Annual cropland mapping using data mining and OLI Landsat-8.

Vista sencilla de ítem
dc.contributorLUCAS VOLOCHEN OLDONI, INPE; CARLOS EDUARDO VIZZOTTO CATTANI, Unioeste; ERIVELTO MERCANTE, Unioeste; JERRY ADRIANI JOHANN, Unioeste; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; LUIZ ALMEIDA, INPE.
dc.creatorOLDONI, L. V.
dc.creatorCATTANI, C. E. V.
dc.creatorMERCANTE, E.
dc.creatorJOHANN, J. A.
dc.creatorANTUNES, J. F. G.
dc.creatorALMEIDA, L.
dc.date2019-11-22T18:21:53Z
dc.date2019-11-22T18:21:53Z
dc.date2019-11-22
dc.date2019
dc.date2019-11-22T18:21:53Z
dc.date.accessioned2026-07-07T04:15:58Z
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%.
dc.identifierRevista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 23, n. 12, p. 952-958, 2019.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1114915
dc.identifierhttp://dx.doi.org/10.1590/1807-1929/agriambi.v23n12p952-958
dc.identifier.urihttp://hdl.handle.net/123456789/455945
dc.languageeng
dc.rightsopenAccess
dc.subjectÁrvore de decisão
dc.subjectMétricas temporais de NDVI
dc.subjectMineração de dados
dc.subjectSéries temporais
dc.subjectDecision tree
dc.subjectNDVI temporal metrics
dc.subjectRandom forest
dc.subjectData mining
dc.subjectNormalized difference vegetation index
dc.subjectTime series analysis
dc.titleAnnual cropland mapping using data mining and OLI Landsat-8.
dc.typeArtigo de periódico

Archivos

Colecciones

Embrapa Agricultura Digital (CNPTIA)