A global coral reef probability map generated using convolutional neural networks
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A global coral reef probability map generated using convolutional neural networks Jiwei Li1 • David E. Knapp1 • Nicholas S. Fabina1 • Emma V. Kennedy2 • Kirk Larsen3 • Mitchell B. Lyons2 • Nicholas J. Murray2,4 • Stuart R. Phinn2 Chris M. Roelfsema2 • Gregory P. Asner1
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Received: 14 April 2020 / Accepted: 11 September 2020 Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Coral reef research and management efforts can be improved when supported by reef maps providing localscale 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-
Topic Editor Morgan S. Pratchett
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00338-020-02005-6) contains supplementary material, which is available to authorized users. & Gregory P. Asner [email protected] 1
Center for Global Discovery and Conservation Science, Arizona State University, Arizona 85281, USA
2
Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, Queensland 4072, Australia
3
Vulcan Inc, Washington 98104, USA
4
College of Science and Engineering, James Cook University, Queensland 4811, Australia
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 C 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. Keywords Coral reef Deep learning Earth observation Planet Dove Millennium Coral Reef Mapping Project Remote sensing
Introduction Accurate and reliable maps are a prerequisite for quantifying and analyzing geospatial patterns and the processes that underpin those patterns. With coral reefs experiencing unprecedented change (Hughes et al. 2018; Eakin et al. 2019), reef management and monitoring agencies, as well as the science communit
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