Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach

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dc.contributor.authorGonzález-Rivero, Manuel
dc.contributor.authorBeijbom, Oscar
dc.contributor.authorRodriguez-Ramirez, Alberto
dc.contributor.authorBryant, Dominic E. P.
dc.contributor.authorGanase, Anjani
dc.contributor.authorGonzalez-Marrero, Yeray
dc.contributor.authorHerrera-Reveles, Ana
dc.contributor.authorKennedy, Emma V.
dc.contributor.authorKim, Catherine J. S.
dc.contributor.authorLopez-Marcano, Sebastian
dc.contributor.authorMarkey, Kathryn
dc.contributor.authorNeal, Benjamin
dc.contributor.authorOsborne, Kate
dc.contributor.authorReyes-Nivia, Catalina
dc.contributor.authorSampayo, Eugenia M.
dc.contributor.authorStolberg, Kristin
dc.contributor.authorTaylor, Abbie
dc.contributor.authorVercelloni, Julie
dc.contributor.authorWyatt, Mathew
dc.contributor.authorHoegh-Guldberg, Ove
dc.date.accessioned2025-03-13T22:32:42Z
dc.date.available2025-03-13T22:32:42Z
dc.date.issued2020
dc.description.abstractEcosystem 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.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipThis research was funded by XL Catlin Ltd. (now AXA XL; to OHG and MGR) and the Australian Research Council (ARC Laureate and ARC Centre for Excellence to OHG). The production of this work was also funded by Vulcan Inc (OHG), including the article processing charges (APC).
dc.identifier.citationGonzález-Rivero, M., Beijbom, O., Rodriguez-Ramirez, A., Bryant, D. E. P., Ganase, A., Gonzalez-Marrero, Y., Herrera-Reveles, A., Kennedy, E. V., Kim, C. J. S., Lopez-Marcano, S., Markey, K., Neal, B. P., Osborne, K., Reyes-Nivia, C., Sampayo, E. M., Stolberg, K., Taylor, A., Vercelloni, J., Wyatt, M., & Hoegh-Guldberg, O. (2020). Monitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach. Remote Sensing, 12(3), 489. https://doi.org/10.3390/rs12030489
dc.identifier.urihttps://doi.org/10.3390/rs12030489
dc.identifier.urihttps://hdl.handle.net/1828/21420
dc.language.isoen
dc.publisherRemote Sensing
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectartificial intelligence
dc.subjectautomated image analysis
dc.subjectcoral reefs
dc.subjectmonitoring
dc.subject.departmentSchool of Environmental Studies
dc.titleMonitoring of coral reefs using artificial intelligence: A feasible and cost-effective approach
dc.typeArticle

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