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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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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
23/08/2016 |
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Data da última atualização: |
23/08/2016 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
PINHEIRO, H. S. K.; OWENS, P. R.; CHAGAS, C. S.; CARVALHO JUNIOR, W.; ANJOS, L. H. C. |
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Afiliação: |
HELENA S. K. PINHEIRO, UFRRJ; P. R. OWENS, PURDUE UNIVERSITY; CESAR DA SILVA CHAGAS, CNPS; WALDIR DE CARVALHO JUNIOR, CNPS; L. H. C. ANJOS, UFRRJ. |
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Título: |
Applying artificial neural networks utilizing geomorphons to predict soil classes in a brazilian watershed. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: ZHANG, G.-L.; BRUS, D.; LIU, F.; SONG, X.-D.; LAGACHERIE, P. (Ed.). Digital soil mapping across paradigms, scales and boundaries. New York: Springer, 2016. cap. 8, p. 89-102. |
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Idioma: |
Inglês |
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Conteúdo: |
The use of landscape terrain attributes associated with the field information in geographic information systems (GISs) helps to improve the methods applied in soil survey. Geomorphons is a novel technique to map surface elements from digital elevation model and visibility distance (search radius) of a central point in the landscape, which can adopt flexible scales. The main goal of this study was to evaluate the potential for incorporating Geomorphons, which is used to recognize landscape patterns and to improve the soil class predictions by artificial neural networks (ANNs). The procedures involved the acquisition of a cartographic database, creating digital models that represent landscape attributes relevant to paedogenesis on the research site (including Geomorphons of different search radius), sample collection and description of one hundred soil profiles in predefined locations, and finally the supervised classification by neural networks. The covariates used were as follows: elevation, slope, curvature, combined topographic index (CTI), euclidean distance, clay minerals, iron oxide, normalized difference vegetation index (NDVI), geology, and Geomorphons. All models for the terrain attributes have 30-m pixel resolution, and these variables correspond to neurons in the input layer of the neural networks. The output layer of the supervised classification corresponded to the nine dominant soil classes in the study area. To define the appropriate scale of Geomorphons map, sixteen sets of neural networks contain each one of the terrain attributes plus a Geomorphons map calculated from different search radius. For comparative purposes, one of the sets included no Geomorphons. Selection of the appropriate Geomorphons search radius was based on the statistical indexes obtained from a confusion matrix. The results showed that the best classification used the Geomorphons map obtained by forty-five pixels of search radius, in combination with other variables. This classifier presented values to kappa index and global accuracy corresponding to 0.74 and 77.0, respectively. MenosThe use of landscape terrain attributes associated with the field information in geographic information systems (GISs) helps to improve the methods applied in soil survey. Geomorphons is a novel technique to map surface elements from digital elevation model and visibility distance (search radius) of a central point in the landscape, which can adopt flexible scales. The main goal of this study was to evaluate the potential for incorporating Geomorphons, which is used to recognize landscape patterns and to improve the soil class predictions by artificial neural networks (ANNs). The procedures involved the acquisition of a cartographic database, creating digital models that represent landscape attributes relevant to paedogenesis on the research site (including Geomorphons of different search radius), sample collection and description of one hundred soil profiles in predefined locations, and finally the supervised classification by neural networks. The covariates used were as follows: elevation, slope, curvature, combined topographic index (CTI), euclidean distance, clay minerals, iron oxide, normalized difference vegetation index (NDVI), geology, and Geomorphons. All models for the terrain attributes have 30-m pixel resolution, and these variables correspond to neurons in the input layer of the neural networks. The output layer of the supervised classification corresponded to the nine dominant soil classes in the study area. To define the appropriate scale of Geomorphons map, s... Mostrar Tudo |
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Palavras-Chave: |
Atributos geomorfométricos; GRASS; Mapeamento digital de solos; Padrões ternários; Redes neurais artificiais. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02953naa a2200229 a 4500
001 2051514
005 2016-08-23
008 2016 bl uuuu u00u1 u #d
100 1 $aPINHEIRO, H. S. K.
245 $aApplying artificial neural networks utilizing geomorphons to predict soil classes in a brazilian watershed.$h[electronic resource]
260 $c2016
520 $aThe use of landscape terrain attributes associated with the field information in geographic information systems (GISs) helps to improve the methods applied in soil survey. Geomorphons is a novel technique to map surface elements from digital elevation model and visibility distance (search radius) of a central point in the landscape, which can adopt flexible scales. The main goal of this study was to evaluate the potential for incorporating Geomorphons, which is used to recognize landscape patterns and to improve the soil class predictions by artificial neural networks (ANNs). The procedures involved the acquisition of a cartographic database, creating digital models that represent landscape attributes relevant to paedogenesis on the research site (including Geomorphons of different search radius), sample collection and description of one hundred soil profiles in predefined locations, and finally the supervised classification by neural networks. The covariates used were as follows: elevation, slope, curvature, combined topographic index (CTI), euclidean distance, clay minerals, iron oxide, normalized difference vegetation index (NDVI), geology, and Geomorphons. All models for the terrain attributes have 30-m pixel resolution, and these variables correspond to neurons in the input layer of the neural networks. The output layer of the supervised classification corresponded to the nine dominant soil classes in the study area. To define the appropriate scale of Geomorphons map, sixteen sets of neural networks contain each one of the terrain attributes plus a Geomorphons map calculated from different search radius. For comparative purposes, one of the sets included no Geomorphons. Selection of the appropriate Geomorphons search radius was based on the statistical indexes obtained from a confusion matrix. The results showed that the best classification used the Geomorphons map obtained by forty-five pixels of search radius, in combination with other variables. This classifier presented values to kappa index and global accuracy corresponding to 0.74 and 77.0, respectively.
653 $aAtributos geomorfométricos
653 $aGRASS
653 $aMapeamento digital de solos
653 $aPadrões ternários
653 $aRedes neurais artificiais
700 1 $aOWENS, P. R.
700 1 $aCHAGAS, C. S.
700 1 $aCARVALHO JUNIOR, W.
700 1 $aANJOS, L. H. C.
773 $tIn: ZHANG, G.-L.; BRUS, D.; LIU, F.; SONG, X.-D.; LAGACHERIE, P. (Ed.). Digital soil mapping across paradigms, scales and boundaries. New York: Springer, 2016. cap. 8, p. 89-102.
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Embrapa Solos (CNPS) |
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| 1. |  | MACIEL NETTO, A.; ANTONINO, A. C. D.; AUDRY, P.; CARNEIRO, C. J. G.; DALL'OLIO, A. Condutividade hidráulica não saturada de um Podzólico Amarelo da Zona da Mata Norte de Pernambuco. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 35, n. 6, p. 1221-28, jun. 2000. Título em inglês: Unsaturated hydraulic conductivity of a yellow podzolic soil in the north forest zone of Pernambuco state.| Biblioteca(s): Embrapa Unidades Centrais. |
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