Submerged aquatic vegetation: Overview of monitoring techniques used for the identification and determination of spatial distribution in European coastal waters

• Published in: Integrated environmental assessment and management 2022-06, Vol.18 (4), p.892-908
• Main Authors: Lønborg, Christian, Thomasberger, Aris, Stæhr, Peter A. U., Stockmarr, Anders, Sengupta, Sayantan, Rasmussen, Mikkel Lydholm, Nielsen, Lisbeth Tangaa, Hansen, Lars Boye, Timmermann, Karen
• Format: Article
• Language: English
• Subjects: Acoustics; Aquatic ecosystems; Aquatic plants; Coastal waters; Community composition; Drone aircraft; Ecological function; Environmental monitoring; Image analysis; Labour; Managers; Marine ecosystems; Marine technology; Monitoring programs; Observations; Perturbation; Remote monitoring; Remote sensing; Spatial distribution; Species composition; Submerged aquatic vegetation; Surveys; Technology; Vegetation
• ISSN: 1551-3777; 1551-3793
• DOI: 10.1002/ieam.4552

Coastal waters are highly productive and diverse ecosystems, often dominated by marine submerged aquatic vegetation (SAV) and strongly affected by a range of human pressures. Due to their important ecosystem functions, for decades, both researchers and managers have investigated changes in SAV abundance and growth dynamics to understand linkages to human perturbations. In European coastal waters, monitoring of marine SAV communities traditionally combines diver observations and/or video recordings to determine, for example, spatial coverage and species composition. While these techniques provide very useful data, they are rather time consuming, labor‐intensive, and limited in their spatial coverage. In this study, we compare traditional and emerging remote sensing technologies used to monitor marine SAV, which include satellite and occupied aircraft operations, aerial drones, and acoustics. We introduce these techniques and identify their main strengths and limitations. Finally, we provide recommendations for researchers and managers to choose the appropriate techniques for future surveys and monitoring programs. Integr Environ Assess Manag 2022;18:892–908. © 2021 SETAC Key Points No technology is perfect; the monitoring objectives, data needs, and budget therefore should be known before the preferred technique is chosen. Studies should combine the different technologies as well as increase the use of machine learning for post processing of the obtained data.