scholarly journals Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers

2020 ◽  
Vol 12 (11) ◽  
pp. 1789 ◽  
Author(s):  
Silvano Fortunato Dal Sasso ◽  
Alonso Pizarro ◽  
Salvatore Manfreda
Keyword(s):  
Water Resource Management ◽  
Large Scale ◽  
River Flow ◽  
Low Cost ◽  
A Priori ◽  
Surface Flow ◽  
Seeding Density ◽  
Unmanned Aerial Systems ◽  
Flow Monitoring ◽  
Image Velocimetry

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 techniques under particle aggregation for streamflow monitoring: a numerical approach

2020 ◽  
Author(s):  
Alonso Pizarro ◽  
Silvano Fortunato Dal Sasso ◽  
Salvatore Manfreda
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Numerical Approach ◽  
Surface Flow ◽  
Particle Aggregation ◽  
Seeding Density ◽  
Stream Flows ◽  
Flow Monitoring ◽  
Time Flow ◽  
Image Velocimetry

<p>Monitoring extreme events with high accuracy and consistency is still a challenge, even by using up-to-date approaches. On the one side, field campaigns are in general expensive and time-consuming, requiring the presence of high-qualified personnel and forward planning. On the other side, non-contact approaches (such as image velocimetry, radars, and microwave systems) have had promising signs of progress in recent years, making now possible real-time flow monitoring. This work focuses on the estimation of surface flow velocities for streamflow monitoring under particle aggregation, in which tracers are not necessarily uniformly distributed across the entire field of view. This issue is extremely relevant for the computing stream flows since velocity errors are transmitted to river discharge estimations. Ad-hoc numerical simulations were performed to consider different levels of particle aggregation, particle colour and shapes, seeding density, and background noise. Particle Tracking Velocimetry (PTV) and Large-Scale Particle Image Velocimetry (LSPIV) were used for image velocimetry estimations due to their widely used worldwide. Comparisons between the theoretical and computed velocities were carried out to determine the associated uncertainty and optimal experimental setup that minimises those errors.</p>

Development and Testing of an Unmanned Aerial Vehicle for Large Scale Particle Image Velocimetry

2014 ◽  
Author(s):  
Christopher Pagano ◽  
Flavia Tauro ◽  
Salvatore Grimaldi ◽  
Maurizio Porfiri
Keyword(s):  
Particle Image Velocimetry ◽  
Large Scale ◽  
Field Experiments ◽  
Particle Image ◽  
Low Cost ◽  
Surface Flow ◽  
Natural Environments ◽  
Natural Stream ◽  
Image Velocimetry ◽  
Scale Particle

Large scale particle image velocimetry (LSPIV) is a nonintrusive environmental monitoring methodology that allows for continuous characterization of surface flows in natural catchments. Despite its promise, the implementation of LSPIV in natural environments is limited to areas accessible to human operators. In this work, we propose a novel experimental configuration that allows for unsupervised LSPIV over large water bodies. Specifically, we design, develop, and characterize a lightweight, low cost, and stable quadricopter hosting a digital acquisition system. An active gimbal maintains the camera lens orthogonal to the water surface, thus preventing severe image distortions. Field experiments are performed to characterize the vehicle and assess the feasibility of the approach. We demonstrate that the quadricopter can hover above an area of 1×1m2 for 4–5 minutes with a payload of 500g. Further, LSPIV measurements on a natural stream confirm that the methodology can be reliably used for surface flow studies.

