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Abstract Particle tracking velocimetry (PTV) is a promising image-based approach for remote streamflow measurements in natural environments. However, most PTV approaches require highly-defined round-shaped tracers, which are often difficult to observe outdoors. PTV-Stream offers a versatile alternative to cross-correlation-based PTV by affording the identification and tracking of features of any shape transiting in the field of view. This nearest-neighbor algorithm is inherently thought for estimating surface flow velocity of streams in outdoor conditions. The procedure allows for reconstructing and filtering the trajectories of features that are more likely to pertain to actual objects transiting in the field of view rather than to water reflections. The procedure is computationally efficient and is demonstrated to yield accurate measurements even in case of downsampled image sequences. Highlights • Particle tracking velocimetry approaches are promising to monitor surface stream flow but are affected by feature shape. • PTV-Stream is a nearest-neighbor algorithm designed for tracking features of any shape. • Trajectory-based filtering guarantees tracking of actual objects in the field of view. • Based on outdoor tests, PTV-Stream is as efficient as traditional PTV. [ABSTRACT FROM AUTHOR]

Fully remote surface flow measurements are crucial for flow monitoring during floods and in difficult to- access areas. Recently, optics-based surface flow monitoring has been enabled through a permanent gauge-cam station on the Tiber River, Rome, Italy. Therein, a system of lasers and an internet protocol camera equipped with two optical modules afford video acquisitions of the river surface every 10 minutes. In this work, we establish a standard video-processing protocol by analyzing more than 10 Gb of footage data captured during low discharge regime from May 2nd to 11th, 2015, through particle tracking velocimetry (PTV). We show that good image-based velocity data can be obtained throughout the day - from 6 am to 8 pm - despite the challenging experimental settings (direct sunlight illumination, mirror-like river surface, and overlying bridge shadow). Further, we demonstrate that images captured with a 27W angle of view optical sensor lead to average velocity measurements in agreement with available radar data. Consistent with similar optical methods, PTV is not applicable in case of adverse illumination and at night; however, it is more robust for dishomogeneous distributions of floaters in the field of view. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Advances in flow monitoring are crucial to increase our knowledge on basin hydrology and to understand the interactions between flow dynamics and infrastructures. In this context, image processing offers great potential for hydraulic monitoring, allowing acquisition of a wide range of measurements with high spatial resolution at relatively low costs. In particular, the particle tracking velocimetry (PTV) algorithm can be used to describe the dynamics of surface flow velocity in both space and time using fixed cameras or unmanned aerial systems (UASs). In this study, analyses allowed exploration of the optimal particle seeding density and frame rate in different configurations. Numerical results provided useful indications for two field experiments that have been carried out with a low-cost quadrocopter equipped with an optical camera to record RGB videos of floating tracers manually distributed over the water surface. Field measurements have been carried out using different natural tracers under diverse hydraulic and morphological conditions; PTV’s processed velocities have been subsequently benchmarked with current meter measurements. The numerical results allowed rapid identification of the experimental configuration (e.g., required particle seeding density, image resolution, particle size, and frame frequency) producing flow velocity fields with high resolution in time and space with good agreement with the benchmark velocity values measured with conventional instruments. [ABSTRACT FROM AUTHOR]