Abstract
Large-scale image velocimetry is a novel approach for non-contact remote sensing of flow in rivers. Research within this topic has largely focussed on developing specific aspects of the image velocimetry work-flow, or alternatively, testing specific tools or software using case studies. This has resulted in the development of a multitude of techniques, with varying practice being employed between groups, and authorities. As such, for those new to image velocimetry, it may be hard to decipher which methods are suited for particular challenges. This research collates, synthesises, and presents current understanding related to the application of particle image velocimetry (PIV) and particle tracking velocimetry (PTV) approaches in a fluvial setting. The image velocimetry work-flow is compartmentalised into sub-systems of: capture optimisation, pre-processing, processing, and post-processing. The focus of each section is to provide examples from the wider literature for best practice, or where this is not possible, to provide an overview of the theoretical basis and provide examples to use as precedence and inform decision making. We present literature from a range of sources from across the hydrology and remote sensing literature to suggest circumstances in which specific approaches are best applied. For most sub-systems, there is clear research or precedence indicating how to best perform analysis. However, there are some stages in the process that are not conclusive with one set method and require user intuition or further research. For example, the role of external environmental conditions on the performance of image velocimetry being a key aspect that is currently lacking research. Further understanding in areas that are lacking, such as environmental challenges, is vital if image velocimetry is to be used as a method for the extraction of river flow information across the range of hydro-geomorphic conditions.
Highlights
Advances in understanding of hydrological processes and the characterisation of river flows have been driven by development of novel sensing instruments, data acquisition platforms, and new analytical techniques (e.g., Chen et al, 2007; Assem et al, 2017; Mishra et al, 2019)
Algorithms have been shown to be capable of estimating surface velocity accurately, the onus is on the user to decipher the ideal methodology
The aim of this research was to present and discuss instances of best practice for image velocimetry processes based on existing papers and case studies, summarised by Figure 2
Summary
Advances in understanding of hydrological processes and the characterisation of river flows have been driven by development of novel sensing instruments, data acquisition platforms, and new analytical techniques (e.g., Chen et al, 2007; Assem et al, 2017; Mishra et al, 2019). Considerations When Applying LS-PIV/PTV columns (Neill and Hashemi, 2018) Whilst this technology offers some advantages over traditional gauging techniques (e.g., ultrasonic sensors, electromagnetic current meters), they fail to overcome a critical limitation in that they require contact with the water-body, which may not be viable during high-flow conditions or where physical access to the channel is not possible. In recognition of these challenges, the development of noncontact sensors for velocity measurements (e.g., surface velocity radar), and non-intrusive approaches for acquiring bathymetric data (e.g., bathymetric LiDAR, radar, photogrammetry) is facilitating the non-contact and autonomous monitoring of fluvial flows (Costa et al, 2006; Flener et al, 2013; Javernick et al, 2014; Alimenti et al, 2020). Interrogation areas set at 64 × 64 pixels and search areas set at 24 pixels
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