Seeding metrics for image velocimetry performances in rivers

2021 ◽  
Author(s):  
Silvano Fortunato Dal Sasso ◽  
Alonso Pizarro ◽  
Sophie Pearce ◽  
Ian Maddock ◽  
Matthew T. Perks ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Optical Sensors ◽  
Large Scale ◽  
Surface Flow ◽  
Surface Velocity ◽  
Seeding Density ◽  
Earth System ◽  
Science Data ◽  
Image Velocimetry ◽  
Hydraulic Conditions

<p>Optical sensors coupled with image velocimetry techniques are becoming popular for river monitoring applications. In this context, new opportunities and challenges are growing for the research community aimed to: i) define standardized practices and methodologies; and ii) overcome some recognized uncertainty at the field scale. At this regard, the accuracy of image velocimetry techniques strongly depends on the occurrence and distribution of visible features on the water surface in consecutive frames. In a natural environment, the amount, spatial distribution and visibility of natural features on river surface are continuously challenging because of environmental factors and hydraulic conditions. The dimensionless seeding distribution index (SDI), recently introduced by Pizarro et al., 2020a,b and Dal Sasso et al., 2020, represents a metric based on seeding density and spatial distribution of tracers for identifying the best frame window (FW) during video footage. In this work, a methodology based on the SDI index was applied to different study cases with the Large Scale Particle Image Velocimetry (LSPIV) technique. Videos adopted are taken from the repository recently created by the COST Action Harmonious, which includes 13 case study across Europe and beyond for image velocimetry applications (Perks et al., 2020). The optimal frame window selection is based on two criteria: i) the maximization of the number of frames and ii) the minimization of SDI index. This methodology allowed an error reduction between 20 and 39% respect to the entire video configuration. This novel idea appears suitable for performing image velocimetry in natural settings where environmental and hydraulic conditions are extremely challenging and particularly useful for real-time observations from fixed river-gauged stations where an extended number of frames are usually recorded and analyzed.</p><p> </p><p><strong>References </strong></p><p>Dal Sasso S.F., Pizarro A., Manfreda S., Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers. Remote Sensing, 12, 1789 (doi: 10.3390/rs12111789), 2020.</p><p>Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., …Manfreda S, Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, https://doi.org/10.5194/essd-12-1545-2020, 12(3), 1545 – 1559, 2020.</p><p>Pizarro A., Dal Sasso S.F., Manfreda S., Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.</p><p>Pizarro A., Dal Sasso S.F., Perks M. and Manfreda S., Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173–5185, (10.5194/hess-24-5173-2020), 2020.</p>

scholarly journals Spatial distribution of tracers for optical sensing of stream surface flow

2020 ◽  
Author(s):  
Alonso Pizarro ◽  
Silvano F. Dal Sasso ◽  
Matthew Perks ◽  
Salvatore Manfreda
Keyword(s):  
Large Scale ◽  
Optical Sensing ◽  
Surface Flow ◽  
Seeding Density ◽  
Stream Surface ◽  
Image Velocimetry ◽  
Level Of Aggregation ◽  
Scale Particle ◽  
The One

Abstract. River monitoring is of particular interest for our society that is facing increasing complexity in water management. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but also generating 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 level of aggregation. Therefore, a requirement 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 particle aggregation, 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) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on density and aggregation) was introduced based on a newly developed metric π. This value can be approximated and used in practice as π = ν0.1 / (ρ / ρcν1) where ν, ρ, and ρcν1 are the aggregation level, the seeding density, and the converging seeding density at ν = 1, respectively. A reduction of image-velocimetry errors was systematically observed by decreasing the values of π; and therefore, the optimal frame window was defined as the one that minimises π. In addition to numerical analyses, the Basento field case study (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and an error reduction of about 15.9 and 16.1 % was calculated – using PTV and PIV, respectively – by employing the optimal frame window.

scholarly journals An evaluation of image velocimetry techniques under low flow conditions and high seeding densities using Unmanned Aerial Systems

2020 ◽  
Author(s):  
Sophie Pearce ◽  
Robert Ljubicic ◽  
Salvador Pena-Haro ◽  
Matthew Perks ◽  
Flavia Tauro ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Surface Flow ◽  
Extraction Rate ◽  
Particle Identification ◽  
Surface Velocity ◽  
Low Flow ◽  
Unmanned Aerial Systems ◽  
Flow Conditions ◽  
Image Velocimetry

