RT Journal Article
SR Electronic
T1 Digitizing the coral reef: machine learning of underwater spectral images enables dense taxonomic mapping of benthic habitats
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.03.28.485758
DO 10.1101/2022.03.28.485758
A1 Daniel Schürholz
A1 Arjun Chennu
YR 2022
UL http://biorxiv.org/content/early/2022/06/21/2022.03.28.485758.abstract
AB Coral reefs are the most biodiverse marine ecosystems, and host a wide range of taxonomic diversity in a complex spatial habitat structure. Existing coral reef survey methods struggle to accurately capture the taxonomic detail within the complex spatial structure of benthic communities.We propose a workflow to leverage underwater hyperspectral transects and two machine learning algorithms to produce dense habitat maps of 1150 m2 of reefs across the Curaçao coastline. Our multi-method workflow labelled all 500+ million pixels with one of 43 classes at taxonomic family, genus or species level for corals, algae, sponges, or to substrate labels such as sediment, turf algae and cyanobacterial mats.With low annotation effort (2% pixels) and no external data, our workflow enables accurate (Fbeta 87%) survey-scale mapping, with unprecedented thematic and spatial detail. Our assessments of the composition and configuration of the benthic communities of 23 transect showed high consistency.Digitizing the reef habitat structure enables validation and novel analysis of pattern and scale in coral reef ecology. Our dense habitat maps reveal the inadequacies of point sampling methods to accurately describe reef benthic communities.Competing Interest StatementThe authors have declared no competing interest.