Computational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives.

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dc.contributorLUCIANA ALVIM SANTOS ROMANI, CNPTIA; AGMA J. M. TRAINA, Ciência da Computação/USP São Carlos; ELAINE P. M. DE SOUSA, Ciência da Computação/USP São Carlos; JURANDIR ZULLO JÚNIOR, CEPAGRI/ UNICAMP; ANA M. H. AVILA, CEPAGRI/UNICAMP; JOSE FERNANDO RODRIGUES JR., UFSCAR; CAETANO TRAINA JÚNIOR, Ciência da Computação/USP São Carlos.
dc.creatorROMANI, L. A. S.
dc.creatorTRAINA, A. J. M.
dc.creatorSOUSA, E. P. M. de
dc.creatorZULLO JÚNIOR, J.
dc.creatorAVILA, A. M. H.
dc.creatorRODRIGUES JR. J. F.
dc.creatorTRAINA JÚNIOR. C.
dc.date2011-04-09T17:37:24Z
dc.date2011-04-09T17:37:24Z
dc.date2009-08-06
dc.date2009
dc.date2020-01-31T11:11:11Z
dc.date.accessioned2026-07-07T04:12:06Z
dc.descriptionIn the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy.
dc.descriptionCSBC 2009.
dc.formatp. 323-337.
dc.identifierIn: CONGRESSO DA SOCIEDADE BRASILEIRA DE COMPUTAÇÃO, 29., 2009, Bento Gonçalves. Anais... Rio Grande do SUL: Instituto de Informática UFRGS.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/256494
dc.identifier.urihttp://hdl.handle.net/123456789/454005
dc.languageeng
dc.rightsopenAccess
dc.subjectDados agrometeorológicos
dc.subjectDados de sensoriamento remoto
dc.subjectDados climáticos
dc.subjectTeoria dos fractais
dc.subjectMineração de dados
dc.subjectTécnicas de visualização
dc.subjectDados massivos
dc.subjectSéries temporais
dc.subjectCana-de-açúcar
dc.subjectData mining
dc.subjectAgricultura
dc.subjectRemote sensing
dc.subjectAgriculture
dc.subjectSugarcane
dc.titleComputational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives.
dc.typeArtigo em anais e proceedings

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