<p>Image velocimetry (IV) is a remote technique which calculates surface flow velocities of rivers (or fluids) via a range of cross-correlation and tracking algorithms. IV can be implemented via a range of camera sensors which can be mounted on tri-pods, or Unmanned Aerial Systems (UAS). IV has proven a powerful technique for monitoring river flows during flood conditions, whereby traditional in-situ techniques would be unsafe to deploy. However, little research has focussed upon the application of such techniques during low flow conditions. The applicability of IV to low flow studies could aid data collection at a higher spatial and temporal resolution than is currently available. Many IV techniques are under-development, that utilise different cross-correlation and tracking algorithms, including, Large Scale Particle Image Velocimetry (LSPIV), Large Scale Particle Tracking Velocimetry (LSPTV), Optical Tracking Velocimetry (OTV), Kanade Lucas Tomasi Image Velocimetry (KLT-IV) and Surface Structure Image Velocimetry (SSIV). Nevertheless, the true applications and limitations of such algorithms have yet to be extensively tested. Therefore, this study aimed to conduct a sensitivity analysis on the commonly relatable 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 (or sub sampled frame rate).</p><p>Fieldwork was carried out on Kolubara River near the city of Obrenovac in Central Serbia. Cross-sectional surface width was relatively constant, varying between 23.30 and 23.45m. During the experiment, low flow conditions were present with a discharge of approx. 3.4m<sup>3 </sup>s<sup>-1</sup> (estimated using a Sontek M9 ADCP), and depths of up to 1.9m. A DJI Phantom 4 Pro UAS was used to collect video data of the surface flow. Artificial seeding material (wood-mulch) was distributed homogenously across the rivers’ surface, in order to improve the conditions for IV techniques during slow flows. Two 30-second videos were utilised for surface velocity analysis.</p><p>This study highlighted that KLT, SSIV, OTV and LSPIV are the least sensitive algorithms to changing parameters when no pre- or post-processing of results are conducted. On the other hand, LSPTV must undergo post-processing procedures in order to avoid spurious results and only then, results may be reliable. Furthermore, KLT and SSIV highlighted a slight sensitivity to changing the feature extraction rate, however changing the particle identification area did not affect significantly the outputted surface velocity results. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area provided a higher variability in the results, whilst 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.</p><p>This analysis has led to the conclusions that during the conditions of sampling with surface velocities of approximately 0.12ms<sup>-1</sup>, and homogeneous seeding on the rivers surface, IV techniques can provide results comparable to traditional techniques such as ADCPs during low flow conditions. All IV algorithms provided results that were, on average, within 0.05ms<sup>-1</sup> of the ADCP measurements.</p><p> </p>

scholarly journals sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

2019 ◽  
Vol 11 (19) ◽  
pp. 2317 ◽  
Author(s):  
Paul Kinzel ◽  
Carl Legleiter
Keyword(s):  
Particle Image Velocimetry ◽  
River Discharge ◽  
Water Surface ◽  
Large Scale ◽  
Particle Image ◽  
Field Evaluation ◽  
Remotely Sensed ◽  
Surface Velocity ◽  
Unmanned Aerial Systems ◽  
Image Velocimetry

This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely ( R 2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections ( R 2 = 0.95), but the agreement was not as strong for the transect with greater depths ( R 2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and <1% at the second.

scholarly journals Real-Time Measurement of Flash-Flood in a Wadi Area by LSPIV and STIV

Hydrology ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 27 ◽  
Author(s):  
Mahmood Al-mamari ◽  
Sameh Kantoush ◽  
Sohei Kobayashi ◽  
Tetsuya Sumi ◽  
Mohamed Saber
Keyword(s):  
Large Scale ◽  
Flash Flood ◽  
Early Warning Systems ◽  
Flash Floods ◽  
Surface Flow ◽  
Unidirectional Flow ◽  
Real Time Measurement ◽  
Positive Performance ◽  
Image Velocimetry ◽  
Scale Particle

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.

scholarly journals A novel permanent gauge-cam station for surface flow observations on the Tiber river

2016 ◽  
Author(s):  
Flavia Tauro ◽  
Andrea Petroselli ◽  
Maurizio Porfiri ◽  
Lorenzo Giandomenico ◽  
Guido Bernardi ◽  
...  
Keyword(s):  
Flow Velocity ◽  
Large Scale ◽  
Surface Flow ◽  
Video Data ◽  
Engineering Practice ◽  
Surface Hydrology ◽  
Flow Monitoring ◽  
Tiber River ◽  
Scale Particle ◽  
Local Water

Abstract. Flow monitoring of riverine environments is crucial for hydrology and hydraulic engineering practice. Besides few experimental implementations, flow gauging relies on local water level and surface flow velocity measurements through ultrasonic meters and radars. In this paper, we describe a novel permanent gauge-cam station for large scale and continuous observation of surface flows, based on remote acquisition and calibration of video data. Located on the Tiber river, in the center of Rome, Italy, the station captures one-minute videos every 10 min over an area oriented along the river cross-section of up to 20.6 × 15.5 m2. In a feasibility study, we demonstrate that accurate surface flow velocity estimations can be obtained by analyzing experimental images via particle tracking velocimetry (PTV). In medium illumination conditions (70–75 lux), PTV leads to velocity estimations in close agreement with radar records and is less affected by uneven lighting than large scale particle image velocimetry. Future efforts will be devoted to the development of a comprehensive testbed infrastructure for investigating the potential of multiple optics-based approaches for surface hydrology.

scholarly journals A novel permanent gauge-cam station for surface-flow observations on the Tiber River

2016 ◽  
Vol 5 (1) ◽  
pp. 241-251 ◽  
Author(s):  
Flavia Tauro ◽  
Andrea Petroselli ◽  
Maurizio Porfiri ◽  
Lorenzo Giandomenico ◽  
Guido Bernardi ◽  
...  
Keyword(s):  
Flow Velocity ◽  
Large Scale ◽  
Surface Flow ◽  
Video Data ◽  
Engineering Practice ◽  
Test Bed ◽  
Surface Hydrology ◽  
Flow Monitoring ◽  
Tiber River ◽  
Scale Particle

Abstract. Flow monitoring of riverine environments is crucial for hydrology and hydraulic engineering practice. Besides few experimental implementations, flow gauging relies on local water level and surface-flow velocity measurements through ultrasonic meters and radars. In this paper, we describe a novel permanent gauge-cam station for large-scale and continuous observation of surface flows, based on remote acquisition and calibration of video data. Located on the Tiber River, in the center of Rome, Italy, the station captures 1 min videos every 10 min over an area oriented along the river cross section of up to 20.6  ×  15.5 m2. In a feasibility study, we demonstrate that accurate surface-flow velocity estimations can be obtained by analyzing experimental images via particle tracking velocimetry (PTV). In medium illumination conditions (70–75 lux), PTV leads to velocity estimations in close agreement with radar records and is less affected by uneven lighting than large-scale particle image velocimetry. Future efforts will be devoted to the development of a comprehensive test bed infrastructure for investigating the potential of multiple optics-based approaches for surface hydrology.

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

2020 ◽  
Vol 24 (11) ◽  
pp. 5173-5185
Author(s):  
Alonso Pizarro ◽  
Silvano F. Dal Sasso ◽  
Matthew T. Perks ◽  
Salvatore Manfreda
Keyword(s):  
Spatial Distribution ◽  
Optical Sensing ◽  
Spatial Clustering ◽  
Surface Flow ◽  
Seeding Density ◽  
Stream Surface ◽  
Image Velocimetry ◽  
Distribution Index ◽  
Research Goal ◽  
The One

Abstract. 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=ν0.1/ρρcν1, where ν, ρ, and ρcν1 are the spatial-clustering level, the seeding density, and the reference seeding density at ν=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.

Sign in / Sign up

Export Citation Format

Share Document