Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/902332
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorOLIVEIRA, D. P. dept_BR
dc.contributor.authorPULIDO, J.pt_BR
dc.contributor.authorFRANCHESCHINI, M. H. D.pt_BR
dc.contributor.authorCOSTA, P. A. dapt_BR
dc.contributor.authorARRUDA, G. P. dept_BR
dc.contributor.authorDEMATTÊ, J. A. M.pt_BR
dc.contributor.authorSOUZA FILHO, C. R. dept_BR
dc.contributor.authorVICENTE, L. E.pt_BR
dc.date.accessioned2011-10-04T11:11:11Zpt_BR
dc.date.accessioned2011-10-04T11:11:11Zpt_BR
dc.date.available2011-10-04T11:11:11Zpt_BR
dc.date.available2011-10-04T11:11:11Zpt_BR
dc.date.created2011-10-04pt_BR
dc.date.issued2011pt_BR
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE, 2011.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/902332pt_BR
dc.descriptionData obtained with hyperspectral remote sensors have the advantage of containing a great spectral resolution, offering more details about spectral behavior of a particular target. The use of these images show high potential to describe soil mineralogical attributes. The main objective of this study was to obtain the spectral and mineralogical attributes of soils using hyperspectral satellite imagery and with data acquired at ground level; evaluation of a supervised classification routine for determination of soils texture; and estimate clay using multivariate analysis. Soil samples were collected at a 0-20cm depth and spectral measurements, texture and mineralogy analysis were made. Using GIS software, image processing and statistical packages, the information obtained in the laboratory has been analyzed. The use of hyperspectral imagery enhanced the mineralogical characterization of the studied area. The maximum likelihood classification algorithm showed great skill in distinguishing between four textures class created with the aim of hyperspectral data. The statistical method PLSR provided a satisfactory prediction of clay and sand, using data collected in the laboratory, with high coefficients of determination and low error values (RMSE).pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectProcessamento de imagenspt_BR
dc.titleCaracterização espectral de solos utilizando espectrorradiômetro em laboratório e imagem de satélite hiperespectral.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2019-05-03T11:11:11Zpt_BR
dc.subject.thesagroGeologiapt_BR
dc.subject.thesagroSensoriamento Remotopt_BR
dc.format.extent2p. 8476-8483.pt_BR
riaa.ainfo.id902332pt_BR
riaa.ainfo.lastupdate2019-05-03 -03:00:00pt_BR
dc.contributor.institutionDANIEL PONTES DE OLIVEIRA, ESALQ/USP - UFC; JAVIER PULIDO, ESALQ/USP - UFC; MARSTON HÉRACLES DOMINGUES FRANCHESCHINI, ESALQ/USP; PRISCILLA ALVES DA COSTA, ESALQ/USP; GUSTAVO PAIS DE ARRUDA, ESALQ/USP; JOSÉ ALEXANDRE MELO DEMATTÊ, ESALQ/USP; CARLOS ROBERTO DE SOUZA FILHO, IG/UNICAMP; LUIZ EDUARDO VICENTE, CNPM.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPM)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
VicenteSBSR.pdf1,12 MBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace