Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System

Abstract

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.

Recent Advancements and Perspectives in UAS-Based Image Velocimetry

“…UAS remotecontrolled systems allow the real-time definition of acquisition frequency based on water flow velocities [19]. The possibility of using filters, polarisers, and changing the flight height based on imposed resolution allows for adapting footage acquisition based on environmental settings (e.g., sunlight, reflections, shadows) to maximise the caption of different patterns in time [20]. Despite the commonly recognised UAS strengths, UAS are subject to several limitations.…”

Comparison of dense optical flow and PIV techniques for mapping surface current flow in tidal stream energy sites

“…Reasonable agreement between ADCP data in the Inner Sound using the dense optical flow and PIVlab may indicate that this flying altitude and resolution are suitable for the measurement of flow at these locations. The effect of flying altitude on LSPIV and optical flow has been investigated in other environments; higher flying altitude resulted in lower resolution and poorer results from LSPIV with a comparable UAV [17], albeit in a riverine environment. The altitude, resolution and coverage, together with spatial and temporal averaging, eventually becomes an application-specific trade off, for example spatial coverage of a tidal stream site for initial site selection and sift.…”

“…Particle tracking techniques have been increasingly used for flow measurement in rivers [12][13][14][15][16]. LSPIV relies on the flow being seeded with artificial or natural particles and is highly accurate in a wide variety of natural flow conditions [17]. However, LSPIV does have drawbacks, as the technique relies on natural particles such as foam or debris on the surface; insufficient particles require artificial seeding which is labour intensive and not appropriate for tidal environments on a large scale.…”

Impact Velocity Measurement Method Based on Trajectory and Impact Position

“…T. B. Dinh [ 8 ] used the PIV method to measure the velocity of bubbles in the fluid. W. C. Liu [ 9 ] measured the flow velocity of the river using the PIV method. The most similar areas in two successive images were identified.…”