Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1035210
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dc.contributor.authorTRABAQUINI, K.pt_BR
dc.contributor.authorLUIZ, A. J. B.pt_BR
dc.contributor.authorEBERHARDT, I. D. R.pt_BR
dc.contributor.authorSCHULTZ, B.pt_BR
dc.contributor.authorFORMAGGIO, A. R.pt_BR
dc.contributor.authorATZBERGER, C.pt_BR
dc.contributor.otherKLEBER TRABAQUINI, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; ISAQUE DANIEL ROCHA EBERHARDT, INPE; BRUNO SCHULTZ, INPE; ANTONIO ROBERTO FORMAGGIO, INPE; CLEMENT ATZBERGER, University of Natural Resources and Life Sciences, Viena.pt_BR
dc.date.accessioned2016-01-26T11:11:11Zpt_BR
dc.date.available2016-01-26T11:11:11Zpt_BR
dc.date.created2016-01-26pt_BR
dc.date.issued2015pt_BR
dc.identifier.other14776pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1035210pt_BR
dc.descriptionAbstract: Brazil still has not a system based in earth observation images to map and monitoring the aimed crops in large scale. Many programs have been made with Landsat-like and MODIS data to monitoring crops in Brazil, but only the CANASAT has worked in operation level. The clouds and unit products (UPS) size in Brazil, have not permitted the use these data to correct classify maize, sugarcane and soybean. The use of sample frame and visual pixels classification with multitemporal OLI images could be a solution to monitor these three crops. The goal of this study was evaluate the sample frame performance to maize (c1), soybean (c2) and sugarcane (c3) in Paraná (PR) State using OLI images and pixel visual classification. Were used four periods to classify 20.000 random pixels over all the Paraná State: (p1) Nov/Dec, (p2) Jan/Feb, (p3) Mar/Apr and (p4) May/Jun. Each period was compost for 4 OLI images, and 5.000 pixels were classified as c1, c2, c3 and others. IBGE data from 2012 were used to determinate the number of random pixels in each PR mesoregion/stratum. The Stratified Random Sample by Maximum Corrected (SRSMC) showed good performance for tree crops. The coefficient of variation (CV) for each period ranged of 1.42 for soybean in p2 until 16.87 for soybean in p4. The sugarcane CVs have not varied ( and maize CV had the minimum value (2.16) in p4.pt_BR
dc.description.uribitstream/item/137814/1/2015AA003.pdfpt_BR
dc.languagept_BRpt_BR
dc.language.isoporpt_BR
dc.publisherIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 4482-4489.pt_BR
dc.relation.ispartofEmbrapa Meio Ambiente - Artigo em anais de congresso (ALICE)pt_BR
dc.rightsopenAccesspt_BR
dc.subjectStatistical samplingpt_BR
dc.subjectAmostragem estatísticapt_BR
dc.subjectRomote sensing.pt_BR
dc.titleMetodologia para monitoramento agrícola com emprego de imagens orbitais e amostragem estatística.pt_BR
dc.typeArtigo em anais de congresso (ALICE)pt_BR
dc.date.updated2016-01-26T11:11:11Zpt_BR
dc.subject.thesagroEstatística agrícolapt_BR
dc.subject.thesagroAgriculturapt_BR
dc.subject.thesagroSensoriamento Remoto.pt_BR
dc.subject.nalthesaurusAgricultural statisticspt_BR
dc.subject.nalthesaurusSamplingpt_BR
dc.subject.nalthesaurusagriculturept_BR
dc.subject.nalthesaurusremote sensing.pt_BR
dc.ainfo.id1035210pt_BR
dc.ainfo.lastupdate2016-01-26pt_BR
Appears in Collections:Artigo em anais de congresso (CNPMA)

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