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Image‐based methods have compelling, demonstrated potential for characterizing flow fields in rivers, but algorithms like particle image velocimetry (PIV) must be further tested and improved to enable more effective use of these techniques. This paper presents a framework designed for this exact purpose: Simulating Hydraulics and Images for Velocimetry Evaluation and Refinement (SHIVER). The approach involves coupling a hydrodynamic model with a synthetic particle generator to advect particles between frames, as dictated by local velocity vectors and thus construct a plausible image sequence specific to the reach of interest. The resulting time series can then be used as input to a velocimetry algorithm to compare image‐derived estimates with known (modelled) velocities to perform an exhaustive, spatially distributed accuracy assessment. As an example application of SHIVER, we examined the effects of interrogation area (IA) size, frame rate, flow velocity, and image sequence duration on the performance of a standard PIV algorithm. This analysis indicated that image‐derived velocities were generally in close agreement with those from the flow model (root mean square error <10% and mean bias <3%), except when small IAs were coupled with low frame rates. Velocity estimates were most accurate for the lowest modelled discharge (R2=0.97 at baseflow) and became less reliable as the mean flow velocity increased (R2=0.92 for an intermediate discharge and R2=0.86 at bankfull). Accuracy was essentially independent of image sequence duration, implying that long occupations might not be necessary. Errors were concentrated along channel margins, where PIV‐based velocities tended to be greater than those from the flow model. Small IAs led to underpredictions of velocity, while larger IAs led to overpredictions. SHIVER is highly modular and could be updated to make use of different hydrodynamic models or image simulators. The framework could also facilitate more thorough sensitivity analyses and comparison of various velocimetry algorithms. [ABSTRACT FROM AUTHOR]

Background. Complex interactions between fuel structure and fire substantially affect fire spread and spatial variability in fire behaviour. Heterogeneous arrangement of the fuel coupled with variability in fuel characteristics can impact heat transfer efficiency, preheating of unburned fuel and consequent ignition and spread. Aim. Study the influence of pre-burn fuel structure (canopy height, spatial arrangement) on fire behaviour (rate of spread, flame residence time) derived from high-resolution video of a prescribed gorse fire. Method. Rate of spread and flame residence time are calculated and mapped from high-resolution overhead visible-spectrum video, and compared with the Canopy Height Model derived from pre-burn Light Detection and Ranging (Lidar) scans. Results. Geospatial analytics can provide precision observations of fire behaviour metrics. Rates of spread under high wind conditions are influenced by local changes in canopy height and may be more dependent on other fuel characteristics, while flame residence time is better correlated with canopy height. Conclusions. These observational technology and spatio-temporal analytical techniques highlight how detailed fire behaviour characteristics can be derived from these data. Implications. The results have implications for wildfire modelling and Wildland--Urban Interface (WUI) building design engineers, as the reported dataset is suitable for model validation and the analysis contributes to further understanding of gorse fire hazard. [ABSTRACT FROM AUTHOR]

Flow measurement in rivers and channels is crucial for water resource management and infrastructure planning, especially under the context of climate change. However, traditional methods like mechanical current meters and hydroacoustic instruments face limitations in terms of cost, intrusiveness, and accessibility. In recent years, image-based velocimetry techniques have emerged as promising alternatives due to their non-contact nature and cost-effectiveness. Nevertheless, persistent challenges remain, particularly concerning the uniform distribution of surface tracers necessary for precise measurements. These challenges are particularly pronounced in cases involving artificial seeding, where ensuring uniform distribution poses a significant obstacle. To address this issue, this study presents a novel methodology for filtering Large Scale Particle Image Velocimetry (LSPIV) data based on indicators of pixel intensity gradients. The methodology was evaluated across various field measurements under low flow conditions, encompassing a wide range of seeding characteristics. The evaluations demonstrated improvements in mean surface velocity profile estimation, showing reductions of up to 70 % in normalized root mean square error compared to not using filters. Additionally, the results were compared with filters typically employed by experienced LSPIV users, such as background subtraction and cross-correlation coefficient thresholds, showing improvements with the proposed filter. Implementation of the proposed strategy reduces the subjectivity in LSPIV implementation, particularly for users with limited knowledge of the technique, but also require minimal post-processing efforts. The methodology is anticipated to be integrated into existing software tools, thereby enhancing the accessibility of LSPIV for individuals with limited expertise in image velocimetry. Overall, this methodology facilitates cost-effective expansion of hydrological information availability, particularly in resource-constrained regions. • New filtering method for image velocimetry in rivers handles heterogeneous seeding. • The method enhances surface velocity estimations for more accurate flow measurement. • The methodology was validated in a wide range of environmental and seeding conditions. • This approach minimizes the need for subjective choices during data analysis. [ABSTRACT FROM AUTHOR]

