Li, Jiwei, Knapp, David E., Fabina, Nicholas S., Kennedy, Emma V., Larsen, Kirk, Lyons, Mitchell B., Murray, Nicholas J., Phinn, Stuart R., Roelfsema, Chris M., and Asner, Gregory P. (2020) A global coral reef probability map generated using convolutional neural networks. Coral Reefs, 39 (6). pp. 1805-1815.

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Abstract

Coral reef research and management efforts can be improved when supported by reef maps providing local-scale details across global extents. However, such maps are difficult to generate due to the broad geographic range of coral reefs, the complexities of relating satellite imagery to geomorphic or ecological realities, and other challenges. However, reef extent maps are one of the most commonly used and most valuable data products from the perspective of reef scientists and managers. Here, we used convolutional neural networks to generate a globally consistent coral reef probability map-a probabilistic estimate of the geospatial extent of reef ecosystems-to facilitate scientific, conservation, and management efforts. We combined a global mosaic of high spatial resolution Planet Dove satellite imagery with regional Millennium Coral Reef Mapping Project reef extents to build training, validation, and application datasets. These datasets trained our reef extent prediction model, a neural network with a dense-unet architecture followed by a random forest classifier, which was used to produce a global coral reef probability map. Based on this probability map, we generated a global coral reef extent map from a 60% threshold of reef probability (reef: probability >= 60%, non-reef: probability < 60%). Our findings provide a proof-of-concept method for global reef extent estimates using a consistent and readily updateable methodology that leverages modern deep learning approaches to support downstream users. These maps are openly-available through the Allen Coral Atlas.

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