Streamflow Observations From Cameras: Large‐Scale Particle Image Velocimetry or Particle Tracking Velocimetry?

Abstract

Image‐based methodologies, such as large scale particle image velocimetry (LSPIV) and particle tracking velocimetry (PTV), have increased our ability to noninvasively conduct streamflow measurements by affording spatially distributed observations at high temporal resolution. However, progress in optical methodologies has not been paralleled by the implementation of image‐based approaches in environmental monitoring practice. We attribute this fact to the sensitivity of LSPIV, by far the most frequently adopted algorithm, to visibility conditions and to the occurrence of visible surface features. In this work, we test both LSPIV and PTV on a data set of 12 videos captured in a natural stream wherein artificial floaters are homogeneously and continuously deployed. Further, we apply both algorithms to a video of a high flow event on the Tiber River, Rome, Italy. In our application, we propose a modified PTV approach that only takes into account realistic trajectories. Based on our findings, LSPIV largely underestimates surface velocities with respect to PTV in both favorable (12 videos in a natural stream) and adverse (high flow event in the Tiber River) conditions. On the other hand, PTV is in closer agreement than LSPIV with benchmark velocities in both experimental settings. In addition, the accuracy of PTV estimations can be directly related to the transit of physical objects in the field of view, thus providing tangible data for uncertainty evaluation.

On the Use of Unmanned Aerial Systems for Environmental Monitoring

“…In fact, compared to fixed implementations, UAS enable capture of larger fields of view with a diffuse rather than direct illumination. Such optical image velocimetry techniques can measure flow velocity fields over extended regions rather than pointwise, and at temporal resolutions comparable to or even better than Acoustic Doppler Velocimetry (ADV) based on the presence of detectable features on the water surface [147].…”

Evaluating Image Tracking Approaches for Surface Velocimetry With Thermal Tracers

“…The low accuracy is attributed to challenging measurement conditions such as inhomogeneous density of tracers and scarce illumination. Tauro, Piscopia, et al () improved measurement accuracies using LSPIV and achieved a coefficient variation of 18% (mean velocity 0.39 m/s with standard deviation of 0.07 m/s). Also, unmanned aerial vehicle‐based imagery could be used for flow velocity estimation.…”

“…The objectives of this work include testing (i) if a combination of thermal cameras and thermal tracers is sui- Water Resources Research control points can be extracted automatically, and (iii) the performance of pyramidal LK with respect to the traditional cross-correlation-based methods (e.g., PIVlab and PTVlab). Note that Tauro, Piscopia, et al, (2017) already tested the efficiency of PIVlab and PTVlab, revealing that PIVlab largely underestimates velocities when compared with PTVlab. However, they use RGB imagery.…”

“…Upper and lower velocity thresholds are set as following (equation ): The mean velocity is represented by and the standard deviation by σ u . The parameter n n is usually set to 3 (Tauro, Piscopia, et al, ). The filtering is applied in the spatial and temporal domain.…”

“…The mean velocity is represented by u and the standard deviation by σ u . The parameter nn is usually set to 3 (Tauro, Piscopia, et al, 2017). The filtering is applied in the spatial and temporal domain.…”