Background: Wildfires propagate through vegetation exhibiting complex spread patterns modulated by ambient atmospheric wind turbulence. Wind gusts at the fire-front extend and intensify flames causing direct convective heating towards unburnt fuels resulting in rapid acceleration of spread. Aims: To characterise ambient and fire turbulence over gorse shrub and explore how this contributes to fire behaviour. Methods: Six experimental burns were carried out in Rakaia, New Zealand under varying meteorological conditions. The ignition process ensured a fire-line propagating through dense gorse bush (1 m high). Two 30-m sonic anemometer towers measured turbulent wind velocity at six different levels above the ground. Visible imagery was captured by cameras mounted on uncrewed aerial vehicles at 200 m AGL. Key results: Using wavelet decomposition, we identified different turbulent time scales that varied between 1 and 128 s relative to height above vegetation. Quadrant analysis identified statistical distributions of atmospheric sweeps (downbursts of turbulence towards vegetation) with sustained events emanating from above the vegetation canopy and impinging at the surface with time scales up to 10 s. Conclusions: Image velocimetry enabled tracking of 'fire sweeps' and characterised for the first time their lifetime and dynamics in comparison with overlying atmospheric turbulent structures. Implications: This methodology can provide a comprehensive toolkit when investigating coupled atmosphere–fire interactions. Multi-modal observations of fire behaviour and overlying atmospheric turbulence were carried out for four wind-driven gorse bush experimental fires. Novel image velocimetry analysis outlined the dynamics and scales of motion of fire sweeps in relation to overlying atmospheric coherent turbulent structures. Results are useful for evaluating coupled fire–atmosphere model simulations. (This paper is part of a Special Issue that includes papers on research presented at the IX International Conference on Forest Fire Research, Coimbra, Portugal, November 2022.) [ABSTRACT FROM AUTHOR]

Large-scale particle image velocimetry (LSPIV) provides a cost-effective, rapid, and secure monitoring tool for streamflow measurements. However, surveys of ground control points (GCPs) might affect the camera parameters through the solution of collinearity equations and then impose uncertainty on the measurement results. In this paper, we explore and present an uncertainty analysis for image-based streamflow measurements with the main focus on the ground control points. The study area was Yufeng Creek, which is upstream of the Shimen Reservoir in Northern Taiwan. A monitoring system with dual cameras was set up on the platform of a gauge station to measure the surface velocity. To evaluate the feasibility and accuracy of image-based LSPIV, a comparison with the conventional measurement using a flow meter was conducted. Furthermore, the degree of uncertainty in LSPIV streamflow measurements influenced by the ground control points was quantified using Monte Carlo simulation (MCS). Different operations (with survey times from one to nine) and standard errors (30 mm, 10 mm, and 3 mm) during GCP measurements were considered. Overall, the impacts in the case of single GCP measurement are apparent, i.e., a shifted and wider confidence interval. This uncertainty can be alleviated if the coordinates of the control points are measured and averaged with three repetitions. In terms of the standard errors, the degrees of uncertainty (i.e., normalized confidence intervals) in the streamflow measurement were 20.7%, 12.8%, and 10.7%. Given a smaller SE in GCPs, less uncertain estimations of the river surface velocity and streamflow from LSPIV could be obtained. [ABSTRACT FROM AUTHOR]

This study presents two new remote sensing approaches that can be used to derive rate of spread and flaming zone velocities of a wildfire at very high spatiotemporal resolution. Time sequential image tracking from thermal or visible video collected on uncrewed aerial vehicles is used to estimate instantaneous spatial rate of spread of a surface fire. The techniques were developed using experimental wheat‐stubble burns carried out near Darfield, New Zealand, in March 2019. The thermal tracking technique is based on Thermal Image Velocimetry, which tracks evolving temperature patterns within an infrared video. The visible tracking technique uses colour thresholding, and tracks fire perimeter progression through time at pixel resolution. Results show that the visible perimeter tracking creates a higher mean rate of spread compared to thermal image velocimetry. The visible perimeter tracking provides rate of spread measurements for fire front progression whereas the thermal tracking techniqueis computationally more expensive, but can resolve velocities of thermal structures within the flaming zone and provides spatiotemporal rate of spread measurements. Both techniques are available as open‐source code and providevital scientific data for new studies concerning e.g. fire–atmospheric interactions or model validation. They may be adapted for operational purposes providing rate of spread at high spatiotemporal resolution. We present two new algorithms that can retrieve rate of spread and flaming velocities of fires from uncrewed aerial vehicles. The thermal tracking is well suited for studying fire–atmospheric interactions whereas the visible image tracking estimates the fire perimeter. Both techniques are available open‐source and may be adapted for operational purposes. [ABSTRACT FROM AUTHOR]

