Your browser doesn't support javascript.
loading
Digital soil mapping using reference area and artificial neural networks
Arruda, Gustavo Pais de; Demattê, José A. M; Chagas, César da Silva; Fiorio, Peterson Ricardo; Souza, Arnaldo Barros e; Fongaro, Caio Troula.
Afiliação
  • Arruda, Gustavo Pais de; APagri Agronomic consultancy. São José do Rio Preto. BR
  • Demattê, José A. M; University of São Paulo. Escola Duperior de Agricultura Luiz de Queiroz. Dept. of Soil Science. Piracicaba. BR
  • Chagas, César da Silva; Embrapa Soils. Rio de Janeiro. BR
  • Fiorio, Peterson Ricardo; University of São Paulo. Escola Duperior de Agricultura Luiz de Queiroz. Dept. of Biosystems Engineering. Piracicaba. BR
  • Souza, Arnaldo Barros e; University of São Paulo. Escola Duperior de Agricultura Luiz de Queiroz. Dept. of Soil Science. Piracicaba. BR
  • Fongaro, Caio Troula; University of São Paulo. Escola Duperior de Agricultura Luiz de Queiroz. Dept. of Soil Science. Piracicaba. BR
Sci. agric ; 73(3): 266-273, 2016. ilus, tab, map, graf
Article em En | VETINDEX | ID: biblio-1497562
Biblioteca responsável: BR68.1
Localização: BR68.1
ABSTRACT
Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soil-landscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area.
Assuntos
Palavras-chave
Texto completo
Adicionar na Minha BVS
Imprimir
XML
Buscar no Google

Texto completo: 1 Base de dados: VETINDEX Assunto principal: Percepção / Mudança Climática / Adaptação a Desastres / Enquete Socioeconômica / Fazendeiros Idioma: En Revista: Sci. agric / Sci. agric. Ano de publicação: 2016 Tipo de documento: Article
Texto completo
Adicionar na Minha BVS
Imprimir
XML
Buscar no Google

Texto completo: 1 Base de dados: VETINDEX Assunto principal: Percepção / Mudança Climática / Adaptação a Desastres / Enquete Socioeconômica / Fazendeiros Idioma: En Revista: Sci. agric / Sci. agric. Ano de publicação: 2016 Tipo de documento: Article