Optical sensing for stream flow observations: A review

Tauro, Flavia; Petroselli, Andrea; Grimaldi, Salvatore

doi:10.4081/jae.2018.836

Cited by 25 publications

(26 citation statements)

References 42 publications

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“…Although the image-based methods for stream velocity have been introduced several years ago, there are still limitations to overcome in order to become a worldwide monitoring system [69]. One of the image-based method limitations is to acquire the correct water level or bathymetry 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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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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“…Optical analysis of the flow may be performed with the use of different methods of data evaluation, including Kanade-Lucas-Tomasi image velocimetry [27], optical tracking velocimetry [28], surface structure image velocimetry [29], space-time image velocimetry [30], and the most explored technique, particle tracking velocimetry (PTV) [31][32][33] and particle image velocimetry (PIV) [34,35]. A comprehensive review of the latter two methods can be found in [36,37]. PTV, a Lagrangian approach, is based on identification and tracking of individual particles across consequent video frames.…”

Section: Introductionmentioning

confidence: 99%

“…PTV, a Lagrangian approach, is based on identification and tracking of individual particles across consequent video frames. It is designed for low density particle distribution and, in most implementations, requires that particles are round-shaped [36]. Some implementations, such as PTV-Stream [38], can deal with tracers of any shape, but they have other limitations, e.g., the necessity to know in advance the direction of the flow average velocity.…”

Section: Introductionmentioning

confidence: 99%

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Drone-Based Optical Measurements of Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams

et al. 2020

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In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The efficiency of fish passages is being constantly monitored and improved. Since the likelihood of fish passages to be discovered by fish depends, inter alia, on flow conditions near their entrances, these conditions have to be monitored as well. In this study, we employ large-scale particle image velocimetry (LSPIV) in seeded flow conditions to analyse images of the area near a fish passage entrance, captured with the help of a ready-to-fly consumer drone. We apply LSPIV to short image sequences and test different LSPIV interrogation area sizes and correlation methods. The study demonstrates that LSPIV based on ensemble correlation yields velocities that are in good agreement with the reference values regarding both magnitude and flow direction. Therefore, this non-intrusive methodology has a potential to be used for flow monitoring near fish passages on a regular basis, enabling timely reaction to undesired changes in flow conditions when possible.

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“…Most of these methods are expensive and some of them require time-consuming experimental campaigns and the presence of qualified personnel. Intrusive and highly user-assisted technology cannot be adopted to monitor abrupt phenomena, such as flash floods, and large flood events that may be risky for personnel and equipment [8].…”

Section: Introductionmentioning

confidence: 99%

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

et al. 2018

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Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been preferred to alternative image-based approaches, such as optical flow, for camera-based surface flow velocity estimate. In this work, we introduce a novel optical flow scheme, optical tracking velocimetry (OTV), that entails automated feature detection, tracking through the differential sparse Lucas-Kanade algorithm, and then a posteriori filtering to retain only realistic trajectories that pertain to the transit of actual objects in the field of view. The method requires minimal input on the flow direction and camera orientation. Tested on two image data sets collected in diverse natural conditions, the approach proved suitable for rapid and accurate surface flow velocity estimations. Five different feature detectors were compared and the features from accelerated segment test (FAST) resulted in the best balance between the number of features identified and successfully tracked as well as computational efficiency. OTV was relatively insensitive to reduced image resolution but was impacted by acquisition frequencies lower than 7-8 Hz. Compared to traditional correlation-based techniques, OTV was less affected by noise and surface seeding. In addition, the scheme is foreseen to be applicable to real-time gauge-cam implementations.

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Optical sensing for stream flow observations: A review

Cited by 25 publications

References 42 publications

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

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

Drone-Based Optical Measurements of Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower Dams

Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations

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