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.

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.

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 Application of an Automated Discharge Imaging System and LSPIV during Typhoon Events in Taiwan

2018 ◽  
Author(s):  
Wei-Che Huang ◽  
Chih-Chieh Young ◽  
Wen-Cheng Liu
Keyword(s):  
Large Scale ◽  
Imaging System ◽  
Flash Flood ◽  
Acoustic Doppler Current Profiler ◽  
Surface Velocity ◽  
Cross Section Area ◽  
Section Area ◽  
Image Velocimetry ◽  
Current Profiler ◽  
Scale Particle

An automated discharge imaging system (ADIS), a non-intrusive and safe approach, was developed for measuring river flows during flash flood events. ADIS consists of dual cameras to capture complete surface images in the near and far fields. Surface velocities are accurately measured using the Large Scale Particle Image Velocimetry (LSPIV) technique. The stream discharges are then obtained from the depth-averaged velocity (based upon an empirical velocity-index relationship) and cross-section area. The ADIS was deployed at the Yu-Feng gauging station in Shimen Reservoir upper catchment, northern Taiwan. For a rigorous validation, surface velocity measurements were conducted using ADIS/LSPIV and other instruments. In terms of the averaged surface velocity, all measured results were in good agreement with small differences, i.e., 0.004 to 0.39 m/s and 0.023 to 0.345 m/s when compared to those from acoustic Doppler current profiler (ADCP) and surface velocity radar (SVR), respectively. The ADIS/LSPIV was further applied to measure surface velocities and discharges during typhoon events (i.e., Chan-Hom, Soudelor, Goni, and Dujuan) in 2015. The measured water level and surface velocity both showed rapid increases due to flash floods. The estimated discharges from ADIS/LSPIV and ADCP were compared, presenting good consistency with correlation coefficient R = 0.996 and normalized root mean square error NRMSE = 7.96%. The results of sensitivity analysis indicate that components till (τ) and roll (θ) of the camera are most sensitive parameter to affect the surface velocity using ADIS/LSPIV. Overall, the ADIS based upon LSPIV technique effectively measures surface velocities for reliable estimations of river discharges during typhoon events.

scholarly journals Innovative Monitoring Techniques for Wadi Flash Flood by Using Image-Based Analysis

2021 ◽  
pp. 251-266
Author(s):  
Mahmood M. Al-Mamari ◽  
Sameh A. Kantoush ◽  
Tetsuya Sumi
Keyword(s):  
Water Resource Management ◽  
Flood Control ◽  
Large Scale ◽  
High Efficiency ◽  
Flash Flood ◽  
Flash Floods ◽  
Scale Particle ◽  
Bridge Crossing ◽  
Sectional Profile ◽  
Elevation Model

AbstractFlash floods in wadi systems are a very important environmental issue, and their monitoring is necessary for many applications, including water resource management, irrigation and flood control. However, monitoring networks are very rare and lack spatial distribution features. In this study, image-based techniques were used to quantify and monitor flash floods in wadi channels by using two different methods. In the first section, we employed photogrammetry processing technique to quantify post-peak flood discharges by using a drone survey to build a digital elevation model (DEM) with a high resolution and calibrated and validated the model with a field survey (levelling measurements). This technique used drone-collected images to construct a DEM for extracting a cross-sectional profile and elevation points to calculate the peak discharge using the slope-area method with the Manning equation. In the second section, we combined the previous technique with the large-scale particle image velocimetry (LSPIV) technique to measure flash flood discharge by installing a fixed camera on a road bridge crossing a wadi channel and using a digitally extracted cross section from the DEM in the analysis. The results of those techniques show a high efficiency that is equivalent to that of conventional methods.

scholarly journals Validation of large-scale particle image velocimetry to acquire free-surface flow fields in vegetated rivers

2016 ◽  
Vol 6 (3) ◽  
pp. 171-182 ◽  
Author(s):  
Stéphan Creëlle ◽  
Rebeca Roldan ◽  
Anke Herremans ◽  
Dieter Meire ◽  
Kerst Buis ◽  
...  
Keyword(s):  
Particle Image Velocimetry ◽  
Free Surface ◽  
Large Scale ◽  
Particle Image ◽  
Free Surface Flow ◽  
Flow Fields ◽  
Surface Flow ◽  
Image Velocimetry ◽  
Scale Particle

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 Stream discharge using mobile large-scale particle image velocimetry: A proof of concept

2008 ◽  
Vol 44 (9) ◽  
Author(s):  
Y. Kim ◽  
M. Muste ◽  
A. Hauet ◽  
W. F. Krajewski ◽  
A. Kruger ◽  
...  
Keyword(s):  
Particle Image Velocimetry ◽  
Large Scale ◽  
Particle Image ◽  
Proof Of Concept ◽  
Stream Discharge ◽  
Image Velocimetry ◽  
Scale Particle

Large scale particle image velocimetry measurement of river surface velocity based on images captured by a camera of the mobile phone

2020 ◽  
Author(s):  
Wen-Cheng Liu ◽  
Wei-Che Huang
Keyword(s):  
Particle Image Velocimetry ◽  
Mobile Phone ◽  
Large Scale ◽  
Particle Image ◽  
Digital Camera ◽  
Real Space ◽  
Surface Velocity ◽  
Image Velocimetry ◽  
Scale Particle ◽  
Perspective Center

<p>In this research, we conducted LSPIV (Large Scale Particle Image Velocimetry) measurements to measure river surface velocity based on images recorded by mobile phone. The realization of this research is based on the developments of two products. The first one is the digital camera, which has been combined with the mobile phone after several years of development. The second one is the three-axis accelerometer, which can measure the attitude of the object. A three-axis accelerometer is one of the necessary parts of the mobile phone nowadays, as many functions of the mobile phone, such as step counting, Do Not Disturb mode, games, require the detection of attitude.</p><p>In LSPIV, there are nine parameters of the collinear equation. Three of parameters are the coordinates of the perspective center in the image space (focus distance d and image center position (u, v)), which can be determined in advance in the laboratory; the other three parameters are the coordinates (x, y, z) of the perspective center in real space, which can be set to (0, 0, 0); the last three parameters are the attitude of the camera (i.e., the mobile phone), which is determined by the depression angle, the horizontal angle, and the left-right rotation angle and can be measured by three-axis accelerometer. Therefore, river surface velocity could be analyzed by LSPIV with not only continuous images captured by a camera of the mobile phone but also the acceleration values obtained by the three-axis accelerometer when each image was captured.</p><p>In the present study, Yufeng gauging station, which is in the upstream catchment of the Shihmen Reservoir in Taiwan, is selected as the study site. Two other measurement methods were used to measure the river surface velocity and the comparison was conducted. One is using a handheld digital flow meter and another is using LSPIV with control points to calculate the parameters for measuring the river surface velocity.</p>

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