scholarly journals Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System

2021 ◽  
Vol 13 (14) ◽  
pp. 2661
Author(s):  
Wen-Cheng Liu ◽  
Chien-Hsing Lu ◽  
Wei-Che Huang
Keyword(s):  
Large Scale ◽  
Particle Image ◽  
Surface Velocity ◽  
Low Flow ◽  
Flow Conditions ◽  
Image Velocimetry ◽  
Flight Height ◽  
Aerial Vehicle ◽  
The Mean ◽  
Scale Particle

The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers.

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>

scholarly journals Large-Scale Particle Image Velocimetry From an Unmanned Aerial Vehicle

2015 ◽  
Vol 20 (6) ◽  
pp. 3269-3275 ◽  
Author(s):  
Flavia Tauro ◽  
Christopher Pagano ◽  
Paul Phamduy ◽  
Salvatore Grimaldi ◽  
Maurizio Porfiri
Keyword(s):  
Particle Image Velocimetry ◽  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Particle Image ◽  
Image Velocimetry ◽  
Aerial Vehicle ◽  
Scale Particle

scholarly journals MEASUREMENT OF INUNDATING FLOW FROM A BROKEN ENBANKMENT BY USING VIDEO IMAGES SHOOT FROM A MEDIA HELICOPTER

2018 ◽  
Vol 40 ◽  
pp. 06001
Author(s):  
Ichiro Fujita ◽  
Yuichi Notoya ◽  
Takanori Furuta
Keyword(s):  
Large Scale ◽  
Particle Image ◽  
Heavy Rain ◽  
Surface Velocity ◽  
Water Volume ◽  
Video Images ◽  
Image Velocimetry ◽  
Flow Information ◽  
Scale Particle ◽  
Time Image

The heavy rain disaster in the Kinugawa River basin that occurred along with the passage of the Typhoon 18 caused the embankment destruction in the middle reach of the river on September 10, 2015. Due to the overflow, the houses in the vicinity of the embankment collapsed, causing a flood inundation spreading over a wide area. Because the embankment breakwater occurred during the daytime, the state of the inundating flow was recorded from various angles by media helicopters or drones. In this study, we developed a method to extract quantitative flow information from a helicopter video image in which the shooting position and angles are changed one after another, because it was taken in emergency. In the analysis, the images were orthorectified after stabilizing the images, from which surface velocity distributions were measured by image-based technique such as the large-scale particle image velocimetry (LSPIV) or the space-time image velocimetry (STIV). As a result, the time change of water entering from the broken embankment and the total inundated water volume during the disaster were estimated. In addition, two-dimensional surface velocity distributions were analysed to show the spreading of inundated flow.

Fast, large-scale, particle image velocimetry-based estimations of river surface velocity

2014 ◽  
Vol 70 ◽  
pp. 35-43 ◽  
Author(s):  
David W. Dobson ◽  
K. Todd Holland ◽  
Joseph Calantoni
Keyword(s):  
Particle Image Velocimetry ◽  
Large Scale ◽  
Particle Image ◽  
Surface Velocity ◽  
Image Velocimetry ◽  
Scale Particle

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

2020 ◽  
Vol 12 (2) ◽  
pp. 232 ◽  
Author(s):  
Sophie Pearce ◽  
Robert Ljubičić ◽  
Salvador Peña-Haro ◽  
Matthew Perks ◽  
Flavia Tauro ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Extraction Rate ◽  
Particle Identification ◽  
Surface Velocity ◽  
Unmanned Aerial Systems ◽  
Flow Conditions ◽  
Image Velocimetry ◽  
Scale Particle ◽  
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.

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.

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