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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. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
01/12/2017 |
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Data da última atualização: |
10/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
PINHEIRO, H. S. K.; OWENS, P. R.; ANJOS, L. H. C.; CARVALHO JUNIOR, W. de; CHAGAS, C. da S. |
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Afiliação: |
H. S. K. PINHEIRO, UFRRJ; P. R. OWENS, USDA Dale Bumpers Small Farms Research Center; L. H. C. ANJOS, UFRRJ; WALDIR DE CARVALHO JUNIOR, CNPS; CESAR DA SILVA CHAGAS, CNPS. |
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Título: |
Tree-based techniques to predict soil units. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Soil Research, v. 55, n. 8, p. 788-798, 2017. |
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DOI: |
https://doi.org/10.1071/SR16060 |
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Idioma: |
Inglês |
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Conteúdo: |
Quantitative soil-landscape models offer a method for conducting soil surveys that use statistical tools to predict natural patterns in the occurrence of particular map units across a landscape. The aim of the present study was to predict soil units in a watershed with wide variation in landscape conditions. The approach relied on a modelling of soil-forming factors in order to understand the variability of the landscape components in the region. Models were generated for landscape attributes related to pedogenesis, specifically elevation, slope, curvature, compound topographic index, Euclidean distance from stream networks, landforms map, clay minerals index, iron oxide index and normalised difference vegetation index, along with an existing geology map. The soil classification was adapted from the World Reference Base System for Soil Resources, and the predominant soil taxonomic orders observed were Ferrasols, Acrisols, Gleysols, Cambisols, Fluvisols and Regosols. The algorithms used to predict the soil units were based on decision tree (DT) and random forest (RF) methods. The criteria used to evaluate the models' performance were statistical indices, coherence between predicted units and the legacy map, as well as accuracy checks based on control samples. The best performing model was found to be the RF algorithm, with resulting statistical indices considered excellent (overall = 0.966, kappa = 0.962). The accuracy of the map as determined by control points was 67.89%, with a kappa value of 61.39%. MenosQuantitative soil-landscape models offer a method for conducting soil surveys that use statistical tools to predict natural patterns in the occurrence of particular map units across a landscape. The aim of the present study was to predict soil units in a watershed with wide variation in landscape conditions. The approach relied on a modelling of soil-forming factors in order to understand the variability of the landscape components in the region. Models were generated for landscape attributes related to pedogenesis, specifically elevation, slope, curvature, compound topographic index, Euclidean distance from stream networks, landforms map, clay minerals index, iron oxide index and normalised difference vegetation index, along with an existing geology map. The soil classification was adapted from the World Reference Base System for Soil Resources, and the predominant soil taxonomic orders observed were Ferrasols, Acrisols, Gleysols, Cambisols, Fluvisols and Regosols. The algorithms used to predict the soil units were based on decision tree (DT) and random forest (RF) methods. The criteria used to evaluate the models' performance were statistical indices, coherence between predicted units and the legacy map, as well as accuracy checks based on control samples. The best performing model was found to be the RF algorithm, with resulting statistical indices considered excellent (overall = 0.966, kappa = 0.962). The accuracy of the map as determined by control points was 67.89%, wi... Mostrar Tudo |
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Palavras-Chave: |
Mapeamento digital do solo. |
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Thesagro: |
Classificação do Solo; Pedologia. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02162naa a2200217 a 4500
001 2081200
005 2021-11-10
008 2017 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1071/SR16060$2DOI
100 1 $aPINHEIRO, H. S. K.
245 $aTree-based techniques to predict soil units.$h[electronic resource]
260 $c2017
520 $aQuantitative soil-landscape models offer a method for conducting soil surveys that use statistical tools to predict natural patterns in the occurrence of particular map units across a landscape. The aim of the present study was to predict soil units in a watershed with wide variation in landscape conditions. The approach relied on a modelling of soil-forming factors in order to understand the variability of the landscape components in the region. Models were generated for landscape attributes related to pedogenesis, specifically elevation, slope, curvature, compound topographic index, Euclidean distance from stream networks, landforms map, clay minerals index, iron oxide index and normalised difference vegetation index, along with an existing geology map. The soil classification was adapted from the World Reference Base System for Soil Resources, and the predominant soil taxonomic orders observed were Ferrasols, Acrisols, Gleysols, Cambisols, Fluvisols and Regosols. The algorithms used to predict the soil units were based on decision tree (DT) and random forest (RF) methods. The criteria used to evaluate the models' performance were statistical indices, coherence between predicted units and the legacy map, as well as accuracy checks based on control samples. The best performing model was found to be the RF algorithm, with resulting statistical indices considered excellent (overall = 0.966, kappa = 0.962). The accuracy of the map as determined by control points was 67.89%, with a kappa value of 61.39%.
650 $aClassificação do Solo
650 $aPedologia
653 $aMapeamento digital do solo
700 1 $aOWENS, P. R.
700 1 $aANJOS, L. H. C.
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aCHAGAS, C. da S.
773 $tSoil Research$gv. 55, n. 8, p. 788-798, 2017.
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Registro original: |
Embrapa Solos (CNPS) |
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Biblioteca |
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Tipo/Formato |
Classificação |
Cutter |
Registro |
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URL |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Recursos Genéticos e Biotecnologia. Para informações adicionais entre em contato com cenargen.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
24/03/2009 |
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Data da última atualização: |
08/05/2024 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
CALDAS, F. M.; MILANE, P. V. G. N.; FONTES, E. M. G.; PIRES, C. S. S.; SUJII, E. R.; RIBEIRO, P. A. |
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Afiliação: |
FRANCISCO MORENO CALDAS, UNB; PALOMA VIRGÍNIA GAMBARRA NITÃO MILANE, UNICEUB; ELIANA MARIA GOUVEIA FONTES, EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA; CARMEN SILVIA SOARES PIRES, EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA; EDISON RYOITI SUJII, EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA; PAULINA DE ARAÚJO RIBEIRO. |
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Título: |
Reprodução de fêmeas do bicudo em estruturas reprodutivas do algodoeiro em laboratório. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
In: ENCONTRO DO TALENTO ESTUDANTIL DA EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA, 13., 2008, Brasília, DF. Anais: resumos dos trabalhos. Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2008. Resumo 112. |
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Páginas: |
p.157. |
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Idioma: |
Português |
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Thesagro: |
Algodão; Anthonomus Grandis; Praga. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00765nam a2200205 a 4500
001 1191070
005 2024-05-08
008 2008 bl uuuu u01u1 u #d
100 1 $aCALDAS, F. M.
245 $aReprodução de fêmeas do bicudo em estruturas reprodutivas do algodoeiro em laboratório.
260 $aIn: ENCONTRO DO TALENTO ESTUDANTIL DA EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA, 13., 2008, Brasília, DF. Anais: resumos dos trabalhos. Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2008. Resumo 112.$c2008
300 $ap.157.
650 $aAlgodão
650 $aAnthonomus Grandis
650 $aPraga
700 1 $aMILANE, P. V. G. N.
700 1 $aFONTES, E. M. G.
700 1 $aPIRES, C. S. S.
700 1 $aSUJII, E. R.
700 1 $aRIBEIRO, P. A.
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Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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