Search

In recent years, there has been a surge of interest in image-based velocimetry methods for river surface velocity (RSV) estimation due to their efficiency and accuracy, including large-scale particle image velocimetry (LSPIV), space-time image velocimetry (STIV) and optical flow velocimetry (OFV). Among these methods, OFV-based methods have received significant attention owing to their high field resolution and low tracer requirements. Deep optical flow estimation (DOFE), which is a powerful approach for accurate and efficient estimation of optical flow, has also been employed in OFV-based methods. However, the immeasurability of optical flow often results in the usage of irrelevant datasets for training, leading to limited generalization due to domain drift. Moreover, the high similarity of river surfaces can lead to ambiguous correlation volumes extracted by DOFE models, resulting in mismatches. To address the domain shift and mismatch challenges, we proposed a method to generate optical flow datasets and these datasets are used as the training set for the DOFE model MRAFT, which incorporates correlation volume modulation. Experiment results demonstrate that our method effectively mitigates underlying domain shift and mismatch issues, enabling accurate and robust RSV estimation under velocity ranges of 0-6.0 m/s. Our work facilitates the application of DOFE on RSV estimation and provides optical flow datasets for fine-tuning to other related researches.

Documenting glacier surface flow velocity from a longer-term perspective is highly relevant to evaluate the past and current state of glaciers worldwide. For this purpose, satellite data are widely used to obtain region-wide coverage of glacier velocity data. Well-established image correlation methods allow for the automated measurement of glacier surface displacements from satellite data (optical and radar) acquired at different dates. Although computationally expensive, image correlation is nowadays relatively simple to implement and allows two-dimensional displacement measurements. Here, we present a data set of annual glacier surface flow velocity maps at the European Alps scale, covering the period 2015–2021 at a 50 m × 50 m resolution. This data set has been quantified by applying the normalized cross-correlation approach on Sentinel-2 optical data. Parameters of the cross-correlation method (e.g., window size, sampling resolution) have been optimized, and the results have been validated by comparing them with in situ data on monitored glaciers showing an RMSE of 10 m/yr. These data can be used to evaluate glacier dynamics and its spatial and temporal evolution (e.g., quantify mass fluxes or calving) or can be used as an input for model calibration/validation or for the early detection of regional hazards associated with glacier destabilization. Dataset:  https://doi.org/10.57745/XHQ7TL Dataset License: CC BY NC 4.0 [ABSTRACT FROM AUTHOR]

Recent advances in streamflow discharge measurement instrumentation have improved upon or following replacements of conventional devices and methodologies. Among the advanced instruments, surface velocity radar (SVR) has been increasingly used for flood measurements and normal flow discharge, thus enabling noncontact measurements of surface velocities by using microwaves. It is effective even when direct access to rivers is difficult, such as during the flood season. Unfortunately, systematic and comprehensive evaluation of surface velocity measurement accuracy in conditions in situ is rarely conducted. The microwave footprint on the water surface, as emitted from the SVR, depends on the monitoring configurations (e.g., tilt and yaw angles) and can affect the accuracy of velocity measurements. In this study, we selected an SVR with a rectangular front shape and evaluated its measurement accuracy in a real-scale river experiment facility wherein surface velocity was validated against concurrently measured, large-scale particle image and acoustic Doppler velocimetry. Comparative results showed that the spatial autocorrelation of the velocity distribution inside the SVR footprint was crucial such that the measurement configuration for low-tilting and high-yawing angles should be avoided especially for locations near the channel edge with substantially reduced velocity. [ABSTRACT FROM AUTHOR]

Documenting glacier surface flow velocity from a longer-term perspective is highly relevant to evaluate the past and current state of glaciers worldwide. For this purpose, satellite data are widely used to obtain region-wide coverage of glacier velocity data. Well-established image correlation methods allow for the automated measurement of glacier surface displacements from satellite data (optical and radar) acquired at different dates. Although computationally expensive, image correlation is nowadays relatively simple to implement and allows two-dimensional displacement measurements. Here, we present a data set of annual glacier surface flow velocity maps at the European Alps scale, covering the period 2015–2021 at a 50 m × 50 m resolution. This data set has been quantified by applying the normalized cross-correlation approach on Sentinel-2 optical data. Parameters of the cross-correlation method (e.g., window size, sampling resolution) have been optimized, and the results have been validated by comparing them with in situ data on monitored glaciers showing an RMSE of 10 m/yr. These data can be used to evaluate glacier dynamics and its spatial and temporal evolution (e.g., quantify mass fluxes or calving) or can be used as an input for model calibration/validation or for the early detection of regional hazards associated with glacier destabilization.

