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River monitoring is of particular interest as society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: i) Particle Tracking Velocimetry (PTV), and ii) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI=ν^0.1/(ρ/ρ_cν1 ) where ν,ρ,and ρ_cν1 are the spatial clustering level, the seeding density, and the reference seeding density at ν=1, respectively. A reduction of image-velocimetry errors was systematically observed for lower values of SDI; and therefore, the optimal frame window (i.e., a subset of the video image sequence) was defined as the one that minimises SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9 and 16.1% were calculated - using PTV and PIV, respectively - by employing the optimal frame window.
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