Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/31577
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dc.contributor.authorLU, D.pt_BR
dc.contributor.authorBATISTELLA, M.pt_BR
dc.contributor.authorMIRANDA, E. E. dept_BR
dc.date.accessioned2014-08-26T06:26:14Z-
dc.date.available2014-08-26T06:26:14Z-
dc.date.created2009-03-02pt_BR
dc.date.issued2008pt_BR
dc.identifier.citationPhotogrammetric Engineering & Remote Sensing, v. 74, n. 3, p. 311-321, mar. 2008.pt_BR
dc.identifier.isbn0099-1112pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/31577pt_BR
dc.descriptionComplex forest structure and abundant tree species in the moist tropical regions often couse difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HBG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG of TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels X 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectComparative studypt_BR
dc.subjectLandsat TM and SPOT HRG Imagespt_BR
dc.subjectBrazilian Amazonpt_BR
dc.subjectMoist tropical regionspt_BR
dc.subjectMachadinho d´Oestept_BR
dc.titleA comparative study of landsat TM and SPOT HRG images of vegetation classification in the brazilian Amazon.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2014-08-26T06:26:14Zpt_BR
riaa.ainfo.id31577pt_BR
riaa.ainfo.lastupdate2014-08-25pt_BR
dc.contributor.institutionDENGSHENG LU, Indiana University; MATEUS BATISTELLA, CNPM; EVARISTO EDUARDO DE MIRANDA, CNPM.pt_BR
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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