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106. | | ASSAD, E. D.; MARIN, F. R.; PINTO, H. S.; ZULLO JÚNIOR, J. Zoneamento agrícola de riscos climáticos do Brasil: base teórica, pesquisa e desenvolvimento. Informe Agropecuário, Belo Horizonte, v. 29, n. 246, p. 47-60, set./out. 2008. Biblioteca(s): Embrapa Agricultura Digital; Embrapa Semiárido; Embrapa Uva e Vinho. |
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107. | | PINTO, H. S.; ZULLO JÚNIOR, J.; ASSAD, E. D.; BRUNINI, O.; ALFONSI, R. R.; CORAL, G. Zoneamento de riscos climáticos para a cafeicultura do estado de São Paulo. Revista Brasileira de Agrometeorologia, Passo Fundo, v. 9, n. 3, p. 495-500, dez. 2001. Número especial. Biblioteca(s): Embrapa Agricultura Digital. |
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108. | | GONÇALVES, R. R. V.; ZULLO JÚNIOR, J.; ROMANI, L. A. S.; NASCIMENTO, C. R.; TRAINA, A. J. M. Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil. International Journal of Remote Sensing, Basingstoke, v. 33, n. 15, p. 4653-4672, Aug. 2012. Biblioteca(s): Embrapa Agricultura Digital. |
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116. | | PALLONE FILHO, W. J.; ZULLO JUNIOR, J.; ASSAD, E. D.; PINTO, H. S.; ROCHA, J. V.; LAMPARELLI, R. A. C. Monitoramento de estiagem durante o verão de regiões tropicais utilizando imagens AVHRR/NOAA-14. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 11., 2003, Belo Horizonte. Anais... São José dos Campos: Inpe, 2003. p. 1193-1201. Biblioteca(s): Embrapa Agricultura Digital. |
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117. | | ROMANI, L. A. S.; ZULLO JÚNIOR, J.; NASCIMENTO, C. R.; GONÇALVES, R. R. V.; TRAINA, C; TRAINA, A. J. M. Monitoring sugar cane crops through DTW-based method for similarity search in NDVI time series. In: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 5., 2009, Groton, Connecticut. Proceedings... Storrs: UConn, 2009. p. 171-178. MultiTemp 2009. Biblioteca(s): Embrapa Agricultura Digital. |
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Registros recuperados : 178 | |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
25/05/2009 |
Data da última atualização: |
15/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
Autoria: |
ROMANI, L. A. S.; SOUSA, E. P. M. de; RIBEIRO, M. X.; ZULLO JÚNIOR. J.; TRAINA JÚNIOR, C.; TRAINA, A. J. M. |
Afiliação: |
LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, ICMC/USP; MARCELA X. RIBEIRO, ICMC/USP; JURANDIR ZULLO JÚNIOR, CEPAGRI/UNICAMP; CAETANO TRAINA JÚNIOR, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP. |
Título: |
Employing fractal dimension to analyze climate and remote sensing data streams. |
Ano de publicação: |
2009 |
Fonte/Imprenta: |
In: SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 9., 2009, Sparks. Proceedings... Society for Industrial and Applied Mathematics, Philadelphia, 2009. |
Páginas: |
Não paginado. |
Idioma: |
Inglês |
Notas: |
SDM 2009. |
Conteúdo: |
Recently, huge amounts of climate data and remote sensing images have been stored by several institutions around the world. Improvements in the data gathering, aiming at making it available to the domain specialist the information needed for decision making. Also, data usually change their behavior over time in climate and remote sensing areas, evolving according to agrometeorological aspects. In this work, we propose a framework to monitor evolving climate and remote sensing data by emplooying a fast and low-cost process based on the fractal dimension extracted from the collected data. Significant changes in data trends are captured by the fractal-based monitoring process. The changes are evaluated by employing a statistical test to compare the data in consecutive time periods, revealing which data attributes are responsible for the trend changes and how they influence them. Therefore, the proposed framework monitors and reveals important variations on the climate that should be considered to make the agribusiness of the remote sense regions more productive. In particular, we show 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. |
Palavras-Chave: |
Dados multimídia; Data streams; Dimensão fractal; Inteligência artificial; Multimedia data; Teoria dos fractais. |
Thesagro: |
Agricultura; Clima; Sensoriamento remoto. |
Thesaurus NAL: |
Climate; Fractal dimensions; Remote sensing. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 02363nam a2200337 a 4500 001 1048990 005 2020-01-15 008 2009 bl uuuu u00u1 u #d 100 1 $aROMANI, L. A. S. 245 $aEmploying fractal dimension to analyze climate and remote sensing data streams.$h[electronic resource] 260 $aIn: SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 9., 2009, Sparks. Proceedings... Society for Industrial and Applied Mathematics, Philadelphia$c2009 300 $aNão paginado. 500 $aSDM 2009. 520 $aRecently, huge amounts of climate data and remote sensing images have been stored by several institutions around the world. Improvements in the data gathering, aiming at making it available to the domain specialist the information needed for decision making. Also, data usually change their behavior over time in climate and remote sensing areas, evolving according to agrometeorological aspects. In this work, we propose a framework to monitor evolving climate and remote sensing data by emplooying a fast and low-cost process based on the fractal dimension extracted from the collected data. Significant changes in data trends are captured by the fractal-based monitoring process. The changes are evaluated by employing a statistical test to compare the data in consecutive time periods, revealing which data attributes are responsible for the trend changes and how they influence them. Therefore, the proposed framework monitors and reveals important variations on the climate that should be considered to make the agribusiness of the remote sense regions more productive. In particular, we show 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. 650 $aClimate 650 $aFractal dimensions 650 $aRemote sensing 650 $aAgricultura 650 $aClima 650 $aSensoriamento remoto 653 $aDados multimídia 653 $aData streams 653 $aDimensão fractal 653 $aInteligência artificial 653 $aMultimedia data 653 $aTeoria dos fractais 700 1 $aSOUSA, E. P. M. de 700 1 $aRIBEIRO, M. X. 700 1 $aZULLO JÚNIOR. J. 700 1 $aTRAINA JÚNIOR, C. 700 1 $aTRAINA, A. J. M.
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