An assessment of machine learning methods for seagrass classification in the Mediterranean

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dc.contributor.authorBakırman, Tolga
dc.contributor.authorGümüşay, Mustafa Ümit
dc.date.accessioned2021-02-19T21:16:19Z
dc.date.available2021-02-19T21:16:19Z
dc.date.issued2020
dc.departmentALKÜ
dc.description1st International Conference on Applied Geoinformatics (ISAG) -- NOV 07-09, 2019 -- Istanbul, TURKEY
dc.descriptionBakirman, Tolga/0000-0001-7828-9666
dc.description.abstractPosidonia oceanica is an endemic seagrass species in the Mediterranean. Even though this species has been put under protection, P. oceanica is currently listed as threatened. Therefore, in order to conserve this species, high resolution, accurate and temporal distribution maps are needed to be produced. In this study, it is aimed to create seagrass distribution maps with machine learning algorithms namely as random forests and support vector machines using WorldView-2 imagery. In-situ data has been collected via underwater video and scuba diving for classification training and testing. Atmospheric, radiometric and water column corrections are applied for preprocessing of the optical satellite image. The light penetration in the water is limited by depth. Therefore, we have limited our study area based on maximum depth of 20 meters. The classification accuracies and Cohen's kappa coefficients are calculated as 94% and 0.89 for random forests, 71% and 0.61 for support vector machines, respectively. According to the results, it can be clearly said that random forests method is superior to support vector machines for seagrass mapping in our study area. The proposed framework in this study enables to rapidly produce seagrass distribution maps which can be used to monitor temporal change for a sustainable ecosystem.
dc.description.sponsorshipYildiz Technical University, Scientific Research Projects OfficeYildiz Technical University [2015-05-03-YL05]
dc.description.sponsorshipThis research was funded by Yildiz Technical University, Scientific Research Projects Office with grant number 2015-05-03-YL05. The authors would also like to thank the Mediterranean Conservation Society for the in situ surveys.
dc.identifier.doi10.22364/bjmc.2020.8.2.07
dc.identifier.endpage326en_US
dc.identifier.issn2255-8942
dc.identifier.issn2255-8950
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage315en_US
dc.identifier.urihttps://doi.org/10.22364/bjmc.2020.8.2.07
dc.identifier.urihttps://hdl.handle.net/20.500.12868/376
dc.identifier.volume8en_US
dc.identifier.wosWOS:000543336000008
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor0-belirlenecek
dc.language.isoen
dc.publisherUniv Latvia
dc.relation.ispartofBaltic Journal of Modern Computing
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectSeagrass
dc.subjectClassification
dc.subjectMachine learning
dc.subjectPosidonia oceanica
dc.subjectMediterranean
dc.titleAn assessment of machine learning methods for seagrass classification in the Mediterranean
dc.typeConference Object

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