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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. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
06/01/2016 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
GONÇALVES, R. R. do V.; ZULLO JÚNIOR, J.; PERON, T. M.; EVANGELISTA, S. R. M.; ROMANI, L. A. S. |
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Afiliação: |
RENATA RIBEIRO DO VALLE GONÇALVES, Unicamp; JURANDIR ZULLO JÚNIOR, Unicamp; TAIS MARQUES PERON, Estagiária CNPTIA; SILVIO ROBERTO MEDEIROS EVANGELISTA, CNPTIA; LUCIANA ALVIM SANTOS ROMANI, CNPTIA. |
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Título: |
Numerical models to forecast the sugarcane production in regional scale based on time series of NDVI/AVHRR images. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES, 8., 2015, Annecy. Proceedings... [Piscataway]: IEEE, 2015. |
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Páginas: |
Não paginado. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: The use of time series of meteorological satellite images, such as the AVHRR/NOAA, and agrometeorological data can be very useful in developing monitoring and forecasting methods for sugarcane crops because they are based on detection changes of space-time behavior. The knowledge about different sugarcane producing areas and climate in a given region is information required to develop models that can be applied simultaneously to several producing municipalities of sugarcane in order to assess the relation between NDVI and WRSI, the estimated productivity and the detection of similarity between the municipalities through distance functions. Thus, the main goal of this paper is to propose numerical models applied to monitor the sugarcane production based on time series of NDVI/AVHRR images and agrometeorological data. The regression method analyzes the relation between a single dependent variable (sugarcane production) and several independent variables (planted area, NDVI, WRSI), that is, use the independent variables whose values are known to predict the values of the selected dependent variable. The models proposed to estimate the sugarcane production using the variables planted area, NDVI and WRSI presented correlation coefficients (R2) around 0.9 and are able to estimate the sugarcane production for the state of São Paulo in Brazil |
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Palavras-Chave: |
Cana-de-açúcar; Dados de sensoriamento remoto; Linear regression; Multiple linear regression; Remote sensing data; Séries temporais. |
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Thesaurus Nal: |
Sugarcane; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02310nam a2200265 a 4500
001 2033024
005 2020-01-07
008 2015 bl uuuu u00u1 u #d
100 1 $aGONÇALVES, R. R. do V.
245 $aNumerical models to forecast the sugarcane production in regional scale based on time series of NDVI/AVHRR images.$h[electronic resource]
260 $aIn: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES, 8., 2015, Annecy. Proceedings... [Piscataway]: IEEE$c2015
300 $aNão paginado.
520 $aAbstract: The use of time series of meteorological satellite images, such as the AVHRR/NOAA, and agrometeorological data can be very useful in developing monitoring and forecasting methods for sugarcane crops because they are based on detection changes of space-time behavior. The knowledge about different sugarcane producing areas and climate in a given region is information required to develop models that can be applied simultaneously to several producing municipalities of sugarcane in order to assess the relation between NDVI and WRSI, the estimated productivity and the detection of similarity between the municipalities through distance functions. Thus, the main goal of this paper is to propose numerical models applied to monitor the sugarcane production based on time series of NDVI/AVHRR images and agrometeorological data. The regression method analyzes the relation between a single dependent variable (sugarcane production) and several independent variables (planted area, NDVI, WRSI), that is, use the independent variables whose values are known to predict the values of the selected dependent variable. The models proposed to estimate the sugarcane production using the variables planted area, NDVI and WRSI presented correlation coefficients (R2) around 0.9 and are able to estimate the sugarcane production for the state of São Paulo in Brazil
650 $aSugarcane
650 $aTime series analysis
653 $aCana-de-açúcar
653 $aDados de sensoriamento remoto
653 $aLinear regression
653 $aMultiple linear regression
653 $aRemote sensing data
653 $aSéries temporais
700 1 $aZULLO JÚNIOR, J.
700 1 $aPERON, T. M.
700 1 $aEVANGELISTA, S. R. M.
700 1 $aROMANI, L. A. S.
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Embrapa Agricultura Digital (CNPTIA) |
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| 3. |  | BOTTON, M.; ARIOLI, C. J.; HARTER, W. da R.; ZART, M.; MÜLLER, C.; BATISTA NETO, O. A. Inseticidas reguladores de crescimento para o manejo de lagartas associadas à fruticultura de clima temperado. In: ENCONTRO NACIONAL SOBRE FRUTICULTURA DE CLIMA TEMPERADO, 11., 2009, Fraiburgo. Anais... Caçador: Epagri, 2009. v. 1 p. 135-140.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
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| 5. | | REGO, T. F. C. do; SANTOS, M. P.; CABRAL, G. B.; CIPRIANO, T. de M.; SOUSA, N. L. de; SOUZA NETO, O. A. de; ARAGÃO, F. J. L. Expression of a DREB 5-A subgroup transcription factor gene from Ricinus communis (RcDREB1) enhanced growth, drought tolerance and pollen viability in tobacco. Plant Cell, Tissue and Organ Culture (PCTOC), v. 146, p. 493-504, 2021. P| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
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| 6. | | MENDONÇA, J. F. M. de; TEIXEIRA, S. R.; CHAVES, F. F.; NETO, O. A.; NAMUR, P. de T.; NEVES, R. de J. das; SILVA, F. F. da. Percepção de agricultores familiares do norte de Minas Gerais quanto à melhoria da qualidade do leite após o uso do Kit Embrapa de Ordenha Manual®. In: CONGRESSO INTERNACIONAL DO LEITE, 13., 2015, Porto Alegre. Anais... Juiz de Fora: Embrapa Gado de Leite, 2015.| Tipo: Artigo em Anais de Congresso |
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