Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-1545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Jul 2020
Data description paper |
| 08 Jul 2020
| 08 Jul 2020
Towards harmonisation of image velocimetry techniques for river surface velocity observations
Matthew T. Perks
CORRESPONDING AUTHOR
School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, UK
Silvano Fortunato Dal Sasso
Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DiCEM), University of Basilicata, 75100 Matera, Italy
Alexandre Hauet
Electricité de France, DTG, Grenoble, France
Elizabeth Jamieson
National Hydrological Services, Environment and Climate Change Canada, Gatineau, Canada
Jérôme Le Coz
INRAE, UR RiverLy, River Hydraulics, Villeurbanne, France
Sophie Pearce
School of Science and the Environment, University of Worcester, Worcester, UK
Salvador Peña-Haro
Photrack AG: Flow Measurements, Ankerstrasse 16a, 8004 Zürich, Switzerland
Alonso Pizarro
Department of European and Mediterranean Cultures: Architecture, Environment and Cultural Heritage (DiCEM), University of Basilicata, 75100 Matera, Italy
Dariia Strelnikova
School of Geoinformation, Carinthia University of Applied Sciences, 9524 Villach, Austria
Flavia Tauro
Department for Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, 10003 Viterbo, Italy
James Bomhof
National Hydrological Services, Environment and Climate Change Canada, Gatineau, Canada
Salvatore Grimaldi
Department for Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, 10003 Viterbo, Italy
Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 10003, USA
Alain Goulet
National Hydrological Services, Environment and Climate Change Canada, Gatineau, Canada
Borbála Hortobágyi
School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, UK
Magali Jodeau
Electricité de France, R&D, Chatou, France
LHSV, Chatou, France
Sabine Käfer
Verbund Hydro Power GmbH, 9500 Villach, Austria
Robert Ljubičić
Faculty of Civil Engineering, University of Belgrade, Belgrade 11120, Serbia
Ian Maddock
School of Science and the Environment, University of Worcester, Worcester, UK
Peter Mayr
flussbau iC, 9500 Villach, Austria
Gernot Paulus
School of Geoinformation, Carinthia University of Applied Sciences, 9524 Villach, Austria
Lionel Pénard
INRAE, UR RiverLy, River Hydraulics, Villeurbanne, France
Leigh Sinclair
National Hydrological Services, Environment and Climate Change Canada, Nanaimo, Canada
Salvatore Manfreda
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Napoli, Italy
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Cited
46 citations as recorded by crossref.
- SSIMS-Flow: Image velocimetry workbench for open-channel flow rate estimation R. Ljubičić et al. 10.1016/j.envsoft.2023.105938
- Recent Advancements and Perspectives in UAS-Based Image Velocimetry S. Dal Sasso et al. 10.3390/drones5030081
- Probabilistic Evaluation and Filtering of Image Velocimetry Measurements E. Rozos et al. 10.3390/w13162206
- Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
- Remote Sensing of Riparian Ecosystems M. Rusnák et al. 10.3390/rs14112645
- Satellite Video Remote Sensing for Estimation of River Discharge C. Masafu et al. 10.1029/2023GL105839
- An automatic ANN-based procedure for detecting optimal image sequences supporting LS-PIV applications for rivers monitoring F. Alongi et al. 10.1016/j.jhydrol.2023.130233
- Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales S. Dal Sasso et al. 10.1016/j.jhydrol.2021.126438
- Challenges with Regard to Unmanned Aerial Systems (UASs) Measurement of River Surface Velocity Using Doppler Radar F. Bandini et al. 10.3390/rs14051277
- River Surface Velocity Estimation Using Optical Flow Velocimetry Improved With Attention Mechanism and Position Encoding Y. Cao et al. 10.1109/JSEN.2022.3186972
- RivQNet: Deep Learning Based River Discharge Estimation Using Close‐Range Water Surface Imagery S. Ansari et al. 10.1029/2021WR031841
- On the Uncertainty of the Image Velocimetry Method Parameters E. Rozos et al. 10.3390/hydrology7030065
- River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar P. Koutalakis & G. Zaimes 10.3390/hydrology9080148
