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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
07/03/2013 |
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Data da última atualização: |
01/09/2017 |
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Autoria: |
SARMENTO, E. C.; GIASSON, E.; WEBER, E.; FLORES, C. A.; HASENACK, H. |
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Afiliação: |
ELIANA CASCO SARMENTO, UFRGS; ELVIO GIASSON, UFRGS; ELISEU WEBER, UFRGS; CARLOS ALBERTO FLORES, CPACT; HEINRICH HASENACK, UFRGS. |
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Título: |
Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 47, n. 9, p. 1395-1403, set. 2012. |
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Páginas: |
p.1395-1403 |
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Idioma: |
Inglês |
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Notas: |
Título em português: Predição de ordens de solos com alta resolução espacial: resposta de diferentes classificadores à densidade de amostragem. |
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Conteúdo: |
The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self-organizing map, SOM; and multi-layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha. |
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Palavras-Chave: |
Appelation of origin; Decision tree; Digital elevation model; Neural Network. |
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Thesaurus NAL: |
Geographic information systems; Soil surveys. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/78412/1/PAB-v.47-n.9-p.1395-1403.pdf
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Marc: |
LEADER 02200naa a2200265 a 4500
001 1952501
005 2017-09-01
008 2012 bl uuuu u00u1 u #d
100 1 $aSARMENTO, E. C.
245 $aPrediction of soil orders with high spatial resolution$bresponse of different classifiers to sampling density.
260 $c2012
300 $ap.1395-1403
500 $aTítulo em português: Predição de ordens de solos com alta resolução espacial: resposta de diferentes classificadores à densidade de amostragem.
520 $aThe objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self-organizing map, SOM; and multi-layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.
650 $aGeographic information systems
650 $aSoil surveys
653 $aAppelation of origin
653 $aDecision tree
653 $aDigital elevation model
653 $aNeural Network
700 1 $aGIASSON, E.
700 1 $aWEBER, E.
700 1 $aFLORES, C. A.
700 1 $aHASENACK, H.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 47, n. 9, p. 1395-1403, set. 2012.
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Embrapa Unidades Centrais (AI-SEDE) |
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