Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1114915
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorOLDONI, L. V.eng
dc.contributor.authorCATTANI, C. E. V.eng
dc.contributor.authorMERCANTE, E.eng
dc.contributor.authorJOHANN, J. A.eng
dc.contributor.authorANTUNES, J. F. G.eng
dc.contributor.authorALMEIDA, L.eng
dc.date.accessioned2019-11-22T18:21:53Z-
dc.date.available2019-11-22T18:21:53Z-
dc.date.created2019-11-22
dc.date.issued2019
dc.identifier.citationRevista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 23, n. 12, p. 952-958, 2019.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1114915-
dc.descriptionABSTRACT: In the state of Paraná, Brazil, there are no major changes in areas cultivated with annual crops, mainly due to environmental laws that do not allow expansions to new areas. There is a great contribution of the annual crops to the domestic demand of food and economic demand in the exports. Thus, the area and distribution of annual crops are information of great importance. New methodologies, such as data mining, are being tested with the objective of analyzing and improving their potential use for classification of land use and land cover. This study used the classifiers decision tree and random forest with Normalized Difference Vegetation Index (NDVI) temporal metrics on images from Operational Land Imager (OLI)/Landsat-8. The results were compared with traditional methods spectral images and Maximum Likelihood Classifier (MLC). At first, seven classes were mapped (water bodies, sugarcane, urban area, annual crops, forest, pasture and reforestation areas); then, only two classes were considered (annual crops and other targets). When classifying the seven targets, both methods had corresponding results, showing global accuracy near 84%. NDVI temporal metrics showed producer?s and user?s accuracy for the annual crop class of 86 and 100%, respectively. However, if considering only two classes, the NDVI temporal metrics reached global accuracy of near 98% and producer?s and user?s accuracy above 94%.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectÁrvore de decisãoeng
dc.subjectMétricas temporais de NDVIeng
dc.subjectMineração de dadoseng
dc.subjectSéries temporaiseng
dc.subjectDecision treeeng
dc.subjectNDVI temporal metricseng
dc.subjectRandom foresteng
dc.subjectData miningeng
dc.titleAnnual cropland mapping using data mining and OLI Landsat-8.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-11-22T18:21:53Z
dc.subject.nalthesaurusNormalized difference vegetation indexeng
dc.subject.nalthesaurusTime series analysiseng
riaa.ainfo.id1114915eng
riaa.ainfo.lastupdate2019-11-22
dc.identifier.doihttp://dx.doi.org/10.1590/1807-1929/agriambi.v23n12p952-958eng
dc.contributor.institutionLUCAS VOLOCHEN OLDONI, INPE; CARLOS EDUARDO VIZZOTTO CATTANI, Unioeste; ERIVELTO MERCANTE, Unioeste; JERRY ADRIANI JOHANN, Unioeste; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LUIZ ALMEIDA, INPE.eng
Aparece nas coleções:Artigo em periódico indexado (CNPTIA)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
APAnnualcropland.pdf3.11 MBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace