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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
07/12/2011 |
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Data da última atualização: |
06/02/2012 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
NORONHA, A. C. da S.; JORDÃO, A. L.; FERREIRA, C. T. |
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Afiliação: |
ALOYSEIA CRISTINA DA SILVA NORONHA, CPATU; ALEXANDRE LUIS JORDÃO, IEPA; CAMILA TAVARES FERREIRA, GRADUANDA UFRA. |
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Título: |
Ocorrência de Brevipalpus obovatus Donnadieu (Acari, Tenuipalpidae) em plantas de mandioca no estado do Pará. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE MANDIOCA, 14.; FEIRA BRASILEIRA DA MANDIOCA, 1., 2011, Maceió. Mandioca: fonte de alimento e energia: anais. Maceió: ABAM: SBM, 2011. |
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Idioma: |
Português |
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Palavras-Chave: |
Pará. |
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Thesagro: |
Ácaro; Mandioca. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/49632/1/Resumon-90.pdf
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Marc: |
LEADER 00599nam a2200157 a 4500
001 1908927
005 2012-02-06
008 2011 bl uuuu u00u1 u #d
100 1 $aNORONHA, A. C. da S.
245 $aOcorrência de Brevipalpus obovatus Donnadieu (Acari, Tenuipalpidae) em plantas de mandioca no estado do Pará.
260 $aIn: CONGRESSO BRASILEIRO DE MANDIOCA, 14.; FEIRA BRASILEIRA DA MANDIOCA, 1., 2011, Maceió. Mandioca: fonte de alimento e energia: anais. Maceió: ABAM: SBM$c2011
650 $aÁcaro
650 $aMandioca
653 $aPará
700 1 $aJORDÃO, A. L.
700 1 $aFERREIRA, C. T.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
01/03/2007 |
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Data da última atualização: |
04/03/2020 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
MENDONÇA-SANTOS, M. de L.; MCBRATNEY, A. B.; MINASNY, B. |
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Afiliação: |
MARIA DE LOURDES M SANTOS BREFIN, CNPS. |
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Título: |
Soil prediction with spatially decomposed environmental factors. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
In: LAGACHERIE, P.; MCBRATNEY, A. B.; VOLTZ, M. (Ed.). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier, 2007. cap. 21, 269-278. |
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Idioma: |
Inglês |
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Conteúdo: |
Prediction of soil attributes and soil classes in digital soil mapping relies on finding relationships between soil and the predictor variables of soil-forming factors and processes. The predictor variables can be remotely or proximally sensed images of soil, landscape, parent material or climatic factors. Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes. There are problems using data layers from different sources, particularly, multicollinearity, and the fact that the relationships between soil and environmental variables can change with spatial scale. To overcome the problem of correlation between variables, principal component analysis can be performed on the predictor variables. With respect to the spatial dependency, each of these variables can be decomposed into separate spatial components and mapped separately. One of the methods of achieving this is wavelet analysis, which decomposes the variables into separate hierarchical spatial components of decreasing spatial resolution. These components could all be derived and subsequently used as separate layers in predicting soil classes or soil attributes. In this chapter, data are decomposed using the wavelet method and examples of predictions of soil classes and surface-clay content are shown, in order to evaluate the effect of using the decomposed layers in comparison with the original data. |
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Palavras-Chave: |
Atributos do solo. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus NAL: |
Remote sensing. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02099naa a2200181 a 4500
001 1338904
005 2020-03-04
008 2007 bl uuuu u00u1 u #d
100 1 $aMENDONÇA-SANTOS, M. de L.
245 $aSoil prediction with spatially decomposed environmental factors.$h[electronic resource]
260 $c2007
520 $aPrediction of soil attributes and soil classes in digital soil mapping relies on finding relationships between soil and the predictor variables of soil-forming factors and processes. The predictor variables can be remotely or proximally sensed images of soil, landscape, parent material or climatic factors. Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes. There are problems using data layers from different sources, particularly, multicollinearity, and the fact that the relationships between soil and environmental variables can change with spatial scale. To overcome the problem of correlation between variables, principal component analysis can be performed on the predictor variables. With respect to the spatial dependency, each of these variables can be decomposed into separate spatial components and mapped separately. One of the methods of achieving this is wavelet analysis, which decomposes the variables into separate hierarchical spatial components of decreasing spatial resolution. These components could all be derived and subsequently used as separate layers in predicting soil classes or soil attributes. In this chapter, data are decomposed using the wavelet method and examples of predictions of soil classes and surface-clay content are shown, in order to evaluate the effect of using the decomposed layers in comparison with the original data.
650 $aRemote sensing
650 $aSensoriamento Remoto
653 $aAtributos do solo
700 1 $aMCBRATNEY, A. B.
700 1 $aMINASNY, B.
773 $tIn: LAGACHERIE, P.; MCBRATNEY, A. B.; VOLTZ, M. (Ed.). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier, 2007. cap. 21, 269-278.
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