Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1072220
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dc.contributor.authorMORAS FILHO, L. O.pt_BR
dc.contributor.authorFIGUEIREDO, E. O.pt_BR
dc.contributor.authorISAAC JÚNIOR, M. A.pt_BR
dc.contributor.authorBARROS, V. C. C. dept_BR
dc.contributor.authorHOTT, M. C.pt_BR
dc.contributor.authorBORGES, L. A. C.pt_BR
dc.date.accessioned2017-07-07T11:11:11Zpt_BR
dc.date.available2017-07-07T11:11:11Zpt_BR
dc.date.created2017-07-07pt_BR
dc.date.issued2017pt_BR
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpe, 2017.pt_BR
dc.identifier.isbn978-85-11-00088-1pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1072220pt_BR
dc.descriptionAmong a variety of digital classification methods based on remote sensing images, the Maximum Likelihood (ML) is widely used in environmental studies, mainly for land cover and vegetation analysis. This study aimed to evaluate the effectiveness of supervised classification by ML technique in a forest management area of dense ombrophilous forest, using one RapidEye image. With this purpose, it was conducted the census of species over 30 cm in diameter at breast height and calculated the Cover Value Index (CVI), and selected the 20 species with the highest CVI as a parameter for classification in a Geographic Information System. 13 of the 20 species selected in the study area were not identified by the classification method, and among the seven identified species, two were underestimated and the others were overestimated. Both the maximum likelihood technique and the spatial resolution of the image used were not suitable for supervised classification of native vegetation, with Kappa index of 0.05 and global accuracy of 5.53%. Studies using spectral characterization in leaf level supported by higher or hyper spectral and spatial resolution images are recommended to increase the accuracy of classification.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectManejo florestalpt_BR
dc.subjectMétodo de classificação digitalpt_BR
dc.subjectMaximum Likelihoodpt_BR
dc.subjectMáxima verossimilhançapt_BR
dc.subjectRio Branco (AC)pt_BR
dc.subjectAcrept_BR
dc.subjectAmazônia Ocidentalpt_BR
dc.subjectWestern Amazonpt_BR
dc.subjectAmazonia Occidentalpt_BR
dc.subjectSistemas de información geográficapt_BR
dc.subjectIdentificación de plantaspt_BR
dc.subjectEstimaciónpt_BR
dc.subjectEspecies nativaspt_BR
dc.subjectBosques tropicalespt_BR
dc.subjectAnálisis estadísticopt_BR
dc.subjectTeledetecciónpt_BR
dc.titleClassificador de máxima verossimilhança aplicado à identificação de espécies nativas na Floresta Amazônica.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2017-11-08T11:11:11Zpt_BR
dc.subject.thesagroFloresta tropicalpt_BR
dc.subject.thesagroEspécie nativapt_BR
dc.subject.thesagroIdentificaçãopt_BR
dc.subject.thesagroEstimativapt_BR
dc.subject.thesagroSensoriamento remotopt_BR
dc.subject.thesagroSistema de informação geográficapt_BR
dc.subject.thesagroAnálise estatísticapt_BR
dc.subject.thesagroMétodo estatísticopt_BR
dc.subject.nalthesaurusTropical forestspt_BR
dc.subject.nalthesaurusIndigenous speciespt_BR
dc.subject.nalthesaurusPlant identificationpt_BR
dc.subject.nalthesaurusEstimationpt_BR
dc.subject.nalthesaurusRemote sensingpt_BR
dc.subject.nalthesaurusGeographic information systemspt_BR
dc.subject.nalthesaurusStatistical analysispt_BR
dc.format.extent26 p.pt_BR
riaa.ainfo.id1072220pt_BR
riaa.ainfo.lastupdate2017-11-08 -02:00:00pt_BR
dc.contributor.institutionLuiz Otávio Moras Filho, Universidade Federal de Lavras (Ufla); EVANDRO ORFANO FIGUEIREDO, CPAF-Acre; Marcos Antônio Isaac Júnior, Universidade Federal de Lavras (Ufla); Vanessa Cabral Costa de Barros, Universidade Federal de Lavras (Ufla); Marcos Cicarini Hott, Universidade Federal de Lavras (Ufla); Luís Antônio Coimbra Borges, Universidade Federal de Lavras (Ufla).pt_BR
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