The application of image velocimetry to measure surface streamflow velocities requires meticulous preparation, including surveying and securing both the existence of floating features on the water surface, and, as in every hydrometry method, appropriate hydraulic conditions (e.g., uniform flow, turbulent velocity profile, etc.). Though these requirements can be easily satisfied when all stages involved in image velocimetry are prepared and executed by specialists, this is not guaranteed when the video footage is recorded by citizens. This kind of spontaneously obtained data are frequently the only available information of extreme flood events; therefore, and despite their non-scientific origin and standardization, these data are very important for hydrology. In this study, we evaluate image velocimetry under a variety of conditions, including conditions resembling citizen videos. Furthermore, we conclude on the manual analysis as a means of verification of the accuracy of the velocity estimations. An interesting finding from the case study with non-uniform flow conditions was that the surface velocities occurring at the middle section of the river, estimated using large-scale particle image velocimetry algorithms, exhibited a significant error, whereas the manual estimation was more accurate. This finding calls for further investigation and a more careful approach in similar conditions. [ABSTRACT FROM AUTHOR]

VISION is open-source software written in MATLAB for video stabilisation using automatic features detection. It can be applied for any use, but it has been developed mainly for image velocimetry applications in rivers. It includes a number of options that can be set depending on the user’s needs and intended application: 1) selection of different feature detection algorithms (seven to be selected with the flexibility to choose two simultaneously), 2) definition of the percentual value of the strongest features detected to be considered for stabilisation, 3) geometric transformation type, 4) definition of a region of interest on which the analysis can be performed, and 5) visualisation in real-time of stabilised frames. One case study was deemed to illustrate VISION stabilisation capabilities on an image velocimetry experiment. In particular, the stabilisation impact was quantified in terms of velocity errors with respect to field measurements obtaining a significant error reduction of velocities. VISION is an easy-to-use software that may support research operating in image processing, but it can also be adopted for educational purposes.

We present novel in‐field vegetation fire observations and the analyses using brightness temperatures recorded by longwave infrared camera and thermal image velocimetry. The brightness temperatures from a wind‐driven stubble wheat fire were obtained with a 60 frames per second (fps) video acquisition. Multi‐level sonic anemometers on a 10m in‐fire tower were used for measurements of turbulent velocity and air temperatures, while fuel level air and flame temperatures were collected by an array of thermocouples (TCs). The camera's image pixel resolution was adequate to discern thermal structures in accordance with the in‐fire TC measurements and its spacing distances. The in‐situ and remotely measured flaming zone dynamics were derived using two different methodologies, Thermal Image Velocimetry (TIV) and Image Segmentation (IS). The results highlight spatial and spectral information of coherent turbulent and mean velocity structures. The power spectra decomposition of the TIV showed similar spectral characteristics to the sonic speed measurements during the fire passage under the tower with a similar inertial subrange slope. This result reveals plausible evidence of interaction between the flaming zone and wind turbulence for a prescribed rapidly moving stubble wheat fire. This research presents a new field measurement methodology for understanding fire‐atmospheric interactions between the flaming zone and the immediate overlying atmospheric turbulent boundary layer. Plain Language Summary: The size and destructive power of wildfires can vary depending on weather and fuel conditions. We have carried out wheat stubble burning experiments using specialized instrumentation in order to study how the wind and fire interact, and the subsequent role of this interaction in the development of an experimental field‐scale burn. We used Long‐Wave InfraRed or LWIR cameras to capture radiation emitted by the fire but invisible to the human eye. We then used the time‐sequenced LWIR images to detect and calculate forward motion and fluctuation of the fire. We demonstrated for the first time the validity of these techniques by comparing the results with wind measurements collected during the passage of the fire using sonic anemometers. This new observation method has provided the first set of two‐dimensional observations of the interaction between the flame zone and the overlying wind. Key Points: A near target cooled infrared camera captures spatial fire thermal perturbations verified with an onsite thermocouple sensor arrayTurbulence derived from Thermal Image Velocimetry of flaming zone reveals similar spectral characteristics with atmospheric measurements [ABSTRACT FROM AUTHOR]

Unoccupied aerial systems (UASs) are frequently used in the field of fluvial geomorphology due to their capabilities for observing the continuum rather than single sample points. We introduce a (semi-)automatic workflow to measure river bathymetry and surface flow velocities of entire river reaches at high resolution, based on UAS videos and imagery. Video frame filtering improved the visibility of the riverbed using frame co-registration and averaging with a median filter. Subsequently, these video frames were incorporated with still images acquired by UASs into a structure from motion (SfM) photogrammetry approach to reconstruct the camera poses (i.e. positions and orientations) and the 3D point cloud of the river reach. The heights of submerged points were further processed using small-angle and multi-view refraction correction approaches to account for the refraction impact. The flow velocity pattern of the river surface was measured using the estimated camera pose from SfM, the reconstructed bathymetric point cloud and the co-registered video frames in combination with image velocimetry analysis. Finally, discharge was estimated at selected cross-sections, considering the average surface velocity and the bathymetry. Three case studies were considered to assess the performance of the workflow under different environmental conditions. The studied river reaches spanned a length between 0.15 and 1 km. The bathymetry was reconstructed with average deviations to RTK-GNSS point measurements as low as 1 cm with a standard deviation of 6 cm. If frames were processed with the median filter, the number of underwater points increased by up to 21%. The image-based surface velocities revealed an average deviation to reference measurements between 0.05 and 0.08 m s-1. The imagebased discharge was estimated with deviations to ADCP references of up to 5%, however this was sensitive to errors in water-level retrieval. The output of our workflow can provide a valuable input to hydro-morphological models. [ABSTRACT FROM AUTHOR]

