•  
  •  
 

The Plymouth Student Scientist

Alex Mallinson

Document Type

Biological and Marine Sciences Article

Abstract

The pressures of modern society have been proven to have widespread negative effects on worldwide seagrass meadows, thus necessitating an increased need for the accurate and long-term monitoring of sites of special interest. These monitoring programs have typically relied on the in-situ measurement of individual seagrass plants. However, technological advancements have increased the prevalence of new surveying techniques, including satellite imagery, aerial photography and bathymetric measurement. With the advent of machine learning, the processing of these methods has increased in efficiency and become more objective in nature, transforming the way surveys are being undertaken. This study focuses on the unique habitat of Higher Town Bay (St Martins) in the Isles of Scilly and provides a comprehensive evaluation of how the seagrass meadows have changed over a 16-year period. A combined dataset of bathymetric and remote sensing data is analysed to map these changes, and two machine learning (ML) methods are suggested to automate this process. A sensitivity analysis of both the ML algorithms and processing methods is undertaken. The resulting trend shows the damage inflicted on the meadow by the 2013/14 winter storms, demonstrating the impact of extreme wave events on the archipelago and the subsequent recovery of the meadow in the following years. This study provides guidance into how to combine bathymetric and remote sensing data types for more comprehensive seagrass surveys and gives suggestions for ML techniques to be used in the broader scope of seagrass detection and management.

Publication Date

2025-3

Publication Title

The Plymouth Student Scientist

Volume

18

Issue

2

ISSN

1754-2383

Deposit Date

2025-12

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Download

Included in

Life Sciences Commons

Share

COinS