Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/88949
DC FieldValueLanguage
dc.contributor.authorMassetti, Andrea-
dc.contributor.authorSequeira, Miguel Menezes-
dc.contributor.authorPupo, Aida-
dc.contributor.authorRodrigues, Albano-
dc.contributor.authorGuiomar, Nuno-
dc.contributor.authorGil, Artur-
dc.date.accessioned2020-03-13T10:44:55Z-
dc.date.available2020-03-13T10:44:55Z-
dc.date.issued2017-02-17-
dc.identifier.issn2279-7254pt
dc.identifier.urihttp://hdl.handle.net/10316/88949-
dc.description.abstractMadeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result.pt
dc.language.isoengpt
dc.publisherTaylor & Francispt
dc.relationSFRH/BPD/100017/2014pt
dc.rightsembargoedAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectLand cover mappingpt
dc.subjectbiodiversity assessmentpt
dc.subjectland use assessmentpt
dc.subjectoceanic islandpt
dc.titleAssessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal)pt
dc.typearticleen
degois.publication.firstPage643pt
degois.publication.lastPage672pt
degois.publication.issue1pt
degois.publication.titleEuropean Journal of Remote Sensingpt
dc.relation.publisherversionhttps://www.tandfonline.com/doi/abs/10.5721/EuJRS20164934pt
dc.peerreviewedyespt
dc.identifier.doi10.5721/EuJRS20164934pt
degois.publication.volume49pt
dc.date.embargo2023-02-16*
uc.date.periodoEmbargo2190pt
item.grantfulltextembargo_20230216-
item.languageiso639-1en-
item.fulltextCom Texto completo-
crisitem.author.researchunitCEGOT – Centre of Studies on Geography and Spatial Planning-
crisitem.author.orcid0000-0003-0142-4764-
Appears in Collections:I&D CEGOT - Artigos em Revistas Internacionais
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