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UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

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dc.contributor.authorSotille, Maria E.
dc.contributor.authorBremer, Ulisses F.
dc.contributor.authorVieira, Gonçalo
dc.contributor.authorVelho, Luiz F.
dc.contributor.authorPetsch, Carina
dc.contributor.authorAuger, Jeffrey D.
dc.contributor.authorSimões, Jefferson C.
dc.date.accessioned2022-10-04T11:41:32Z
dc.date.available2022-10-04T11:41:32Z
dc.date.issued2022
dc.description.abstractDevelopment of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolutionpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSotille, M. E., Bremer, U. F., Vieira, G., Velho, Luiz F., Petsch, C., Auger, J. D. Simões, J. C. (2022). UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest. Ecological Informatics, 71, 101768, https://doi.org/10.1016/j.ecoinf.2022.101768pt_PT
dc.identifier.doi10.1016/j.ecoinf.2022.101768pt_PT
dc.identifier.issn1574-9541
dc.identifier.urihttp://hdl.handle.net/10451/54694
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relatione Brazilian National Council for Scientific and Technological Development - CNPq [Grant 421743/2017-4]pt_PT
dc.relationProcess 465680/2014-3 – INCT Criosferapt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1574954122002187?pes=vorpt_PT
dc.subjectVegetation mappingpt_PT
dc.subjectAntarcticapt_PT
dc.subjectUAVpt_PT
dc.subjectGEOBIApt_PT
dc.subjectImage classificationpt_PT
dc.subjectRemote sensingpt_PT
dc.titleUAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forestpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage101768pt_PT
oaire.citation.titleEcological Informaticspt_PT
oaire.citation.volume71pt_PT
person.familyNameBrito Guapo Teles Vieira
person.givenNameGonçalo
person.identifierG-5958-2010
person.identifier.ciencia-id2519-6583-CAEA
person.identifier.orcid0000-0001-7611-3464
person.identifier.scopus-author-id7005863976
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication7039fbb2-e1f8-4c3e-80f1-603b12d33c1c
relation.isAuthorOfPublication.latestForDiscovery7039fbb2-e1f8-4c3e-80f1-603b12d33c1c

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