Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/17039
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dc.contributor.authorLU, D.pt_BR
dc.contributor.authorMAUSEL, P.pt_BR
dc.contributor.authorBATISTELLA, M.pt_BR
dc.contributor.authorMORAN, E.pt_BR
dc.date.accessioned2011-04-10T11:11:11Zpt_BR
dc.date.available2011-04-10T11:11:11Zpt_BR
dc.date.created2004-04-29pt_BR
dc.date.issued2004pt_BR
dc.identifier.citationPhotogrammetric Engineering & Remote Sensing, v. 70, n. 6, p. 723-731, jun. 2004.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/17039pt_BR
dc.descriptionFour distinctly different classifiers were used to analyze multispectral data. Which of these classifiers is most suitable for a specific study area is not always clear. This paper provides a comparison of minimum-distance classifier (MDC), maximumlikelihood classifier (MLC), extraction and classification of homogeneous objects (ECHO), and decision-tree classifier based on linear spectral mixture analysis (DTC-LSMA). Each of the classifiers used both Landsat Thematic Mapper data and identical field-based training sample datasets in a western Brazilian Amazon study area. Seven land-cover classes? mature forest, advanced secondary succession, initial secondary succession, pasture lands, agricultural lands, bare lands, and water?were classified. Classification results indicate that the DTC-LSMA and ECHO classifiers were more accurate than were the MDC and MLC. The overall accuracy of the DTCLSMA approach was 86 percent with a 0.82 kappa coefficient and ECHO had an accuracy of 83 percent with a 0.79 kappa coefficient. The accuracy of the other classifiers ranged from 77 to 80 percent with kappa coefficients from 0.72 to 0.75.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectMapeamentopt_BR
dc.subjectAmazonia brasileirapt_BR
dc.subjectAmazonaspt_BR
dc.titleComparison of land-cover classification methods in the Brazilian Amazon Basin.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2015-03-30T11:11:11Zpt_BR
dc.subject.thesagroBacia Hidrográficapt_BR
dc.subject.thesagroFloresta Tropical Úmidapt_BR
dc.subject.thesagroSatélitept_BR
riaa.ainfo.id17039pt_BR
riaa.ainfo.lastupdate2015-03-30pt_BR
dc.contributor.institution1-2 e 4: Indiana University; 3: Embrapa Monitoramento por Satélite.pt_BR
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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