The volumetric flow rate in rivers is essential to analyze hydrological processes and at the same time it is one of the most difficult variables to measure. Image based discharge measurements possess several advantages, one of them being that the sensor (camera) is not in contact with the water, it can be placed safe of floods, its mounting position is very flexible and there is no need of expensive structures/constructions. During the last years several image-based methods for measuring the surface velocity in rivers and canals have been proposed and successfully tested under different conditions. However, these methods have been used and configured to perform well under the particular conditions of a single recording or single site. The objective of this paper is to present a system which has reached a Technology Readiness Level (TRL) 9. The system is able to measure the volumetric flow under different conditions day and night and all year long, the system is able to perform in rivers or canals of different sizes and flow velocities and under different conditions of visibility. In addition, the system is capable of measuring the river stage optically without the need of a stage, but it can also integrate external level sensor. Important for a wide set of customers, the system must be able to interface with the various common signal input and output standards, such as 4–20 mAmp, modbus, SDI-12, ZRXP, and even with customer specific formats. Additionally, the developed technology can be implemented as an edge or as a cloud system. The cloud system only needs a camera with Internet connection to send videos to the cloud where they are processed, while the edge systems have a processing unit installed at the site where the processing is done. This paper presents the key aspects needed to move from prototype with TRL5-7 and lower toward the presented field proven system with a TRL 9.

Large-scale image velocimetry is a novel approach for non-contact remote sensing of flow in rivers. Research within this topic has largely focussed on developing specific aspects of the image velocimetry work-flow, or alternatively, testing specific tools or software using case studies. This has resulted in the development of a multitude of techniques, with varying practice being employed between groups, and authorities. As such, for those new to image velocimetry, it may be hard to decipher which methods are suited for particular challenges. This research collates, synthesises, and presents current understanding related to the application of particle image velocimetry (PIV) and particle tracking velocimetry (PTV) approaches in a fluvial setting. The image velocimetry work-flow is compartmentalised into sub-systems of: capture optimisation, pre-processing, processing, and post-processing. The focus of each section is to provide examples from the wider literature for best practice, or where this is not possible, to provide an overview of the theoretical basis and provide examples to use as precedence and inform decision making. We present literature from a range of sources from across the hydrology and remote sensing literature to suggest circumstances in which specific approaches are best applied. For most sub-systems, there is clear research or precedence indicating how to best perform analysis. However, there are some stages in the process that are not conclusive with one set method and require user intuition or further research. For example, the role of external environmental conditions on the performance of image velocimetry being a key aspect that is currently lacking research. Further understanding in areas that are lacking, such as environmental challenges, is vital if image velocimetry is to be used as a method for the extraction of river flow information across the range of hydro-geomorphic conditions.

Non-intrusive technologies for the in-situ measurement of river morphological features are increasingly popular in the scientific and practice communities due to their efficient and productive data acquisition. While the measurement of suspended load with optical and acoustic technologies is currently an active area of research, the measurement of bedform dynamics has not experienced similar progress. We have successfully demonstrated through laboratory experiments that, by combining acoustic mapping with image velocimetry concepts, we can characterize the planar dynamics of the bedform migration. The technique, labeled Acoustic Mapping Velocimetry (AMV), is currently transferred to field conditions using multiple-beam echo-sounders (MBES) for producing acoustic maps. During this transfer, new questions emerged because, in field conditions, many of the morphologic features targeted by AMV measurements are not a priori known. Moreover, the image velocimetry processing can be approached with several alternatives, each of them characterized by strength and limitations. This paper assembles guidelines for establishing optimal parameters for the acquisition of the acoustic maps based on analytical considerations, and for selecting essential features of the processing for image velocimetry. We test these guidelines using MBES data acquired in the Mississippi River.

Video‐based hydrometry continues to develop for contactless discharge measurements, automated flood gauging stations and the use of crowd‐sourced flood videos for discharge reconstruction. Irrespective of the velocimetry algorithm used (LSPIV, STIV, PTV...), orthorectification of the images is necessary beforehand, so that each pixel has the same known physical size. Most times, the orthorectification transformation is a plane‐to‐plane projection from the water surface to the camera sensor. Two approaches are typically used to compute the coefficients of this transformation: their calibration from ground reference points (GRPs) with known image and real‐world coordinates ("implicit calibration") or their calculation from the values of the intrinsic (focal length, sensor size) and extrinsic (position, angles) parameters of the camera ("explicit calibration"). In this paper, we develop a Bayesian method which makes it possible to combine the implicit and explicit approaches in a probabilistic framework. The Bayesian approach can be used from situations suitable for the implicit approach (plenty of GRPs) to situations propitious to the explicit approach (well‐known camera parameters). The method is illustrated using synthetic views of a typical streamgauging scene with known true values of the parameters and GRP coordinates. We show that combining observational and prior information is generally beneficial to get precise estimates. Further tests carried out with a real scene of the Arc River at Randens, France, in flood conditions illustrate the impact of the number, uncertainty and spatial distribution of GRPs on the final uncertainty of flow velocity and discharge. [ABSTRACT FROM AUTHOR]

