensors which can be mounted on tri-pods, or Unmanned Aerial Systems (UAS). IV has proven a powerful technique for monitoring river flows during flood conditions, whereby traditional in-situ techniques would be unsafe to deploy. However, little research has focussed upon the application of such techniques during low flow conditions. The applicability of IV to low flow studies could aid data collection at a higher spatial and temporal resolution than is currently available. Many IV techniques are under-development, that utilise different cross-correlation and tracking algorithms, including, Large Scale Particle Image Velocimetry (LSPIV), Large Scale Particle Tracking Velocimetry (LSPTV), Optical Tracking Velocimetry (OTV), Kanade Lucas Tomasi Image Velocimetry (KLT-IV) and Surface Structure Image Velocimetry (SSIV). Nevertheless, the true applications and limitations of such algorithms have yet to be extensively tested. Therefore, this study aimed to conduct a sensitivity analysis on the commonly relatable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate (or sub sampled frame rate).&lt;/p&gt;&lt;p&gt;Fieldwork was carried out on Kolubara River near the city of Obrenovac in Central Serbia. Cross-sectional surface width was relatively constant, varying between 23.30 and 23.45m. During the experiment, low flow conditions were present with a discharge of approx. 3.4m&lt;sup&gt;3 &lt;/sup&gt;s&lt;sup&gt;-1&lt;/sup&gt; (estimated using a Sontek M9 ADCP), and depths of up to 1.9m. A DJI Phantom 4 Pro UAS was used to collect video data of the surface flow. Artificial seeding material (wood-mulch) was distributed homogenously across the rivers&amp;#8217; surface, in order to improve the conditions for IV techniques during slow flows. Two 30-second videos were utilised for surface velocity analysis.&lt;/p&gt;&lt;p&gt;This study highlighted that KLT, SSIV, OTV and LSPIV are the least sensitive algorithms to changing parameters when no pre- or post-processing of results are conducted. On the other hand, LSPTV must undergo post-processing procedures in order to avoid spurious results and only then, results may be reliable. Furthermore, KLT and SSIV highlighted a slight sensitivity to changing the feature extraction rate, however changing the particle identification area did not affect significantly the outputted surface velocity results. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area provided a higher variability in the results, whilst changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area.&lt;/p&gt;&lt;p&gt;This analysis has led to the conclusions that during the conditions of sampling with surface velocities of approximately 0.12ms&lt;sup&gt;-1&lt;/sup&gt;, and homogeneous seeding on the rivers surface, IV techniques can provide results comparable to traditional techniques such as ADCPs during low flow conditions. All IV algorithms provided results that were, on average, within 0.05ms&lt;sup&gt;-1&lt;/sup&gt; of the ADCP measurements.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems

2020 ◽  
Vol 12 (2) ◽  
pp. 232 ◽  
Author(s):  
Sophie Pearce ◽  
Robert Ljubičić ◽  
Salvador Peña-Haro ◽  
Matthew Perks ◽  
Flavia Tauro ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Extraction Rate ◽  
Particle Identification ◽  
Surface Velocity ◽  
Unmanned Aerial Systems ◽  
Flow Conditions ◽  
Image Velocimetry ◽  
Scale Particle ◽  
Aerial Systems

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 m s − 1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 m s − 1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 m s − 1 of the ADCP measurements, on average.

Seismic Processing in Areas of Complex Near-Surface Velocity Fields: a Case Study

1997 ◽  
Vol 28 (1-2) ◽  
pp. 161-165
Author(s):  
Peter Whiting ◽  
Wal Muir ◽  
Natasha Hendrick ◽  
Belinda Suthers
Keyword(s):  
Velocity Fields ◽  
Surface Velocity ◽  
Near Surface ◽  
Seismic Processing

PIV MEASUREMENTS AND NUMERICAL VALIDATION OF END-TO-SIDE ANASTOMOSIS

2010 ◽  
Vol 10 (01) ◽  
pp. 123-138 ◽  
Author(s):  
AMAL OWIDA ◽  
HUNG DO ◽  
WILLIAM YANG ◽  
YOS S. MORSI
Keyword(s):  
Particle Image ◽  
Three Dimensional ◽  
Quantitative Information ◽  
Velocity Fields ◽  
Flow Conditions ◽  
Ansys Software ◽  
Piv Measurements ◽  
Piv Technique ◽  
Image Velocimetry ◽  
Glass Model

In this article, particle image velocimetry (PIV) technique was used to determine the instantaneous velocity fields inside a model of end-to-side anastomosis under various physiological flow conditions. Using ANSYS software, a three-dimensional (3D) computational model at the peak systolic blood flow was simulated. The numerical and experimental results were presented and discussed in terms of velocity fields at various locations along the graft and the host artery. The numerical results were then compared with the experimental data and a large difference was found, which was attributed to the imperfection of manufacturing the glass model and measurements error associated with PIV. The findings indicated in general that the analysis at peak systole, steady flow could help in providing essential quantitative information of the hemodynamics in anastomotic artery.

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