This study focuses on utilizing image techniques for river velocity measurement, with a specific emphasis on natural surface floating patterns. Employing a multi-camera system, we conducted 3D measurements on river surfaces, including surface velocity and water surface reconstruction. A pattern-based tracking approach has been adopted to improve the performance of image measurements on different types of natural floating tracers. The study employs the following approaches: 3D Lagrangian Pattern Tracking Velocimetry (3D-LPTV), 2D Lagrangian Pattern Velocimetry (2D- LPTV), and Large-scale Particle Image Velocimetry (LSPIV), for surface velocity estimation. The outcomes revealed that all three approaches yielded consistent results in terms of averaged velocity. However, the LSPIV method produced about two times higher uncertainty in measured velocities compared to the other methods. A strategy to assess the quality of river surface patterns in velocity estimation is presented. Specifically, the sum of squared interrogation area intensity gradient (SSIAIG) was found to be strongly correlated with measurement uncertainty. Additionally, a term related to the peak sidelobe ratio (PSR) of the cross-correlation map was found as an effective constraint, ensuring the image-tracking process achieves high reliability. The precision of measurements increases corresponding to the increase of image intensity gradient and PSR. • Multi-camera system is employed for 3D measurements of river surface velocity and water surface reconstruction. • A pattern-based tracking approach enhances measurement accuracy with different types of natural surface tracers. • Surface pattern metrics assess the quality and reliability of river surface patterns in velocity estimation. [ABSTRACT FROM AUTHOR]

Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.

Recent advances in image-based methods for environmental monitoring are opening new frontiers for remote streamflow measurements in natural environments. Such techniques offer numerous advantages compared to traditional approaches. Despite the wide availability of cost-effective devices and software for image processing, these techniques are still rarely systematically implemented in practical applications, probably due to the lack of consistent operational protocols for both phases of images acquisition and processing. In this work, the optimal experimental setup for LSPIV based flow velocity measurements under different conditions is explored using the software PIVlab, investigating performance and sensitivity to some key factors. Different synthetic image sequences, reproducing a river flow with a realistic velocity profile and uniformly distributed floating tracers, are generated under controlled conditions. Different parametric scenarios are created considering diverse combinations of flow velocity, tracer size, seeding density, and environmental conditions. Multiple replications per scenario are processed, using descriptive statistics to characterize errors in PIVlab estimates. Simulations highlight the crucial role of some parameters (e.g., seeding density) and demonstrate how appropriate video duration, frame-rate and parameters setting in relation to the hydraulic conditions can efficiently counterbalance many of the typical operative issues (i.e., scarce tracer concentration) and improve algorithms performance. [ABSTRACT FROM AUTHOR]

Glacier surge, which causes a quick movement of ice mass from high to low elevation, is closely associated to the glacial hazards of debris flows and glacial lake outburst floods. Over the West Kunlun Shan, surge events have been detected for some glaciers, however, the characteristics (e.g., the active phase) of the identified surge-type glaciers are not fully understood due to the paucity of long-term observations of glacier changes. In this study, we investigated the geometric evolution of the Chongce Glacier (a surge-type glacier) over the past five decades. Glacier elevation changes were observed by comparing topographic data from different times. Surface velocity and terminus position were derived using a cross-correlation algorithm and band ratio method, respectively. A decreasing rate of glacier surface thinning was found for the Chongce Glacier during the studied period. Glacier elevation changes of −0.46 ± 0.12, −0.12 ± 0.05, and 0.27 ± 0.11 m yr−1 were estimated for the periods of 1970–2000, 2000–2012, and 2012–2018, respectively. Moreover, this glacier experienced obvious surface lowering over the terminus zone and clear surface thickening over the upper zone during 1970–2000, and the opposite during 2000–2018. Surface velocity of the Chongce Glacier was less than 300 m yr−1 in 1990–1993, and then quickly increased to more than 1000 m yr−1 between 1994 and 1998, and dropped to less than 50 m yr−1 in 1999–2020. Over the past five decades, the Chongce Glacier generally experienced a slight retreat, except for a terminus advance from 1995 to 1999. According to the spatial pattern of glacier elevation changes in 1970–2000 and the long-term changes of glacier velocity and terminus position, the recent surge event at the Chongce Glacier likely initiated in winter 1993 and terminated in winter 1998. Furthermore, the start date, end date, and duration of the active phase indicate that the detected surge event was likely triggered by a thermal mechanism.

