Data/hora: 09/06/2024 14:12:25

Provedor de dados: 	Pesquisa Agropecuária Brasileira	
País: 	Brazil	
Título: 	Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density	
Autores: 	Sarmento,Eliana Casco; Giasson,Elvio; Weber,Eliseu; Flores,Carlos Alberto; Hasenack,Heinrich.	
Data: 	2012-09-01	
Ano: 	2012	
Palavras-chave: 	Appellation of origin; Decision tree; Digital elevation model; Geographic information systems; Neural network; Soil mapping.	
Resumo: 	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.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2012000900025	
Editor: 	Embrapa Secretaria de Pesquisa e Desenvolvimento

Pesquisa Agropecuária Brasileira	
Relação: 	10.1590/S0100-204X2012000900025	
Formato: 	text/html	
Fonte: 	Pesquisa Agropecuária Brasileira  v.47 n.9 2012	
Fonte: 	info:eu-repo/semantics/openAccess