- Discharge Estimation Using Video Recordings from Small Unoccupied Aircraft Systems J. Duan et al. 10.1061/JHEND8.HYENG-13591
- Synthetic River Flow Videos for Evaluating Image‐Based Velocimetry Methods G. Bodart et al. 10.1029/2022WR032251
- River velocity measurements using optical flow algorithm and unoccupied aerial vehicles: A case study J. Jyoti et al. 10.1016/j.flowmeasinst.2023.102341
- Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization A. Pizarro et al. 10.1002/hyp.13919
- A bird's-eye view on turbulence: seabird foraging associations with evolving surface flow features L. Lieber et al. 10.1098/rspb.2021.0592
- Considerations When Applying Large-Scale PIV and PTV for Determining River Flow Velocity M. Jolley et al. 10.3389/frwa.2021.709269
- On the Accuracy of Particle Image Velocimetry with Citizen Videos—Five Typical Case Studies E. Rozos et al. 10.3390/hydrology9050072
- Technologies for the study of hydropeaking impacts on fish populations: Applications, advantages, outcomes, and future developments C. Alexandre et al. 10.1002/rra.4039
- Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow A. Pizarro et al. 10.5194/hess-24-5173-2020
- Optical Methods for River Monitoring: A Simulation-Based Approach to Explore Optimal Experimental Setup for LSPIV D. Pumo et al. 10.3390/w13030247
- Quantifying and Reducing the Operator Effect in LSPIV Discharge Measurements G. Bodart et al. 10.1029/2023WR034740
- Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives B. Acharya et al. 10.1029/2021WR029925
- Study on flow distribution of irrigation canal system based on image velocimetry S. Li et al. 10.1016/j.compag.2022.106828
- Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions A. Pizarro et al. 10.3390/hydrology9070126
- Satellite Video Remote Sensing for Flood Model Validation C. Masafu & R. Williams 10.1029/2023WR034545
- Enhancing the Monitoring Protocols of Intermittent Flow Rivers with UAV-Based Optical Methods to Estimate the River Flow and Evaluate Their Environmental Status P. Koutalakis et al. 10.35534/dav.2023.10006
- Robust Image-Based Streamflow Measurements for Real-Time Continuous Monitoring S. Peña-Haro et al. 10.3389/frwa.2021.766918
- The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years A. Pizarro et al. 10.3390/hydrology11060080
- Measuring zero water level in stream reaches: A comparison of an image‐based versus a conventional method A. Herzog et al. 10.1002/hyp.14658
- Open-channel flow rate measurement estimation using videos R. Ljubičić & D. Ivetić 10.5937/GK23055045L
- Optical Ortho-Rectification for Image-Based Stream Surface Flow Observations Using a Ground Camera R. Tsubaki & R. Zhu 10.3389/frwa.2021.700946
- The impacts of low flow, ice‐cover and ice thickness on sediment load in a sub‐arctic river – Modelling sediment transport with particle image velocimetry calibration data sets V. Pajunen et al. 10.1002/esp.5809
- Hydro‐morphological mapping of river reaches using videos captured with UAS A. Eltner et al. 10.1002/esp.5205
- Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. 10.5194/hess-25-4435-2021
- Imaging-based 3D particle tracking system for field characterization of particle dynamics in atmospheric flows N. Bristow et al. 10.1007/s00348-023-03619-6
- Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
- Adaptively monitoring streamflow using a stereo computer vision system N. Hutley et al. 10.5194/hess-27-2051-2023
- How computer vision can facilitate flood management: A systematic review U. Iqbal et al. 10.1016/j.ijdrr.2020.102030
- Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System W. Liu et al. 10.3390/rs13142661
- A robust filtering algorithm based on the estimation of tracer visibility and stability for large scale particle image velocimetry L. Li & H. Yan 10.1016/j.flowmeasinst.2022.102204
- Dimensionless Stage-Discharge Relationship for a Non-Linear Water Reservoir: Theory and Experiments G. Baiamonte 10.3390/hydrology7020023
- Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations C. Masafu et al. 10.1016/j.jhydrol.2022.128217
- Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers S. Dal Sasso et al. 10.3390/rs12111789
43 citations as recorded by crossref.
