52 citations as recorded by crossref.

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8. Considerations When Applying Large-Scale PIV and PTV for Determining River Flow Velocity M. Jolley et al. 10.3389/frwa.2021.709269
9. Technologies for the study of hydropeaking impacts on fish populations: Applications, advantages, outcomes, and future developments C. Alexandre et al. 10.1002/rra.4039
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12. 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
13. Satellite Video Remote Sensing for Flood Model Validation C. Masafu & R. Williams 10.1029/2023WR034545
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15. UAS hydrometry: contactless river water level, bathymetry, and flow velocity – the Rӧnne river dataset Z. Zhou et al. 10.1038/s41597-025-04611-x
16. Optical Ortho-Rectification for Image-Based Stream Surface Flow Observations Using a Ground Camera R. Tsubaki & R. Zhu 10.3389/frwa.2021.700946
17. 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
18. 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
19. SSIMS-Flow: Image velocimetry workbench for open-channel flow rate estimation R. Ljubičić et al. 10.1016/j.envsoft.2023.105938
20. Baseflow measurement in mountain rivers using LSPIV: A case study of the Tarqui and Yanuncay rivers in the Ecuadorian Andes S. Ochoa-García et al. 10.4995/raet.2025.22733
21. Recent Advancements and Perspectives in UAS-Based Image Velocimetry S. Dal Sasso et al. 10.3390/drones5030081
22. A machine-learning enabled digital-twin framework for tactical drone-swarm design T. Zohdi 10.1016/j.cma.2025.117999
23. Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
24. Remote Sensing of Riparian Ecosystems M. Rusnák et al. 10.3390/rs14112645
25. Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales S. Dal Sasso et al. 10.1016/j.jhydrol.2021.126438
26. A Review on the Video-Based River Discharge Measurement Technique M. Chen et al. 10.3390/s24144655
27. RivQNet: Deep Learning Based River Discharge Estimation Using Close‐Range Water Surface Imagery S. Ansari et al. 10.1029/2021WR031841
28. On the Uncertainty of the Image Velocimetry Method Parameters E. Rozos et al. 10.3390/hydrology7030065
29. River Flow Measurements Utilizing UAV-Based Surface Velocimetry and Bathymetry Coupled with Sonar P. Koutalakis & G. Zaimes 10.3390/hydrology9080148
30. Validity Identification and Rectification of Water Surface Fast Fourier Transform-Based Space-Time Image Velocimetry (FFT-STIV) Results Z. Zhang et al. 10.3390/s25010257
31. Discharge Estimation Using Video Recordings from Small Unoccupied Aircraft Systems J. Duan et al. 10.1061/JHEND8.HYENG-13591
32. River velocity measurements using optical flow algorithm and unoccupied aerial vehicles: A case study J. Jyoti et al. 10.1016/j.flowmeasinst.2023.102341
33. Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization A. Pizarro et al. 10.1002/hyp.13919
34. A bird's-eye view on turbulence: seabird foraging associations with evolving surface flow features L. Lieber et al. 10.1098/rspb.2021.0592
35. On the Accuracy of Particle Image Velocimetry with Citizen Videos—Five Typical Case Studies E. Rozos et al. 10.3390/hydrology9050072
36. Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow A. Pizarro et al. 10.5194/hess-24-5173-2020
37. Optical Methods for River Monitoring: A Simulation-Based Approach to Explore Optimal Experimental Setup for LSPIV D. Pumo et al. 10.3390/w13030247
38. Study on flow distribution of irrigation canal system based on image velocimetry S. Li et al. 10.1016/j.compag.2022.106828
39. 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
40. Robust Image-Based Streamflow Measurements for Real-Time Continuous Monitoring S. Peña-Haro et al. 10.3389/frwa.2021.766918
41. The Use of Unmanned Aerial Systems for River Monitoring: A Bibliometric Analysis Covering the Last 25 Years A. Pizarro et al. 10.3390/hydrology11060080
42. From PIV to LSPIV: Harnessing deep learning for environmental flow velocimetry J. Tlhomole et al. 10.1016/j.jhydrol.2024.132446
43. Open-channel flow rate measurement estimation using videos R. Ljubičić & D. Ivetić 10.5937/GK23055045L
44. 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
45. Hydro‐morphological mapping of river reaches using videos captured with UAS A. Eltner et al. 10.1002/esp.5205
46. 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
47. Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
48. Adaptively monitoring streamflow using a stereo computer vision system N. Hutley et al. 10.5194/hess-27-2051-2023
49. How computer vision can facilitate flood management: A systematic review U. Iqbal et al. 10.1016/j.ijdrr.2020.102030
50. 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
51. Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations C. Masafu et al. 10.1016/j.jhydrol.2022.128217
52. Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers S. Dal Sasso et al. 10.3390/rs12111789