Spatial Measurements of Snowmelt Flood by Image Analysis With Multiple-Angle Images and Radio-Controlled Adcp

Fujita, Ichiro; Kitada, Masaki; Shimono, Mitsuru; Kitsuda, Takashi; Yorozuya, Atsuhiro; Motonaga, Yoshiki

doi:10.2208/journalofjsce.5.1_305

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…The most possible solution to the problem is to use image-based techniques such as the large-scale particle image velocimetry (LSPIV) [2, 3, 6, 8-10, 12, 23, 24, 26-29] or the space-time image velocimetry (STIV) [1,13,14,19,30]. These techniques can measure surface velocity distributions by analyzing surface images captured from a river bank.…”

Section: Introductionmentioning

confidence: 99%

Efficient and accurate estimation of water surface velocity in STIV

et al. 2018

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In shallow flow conditions, turbulence effects appear on a water surface as a form of irregularity of surface shape composed of a large number of fluctuating ripples. The intensity of such a fluctuation increases with the Froude number and also with the Reynolds number as can be observed in flooding river flow. In such a flow condition, surface irregularities are viewed as surface features or textures moving with the flow. Although there has been a discussion in terms of the traceability of surface features, the advection speed of surface features agrees well with the surface velocity from a practical point of view. Based on the assumption about the traceability of surface features, image-based techniques have been developed in the past decades. The space-time image velocimetry (STIV) is one of those techniques developed by Fujita et al. (Int J River Basin Man 5(2):105-114, 2007), with success of measuring river surface velocity distributions without seeding the flow. However, there is still some room for improvement in determining accurate surface velocity from a space-time image (STI) used in STIV. For that purpose, a novel technique was developed that utilizes the two dimensional auto-correlation function of the image intensity in an STI together with quality indices of STI. The performance of the new technique was verified using synthetic images as well as its application to the measurement of snowmelt flood.

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Section: Introductionmentioning

confidence: 99%

Efficient and accurate estimation of water surface velocity in STIV

et al. 2018

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“…This allows the images to be captured without any angles, that allow the orthorectification process to be avoided [63]. All studied methods have been approved by the scientific community and provide satisfactory performance and results, as they have been tested coupling other verification methods, such as data from gauged stations or acoustic Doppler devices [64][65][66]. In addition, these image-based methods are very useful tools when the studied area is not very easily accessible for field measurements.…”

Section: Discussionmentioning

confidence: 99%

UAVs for Hydrologic Scopes: Application of a Low-Cost UAV to Estimate Surface Water Velocity by Using Three Different Image-Based Methods

Koutalakis

Tzoraki

Zaimes

2019

Drones

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Stream velocity and flow are very important parameters that must be measured accurately to develop effective water resource management plans. There are various methods and tools to measure the velocity but, nowadays, image-based methods are a promising alternative that does not require physical contact with the water body. The current study describes the application of a low cost unmanned aerial vehicle that was selected in order to capture a video over a specific reach of Aggitis River in Greece. The captured frames were analyzed by three different software (PIVlab, PTVlab, and KU-STIV) in order to estimate accurately the surface water velocity. These three software also represent three different image-based methodologies. Although there are differences among these three methods, the analysis produced similar trends for all. The velocity ranged between 0.02 and 3.98 m/s for PIVlab, 0.12 and 3.44 m/s for PTVlab, and 0.04 and 3.99 m/s for KU-STIV software. There were parts, especially in the existing vegetation, where differences were observed. Further applications will be examined in the same or different reaches, to study the parameters affecting the analysis. Finally, the image-based methods will be coupled with verification measurements by a current meter to produce more rigorous results.

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“…In contrast to regular riverbeds, natural river channels are highly irregular and unpredictable. Deploying velocitymeasuring lines parallel to the frame through image rectification and camera angles adjustment is difficult [17]. This process is often manual and lacks a reliable basis.…”

Section: Introductionmentioning

confidence: 99%

River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics

Yuan,

Che,

Wang

et al. 2024

Meas. Sci. Technol.

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River flow monitoring serves as a crucial data source in hydrological stations, employing the widely adopted space-time image velocimetry (STIV) method both domestically and internationally. However, current methods yield space-time image trajectories with limited usable information, particularly due to the interference of complex flow characteristics and harsh outdoor environments in natural rivers. This interference leads to errors in detecting the main orientation of texture, consequently reducing measurement accuracy. To overcome these challenges, this paper proposes an river video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics. The Lucas-Kanade tracking algorithm is employed to track velocity points and generate tracking trajectories. These trajectories' coordinates are then fitted into straight lines to automatically arrange velocity-measuring lines in natural rivers. The algorithm leverages multi-scale detail Boosting and Gaussian directional stretch filtering to enhance the quality and texture trajectories of space-time images. Furthermore, background noise filtering and stripe noise filtering are applied to denoise the space-time images, effectively mitigating the impact of noise caused by the natural river environment and camera equipment. The proposed algorithm incorporates projection statistical characteristics algorithms to accurately detect the dominant texture direction, enabling precise calculation of Mean Velocity and cross-sectional discharge of the river. Experimental validation was conducted in both natural and regular river channels, demonstrating the superior performance of the proposed solution compared to widely used LSPIV, STIV, MobileNet-STIV and FD-DIS-G algorithms. The algorithm exhibited higher accuracy, stability, and applicability, with errors at each velocity-measuring point below 10%, and relative errors of the calculated Mean Velocity and cross-sectional discharge below 3%. This affirms the algorithm's superior accuracy, stability, and applicability.

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Spatial Measurements of Snowmelt Flood by Image Analysis With Multiple-Angle Images and Radio-Controlled Adcp

Cited by 11 publications

References 13 publications

Efficient and accurate estimation of water surface velocity in STIV

Efficient and accurate estimation of water surface velocity in STIV

UAVs for Hydrologic Scopes: Application of a Low-Cost UAV to Estimate Surface Water Velocity by Using Three Different Image-Based Methods

River video flow measurement algorithm with space-time image fusion of object tracking and statistical characteristics

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