- SSIMS-Flow: Image velocimetry workbench for open-channel flow rate estimation R. Ljubičić et al. 10.1016/j.envsoft.2023.105938
- Recent Advancements and Perspectives in UAS-Based Image Velocimetry S. Dal Sasso et al. 10.3390/drones5030081
- Probabilistic Evaluation and Filtering of Image Velocimetry Measurements E. Rozos et al. 10.3390/w13162206
- Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
- Remote Sensing of Riparian Ecosystems M. Rusnák et al. 10.3390/rs14112645
- Satellite Video Remote Sensing for Estimation of River Discharge C. Masafu et al. 10.1029/2023GL105839
- An automatic ANN-based procedure for detecting optimal image sequences supporting LS-PIV applications for rivers monitoring F. Alongi et al. 10.1016/j.jhydrol.2023.130233
- Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales S. Dal Sasso et al. 10.1016/j.jhydrol.2021.126438
- Challenges with Regard to Unmanned Aerial Systems (UASs) Measurement of River Surface Velocity Using Doppler Radar F. Bandini et al. 10.3390/rs14051277
- River Surface Velocity Estimation Using Optical Flow Velocimetry Improved With Attention Mechanism and Position Encoding Y. Cao et al. 10.1109/JSEN.2022.3186972
- RivQNet: Deep Learning Based River Discharge Estimation Using Close‐Range Water Surface Imagery S. Ansari et al. 10.1029/2021WR031841
- On the Uncertainty of the Image Velocimetry Method Parameters E. Rozos et al. 10.3390/hydrology7030065
- River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar P. Koutalakis & G. Zaimes 10.3390/hydrology9080148
- Discharge Estimation Using Video Recordings from Small Unoccupied Aircraft Systems J. Duan et al. 10.1061/JHEND8.HYENG-13591
- Synthetic River Flow Videos for Evaluating Image‐Based Velocimetry Methods G. Bodart et al. 10.1029/2022WR032251
- River velocity measurements using optical flow algorithm and unoccupied aerial vehicles: A case study J. Jyoti et al. 10.1016/j.flowmeasinst.2023.102341
- Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization A. Pizarro et al. 10.1002/hyp.13919
- A bird's-eye view on turbulence: seabird foraging associations with evolving surface flow features L. Lieber et al. 10.1098/rspb.2021.0592
- Considerations When Applying Large-Scale PIV and PTV for Determining River Flow Velocity M. Jolley et al. 10.3389/frwa.2021.709269
- On the Accuracy of Particle Image Velocimetry with Citizen Videos—Five Typical Case Studies E. Rozos et al. 10.3390/hydrology9050072
- Technologies for the study of hydropeaking impacts on fish populations: Applications, advantages, outcomes, and future developments C. Alexandre et al. 10.1002/rra.4039
- Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow A. Pizarro et al. 10.5194/hess-24-5173-2020
- Optical Methods for River Monitoring: A Simulation-Based Approach to Explore Optimal Experimental Setup for LSPIV D. Pumo et al. 10.3390/w13030247
- Quantifying and Reducing the Operator Effect in LSPIV Discharge Measurements G. Bodart et al. 10.1029/2023WR034740
- Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives B. Acharya et al. 10.1029/2021WR029925
- Study on flow distribution of irrigation canal system based on image velocimetry S. Li et al. 10.1016/j.compag.2022.106828
- Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions A. Pizarro et al. 10.3390/hydrology9070126
- Satellite Video Remote Sensing for Flood Model Validation C. Masafu & R. Williams 10.1029/2023WR034545
- Enhancing the Monitoring Protocols of Intermittent Flow Rivers with UAV-Based Optical Methods to Estimate the River Flow and Evaluate Their Environmental Status P. Koutalakis et al. 10.35534/dav.2023.10006
- Robust Image-Based Streamflow Measurements for Real-Time Continuous Monitoring S. Peña-Haro et al. 10.3389/frwa.2021.766918
- The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years A. Pizarro et al. 10.3390/hydrology11060080
- Measuring zero water level in stream reaches: A comparison of an image‐based versus a conventional method A. Herzog et al. 10.1002/hyp.14658
- Open-channel flow rate measurement estimation using videos R. Ljubičić & D. Ivetić 10.5937/GK23055045L
- Optical Ortho-Rectification for Image-Based Stream Surface Flow Observations Using a Ground Camera R. Tsubaki & R. Zhu 10.3389/frwa.2021.700946
- The impacts of low flow, ice‐cover and ice thickness on sediment load in a sub‐arctic river – Modelling sediment transport with particle image velocimetry calibration data sets V. Pajunen et al. 10.1002/esp.5809
- Hydro‐morphological mapping of river reaches using videos captured with UAS A. Eltner et al. 10.1002/esp.5205
- Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning R. Vandaele et al. 10.5194/hess-25-4435-2021
- Imaging-based 3D particle tracking system for field characterization of particle dynamics in atmospheric flows N. Bristow et al. 10.1007/s00348-023-03619-6
- Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
- Adaptively monitoring streamflow using a stereo computer vision system N. Hutley et al. 10.5194/hess-27-2051-2023
- How computer vision can facilitate flood management: A systematic review U. Iqbal et al. 10.1016/j.ijdrr.2020.102030
- Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System W. Liu et al. 10.3390/rs13142661
- A robust filtering algorithm based on the estimation of tracer visibility and stability for large scale particle image velocimetry L. Li & H. Yan 10.1016/j.flowmeasinst.2022.102204
3 citations as recorded by crossref.
- Dimensionless Stage-Discharge Relationship for a Non-Linear Water Reservoir: Theory and Experiments G. Baiamonte 10.3390/hydrology7020023
- Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations C. Masafu et al. 10.1016/j.jhydrol.2022.128217
- Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers S. Dal Sasso et al. 10.3390/rs12111789
Discussed (final revised paper)
Latest update: 29 Jun 2024
Short summary
We present datasets acquired from seven countries across Europe and North America consisting of image sequences. These have been subjected to a range of pre-processing methods in preparation for image velocimetry analysis. These datasets and accompanying reference data are a resource that may be used for conducting benchmarking experiments, assessing algorithm performances, and focusing future software development.
We present datasets acquired from seven countries across Europe and North America consisting of...
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