Sustainable water resources management plans require advanced instrumentation to measure accurately both the water velocity and the discharge of the aquatic bodies. An innovative way to achieve this goal is with the use of non-contact, cost-effective, and efficient image-based methods. A studied water surface can be captured by subsequent images while surface velocity can be estimated by the movement of tracers on the water’s surface. Various software have been developed in order to utilize such tracing algorithms and to monitor the surface velocity of fluids. The objective of this study was to present the current status of these software, describe the main parameters, and provide applied examples of image-based velocimetry application. To achieve this firstly, a comprehensive review was conducted that focused on the scientific publications that have utilized particle image and tracking velocimetry methods on water bodies. Secondly, the main scope of this research was to record and analyze these publications based on the software that were used. This paper presents 30 software with their main features and few of their application examples. This should help other researchers choose the appropriate image-based velocimetry software to measure water velocity and discharge based on the needs of their study. [ABSTRACT FROM AUTHOR]

The application of image velocimetry to measure surface streamflow velocities requires meticulous preparation, including surveying and securing both the existence of floating features on the water surface, and, as in every hydrometry method, appropriate hydraulic conditions (e.g., uniform flow, turbulent velocity profile, etc.). Though these requirements can be easily satisfied when all stages involved in image velocimetry are prepared and executed by specialists, this is not guaranteed when the video footage is recorded by citizens. This kind of spontaneously obtained data are frequently the only available information of extreme flood events; therefore, and despite their non-scientific origin and standardization, these data are very important for hydrology. In this study, we evaluate image velocimetry under a variety of conditions, including conditions resembling citizen videos. Furthermore, we conclude on the manual analysis as a means of verification of the accuracy of the velocity estimations. An interesting finding from the case study with non-uniform flow conditions was that the surface velocities occurring at the middle section of the river, estimated using large-scale particle image velocimetry algorithms, exhibited a significant error, whereas the manual estimation was more accurate. This finding calls for further investigation and a more careful approach in similar conditions.

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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]

Image-based hydrological measurement techniques have surged in popularity over the last decade, with the most common type of data obtained being the open-channel surface velocity field and flow rate. The lack of a feature-rich image velocimetry software often forces practitioners to use multiple tools as part of a cumbersome workflow, which restricts the uptake of such methods and reduces the reproducibility of results. We present the free and open-source software SSIMS-Flow, which handles extraction of video frames, removal of geometric distortions, video stabilization, orthorectification, image enhancement, and velocity and flow rate estimation using the Farnebäck optical flow method improved with specific pooling techniques developed in this study. SSIMS-Flow performance is tested in two case studies and compared with known reference values and those obtained using industry-standard PIVlab software. In low seeding and tracerless conditions, SSIMS-Flow demonstrates higher velocity estimation accuracy, while delivering several times faster computational runtime. • SSIMS-Flow is a feature-rich image velocimetry software for open-channel flow. • Novel pooling methods are developed for obtaining time-averaged velocities. • More accurate results are obtained under tracerless conditions than with PIV. • Computational runtime of SSIMS-Flow is several times faster than with PIVlab. [ABSTRACT FROM AUTHOR]

Abstract Venus is covered with thick clouds. Ultraviolet (UV) images at 0.3–0.4 microns show detailed cloud features at the cloud-top level at about 70 km, which are created by an unknown UV-absorbing substance. Images acquired in this wavelength range have traditionally been used to measure winds at the cloud top. In this study, we report low-latitude winds obtained from the images taken by the UV imager, UVI, onboard the Akatsuki orbiter from December 2015 to March 2017. UVI provides images with two filters centered at 365 and 283 nm. While the 365-nm images enable continuation of traditional Venus observations, the 283-nm images visualize cloud features at an SO2 absorption band, which is novel. We used a sophisticated automated cloud-tracking method and thorough quality control to estimate winds with high precision. Horizontal winds obtained from the 283-nm images are generally similar to those from the 365-nm images, but in many cases, westward winds from the former are faster than the latter by a few m/s. From previous studies, one can argue that the 283-nm images likely reflect cloud features at higher altitude than the 365-nm images. If this is the case, the superrotation of the Venusian atmosphere generally increases with height at the cloud-top level, where it has been thought to roughly peak. The mean winds obtained from the 365-nm images exhibit local time dependence consistent with known tidal features. Mean zonal winds exhibit asymmetry with respect to the equator in the latter half of the analysis period, significantly at 365 nm and weakly at 283 nm. This contrast indicates that the relative altitude may vary with time and latitude, and so are the observed altitudes. In contrast, mean meridional winds do not exhibit much long-term variability. A previous study suggested that the geographic distribution of temporal mean zonal winds obtained from UV images from the Venus Express orbiter during 2006–2012 can be interpreted as forced by topographically induced stationary gravity waves. However, the geographic distribution of temporal mean zonal winds we obtained is not consistent with that distribution, which suggests that the distribution may not be persistent.

