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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
10/08/2009 |
Data da última atualização: |
15/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
Autoria: |
NASCIMENTO, C. R.; ZULLO JÚNIOR, J.; ROMANI, L. A. S.; RODRIGUES, L. H. A. |
Afiliação: |
C. R. NASCIMENTO, FEAGRI/UNICAMP; J. ZULLO JÚNIOR, CEPAGRI/UNICAMP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; L. H. A. RODRIGUES, FEAGRI/UNICAMP. |
Título: |
Identification of sugar cane fields in the state of sao paulo using a time series of AVHRR/NOAA satellite images. |
Ano de publicação: |
2009 |
Fonte/Imprenta: |
In: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 5., 2009, Groton, Connecticut. Proceedings... Storrs: UConn, 2009. |
Páginas: |
p. 104-111. |
Idioma: |
Inglês |
Notas: |
MultiTemp 2009. |
Conteúdo: |
Brazil is the first world producer of sugar cane. Despite the economic and social importance of agribusiness to Brazil, it is still difficult to estimate the harvest of the its main agricultural crops with the precision and anticipation needed, justifying the study and development of new methods based on the use of remote sensing data, for example. Even considering the increasing availability of high spatial resolution images in recent years, AVHRR/NOAA satellites have some characteristics suitable for application in operational methods of agricultural monitoring specially for a crop like sugar cane. Thus, this paper had the main objective to identify sugar cane areas in the state of Sao Paulo using harmonic analysis applied to a time series of AVHRR/NOAA-17 available at CEPAGRI/UNICAMP. A decision tree was calculated to search patterns that could represent the sugar cane along the crop season 2006/2007. Application of these two methods (harmonic analysis and decision tree) on the time series used was able to identify sugar cane areas in the state of Sao Paulo (Brazil) with 92% confidence when compared to ground truth based on LANDSAT/TM images and available at CANASAT?s webpage (http://www.dsr.inpe.br/canasat). This methodology can be applied in an operational way to support the official systems of harvest forecasting. |
Palavras-Chave: |
Análise harmônica; Árvore de decisão; Georreferenciamento; Processamento de imagens NDVI; Séries temporais de AVHRR/NOAA-17. |
Thesagro: |
Agricultura; Cana de açúcar; Sensoriamento remoto. |
Thesaurus Nal: |
Image analysis; Remote sensing; Sugarcane. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 02387nam a2200301 a 4500 001 1256611 005 2020-01-15 008 2009 bl uuuu u00u1 u #d 100 1 $aNASCIMENTO, C. R. 245 $aIdentification of sugar cane fields in the state of sao paulo using a time series of AVHRR/NOAA satellite images.$h[electronic resource] 260 $aIn: INTERNATIONAL WORKSHOP ON THE ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 5., 2009, Groton, Connecticut. Proceedings... Storrs: UConn$c2009 300 $ap. 104-111. 500 $aMultiTemp 2009. 520 $aBrazil is the first world producer of sugar cane. Despite the economic and social importance of agribusiness to Brazil, it is still difficult to estimate the harvest of the its main agricultural crops with the precision and anticipation needed, justifying the study and development of new methods based on the use of remote sensing data, for example. Even considering the increasing availability of high spatial resolution images in recent years, AVHRR/NOAA satellites have some characteristics suitable for application in operational methods of agricultural monitoring specially for a crop like sugar cane. Thus, this paper had the main objective to identify sugar cane areas in the state of Sao Paulo using harmonic analysis applied to a time series of AVHRR/NOAA-17 available at CEPAGRI/UNICAMP. A decision tree was calculated to search patterns that could represent the sugar cane along the crop season 2006/2007. Application of these two methods (harmonic analysis and decision tree) on the time series used was able to identify sugar cane areas in the state of Sao Paulo (Brazil) with 92% confidence when compared to ground truth based on LANDSAT/TM images and available at CANASAT?s webpage (http://www.dsr.inpe.br/canasat). This methodology can be applied in an operational way to support the official systems of harvest forecasting. 650 $aImage analysis 650 $aRemote sensing 650 $aSugarcane 650 $aAgricultura 650 $aCana de açúcar 650 $aSensoriamento remoto 653 $aAnálise harmônica 653 $aÁrvore de decisão 653 $aGeorreferenciamento 653 $aProcessamento de imagens NDVI 653 $aSéries temporais de AVHRR/NOAA-17 700 1 $aZULLO JÚNIOR, J. 700 1 $aROMANI, L. A. S. 700 1 $aRODRIGUES, L. H. A.
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14. | | RIBEIRO, M. V.; CUNHA, L. M. S.; CAMARGO, H. A.; RODRIGUES, L. H. A. Applying a fuzzy decision tree approach to soil classification. In: INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, 15., 2014, Montpellier. Proceedings... Cham: Springer, 2014. p. 87-96. Part I. (Communications in computer and information science, 442). Editores: Anne Laurent Olivier Strauss, Bernadette Bouchon-Meunier, Ronald R. Yager. IPMU 2014.Tipo: Artigo em Anais de Congresso |
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