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Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery

dc.contributor.authorMarquez, L.
dc.contributor.authorFragkopoulou, Eliza
dc.contributor.authorCavanaugh, K. C.
dc.contributor.authorHouskeeper, H. F.
dc.contributor.authorAssis, J.
dc.date.accessioned2023-05-11T09:17:11Z
dc.date.available2023-05-11T09:17:11Z
dc.date.issued2022
dc.description.abstractClimate change is producing shifts in the distribution and abundance of marine species. Such is the case of kelp forests, important marine ecosystem-structuring species whose distributional range limits have been shifting worldwide. Synthesizing long-term time series of kelp forest observations is therefore vital for understanding the drivers shaping ecosystem dynamics and for predicting responses to ongoing and future climate changes. Traditional methods of mapping kelp from satellite imagery are time-consuming and expensive, as they require high amount of human effort for image processing and algorithm optimization. Here we propose the use of mask region-based convolutional neural networks (Mask R-CNN) to automatically assimilate data from open-source satellite imagery (Landsat Thematic Mapper) and detect kelp forest canopy cover. The analyses focused on the giant kelp Macrocystis pyrifera along the shorelines of southern California and Baja California in the northeastern Pacific. Model hyper-parameterization was tuned through cross-validation procedures testing the effect of data augmentation, and different learning rates and anchor sizes. The optimal model detected kelp forests with high performance and low levels of overprediction (Jaccard's index: 0.87 +/- 0.07; Dice index: 0.93 +/- 0.04; over prediction: 0.06) and allowed reconstructing a time series of 32 years in Baja California (Mexico), a region known for its high variability in kelp owing to El Nino events. The proposed framework based on Mask R-CNN now joins the list of cost-efficient tools for long-term marine ecological monitoring, facilitating well-informed biodiversity conservation, management and decision making.pt_PT
dc.description.sponsorshipLA/P/0101/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1038/s41598-022-26439-wpt_PT
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10400.1/19550
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherNature Portfoliopt_PT
dc.relationAlgarve Centre for Marine Sciences
dc.relationAlgarve Centre for Marine Sciences
dc.relationPotential consequences of future climate changes for marine forest gene pools
dc.relationNot Available
dc.subjectClimate changept_PT
dc.subjectOceanpt_PT
dc.subjectDynamicspt_PT
dc.subjectLimitpt_PT
dc.titleArtificial intelligence convolutional neural networks map giant kelp forests from satellite imagerypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitlePotential consequences of future climate changes for marine forest gene pools
oaire.awardTitleNot Available
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FBIA-CBI%2F6515%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/SFRH%2FBD%2F144878%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/DL 57%2F2016/DL 57%2F2016%2FCP1361%2FCT0035/PT
oaire.citation.issue1pt_PT
oaire.citation.startPage22196pt_PT
oaire.citation.titleScientific Reportspt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
oaire.fundingStreamOE
oaire.fundingStreamDL 57/2016
person.familyNameFragkopoulou
person.familyNameAssis
person.givenNameEliza
person.givenNameJorge
person.identifier.ciencia-id5B1F-9922-2CB5
person.identifier.ciencia-id5C1D-05B6-29F7
person.identifier.orcid0000-0002-0557-3954
person.identifier.orcid0000-0002-6624-4820
person.identifier.ridG-9688-2012
person.identifier.scopus-author-id53463298700
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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