Videos acquired from Unmanned Aerial Systems (UAS) allow for monitoring river systems at high spatial and temporal resolutions providing unprecedented datasets for hydrological and hydraulic applications. The cost-effectiveness of these measurement methods stimulated the diffusion of image-based frameworks and approaches at scientific and operational levels. Moreover, their application in different environmental contexts gives us the opportunity to explore their reliability, potentialities and limitations, and future perspectives and developments. This paper analyses the recent progress on this topic, with a special focus on the main challenges to foster future research studies.

Image velocimetry is a popular remote sensing method mainly because of the very modest cost of the necessary equipment. However, image velocimetry methods employ parameters that require high expertise to select appropriate values in order to obtain accurate surface flow velocity estimations. This introduces considerations regarding the subjectivity introduced in the definition of the parameter values and its impact on the estimated surface velocity. Alternatively, a statistical approach can be employed instead of directly selecting a value for each image velocimetry parameter. First, probability distribution should be defined for each model parameter, and then Monte Carlo simulations should be employed. In this paper, we demonstrate how this statistical approach can be used to simultaneously produce the confidence intervals of the estimated surface velocity, reduce the uncertainty of some parameters (more specifically, the size of the interrogation area), and reduce the subjectivity. Since image velocimetry algorithms are CPU-intensive, an alternative random number generator that allows obtaining the confidence intervals with a limited number of iterations is suggested. The case study indicated that if the statistical approach is applied diligently, one can achieve the previously mentioned threefold objective.

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.

Wildfires currently represent an imminent danger to communities around the world due to their increasing severity and frequency. It is critical to expand current knowledge on wildfire behaviour to improve risk management practices, firefighting operation and engineering design in the Wildland Urban Interface (WUI). This paper reports 2D high resolution measurements of Byram's fireline intensity generated during a 150 × 200 m2 gorse shrub burn generated from visual and infrared video footage of the prescribed fire, and Light-Detection-and-Ranging (LiDAR) data of the vegetation canopy, both acquired using Unmanned Aerial Vehicles (UAV) technology. The results show that the fireline intensity was highly variable across the plot, reaching localized peak values over 40,000 kW/m and an averaged intensity of 14,300 kW/m approximately. An empirical correlation for estimating the average flame height was derived from the fireline intensity maps and from experimental measurements carried out during the prescribed burn. The correlation was used to produce a 2D detailed map of the average flame height of the prescribed fire, achieving a 11% of error compared to in-fire measurements. The findings from this research suggests that non-linear variabilities of fireline intensity should be carefully considered to adequately describe wildifire behavior of shrubland fires. • UAV-based observations were used to map and characterize the fireline intensity of a 150 × 200 m2 gorse prescribed burn. • Fireline intensity varied greatly within the plot, presenting intermixed areas of very low and higher intensity of roughly an order of magnitude difference within a few meters and seconds. • An empirical correlation for estimating the average flame height was derived and mapped achieving 11% of error compared to in-fire measurements. [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.

Venus is covered with thick clouds. Ultraviolet (UV) images at 0.3-0.4 microns show detailed cloud features at the cloud-top level at about 70 km, which are created by an unknown UV-absorbing substance. Images acquired in this wavelength range have traditionally been used to measure winds at the cloud top. In this study, we report low-latitude winds obtained from the images taken by the UV imager, UVI, onboard the Akatsuki orbiter from December 2015 to March 2017. UVI provides images with two filters centered at 365 and 283 nm. While the 365-nm images enable continuation of traditional Venus observations, the 283-nm images visualize cloud features at an SO absorption band, which is novel. We used a sophisticated automated cloud-tracking method and thorough quality control to estimate winds with high precision. Horizontal winds obtained from the 283-nm images are generally similar to those from the 365-nm images, but in many cases, westward winds from the former are faster than the latter by a few m/s. From previous studies, one can argue that the 283-nm images likely reflect cloud features at higher altitude than the 365-nm images. If this is the case, the superrotation of the Venusian atmosphere generally increases with height at the cloud-top level, where it has been thought to roughly peak. The mean winds obtained from the 365-nm images exhibit local time dependence consistent with known tidal features. Mean zonal winds exhibit asymmetry with respect to the equator in the latter half of the analysis period, significantly at 365 nm and weakly at 283 nm. This contrast indicates that the relative altitude may vary with time and latitude, and so are the observed altitudes. In contrast, mean meridional winds do not exhibit much long-term variability. A previous study suggested that the geographic distribution of temporal mean zonal winds obtained from UV images from the Venus Express orbiter during 2006-2012 can be interpreted as forced by topographically induced stationary gravity waves. However, the geographic distribution of temporal mean zonal winds we obtained is not consistent with that distribution, which suggests that the distribution may not be persistent. [ABSTRACT FROM AUTHOR]

