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| Registros recuperados : 8 | |
| 5. |  | HARTEMINK, A. E.; HEMPEL, J.; LAGACHERIE, P.; MCBRATNEY, A.; MCKENZIE, N.; MACMILLAN, R. A.; MINASNY, B.; MONTANARELLA, L.; MENDONÇA-SANTOS, M. de L.; SANCHEZ, P.; WALSH, M.; ZHANG, G. GlobalSoilMap.net: a new digital soil map of the world. In: BOETTINGER, J. L.; HOWELL, D. W.; MOORE, A. C.; HARTEMINK, A. E.; KIENAST-BROWN, S. (Ed.). Digital soil mapping: bridging research, environmental application, and operation. New York: Springer, 2010. p. 423-427.| Biblioteca(s): Embrapa Solos. |
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| 6. | | ARROUAYS, D.; GRUNDY, M. G.; HARTEMINK, A. E.; HEMPEL, J. W.; HEUVELINK, G. B. M.; HONG, S. Y.; LAGACHERIE, P.; LELYK, G.; MCBRATNEY, A. B.; MCKENZIE, N. J.; MENDONCA-SANTOS, M. D. L.; MINASNY, B.; MONTANARELLA, L.; ODEH, I. O. A.; SANCHEZ, P. A.; THOMPSON, J. A.; ZHANG, G.-L. GlobalSoilMap: toward a fine-resolution global grid of soil properties. Advances in Agronomy, v. 125, p. 93-134, 2014.| Biblioteca(s): Embrapa Solos. |
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| 7. | | ARROUAYS, D.; LEENAARS, J. G. B.; RICHER-DE-FORGES, A. C.; ADHIKARI, K.; BALLABIO, C.; GREVE, M.; GRUNDY, M.; GUERRERO, E.; HEMPEL, J.; HENGL, T.; HEUVELINK, G.; BATJES, N.; CARVALHO, E.; HARTEMINK, A.; HEWITT, A.; HONG, S.-Y.; KRASILNIKOV, P.; LAGACHERIE, P.; LELYK, G.; LIBOHOVA, Z.; LILLY, A.; MCBRATNEY, A.; MCKENZIE, N.; VASQUES, G. de M. Soil legacy data rescue via GlobalSoilMap and other international and national initiatives. GeoResJ, v. 14, p. 1-19, Dec. 2017.| Biblioteca(s): Embrapa Solos. |
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| 8. | | SANCHEZ, P. A.; AHAMED, S.; CARRÉ, F.; HARTEMINK, A. E.; HEMPEL, J.; HUISING, J.; LAGACHERIE, P.; MACBRATNEY, A. B.; MCKENZIE, N. J.; MENDONÇA-SANTOS, M. de L.; MINASNY, B.; MONTANARELLA, L.; OKOTH, P.; PALM, C. A.; SACHS, J. D.; SHEPHERD, K. D.; VAGEN, T. G.; VANLAUWE, B.; WALSH, M. G.; WINOWIECKI, L. A.; ZHANG, G. L. Digital soil map of the world. Science, v. 325, n. 5941, p. 680-681, 2009.| Biblioteca(s): Embrapa Solos. |
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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
|
Biblioteca(s): |
Embrapa Solos. |
|
Data corrente: |
23/06/2014 |
|
Data da última atualização: |
08/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
Autoria: |
CARVALHO JUNIOR, W. de; LAGACHERIE, P.; CHAGAS, C. da S.; CALDERANO FILHO, B.; BHERING, S. B. |
|
Afiliação: |
WALDIR DE CARVALHO JUNIOR, CNPS; Philippe Lagacherie, INRA, LISAH, Montpellier, France; CESAR DA SILVA CHAGAS, CNPS; BRAZ CALDERANO FILHO, CNPS; SILVIO BARGE BHERING, CNPS. |
|
Título: |
A regional-scale assessment of digital mapping of soil attributes in a tropical hillslope environment. |
|
Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Geoderma, v. 232/234, p. 479-486, 2014. |
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DOI: |
https://doi.org/10.1016/j.geoderma.2014.06.007 |
|
Idioma: |
Inglês |
|
Conteúdo: |
The purpose of this study was to analyze the relationships between soil attributes and environmental covariates in a tropical hillslope environment on a regional scale to estimate spatial distribution of soil attributes and identify statistical and geostatistical techniques that could represent the variation of the soil attributes. The studywas performed in Bom Jardim County, Brazil, and covered an area of 390 km2 with a soil database of 208 sample points distributed in six depth layers (0.53 pts/km2). The study used 18 environmental covariates derived from DEM and satellite imagery. The models evaluated were linear regression, regression trees and ordinary and regression kriging. An exploratory analysis showed that DEM, NDVI, MRVBF, MSP, b3/b2, b5/b7, SPI, SWI, SLOPE and ASPECT were correlated with soil properties. The models performance had a mean crossvalidation r2 of 0.13. The best results were achieved with kriging models, with a crossvalidation r2 of 0.19. A comparison between multiple linear regression and regression trees showed that the tree model yielded the best results. The sample density alone could not explain the results, but an interaction between DEM accuracy, sample density, covariates and geological conditions was suitable as an explanatory factor. Studies of tropical hillslope digital soil mapping on regional scales need to be more exhaustively focused to develop this research area. |
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Palavras-Chave: |
Regression tree. |
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Thesaurus NAL: |
kriging; linear models. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
|
Marc: |
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001 1988756
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024 7 $ahttps://doi.org/10.1016/j.geoderma.2014.06.007$2DOI
100 1 $aCARVALHO JUNIOR, W. de
245 $aA regional-scale assessment of digital mapping of soil attributes in a tropical hillslope environment.$h[electronic resource]
260 $c2014
520 $aThe purpose of this study was to analyze the relationships between soil attributes and environmental covariates in a tropical hillslope environment on a regional scale to estimate spatial distribution of soil attributes and identify statistical and geostatistical techniques that could represent the variation of the soil attributes. The studywas performed in Bom Jardim County, Brazil, and covered an area of 390 km2 with a soil database of 208 sample points distributed in six depth layers (0.53 pts/km2). The study used 18 environmental covariates derived from DEM and satellite imagery. The models evaluated were linear regression, regression trees and ordinary and regression kriging. An exploratory analysis showed that DEM, NDVI, MRVBF, MSP, b3/b2, b5/b7, SPI, SWI, SLOPE and ASPECT were correlated with soil properties. The models performance had a mean crossvalidation r2 of 0.13. The best results were achieved with kriging models, with a crossvalidation r2 of 0.19. A comparison between multiple linear regression and regression trees showed that the tree model yielded the best results. The sample density alone could not explain the results, but an interaction between DEM accuracy, sample density, covariates and geological conditions was suitable as an explanatory factor. Studies of tropical hillslope digital soil mapping on regional scales need to be more exhaustively focused to develop this research area.
650 $akriging
650 $alinear models
653 $aRegression tree
700 1 $aLAGACHERIE, P.
700 1 $aCHAGAS, C. da S.
700 1 $aCALDERANO FILHO, B.
700 1 $aBHERING, S. B.
773 $tGeoderma$gv. 232/234, p. 479-486, 2014.
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