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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 |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
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Registro Completo
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Biblioteca(s): |
Embrapa Agroenergia; Embrapa Agroindústria Tropical; Embrapa Gado de Leite; Embrapa Suínos e Aves; Embrapa Unidades Centrais. |
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Data corrente: |
18/04/2018 |
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Data da última atualização: |
17/12/2020 |
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Tipo da produção científica: |
Autoria/Organização/Edição de Livros |
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Autoria: |
KUNZ, A.; OTENIO, M. H.; LEITAO, R. C.; GAMBETTA, R. (ed.). |
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Afiliação: |
AIRTON KUNZ, CNPSA; MARCELO HENRIQUE OTENIO, CNPGL; RENATO CARRHA LEITAO, CNPAT; ROSSANO GAMBETTA, CNPAE. |
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Título: |
Energia limpa e acessível: contribuições da Embrapa. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Brasília, DF: Embrapa, 2018. |
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Descrição Física: |
E-book. |
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Série: |
(Objetivos de Desenvolvimento Sustentável, 7). |
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ISBN: |
978-85-7035-787-8 |
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Idioma: |
Português |
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Conteúdo: |
Trata de biomassa na matriz energética, processos de produção de energia sustentável e eficiência energética, além dos desafios futuros para garantir acesso à energia sustentável para todas e todos. |
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Palavras-Chave: |
Energia sustentável; Fontes de energia. |
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Thesagro: |
Agricultura Sustentável; Biogás; Biomassa; Desenvolvimento Sustentável; Energia; Energia eólica; Energia solar. |
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Thesaurus NAL: |
Sustainable agriculture; Sustainable development. |
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Categoria do assunto: |
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P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/184236/1/ODS-7-energia-limpa-e-acessivel.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/180725/1/ODS-7-energia-limpa-e-acess-vel-contribui-es-da-embrapa-.epub
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/188701/1/ODS-7-energia-limpa-e-acessivel.pdf
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Marc: |
LEADER 01094nam a2200313 a 4500
001 2090708
005 2020-12-17
008 2018 bl uuuu 00u1 u #d
020 $a978-85-7035-787-8
100 1 $aKUNZ, A.
245 $aEnergia limpa e acessível$bcontribuições da Embrapa.$h[electronic resource]
260 $aBrasília, DF: Embrapa$c2018
300 $cE-book.
490 $a(Objetivos de Desenvolvimento Sustentável, 7).
520 $aTrata de biomassa na matriz energética, processos de produção de energia sustentável e eficiência energética, além dos desafios futuros para garantir acesso à energia sustentável para todas e todos.
650 $aSustainable agriculture
650 $aSustainable development
650 $aAgricultura Sustentável
650 $aBiogás
650 $aBiomassa
650 $aDesenvolvimento Sustentável
650 $aEnergia
650 $aEnergia eólica
650 $aEnergia solar
653 $aEnergia sustentável
653 $aFontes de energia
700 1 $aOTENIO, M. H.
700 1 $aLEITAO, R. C.
700 1 $aGAMBETTA, R.
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Embrapa Unidades Centrais (AI-SEDE) |
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