This paper presents a novel non-contact method for simultaneous analysis of pressure and velocity conditions in cavitating flows. The method (implemented in our software ADMflow ) is based on high-speed camera flow visualization and was evaluated in an experiment with ultrasonically induced acoustic cavitation of different intensities. Attached cavitation with clearly visible cavitation structures occurred on the tip of an ultrasonic probe immersed in distilled water. Using the high-speed imaging data, pressure fluctuations were calculated by a computer-aided algorithm based on the Brennen’s theory of cavitation cloud kinematics and a modified version of the Rayleigh-Plesset equation. Reference measurements of pressure pulsations were conducted by a hydrophone installed at the bottom of the liquid container. The analysis of cavitation structure dynamics was complemented by calculation of velocity fields from the imaging data, the algorithm for which is based on the advection-diffusion equation. Calculated pressure fluctuations were analyzed in the spatial, temporal and spectral domain. Presented results indicate a strong correlation between the fields of velocity and pressure fluctuations during the growth and collapse of cavitation structures. A comparison of time series and power spectra demonstrates that our cavitation analysis method is in a reasonably good agreement with results of the reference measurement methods and can therefore be used for non-contact analysis of pressure and velocity conditions in cavitating flows. [ABSTRACT FROM AUTHOR]

Background: Haemodynamic performance of heart valve prosthesis can be defined as its ability to fully open and completely close during the cardiac cycle, neither overloading heart work nor damaging blood particles when passing through the valve. In this perspective, global and local flow parameters, valve dynamics and blood damage safety of the prosthesis, as well as their mutual interactions, have all to be accounted for when assessing the device functionality. Even though all these issues have been and continue to be widely investigated, they are not usually studied through an integrated approach yet, i.e. by analyzing them simultaneously and highlighting their connections.Results: An in vitro test campaign of flow through a bileaflet mechanical heart valve (Sorin Slimline 25 mm) was performed in a suitably arranged pulsatile mock loop able to reproduce human systemic pressure and flow curves. The valve was placed in an elastic, transparent, and anatomically accurate model of healthy aorta, and tested under several pulsatile flow conditions. Global and local hydrodynamics measurements and leaflet dynamics were analysed focusing on correlations between flow characteristics and valve motion. The haemolysis index due to the valve was estimated according to a literature power law model and related to hydrodynamic conditions, and a correlation between the spatial distribution of experimental shear stress and pannus/thrombotic deposits on mechanical valves was suggested. As main and general result, this study validates the potential of the integrated strategy for performance assessment of any prosthetic valve thanks to its capability of highlighting the complex interaction between the different physical mechanisms that govern transvalvular haemodynamics.Conclusions: We have defined an in vitro procedure for a comprehensive analysis of aortic valve prosthesis performance; the rationale for this study was the belief that a proper and overall characterization of the device should be based on the simultaneous measurement of all different quantities of interest for haemodynamic performance and the analysis of their mutual interactions. [ABSTRACT FROM AUTHOR]

The qualitative and quantitative assessment of bedform dynamics has been a continuous challenge for conventional river monitoring methods due to the vexing problems associated with the limited capabilities to observe the bedforms in-situ and the intrusive nature of the instruments used for quantification of their movement. The advent of the new generation of acoustic- and image-based instruments has brought considerable promise for the experimental investigations in this area by allowing observations of the bedforms from close range along with possibilities of non-intrusive quantification of their movement with unprecedented spatial and temporal resolutions. This paper reports proof-of-concept tests for quantifying the dynamics of the bedform migration using a combination of concepts from two representatives of the new nonintrusive instruments: acoustic survey and processing techniques associated with particle image velocimetry. Given the combination of mapping and velocimetry operating principles, we call the technique acoustic mapping velocimetry (AMV). This hybrid technique has potential to track the planar migration of the bedform and quantify the velocity of the moving bedform fronts. Described herein is a laboratory experiment illustrating the implementation of the AMV concept for quantifying bedform migration. [ABSTRACT FROM AUTHOR]

Knowledge of sediment dynamics in rivers is of great importance for various practical purposes. Despite its high relevance in riverine environment processes, the monitoring of sediment rates remains a major and challenging task for both suspended and bed load estimation. While the measurement of suspended load is currently an active area of testing with nonintrusive technologies (optical and acoustic), bed load measurement does not mark a similar progress. This paper describes an innovative combination of measurement techniques and analysis protocols that establishes the proof-of-concept for a promising technique, labeled herein Acoustic Mapping Velocimetry (AMV). The technique estimates bed load rates in rivers developing bed forms using a nonintrusive measurements approach. The raw information for AMV is collected with acoustic multibeam technology that in turn provides maps of the bathymetry over longitudinal swaths. As long as the acoustic maps can be acquired relatively quickly and the repetition rate for the mapping is commensurate with the movement of the bed forms, successive acoustic maps capture the progression of the bed form movement. Two-dimensional velocity maps associated with the bed form migration are obtained by implementing algorithms typically used in particle image velocimetry to acoustic maps converted in gray-level images. Furthermore, use of the obtained acoustic and velocity maps in conjunction with analytical formulations (e.g., Exner equation) enables estimation of multidirectional bed load rates over the whole imaged area. This paper presents a validation study of the AMV technique using a set of laboratory experiments. [ABSTRACT FROM AUTHOR]

