E-Thesis 84 views 15 downloads
Quantifying functional group abundances of intertidal canopy-forming brown macroalgae: combining unmanned aerial vehicle and satellite imagery with machine learning / JOSHUA MUTTER
Swansea University Author: JOSHUA MUTTER
Abstract
Canopy-forming brown macroalgae provide structural support for a diverse range of rocky shore organisms, altering rocky coastline into vibrant coastal macroalgal forests. Intertidal macroalgae provide a range of ecosystem services, and macroalgal forest cover is considered an essential ocean variabl...
| Published: |
Swansea University, Wales, UK
2024
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| Institution: | Swansea University |
| Degree level: | Master of Research |
| Degree name: | MRes |
| Supervisor: | Griffin, J. and Fowler, M. |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa68371 |
| Abstract: |
Canopy-forming brown macroalgae provide structural support for a diverse range of rocky shore organisms, altering rocky coastline into vibrant coastal macroalgal forests. Intertidal macroalgae provide a range of ecosystem services, and macroalgal forest cover is considered an essential ocean variable for monitoring the anthropogenic degradation of coastal ecosystems. Advances in remote sensing techniques, including machine learning and brown algae index (BAI), could upscale efforts to remotely map intertidal macroalgal forests using multispectral satellite imagery. However, previous machine learning application is limited to higher resolution images from small aircraft, whereas BAI regression techniques lack validation on heterogeneous or diverse intertidal forests and are limited to broad taxonomic groups containing multiple spectrally variable species. Random Forests classifiers were trained using unmanned aerial vehicle (UAV) data from four shores around the UK. I used this to predict functional group cover based on multispectral images from the European Space Agency’s Sentinel-2 satellite both i) within the original training shores, and ii) two new sites independent of model training. Total cover estimates were compared to those from previously employed BAI regression models, and re-parameterised BAI regression models using data from the four training shores containing greater heterogeneity and diversity. Random forest models accurately predicted functional group cover during within-set cross-validation but require the incorporation of intra-species variation in reflectivity to predict group cover on novel shores containing different environmental conditions and species traits. BAI regression models provided more robust estimates of total brown macroalgal cover when fitted to data that reflects the natural range heterogeneity and diversity present in intertidal macroalgae habitats. Caution is advised when applying a single BAI regression model as factors that impact near-infra-red or green reflectance can weaken predictability, such as variations in species reflectivity. Nevertheless, results revealed that multispectral satellite imagery can upscale the mapping of intertidal macroalgal coverage around heterogeneous UK shores, improving estimations of ecosystem services and monitoring of anthropogenic degradation. |
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| Item Description: |
A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. |
| Keywords: |
Coastal Ecology, Remote Sensing, Marine Biology, Macroalgae, Intertidal, Ecosystem Functions |
| College: |
Faculty of Science and Engineering |