Abstract

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.

Details

Title
Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles
Author
Johansen Kasper 1 ; Dunne, Aislinn F 2 ; Yu-Hsuan, Tu 1 ; Almashharawi Samir 1 ; Jones, Burton H 2 ; McCabe, Matthew F 1 

 King Abdullah University of Science and Technology, Hydrology, Agriculture and Land Observation Group, Biological and Environmental Science and Engineering Division, Water Desalination and Reuse Center, Thuwal, Saudi Arabia (GRID:grid.45672.32) (ISNI:0000 0001 1926 5090) 
 King Abdullah University of Science and Technology, Reef Ecology Lab, Biological and Environmental Science and Engineering Division, Red Sea Research Center, Thuwal, Saudi Arabia (GRID:grid.45672.32) (ISNI:0000 0001 1926 5090) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2621831645
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.