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| Registros recuperados : 74 | |
| 15. |  | MENDONÇA-SANTOS, M. de L.; DART, R. de O.; BERBARA, R. L. L. Applying Conditional Latin Hypercube (cLHS) for selecting soil sampling location for Digital Soil Mapping at Parque Estadual da Mata Seca, MG, Brazil. In: GLOBAL WORKSHOP ON DIGITAL SOIL MAPPING, 3., 2008, Logan, Utah. Bridging research, production, and environmental applications: papers. Logan, UT: University of Utah, 2008. 1 CD-ROM.| Biblioteca(s): Embrapa Solos. |
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| 19. | | MENDONÇA-SANTOS, M. de L.; SANTOS, H. G. dos; DART, R. de O.; PARES, J. G. Digital mapping of soil classes in Rio de Janeiro State, Brazil: data, modelling and prediction. In: HARTEMINK, A. E.; McBRATNEY, A.; MENDONÇA-SANTOS, M. de L. (ed.). Digital soil mapping with limited data. Dordrecht: Springer, 2008. cap. 34, p. 381-396.| Biblioteca(s): Embrapa Solos. |
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| Registros recuperados : 74 | |
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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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