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Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles
Coastal water flows facilitate important nutrient exchanges between mangroves, seagrasses and coral reefs. However, due to the complex nature of tidal interactions, their spatiotemporal development can be difficult to trace via traditional field instrumentations. Unmanned aerial vehicles (UAVs) serv...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783014/ https://www.ncbi.nlm.nih.gov/pubmed/35064186 http://dx.doi.org/10.1038/s41598-022-05189-9 |
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author | Johansen, Kasper Dunne, Aislinn F. Tu, Yu-Hsuan Almashharawi, Samir Jones, Burton H. McCabe, Matthew F. |
author_facet | Johansen, Kasper Dunne, Aislinn F. Tu, Yu-Hsuan Almashharawi, Samir Jones, Burton H. McCabe, Matthew F. |
author_sort | Johansen, Kasper |
collection | PubMed |
description | Coastal water flows facilitate important nutrient exchanges between mangroves, seagrasses and coral reefs. However, due to the complex nature of tidal interactions, their spatiotemporal development can be difficult to trace via traditional field instrumentations. Unmanned aerial vehicles (UAVs) serve as ideal platforms from which to capture such dynamic responses. Here, we provide a UAV-based approach for tracing coastal water flows using object-based detection of dye plume extent coupled with a regression approach for mapping dye concentration. From hovering UAV images and nine subsequent flight surveys covering the duration of an ebbing tide in the Red Sea, our results show that dye plume extent can be mapped with low omission and commission errors when assessed against manual delineations. Our results also demonstrated that the interaction term of two UAV-derived indices may be employed to accurately map dye concentration (coefficient of determination = 0.96, root mean square error = 7.78 ppb), providing insights into vertical and horizontal transportation and dilution of materials in the water column. We showcase the capabilities of high-frequency UAV-derived data and demonstrate how field-based dye concentration measurements can be integrated with UAV data for future studies of coastal water flow dynamics. |
format | Online Article Text |
id | pubmed-8783014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87830142022-01-25 Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles Johansen, Kasper Dunne, Aislinn F. Tu, Yu-Hsuan Almashharawi, Samir Jones, Burton H. McCabe, Matthew F. Sci Rep Article Coastal water flows facilitate important nutrient exchanges between mangroves, seagrasses and coral reefs. However, due to the complex nature of tidal interactions, their spatiotemporal development can be difficult to trace via traditional field instrumentations. Unmanned aerial vehicles (UAVs) serve as ideal platforms from which to capture such dynamic responses. Here, we provide a UAV-based approach for tracing coastal water flows using object-based detection of dye plume extent coupled with a regression approach for mapping dye concentration. From hovering UAV images and nine subsequent flight surveys covering the duration of an ebbing tide in the Red Sea, our results show that dye plume extent can be mapped with low omission and commission errors when assessed against manual delineations. Our results also demonstrated that the interaction term of two UAV-derived indices may be employed to accurately map dye concentration (coefficient of determination = 0.96, root mean square error = 7.78 ppb), providing insights into vertical and horizontal transportation and dilution of materials in the water column. We showcase the capabilities of high-frequency UAV-derived data and demonstrate how field-based dye concentration measurements can be integrated with UAV data for future studies of coastal water flow dynamics. Nature Publishing Group UK 2022-01-21 /pmc/articles/PMC8783014/ /pubmed/35064186 http://dx.doi.org/10.1038/s41598-022-05189-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Johansen, Kasper Dunne, Aislinn F. Tu, Yu-Hsuan Almashharawi, Samir Jones, Burton H. McCabe, Matthew F. Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles |
title | Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles |
title_full | Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles |
title_fullStr | Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles |
title_full_unstemmed | Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles |
title_short | Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles |
title_sort | dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783014/ https://www.ncbi.nlm.nih.gov/pubmed/35064186 http://dx.doi.org/10.1038/s41598-022-05189-9 |
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