Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5173-2020
© 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/hess-24-5173-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2020
Research article |
| 09 Nov 2020
| 09 Nov 2020
Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow
Department of European and Mediterranean Cultures, University of
Basilicata, Matera, 75100, Italy
Silvano F. Dal Sasso
Department of European and Mediterranean Cultures, University of
Basilicata, Matera, 75100, Italy
Matthew T. Perks
School of Geography, Politics and Sociology, Newcastle University,
Newcastle-upon-Tyne, NE1 7RU, UK
Salvatore Manfreda
Department of Civil, Architectural and Environmental Engineering,
University of Naples Federico II, Naples, 80125, Italy
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Cited
30 citations as recorded by crossref.
- 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
- The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years A. Pizarro et al. 10.3390/hydrology11060080
- Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives B. Acharya et al. 10.1029/2021WR029925
- Optical Methods for River Monitoring: A Simulation-Based Approach to Explore Optimal Experimental Setup for LSPIV D. Pumo et al. 10.3390/w13030247
- River velocity measurements using optical flow algorithm and unoccupied aerial vehicles: A case study J. Jyoti et al. 10.1016/j.flowmeasinst.2023.102341
- Technologies for the study of hydropeaking impacts on fish populations: Applications, advantages, outcomes, and future developments C. Alexandre et al. 10.1002/rra.4039
- An Overview of Flood Concepts, Challenges, and Future Directions A. Mishra et al. 10.1061/(ASCE)HE.1943-5584.0002164
- A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations R. Ljubičić et al. 10.5194/hess-25-5105-2021
- 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
- On the Accuracy of Particle Image Velocimetry with Citizen Videos—Five Typical Case Studies E. Rozos et al. 10.3390/hydrology9050072
- Adaptively monitoring streamflow using a stereo computer vision system N. Hutley et al. 10.5194/hess-27-2051-2023
- Recent Advancements and Perspectives in UAS-Based Image Velocimetry S. Dal Sasso et al. 10.3390/drones5030081
- Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations C. Masafu et al. 10.1016/j.jhydrol.2022.128217
- Discharge Estimation Using Video Recordings from Small Unoccupied Aircraft Systems J. Duan et al. 10.1061/JHEND8.HYENG-13591
- A Drone‐Borne Method to Jointly Estimate Discharge and Manning's Roughness of Natural Streams F. Bandini et al. 10.1029/2020WR028266
- A framework to facilitate development and testing of image‐based river velocimetry algorithms C. Legleiter & P. Kinzel 10.1002/esp.5772
- Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images G. Lama et al. 10.3390/w13101333
- SSIMS-Flow: Image velocimetry workbench for open-channel flow rate estimation R. Ljubičić et al. 10.1016/j.envsoft.2023.105938
- Synthetic River Flow Videos for Evaluating Image‐Based Velocimetry Methods G. Bodart et al. 10.1029/2022WR032251
- Uncertainty Analysis for Image-Based Streamflow Measurement: The Influence of Ground Control Points W. Liu et al. 10.3390/w15010123
- 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
- Video velocity measurement: A two-stage flow velocity prediction method based on deep learning X. Wang et al. 10.2166/nh.2024.128
- Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization A. Pizarro et al. 10.1002/hyp.13919
- 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
- Towards harmonisation of image velocimetry techniques for river surface velocity observations M. Perks et al. 10.5194/essd-12-1545-2020
- Quantifying and Reducing the Operator Effect in LSPIV Discharge Measurements G. Bodart et al. 10.1029/2023WR034740
- Considerations When Applying Large-Scale PIV and PTV for Determining River Flow Velocity M. Jolley et al. 10.3389/frwa.2021.709269
- Moving Aircraft River Velocimetry (MARV): Framework and Proof‐of‐Concept on the Tanana River C. Legleiter et al. 10.1029/2022WR033822
24 citations as recorded by crossref.
- 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
- The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years A. Pizarro et al. 10.3390/hydrology11060080
- Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives B. Acharya et al. 10.1029/2021WR029925
- Optical Methods for River Monitoring: A Simulation-Based Approach to Explore Optimal Experimental Setup for LSPIV D. Pumo et al. 10.3390/w13030247
- River velocity measurements using optical flow algorithm and unoccupied aerial vehicles: A case study J. Jyoti et al. 10.1016/j.flowmeasinst.2023.102341
- Technologies for the study of hydropeaking impacts on fish populations: Applications, advantages, outcomes, and future developments C. Alexandre et al. 10.1002/rra.4039
- An Overview of Flood Concepts, Challenges, and Future Directions A. Mishra et al. 10.1061/(ASCE)HE.1943-5584.0002164
- A comparison of tools and techniques for stabilising unmanned aerial system (UAS) imagery for surface flow observations R. Ljubičić et al. 10.5194/hess-25-5105-2021
- 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
- On the Accuracy of Particle Image Velocimetry with Citizen Videos—Five Typical Case Studies E. Rozos et al. 10.3390/hydrology9050072
- Adaptively monitoring streamflow using a stereo computer vision system N. Hutley et al. 10.5194/hess-27-2051-2023
- Recent Advancements and Perspectives in UAS-Based Image Velocimetry S. Dal Sasso et al. 10.3390/drones5030081
- Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations C. Masafu et al. 10.1016/j.jhydrol.2022.128217
- Discharge Estimation Using Video Recordings from Small Unoccupied Aircraft Systems J. Duan et al. 10.1061/JHEND8.HYENG-13591
- A Drone‐Borne Method to Jointly Estimate Discharge and Manning's Roughness of Natural Streams F. Bandini et al. 10.1029/2020WR028266
- A framework to facilitate development and testing of image‐based river velocimetry algorithms C. Legleiter & P. Kinzel 10.1002/esp.5772
- Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images G. Lama et al. 10.3390/w13101333
- SSIMS-Flow: Image velocimetry workbench for open-channel flow rate estimation R. Ljubičić et al. 10.1016/j.envsoft.2023.105938
- Synthetic River Flow Videos for Evaluating Image‐Based Velocimetry Methods G. Bodart et al. 10.1029/2022WR032251
- Uncertainty Analysis for Image-Based Streamflow Measurement: The Influence of Ground Control Points W. Liu et al. 10.3390/w15010123
- 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
- Video velocity measurement: A two-stage flow velocity prediction method based on deep learning X. Wang et al. 10.2166/nh.2024.128
- Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization A. Pizarro et al. 10.1002/hyp.13919
6 citations as recorded by crossref.
- 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
- Towards harmonisation of image velocimetry techniques for river surface velocity observations M. Perks et al. 10.5194/essd-12-1545-2020
- Quantifying and Reducing the Operator Effect in LSPIV Discharge Measurements G. Bodart et al. 10.1029/2023WR034740
- Considerations When Applying Large-Scale PIV and PTV for Determining River Flow Velocity M. Jolley et al. 10.3389/frwa.2021.709269
- Moving Aircraft River Velocimetry (MARV): Framework and Proof‐of‐Concept on the Tanana River C. Legleiter et al. 10.1029/2022WR033822
Latest update: 29 Jun 2024
Short summary
An innovative approach is presented to optimise image-velocimetry performances for surface flow velocity estimates (and thus remotely sensed river discharges). Synthetic images were generated under different tracer characteristics using a numerical approach. Based on the results, the Seeding Distribution Index was introduced as a descriptor of the optimal portion of the video to analyse. A field case study was considered as a proof of concept of the proposed framework showing error reductions.
An innovative approach is presented to optimise image-velocimetry performances for surface flow...
