A COMPARISON of TREE-BASED ALGORITHMS for COMPLEX WETLAND CLASSIFICATION USING the GOOGLE EARTH ENGINE

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dc.contributor.authorJamali, A.
dc.contributor.authorMahdianpari, M.
dc.contributor.authorKaras, I.R.
dc.date.accessioned2024-09-29T16:16:04Z
dc.date.available2024-09-29T16:16:04Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description6th International Conference on Smart City Applications -- 27 October 2021 through 29 October 2021 -- Safranbolu -- 175815en_US
dc.description.abstractWetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers. © Author(s) 2021. CC BY 4.0 License.en_US
dc.identifier.doi10.5194/isprs-Archives-XLVI-4-W5-2021-313-2021
dc.identifier.endpage319en_US
dc.identifier.issn1682-1750
dc.identifier.issue4/W5-2021en_US
dc.identifier.scopus2-s2.0-85122307937en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage313en_US
dc.identifier.urihttps://doi.org/10.5194/isprs-Archives-XLVI-4-W5-2021-313-2021
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8837
dc.identifier.volume46en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archivesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBig dataen_US
dc.subjectDecision Treeen_US
dc.subjectExtreme Gradient Boostingen_US
dc.subjectGoogle Earth Engineen_US
dc.subjectRandom Foresten_US
dc.subjectSentinel Imageryen_US
dc.subjectWetland Mappingen_US
dc.titleA COMPARISON of TREE-BASED ALGORITHMS for COMPLEX WETLAND CLASSIFICATION USING the GOOGLE EARTH ENGINEen_US
dc.typeConference Objecten_US

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