Abstract
Abstract. Traditional flow velocity measurements in natural environments require contact with the fluid and are usually costly, time-consuming and, sometimes, even dangerous. Particle Image Velocimetry allows the flow velocity field to be remotely characterized from the shift of intensity patterns of sub-image areas in at least two video frames with a known time lag. Recently, Airborne Image Velocimetry has enabled the surface velocity field of large-scale water bodies to be determined by applying Particle Image Velocimetry on videos recorded by cameras mounted on unmanned aerial vehicles. This work presents a comparison of three Airborne Image Velocimetry approaches: BASESURV, Fudaa-LSPIV and RIVeR. For the evaluation, two nadiral videos were acquired with a low-cost quadcopter. The first was recorded under low flow and seeded conditions, the second during a flood event. According to the results obtained, BASESURV is an accurate and complete research oriented approach but it is time-consuming and neither a graphical interface nor documentation are yet provided. Fudaa-LSPIV is a well-developed software package, with a user-friendly graphical interface and good documentation. However it lacks some features and the source code is closed. RIVeR may be suitable for real time monitoring thanks to the rectification of velocity vectors only. Overall, all the codes are found to be effective in performing Airborne Image Velocimetry in riverine environments.
Highlights
Measuring flow velocities is one of the main issues of hydraulic engineering
The flow is homogeneous with surface velocities ranging from 0.6 m s−1 near the riparian sides to 1.7 m s−1 in the centre
The SVFs obtained with Fudaa-Large-Scale Particle Image Velocimetry (LSPIV) and RIVeR (Figures 3b and 3d) show an underestimation of the velocities compared to that of BASESURV (Figure 3a), especially in the downstream area and in the central-upstream part of the river
Summary
Traditional flow measurements require contact with the fluid and are usually costly, time-consuming and, sometimes, even dangerous. An image-based technique called Particle Image Velocimetry (PIV) has been widely used to remotely capture the whole velocity field of a fluid (Adrian, 1991; Raffel et al, 1998). In PIV, velocity vectors are computed from the shift of characteristic intensity patterns of sub-image areas, called Interrogation Areas (IAs), in at least two video frames with a known time lag. For each IA in the first frame, a local integer displacement vector is computed by evaluating the cross-correlation with the corresponding IA in the second frame (Keane and Adrian, 1992). Instantaneous flow velocity fields are derived by dividing the displacement vectors by the time lag
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