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]

[Display omitted] • Tracer density and its space–time distribution variations influence LS-PIV analyses. • Analyzing a small portion of the raw sequence may improve LS-PIV software performance. • An automatic procedure detecting best sub-sequence for LS-PIV analyses is proposed. • The procedure, tested on 12 field cases, allowed for a RMSE reduction up to 65%. River flow monitoring has recently experienced rapid development due to advancements in optical methods, which are non-intrusive and enhance safety conditions for operators. Surface velocity fields are obtained recording and analyzing displacements of floating tracer materials, artificially introduced or already present on the water surface. River discharge can be assessed coupling the surface velocity fields with geometric data of a cross section. The accuracy of optical techniques is strongly affected by different environmental and hydraulic factors, and software parameterization, with tracer features that often play a prominent role. An adequate density and spatial distribution of tracer is required to ensure a complete characterization of surface velocity fields. In practical applications such conditions might occur only for a limited portion of the entire acquired images sequence. This work proposes an automatic procedure for identifying and extracting the best portion of a recorded video in terms of seeding characteristics and demonstrates how LS-PIV software performances can be enhanced through this approach. The procedure is implemented through a data-driven empirical approach based on an Artificial Neural Network, trained using data collected during an extensive measurement campaign across different rivers in Sicily (Italy). Performances are evaluated in terms of error in reproducing surface velocity profiles along specific transects, where benchmark profiles derived using an Acoustic Doppler Current Profiler are available. The procedure, also tested via numerical simulations on synthetic image sequences, outperformed an approach based on an existing metric for seeding characterization and represents a simple and useful tool for LS-PIV based applications. [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.

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.

Flash floods in wadi systems discharge large volumes of water to either the sea or the desert areas after high-intensity rainfall events. Recently, wadi flash floods have frequently occurred in arid regions and caused damage to roads, houses, and properties. Therefore, monitoring and quantifying these events by accurately measuring wadi discharge has become important for the installation of mitigation structures and early warning systems. In this study, image-based methods were used to measure surface flow velocities during a wadi flash flood in 2018 to test the usefulness of large-scale particle image velocimetry (LSPIV) and space–time image velocimetry (STIV) techniques for the estimation of wadi discharge. The results, which indicated the positive performance of the image-based methods, strengthened our hypothesis that the application of LSPIV and STIV techniques is appropriate for the analysis of wadi flash flood velocities. STIV is suitable for unidirectional flow velocity and LSPIV is reliable and stable for two-dimensional measurement along the wadi channel, the direction of flow pattern which varies with time.

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]

Abstract: The relevance of hydrological surface processes, such as drainage formation, landscape's evolution, pollutant diffusion, and sediment transport, has recently fostered the development of an array of novel sensing systems. In this context, the deployment and observation of enhanced fluorescent particles is studied in [Tauro et al., 2012] to non- intrusively estimate surface flow velocities and travel times. Such insoluble and buoyant fluorescent particles are not affected by adsorption issues, thus requiring minimal amounts of tracing material to be released in the environment, and are detected through commercially available inexpensive digital cameras. In this paper, we complement previously developed unsupervised particle detection tools with Large Scale Particle Image Velocimetry (LSPIV) analysis for surface flow estimations in natural settings. Specifically, we conduct several proof of concept experiments by deploying few grams of fluorescent particles in a natural brook and analyzing acquired videos with both unsupervised image analysis tools and LSPIV. Results from both methodologies suggest their complementary use for enhanced surface flow monitoring. In addition, the use of environmentally friendly highly visible fluorescent particles would sensibly improve the joint performance of such non-intrusive sensing systems. [Copyright &y& Elsevier]

Glacier surge, which causes a quick movement of ice mass from high to low elevation, is closely associated to the glacial hazards of debris flows and glacial lake outburst floods. Over the West Kunlun Shan, surge events have been detected for some glaciers, however, the characteristics (e.g., the active phase) of the identified surge-type glaciers are not fully understood due to the paucity of long-term observations of glacier changes. In this study, we investigated the geometric evolution of the Chongce Glacier (a surge-type glacier) over the past five decades. Glacier elevation changes were observed by comparing topographic data from different times. Surface velocity and terminus position were derived using a cross-correlation algorithm and band ratio method, respectively. A decreasing rate of glacier surface thinning was found for the Chongce Glacier during the studied period. Glacier elevation changes of −0.46 ± 0.12, −0.12 ± 0.05, and 0.27 ± 0.11 m yr−1 were estimated for the periods of 1970–2000, 2000–2012, and 2012–2018, respectively. Moreover, this glacier experienced obvious surface lowering over the terminus zone and clear surface thickening over the upper zone during 1970–2000, and the opposite during 2000–2018. Surface velocity of the Chongce Glacier was less than 300 m yr−1 in 1990–1993, and then quickly increased to more than 1000 m yr−1 between 1994 and 1998, and dropped to less than 50 m yr−1 in 1999–2020. Over the past five decades, the Chongce Glacier generally experienced a slight retreat, except for a terminus advance from 1995 to 1999. According to the spatial pattern of glacier elevation changes in 1970–2000 and the long-term changes of glacier velocity and terminus position, the recent surge event at the Chongce Glacier likely initiated in winter 1993 and terminated in winter 1998. Furthermore, the start date, end date, and duration of the active phase indicate that the detected surge event was likely triggered by a thermal mechanism. [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]