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dc.contributor.authorARRUDA, G. P. dept_BR
dc.contributor.authorDEMATTÊ, J. A. M.pt_BR
dc.contributor.authorCHAGAS, C. da S.pt_BR
dc.contributor.authorFIORIO, P. R.pt_BR
dc.contributor.authorSOUZA, A. B. e.pt_BR
dc.date.accessioned2016-08-24T11:11:11Zpt_BR
dc.date.available2016-08-24T11:11:11Zpt_BR
dc.date.created2016-08-24pt_BR
dc.date.issued2016pt_BR
dc.identifier.citationScientia Agricola, Piracicaba, v. 73, n. 3, p. 266-273, May/Jun. 2016.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1051574pt_BR
dc.descriptionDigital 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 soillandscape 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 areapt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectExtrapolação de mapaspt_BR
dc.subjectPesquisa pedológicapt_BR
dc.subjectAtributos da paisagempt_BR
dc.subjectAulas pedológicaspt_BR
dc.subjectMineração de dadospt_BR
dc.titleDigital soil mapping using reference area and artificial neural networks.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2016-08-24T11:11:11Zpt_BR
riaa.ainfo.id1051574pt_BR
riaa.ainfo.lastupdate2016-08-24pt_BR
dc.identifier.doi10.1590/0103-9016-2015-0131pt_BR
dc.contributor.institutionGUSTAVO PAIS DE ARRUDA, APagri Agronomic Consultancy; JOSÉ A. M. DEMATTÊ, USP/ESALQ; CESAR DA SILVA CHAGAS, CNPS; PETERSON RICARDO FIORIO, USP/ESALQ; ARNALDO BARROS E SOUZA, USP/ESALQ.pt_BR
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