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

Abstract

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.

Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

“…Near-field remote sensing of streamflow in Alaska presents a number of unique challenges that can complicate if not preclude application of methods developed for smaller rivers and streams in more readily accessible locations. For example, although many prior investigations have estimated surface flow velocities by tracking features visible in image sequences acquired from sUAS, most of these studies seeded the flow with tracer particles to enable the use of various motion estimation algorithms including PIV, particle tracking velocimetry (PTV), and optical flow (e.g., [7][8][9][10][11]). However, introducing artificial tracers in a large Alaskan river like the Yukon, which is the third longest river in the United States and the second largest by flow volume [1], is simply not practical.…”

“…Our work builds upon a growing body of literature on remote sensing of surface flow velocities, summarized in the recent study by Strelnikova et al [11]. Both Koutalakis et al [23] and Pearce et al [10] compared algorithms for inferring flow velocities from optical image sequences, including PIV, PTV, space-time image velocimetry (STIV), a Kanade-Lucas Tomasi optical flow technique, and several other variants.…”

Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization

“…This drive has been likely motivated by the unprecedented increment in technology leading to the improvement of user accessibility (costs and friendly interface) and sensors' performance, including their miniaturization. Large Scale Particle Image Velocimetry (LSPIV) is often used to process images to estimate surface flow velocities in rivers (Fujita, Muste, & Kruger, 1998; Manfreda, Dal Sasso, Pizarro, & Tauro, 2019; Pearce et al, 2020; Strelnikova et al, 2020; Tauro & Salvatori, 2017). However, several other algorithms – adopting alternative approaches – have been developed and currently used for surface flow velocity calculations.…”

KLT-IV v1.0: image velocimetry software for use with fixed and mobile platforms

“…These features are subsequently tracked from frame to frame using optical flow techniques. This approach has only recently been used for the characterisation of hydrological processes with examples including monitoring of a fluvial flash flood using a UAS , application of optical tracking velocimetry (OTV) on the Tiber and Brenta rivers using fixed gauge cams (Tauro et al, 2018), and in the development of FlowVeloTool (Eltner et al, 2020). Optical flow-based approaches have the benefit of being computationally efficient whilst being capable of automatically extracting and tracking many thousands of visible features within the field of view.…”

“…When using PTV-based approaches, it is common for trajectory filtering to be undertaken in order to eliminate the influence of tracked features that do not accurately represent movement of the free surface (e.g. Tauro et al, 2018;Lin et al, 2019;Eltner et al, 2020). Erroneous reconstructions of the flow field may be caused by environmental factors including, but not limited to, the presence of a visible river bed causing near-zero velocities, differential illumination, hydraulic jumps, and standing waves.…”