An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems

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

Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow

River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical-sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: (i) particle-tracking velocimetry (PTV) and (ii) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI = nu(0.1)/(rho/rho(c nu 1)), where nu, rho, and rho(c nu 1 )are the spatial-clustering level, the seeding density, and the reference seeding density at nu = 1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9 % and 16.1 % were calculated - using PTV and PIV, respectively - by employing the optimal frame window

An automatic ANN-based procedure for detecting optimal image sequences supporting LS-PIV applications for rivers monitoring

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

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

Comparison of dense optical flow and PIV techniques for mapping surface current flow in tidal stream energy sites

Marine renewable energy site and resource characterisation, in particular tidal stream energy, require detailed flow measurements which often rely on high-cost in situ instrumentation which is limited in spatial extent. We hypothesise uncrewed aerial vehicles (UAV) offer a low-cost and low-risk data collection method for tidal stream environments, as recently techniques have been developed to derive flow from optical videography. This may benefit tidal and floating renewable energy developments, providing additional insight into flow conditions and complement traditional instrumentation. Benefits to existing data collection methods include capturing flow over a large spatial extent synchronously, which could be used to analyse flow around structures or for site characterisation; however, uncertainty and method application to tidal energy sites is unclear. Here, two algorithms are tested: large-scale particle image velocimetry using PIVlab and dense optical flow. The methods are applied on video data collected at two tidal stream energy sites (Pentland Firth, Scotland, and Ramsey Sound, Wales) for a range of flow and environmental conditions. Although average validation measures were similar (~ 20–30% error), we recommend PIVlab processed velocity data at tidal energy sites because we find bias (underprediction) in optical flow for higher velocities (> 1 m/s)

River flow monitoring: LS-PIV technique, an image-based method to assess discharge

The measurement of the river discharge within a natural ort artificial channel is still one of the most challenging tasks for hydrologists and the scientific community. Although discharge is a physical quantity that theoretically can be measured with very high accuracy, since the volume of water flows in a well-defined domain, there are numerous critical issues in obtaining a reliable value. Discharge cannot be measured directly, so its value is obtained by coupling a measurement of a quantity related to the volume of flowing water and the area of a channel cross-section. Direct measurements of current velocity are made, traditionally with instruments such as current meters. Although measurements with current meters are sufficiently accurate and even if there are universally recognized standards for the current application of such instruments, they are often unusable under specific flow conditions. In flood conditions, for example, due to the need for personnel to dive into the watercourse, it is impossible to ensure adequate safety conditions to operators for carrying out flow measures. Critical issue arising from the use of current meters has been partially addressed thanks to technological development and the adoption of acoustic sensors. In particular, with the advent of Acoustic Doppler Current Profilers (ADCPs), flow measurements can take place without personnel having direct contact with the flow, performing measurements either from the bridge or from the banks. This made it possible to extend the available range of discharge measurements. However, the flood conditions of a watercourse also limit the technology of ADCPs. The introduction of the instrument into the current with high velocities and turbulence would put the instrument itself at serious risk, making it vulnerable and exposed to damage. In the most critical case, the instrument could be torn away by the turbulent current. On the other hand, considering smaller discharges, both current meters and ADCPs are technologically limited in their measurement as there are no adequate water levels for the use of the devices. The difficulty in obtaining information on the lowest and highest values of discharge has important implications on how to define the relationships linking flows to water levels. The stage-discharge relationship is one of the tools through which it is possible to monitor the flow in a specific section of a watercourse. Through this curve, a discharge value can be obtained from knowing the water stage. Curves are site-specific and must be continuously updated to account for changes in geometry that the sections for which they are defined may experience over time. They are determined by making simultaneous discharge and stage measurements. Since instruments such as current meters and ADCPs are traditionally used, stage-discharge curves suffer from instrumental limitations. So, rating curves are usually obtained by interpolation of field-measured data and by extrapolate them for the highest and the lowest discharge values, with a consequent reduction in accuracy. This thesis aims to identify a valid alternative to traditional flow measurements and to show the advantages of using new methods of monitoring to support traditional techniques, or to replace them. Optical techniques represent the best solution for overcoming the difficulties arising from the adoption of a traditional approach to flow measurement. Among these, the most widely used techniques are the Large-Scale Particle Image Velocimetry (LS-PIV) and the Large-Scale Particle Tracking Velocimetry. They are able to estimate the surface velocity fields by processing images representing a moving tracer, suitably dispersed on the liquid surface. By coupling velocity data obtained from optical techniques with geometry of a cross-section, a discharge value can easily be calculated. In this thesis, the study of the LS-PIV technique was deepened, analysing the performance of the technique, and studying the physical and environmental parameters and factors on which the optical results depend. As the LS-PIV technique is relatively new, there are no recognized standards available for the proper application of the technique. A preliminary numerical analysis was conducted to identify the factors on which the technique is significantly dependent. The results of these analyses enabled the development of specific guidelines through which the LS-PIV technique could subsequently be applied in open field during flow measurement campaigns in Sicily. In this way it was possible to observe experimentally the criticalities involved in applying the technique on real cases. These measurement campaigns provided the opportunity to carry out analyses on field case studies and structure an automatic procedure for optimising the LS-PIV technique. In all case studies it was possible to observe how the turbulence phenomenon is a worsening factor in the output results of the LS-PIV technique. A final numerical analysis was therefore performed to understand the influence of turbulence factor on the performance of the technique. The results obtained represent an important step for future development of the topic