Background: This paper presents a Tracer Liquid Image Velocimetry (TLIV) for multi-layer radial flow in bioreactors used for cells cultivation of tissue engineering. The goal of this approach is to use simple devices to get good measuring precision, specialized for the case in which the uniform level of fluid shear stress was required while fluid velocity varied smoothly. Methods: Compared to the widely used Particles Image Velocimetry (PIV), this method adopted a bit of liquid as tracer, without the need of laser source. Sub-pixel positioning algorithm was used to overcome the adverse effects of the tracer liquid deformation. In addition, a neighborhood smoothing algorithm was used to restrict the measurement perturbation caused by diffusion. Experiments were carried out in a parallel plates flow chamber. And mathematical models of the flow chamber and Computational Fluid Dynamics (CFD) simulation were separately employed to validate the measurement precision of TLIV. Results: The mean relative error between the simulated and measured data can be less than 2%, while in similar validations using PIV, the error was around 8.8%. Conclusions: TLIV avoided the contradiction between the particles' visibility and following performance with tested fluid, which is difficult to overcome in PIV. And TLIV is easier to popularize for its simple experimental condition and low cost. [ABSTRACT FROM AUTHOR]

• A 2D hydraulic model fully calibrated/validated using LSPIV surface velocities. • Performance of LSPIV on par with aDcp calibrated/validated model. • Velocity depth-averaging using the Probability Concept significantly constrains model prediction errors. • LSPIV is justified as an alternative dataset for parametrising 2D hydraulic models. Non-intrusive image-based techniques for measuring surface river velocities have rapidly evolved as a cost-effective and safe means for quantifying flow patterns. Large-scale particle image velocimetry (LSPIV) can provide instantaneous surface velocities over a large spatial footprint rapidly and with little pre-calibration as compared to traditional techniques. Assessment of the spatial distribution of flow velocities in hydraulic models has been comparatively harder to achieve than assessment of depth due to logistical challenges but would be aided using large observational datasets that represent the variability and distribution of flow hydraulics. Additionally, the efficacy of image velocimetry in assessing the accuracy of outputs from 2D hydraulic models has not been addressed. Here, we demonstrate how LSPIV can be used to calibrate and validate 2D model predictions in a gravel bed river reach. LSPIV velocities are depth-averaged using standard velocity coefficients (α) and then using the Probability Concept (PC) - a probabilistic formulation of velocity distributions that accounts for non-standard velocity profiles, typical in field settings. UAV surveys were used to acquire video for LSPIV and imagery for Structure from Motion (SfM) topographic modelling. We use spatially dense acoustic doppler current profiler (aDcp) velocity data for benchmark assessment of the velocity outputs of HEC-RAS 2D model simulations. 2D model prediction error, based on seeded LSPIV velocities, was within range (4.2%) of the aDcp parametrised model, with improvements in modelled versus predicted velocity correlations (up to 7.7%) when using PC to depth average LSPIV velocities. Validation bias reduced significantly (11%) with tighter error distributions when compared to the aDcp based model. Although additional hydraulic measurements are required to parametrise the Probability Concept algorithm, the performance of 2D hydraulic models calibrated/validated with LSPIV velocities is on par with traditional techniques, demonstrating the potential of this non-intrusive, low-cost approach. [ABSTRACT FROM AUTHOR]

This paper presents a non-contact method for velocity field calculation from a series of images containing illuminated planar layer of fluid with a pollutant mixed in. Velocity field is calculated using a model similar to optical flow based on the advection–diffusion equation. The model which was also implemented into our software, ADMflow, is evaluated on different sets of synthetic images. Calculated velocity fields are in a good agreement with the true velocity fields, mostly deviating by less than 10% in magnitude and 1° in flow direction. [ABSTRACT FROM AUTHOR]

Highlights: [•] The Image Velocimetry (IV) techniques are reviewed by this paper. [•] Free-surface IV is the non-intrusive technique to measure velocities in shallow water. [•] A suite of shallow water flow experiments are conducted with IV techniques. [•] IV technique is the promising means to capture flow hydrodynamics in shallow water. [ABSTRACT FROM AUTHOR]

This paper presents a non-contact method for velocity field calculation from a series of fluid flow images with illuminated planar layer of the flow and a mixed-in pollutant for flow visualization. The velocity field is calculated using a model similar to optical flow that is based on the advection-diffusion equation. The model is evaluated using a set of synthetic airfoil flow visualization images generated by a combination of computational fluid dynamics and inverse advection-diffusion equation approaches. The calculated velocity fields are in a good agreement with the true velocity fields. [ABSTRACT FROM AUTHOR]