Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5173-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-5173-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2020
Research article |
| 09 Nov 2020
| 09 Nov 2020
Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow
Department of European and Mediterranean Cultures, University of
Basilicata, Matera, 75100, Italy
Silvano F. Dal Sasso
Department of European and Mediterranean Cultures, University of
Basilicata, Matera, 75100, Italy
Matthew T. Perks
School of Geography, Politics and Sociology, Newcastle University,
Newcastle-upon-Tyne, NE1 7RU, UK
Salvatore Manfreda
Department of Civil, Architectural and Environmental Engineering,
University of Naples Federico II, Naples, 80125, Italy
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Short summary
An innovative approach is presented to optimise image-velocimetry performances for surface flow velocity estimates (and thus remotely sensed river discharges). Synthetic images were generated under different tracer characteristics using a numerical approach. Based on the results, the Seeding Distribution Index was introduced as a descriptor of the optimal portion of the video to analyse. A field case study was considered as a proof of concept of the proposed framework showing error reductions.
An innovative approach is presented to optimise image-velocimetry performances for surface flow...