Water Level Detection for Flood Disaster Management Based on Real-time Color Object Detection

Currently, the water level monitoring system for a river uses instruments installed on the banks of the river and must be checked continuously and manually. This study proposes a real-time water level detection system based on a computer vision algorithm. In the proposed system, we use color object tracking technique with a bar indicator as a reference’s level. We set three bar indicators to determine the status of the water level, namely NORMAL, ALERT and DANGER. A camera was installed across the bar level indicators to capture bar indicator and monitoring the water level. In the simulation, the monitoring system was installed in 5-100 lux lighting conditions. For experimental purposes, we set various distances of the camera, which is set of 40-80 centimeters and the camera angle is set of 30-60 degrees. The experiment results showed that this system has an accuracy of 94% at camera distance is in range 50-80 centimeters and camera angle is 60o. Based on these results, it can be concluded that this proposed system can determine the water level well in varying lighting conditions

Fluorescent particle tracers for surface hydrology

Surface water processes control downstream runoff phenomena, waste and pollutant diffusion, erosion mechanics, and sediment transport. However, current observational methodologies do not allow for the identification and kinematic characterization of the physical processes contributing to catchment dynamics. Traditional methodologies are not capable to cope with extreme in-situ conditions, including practical logistic challenges as well as inherent flow complexity. In addition, available observational techniques are non-exhaustive for describing multiscale hydrological processes. This research addresses the need for novel observations of the hydrological community by developing pioneer flow characterization approaches that rely on the mutual integration of traditional tracing techniques and state-of-the-art image-based sensing procedures. These novel methodologies enable the in-situ direct observation of surface water processes through remote and unsupervised procedures, thus paving the way to the development of distributed networks of sensing platforms for catchment-scale environmental sensing. More specifically, the proposed flow characterization methodology is a low-cost measurement system that can be applied to a variety of real-world settings spanning from few centimeters rills in natural catchments to riverine ecosystems. The technique is based on the use of in-house synthesized environmentally-friendly fluorescent particle tracers through digital cameras for direct flow measurement and travel time estimations. Automated image analysis-based procedures are developed for real-time flow characterization based on image manipulation, template-based correlation, particle image velocimetry, and dimensionality reduction methodologies. The feasibility of the approach is assessed through laboratory-designed experiments, where the accuracy of the methodology is investigated with respect to well-established flow visualization techniques. Further, the transition of the proposed flow characterization approach to natural settings is studied through paradigmatic observations of natural stream flows in small scale channel and riverine settings and overland flows in hillslope environments. The integration of the proposed flow sensing system in a stand-alone, remote, and mobile platform is explored through the design, development, and testing of a miniature aerial vehicle for environmental monitoring through video acquisition and processing

Flood dynamics derived from video remote sensing

Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science