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.

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 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.

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 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 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
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