Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach

Last modified: 
February 10, 2020 - 12:53pm
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Type: Journal Article
Year of publication: 2020
Date published: 02/2020
Authors: Manuel González-Rivero, Oscar Beijbom, Alberto Rodriguez-Ramirez, Dominic Bryant, Anjani Ganase, Yeray Gonzalez-Marrero, Ana Herrera-Reveles, Emma Kennedy, Catherine Kim, Sebastian Lopez-Marcano, Kathryn Markey, Benjamin Neal, Kate Osborne, Catalina Reyes-Nivia, Eugenia Sampayo, Kristin Stolberg, Abbie Taylor, Julie Vercelloni, Mathew Wyatt, Ove Hoegh-Guldberg
Journal title: Remote Sensing
Volume: 12
Issue: 3
Pages: 489

Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas.

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