Coral reefs are the most biodiverse marine ecosystems, and host a wide range of taxonomic diversity in a complex spatial community 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 image 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 (only 2% of pixels) and no external data, our workflow enables accurate (Fbeta of 87%) survey-scale mapping, with unprecedented thematic detail and with fine spatial resolution (2.5 cm/pixel). Our assessments of the composition and configuration of the benthic communities of 23 image transects showed high consistency. Digitizing the reef habitat and community 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.

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Methods in Ecology and Evolution

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Staff publications

Schürholz, Daniel, & Chennu, Arjun. (2022). Digitizing the coral reef: Machine learning of underwater spectral images enables dense taxonomic mapping of benthic habitats. Methods in Ecology and Evolution, 14(2), 596–613. doi:10.1111/2041-210x.14029