Development and Application of an Automated River-Estuary Discharge Imaging System

Bechle, Adam J.; Wu, Chin H.; Liu, Wen‐Cheng; Kimura, Nobuaki

doi:10.1061/(asce)hy.1943-7900.0000521

Cited by 45 publications

(41 citation statements)

References 2 publications

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“…In Figure 5, d is the focal length of camera lens; x c , y c , z c are the physical locations of the cameras; coefficients r ij are rotational angles (i.e., components of azimuth α, tilt τ, and roll θ) of the camera [22]. The detailed formulas to express a geometric relationship between an image point (u, v) and physical position in space (x, y, z) can be found in Bechle et al [13].…”

Section: (I) Image Calibrationmentioning

confidence: 99%

“…The basic idea was to rotate both of the cameras between the observation region (river channel) and an area (stairs) where the control points existed. The rotational calibration can be conducted before/after image acquisition since our cameras always can be rotated back to the initial orientation α [13]. The main reason that the control points are selected at stairs in the gauging station is to prevent the researchers who intrude the river channel region for measuring the physical coordinates using total station.…”

Section: (I) Image Calibrationmentioning

confidence: 99%

“…Two images (at least) were taken from different vantage points to triangulate three-dimensional coordinates of water surface [13,21,23]. Based on the camera orientation Figure 5.…”

Section: (Ii) Image Mappingmentioning

confidence: 99%

“…three-dimensional coordinates of water surface [13,21,23]. Based on the camera orientation parameters, two conditions are insufficient to resolve the three unknowns (x, y, z) for image points (u, v) in either near-or far-field observation area.…”

Section: (Ii) Image Mappingmentioning

confidence: 99%

“…In recent years, non-intrusive approaches based on radar (e.g., [5]) and/or imaging systems (e.g., [6][7][8][9][10][11][12][13][14][15][16]) have been increasingly applied, providing a safer alternative for measuring flood discharge. Without the needs of human-operated physical sampling and data acquisition, surface velocities are characterized from continuous images of observed (seeded fluid) flows mainly using two types of algorithms, i.e., particle tracers and imagine analyses (e.g., [17]).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Huang

Young

Liu

2018

Water

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An automated discharge imaging system (ADIS), which is 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 of the 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 the components till (τ) and roll (θ) of the camera are most sensitive parameters 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.

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Section: (I) Image Calibrationmentioning

confidence: 99%

Section: (I) Image Calibrationmentioning

confidence: 99%

“…Two images (at least) were taken from different vantage points to triangulate three-dimensional coordinates of water surface [13,21,23]. Based on the camera orientation Figure 5.…”

Section: (Ii) Image Mappingmentioning

confidence: 99%

Section: (Ii) Image Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

Young

Liu

2018

Water

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An entropy‐based surface velocity method for estuarine discharge measurement

Bechle

2014

Water Resources Research

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An entropy-based method is developed to estimate estuarine river discharge from surface velocity measurements. A two-dimensional velocity profile based on the principle of maximum entropy is employed to express the mean velocity as a function of average surface velocity. The entropy-based flow profile is parameterized by the location of maximum velocity in the channel and the shape of the velocity distribution. The entropy parameters are quantified over the tidal cycle to account for the unsteady nature of estuarine flow. The method was tested using experiments conducted at the Danshui River, the largest estuarine system in Taiwan. Surface velocities were measured using an Automated River-Estuary Discharge Imaging System (AREDIS), and full-channel velocity profiles were measured with a moving-boat ADP survey. Entropy parameters were calibrated over the tidal cycle and linearly correlated with the average surface velocity to facilitate estimation from AREDIS measurements. The discharge calculated from average surface velocity and entropy relationships exhibits a 7.7% relative error compared to the ADP velocity profiles. The error nearly doubles when the mean values for entropy parameters are used instead of the variable parameters, indicating the importance of accounting for the unsteady nature of estuarine flows. Furthermore, the effects of measurement coverage area, types of entropy distribution, and wind-induced drift current on the surface velocity-based discharge measurement are evaluated and discussed. Overall, surface velocity measurements in conjunction with the entropy profiles well represent the flow in a complex estuarine environment to provide a reliable estimate of discharge.

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Orienting the camera and firing lasers to enhance large scale particle image velocimetry for streamflow monitoring

Tauro

Porfiri

Grimaldi

2014

Water Resources Research

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Large scale particle image velocimetry (LSPIV) is a nonintrusive methodology for continuous surface flow monitoring in natural environments. Recent experimental studies demonstrate that LSPIV is a promising technique to estimate flow discharge in riverine systems. Traditionally, LSPIV implementations are based on the use of angled cameras to capture extended fields of view; images are then orthorectified and calibrated through the acquisition of ground reference points. As widely documented in the literature, the identification of ground reference points and image orthorectification are major hurdles in LSPIV. Here we develop an experimental apparatus to address both of these issues. The proposed platform includes a laser system for remote frame calibration and a low-cost camera that is maintained orthogonal with respect to the water surface to minimize image distortions. We study the feasibility of the apparatus on two complex natural riverine environments where the acquisition of ground reference points is prevented and illumination and seeding density conditions are challenging. While our results confirm that velocity estimations can be severely affected by inhomogeneously seeded surface tracers and adverse illumination settings, they demonstrate that LSPIV implementations can benefit from the proposed apparatus. Specifically, the presented system opens novel avenues in the development of stand-alone platforms for remote surface flow monitoring.

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Development and Application of an Automated River-Estuary Discharge Imaging System

Cited by 45 publications

References 2 publications

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

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

An entropy‐based surface velocity method for estuarine discharge measurement

Orienting the camera and firing lasers to enhance large scale particle image velocimetry for streamflow monitoring

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