Abstract
River flow monitoring is essential for many hydraulic and hydrologic applications related to water resource management and flood forecasting. Currently, unmanned aerial systems (UASs) combined with image velocimetry techniques provide a significant low-cost alternative for hydraulic monitoring, allowing the estimation of river stream flows and surface flow velocities based on video acquisitions. The accuracy of these methods tends to be sensitive to several factors, such as the presence of floating materials (transiting onto the stream surface), challenging environmental conditions, and the choice of a proper experimental setting. In most real-world cases, the seeding density is not constant during the acquisition period, so it is not unusual for the patterns generated by tracers to have non-uniform distribution. As a consequence, these patterns are not easily identifiable and are thus not trackable, especially during floods. We aimed to quantify the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) techniques under different hydrological and seeding conditions using footage acquired by UASs. With this aim, three metrics were adopted to explore the relationship between seeding density, tracer characteristics, and their spatial distribution in image velocimetry accuracy. The results demonstrate that prior knowledge of seeding characteristics in the field can help with the use of these techniques, providing a priori evaluation of the quality of the frame sequence for post-processing.
Highlights
Image processing techniques provide up-to-date and valuable strategies for estimating surface-water velocities in artificial laboratory flumes and natural rivers surrounded by complex hydraulic conditions [1,2]
Based on the lack of practical guidelines for image velocimetry analysis based on different seeding properties, we aimed to investigate the accuracy of particle tracking velocimetry (PTV) and large-scale particle image velocimetry (LSPIV) on three real case studies characterised by different seeding and environmental conditions
We focused on the accuracy of PTV and LSPIV image velocimetry techniques under different seeding conditions for three case study locations in Southern Italy
Summary
Image processing techniques provide up-to-date and valuable strategies for estimating surface-water velocities in artificial laboratory flumes and natural rivers surrounded by complex hydraulic conditions [1,2]. Image velocimetry techniques have gained popularity in estimating surface flow velocities and river stream flows in natural and artificial water bodies. Image velocimetry techniques support standard measuring networks and expand hydrological and hydraulic information of rivers in basins that are not densely instrumented or have limited accessibility [4,5]. The versatility of those techniques enables the analysis of a large amount of data on different spatial and temporal scales. The analysis can be performed with several techniques [6,7,8,9]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
