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| Registros recuperados : 53 | |
| Registros recuperados : 53 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
04/10/2011 |
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Data da última atualização: |
03/05/2019 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
OLIVEIRA, D. P. de; PULIDO, J.; FRANCHESCHINI, M. H. D.; COSTA, P. A. da; ARRUDA, G. P. de; DEMATTÊ, J. A. M.; SOUZA FILHO, C. R. de; VICENTE, L. E. |
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Afiliação: |
DANIEL 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. |
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Título: |
Caracterização espectral de solos utilizando espectrorradiômetro em laboratório e imagem de satélite hiperespectral. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE, 2011. |
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Páginas: |
p. 8476-8483. |
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Idioma: |
Português |
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Conteúdo: |
Data 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). |
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Palavras-Chave: |
Processamento de imagens. |
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Thesagro: |
Geologia; Sensoriamento Remoto. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/42859/1/Vicente-SBSR.pdf
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Marc: |
LEADER 02072nam a2200241 a 4500
001 1902332
005 2019-05-03
008 2011 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, D. P. de
245 $aCaracterização espectral de solos utilizando espectrorradiômetro em laboratório e imagem de satélite hiperespectral.
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: INPE$c2011
300 $ap. 8476-8483.
520 $aData 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).
650 $aGeologia
650 $aSensoriamento Remoto
653 $aProcessamento de imagens
700 1 $aPULIDO, J.
700 1 $aFRANCHESCHINI, M. H. D.
700 1 $aCOSTA, P. A. da
700 1 $aARRUDA, G. P. de
700 1 $aDEMATTÊ, J. A. M.
700 1 $aSOUZA FILHO, C. R. de
700 1 $aVICENTE, L. E.
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Registro original: |
Embrapa Territorial (CNPM) |
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