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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: |
11/10/2011 |
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Data da última atualização: |
24/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
GONÇALVES, R. R. V.; ZULLO JUNIOR, J.; FERRARESSO, C. S.; SOUSA, E. P. M.; ROMANI, L. A. S.; TRAINA, A. J. M. |
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Afiliação: |
R. R. V. GONÇALVES, Cepagri/Unicamp; JURANDIR ZULLO JUNIOR, Cepagri/Unicamp; C. S. FERRARESSO, Cepagri/Unicamp; E. P. M. SOUSA, USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; A. J. M. TRAINA, USP. |
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Título: |
Analysis of NOAA/VHRR multitemporal images, climate conditions and cultivated land of sugarcane fields applied to agricultural monitoring. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 6., 2011, Trento. Proceedings... Piscataway: IEEE; Italy: University of Trento, 2011. |
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Páginas: |
p. 12-14. |
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Idioma: |
Inglês |
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Notas: |
MultiTemp-2011. |
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Conteúdo: |
The purpose of this work is to assess the sugarcane yield variation in regional scale through NDVI images from a low resolution spatial satellite. We have used Principal Component Analysis (PCA) and Cluster Analysis to correlate sugarcane cultivated land with multitemporal NDVI images also verifying the influence of climate conditions to them. According to both techniques (PCA and clustering), clusters for different set of variables are distinct only when cultivated land was included in the dataset. On the contrary, climate variables determine the clustering formation. Exploring multitemporal images from high resolution satellites through data mining techniques, such as cluster analysis, is a valuable way to improve crops monitoring specially at a time when it becomes increasingly important to understand the impact of climate change on agriculture. |
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Palavras-Chave: |
Análise de componentes principais; Cana-de-açúcar; Clusterização; Imagens NDVI; Monitoramento agrícola; Séries temporais. |
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Thesaurus Nal: |
Cluster analysis; Principal component analysis; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01956nam a2200301 a 4500
001 1902845
005 2020-01-24
008 2011 bl uuuu u00u1 u #d
100 1 $aGONÇALVES, R. R. V.
245 $aAnalysis of NOAA/VHRR multitemporal images, climate conditions and cultivated land of sugarcane fields applied to agricultural monitoring.$h[electronic resource]
260 $aIn: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 6., 2011, Trento. Proceedings... Piscataway: IEEE; Italy: University of Trento$c2011
300 $ap. 12-14.
500 $aMultiTemp-2011.
520 $aThe purpose of this work is to assess the sugarcane yield variation in regional scale through NDVI images from a low resolution spatial satellite. We have used Principal Component Analysis (PCA) and Cluster Analysis to correlate sugarcane cultivated land with multitemporal NDVI images also verifying the influence of climate conditions to them. According to both techniques (PCA and clustering), clusters for different set of variables are distinct only when cultivated land was included in the dataset. On the contrary, climate variables determine the clustering formation. Exploring multitemporal images from high resolution satellites through data mining techniques, such as cluster analysis, is a valuable way to improve crops monitoring specially at a time when it becomes increasingly important to understand the impact of climate change on agriculture.
650 $aCluster analysis
650 $aPrincipal component analysis
650 $aTime series analysis
653 $aAnálise de componentes principais
653 $aCana-de-açúcar
653 $aClusterização
653 $aImagens NDVI
653 $aMonitoramento agrícola
653 $aSéries temporais
700 1 $aZULLO JUNIOR, J.
700 1 $aFERRARESSO, C. S.
700 1 $aSOUSA, E. P. M.
700 1 $aROMANI, L. A. S.
700 1 $aTRAINA, A. J. M.
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