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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
06/01/2016 |
Data da última atualização: |
07/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
GONÇALVES, R. R. do V.; ZULLO JÚNIOR, J.; PERON, T. M.; EVANGELISTA, S. R. M.; ROMANI, L. A. S. |
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. |
Título: |
Numerical models to forecast the sugarcane production in regional scale based on time series of NDVI/AVHRR images. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
In: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTITEMPORAL REMOTE SENSING IMAGES, 8., 2015, Annecy. Proceedings... [Piscataway]: IEEE, 2015. |
Páginas: |
Não paginado. |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Cana-de-açúcar; Dados de sensoriamento remoto; Linear regression; Multiple linear regression; Remote sensing data; Séries temporais. |
Thesaurus Nal: |
Sugarcane; Time series analysis. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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10. | | SANCHES, M. C.; ZULLO JUNIOR, J.; ROMANI, L. A. S. Comparação do risco climático da soja, cana-de-açúcar e café arábica, para o estado de São Paulo, calculado com dados terrestres e orbitais de precipitação pluvial. Agrometeoros, v. 26, n. 1, p. 25-36, jul. 2018.Tipo: Artigo em Periódico Indexado | Circulação/Nível: C - 0 |
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