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dc.contributor.authorGonzalez-Rivero, Manuel
dc.contributor.authorBeijbom, Oscar
dc.contributor.authorRodriguez-Ramirez, Alberto
dc.contributor.authorBryant, Dominic EP
dc.contributor.authorGanase, Anjani
dc.contributor.authorGonzalez-Marrero, Yeray
dc.contributor.authorHerrera-Reveles, Ana
dc.contributor.authorKennedy, Emma
dc.contributor.authorKim, Catherine JS
dc.contributor.authorLopez-Marcano, Sebastian
dc.contributor.authorMarkey, Kathryn
dc.contributor.authorNeal, Benjamin P
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.accessioned2020-08-05T03:28:05Z
dc.date.available2020-08-05T03:28:05Z
dc.date.issued2020
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs12030489
dc.identifier.urihttp://hdl.handle.net/10072/396201
dc.description.abstractEcosystemmonitoring is central to effectivemanagement, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection formonitoring 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 reefmonitoring 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 inmonitoring with automated image annotation can dramatically improve how wemeasure andmonitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and acrossmanagement areas.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherMDPI
dc.relation.ispartofpagefrom489
dc.relation.ispartofissue3
dc.relation.ispartofjournalRemote Sensing
dc.relation.ispartofvolume12
dc.subject.fieldofresearchClassical Physics
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode0203
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.subject.keywordsScience & Technology
dc.subject.keywordsRemote Sensing
dc.subject.keywordscoral reefs
dc.subject.keywordsmonitoring
dc.titleMonitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationGonzalez-Rivero, M; Beijbom, O; Rodriguez-Ramirez, A; Bryant, DEP; Ganase, A; Gonzalez-Marrero, Y; Herrera-Reveles, A; Kennedy, E; Kim, CJS; Lopez-Marcano, S; Markey, K; Neal, BP; Osborne, K; Reyes-Nivia, C; et al, Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach, Remote Sensing, 2020, 12 (3), pp. 489
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-08-05T02:56:24Z
dc.description.versionPublished
gro.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
gro.hasfulltextFull Text
gro.griffith.authorLopez-Marcano, Sebastian E.


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