Seafloor morphology and substrate mapping in the Gulf of St Lawrence, Canada, using machine learning approaches

Seafloor morphology and substrate mapping in the Gulf of St Lawrence, Canada, using machine learning approaches

Detailed maps of seafloor substrata and morphology can act as valuable proxies for predicting and understanding the distributions of benthic communities and are important for guiding conservation initiatives. High resolution acoustic remote sensing data can facilitate the production of detailed seaf...

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Bibliographic Details
Main Authors: Emily Sklar, Esther Bushuev, Benjamin Misiuk, Guillaume Labbé-Morissette, Craig J. Brown
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Marine Science
Subjects:
random forest
principal component analysis
K-means (KM) clustering
seafloor morphology
seafloor mapping
sediment
Online Access:https://www.frontiersin.org/articles/10.3389/fmars.2024.1306396/full

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