Abstract
Recent advances in image-based methods for environmental monitoring are opening new frontiers for remote streamflow measurements in natural environments. Such techniques offer numerous advantages compared to traditional approaches. Despite the wide availability of cost-effective devices and software for image processing, these techniques are still rarely systematically implemented in practical applications, probably due to the lack of consistent operational protocols for both phases of images acquisition and processing. In this work, the optimal experimental setup for LSPIV based flow velocity measurements under different conditions is explored using the software PIVlab, investigating performance and sensitivity to some key factors. Different synthetic image sequences, reproducing a river flow with a realistic velocity profile and uniformly distributed floating tracers, are generated under controlled conditions. Different parametric scenarios are created considering diverse combinations of flow velocity, tracer size, seeding density, and environmental conditions. Multiple replications per scenario are processed, using descriptive statistics to characterize errors in PIVlab estimates. Simulations highlight the crucial role of some parameters (e.g., seeding density) and demonstrate how appropriate video duration, frame-rate and parameters setting in relation to the hydraulic conditions can efficiently counterbalance many of the typical operative issues (i.e., scarce tracer concentration) and improve algorithms performance.
Highlights
PIVlab offers a full suite of tools for results analysis and visualization, since in this work a considerable number of synthetic image sequences have to be managed, it has been preferred to create a specific MATLAB script, named Results Analysis and Visualization (RAV), dedicated to the analysis and the statistical characterization of the results relative to each parametric scenario
The APEi values arises from the comparison at each computational grid node between the surface velocity estimated by PIVlab (v PIVLab,i ) and the velocity imposed in the Image Sequence Generator (ISG), and are computed as: APEi =
The frame was initially entirely selected as area of analysis, considering a computational grid consisting of a total of 121 computational nodes arranged on 11 horizontal rows and 11 vertical columns; the aim of this type of analysis is to investigate possible frame-border effects and to identify the region of the frame not significantly affected by frame-border effects
Summary
Discharge estimation in natural rivers is conducted through a velocity-area method, based on: (i) the discrete point sampling of flow velocity along transects in a specific cross section of interest; (ii) the derivation of both average flow velocity and associated wetted area [4,5,6]. This approach traditionally involves the use of current meters, requires a large employment of highly specialized personnel, and it is extremely expensive and time-consuming.
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