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Registro Completo |
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Biblioteca(s): |
Embrapa Agrobiologia. |
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Data corrente: |
16/04/2008 |
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Data da última atualização: |
16/04/2008 |
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Autoria: |
VIDEIRA, S. S.; SIMÕES ARAÚJO, J. L.; BALDANI, J. I.; BALDANI, V. L. D. |
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Título: |
Identificação de Sphingomonas spp. fixadoras de nitrogênio isoladas de arroz (Oryza sativa L.). |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
Seropédica: Embrapa Agrobiologia, 2006. |
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Páginas: |
4 p. |
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Idioma: |
Português |
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Notas: |
Trabalho apresentado na XXVII Reunião Brasileira de Fertilidade do Solo e Nutrição de Plantas, XI Reunião Brasileira sobre Micorrizas, IX Simpósio Brasileiro de Microbiologia do Solo; VI Reunião Brasileira de Biologia do Solo, Bonito/MS, 17 a 22 de setembro de 2006. |
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Palavras-Chave: |
N. |
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Thesagro: |
Arroz; Microrganismo; Nitrogênio; Planta. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00859nam a2200217 a 4500
001 1629399
005 2008-04-16
008 2006 bl uuuu u0uu1 u #d
100 1 $aVIDEIRA, S. S.
245 $aIdentificação de Sphingomonas spp. fixadoras de nitrogênio isoladas de arroz (Oryza sativa L.).
260 $aSeropédica: Embrapa Agrobiologia$c2006
300 $a4 p.
500 $aTrabalho apresentado na XXVII Reunião Brasileira de Fertilidade do Solo e Nutrição de Plantas, XI Reunião Brasileira sobre Micorrizas, IX Simpósio Brasileiro de Microbiologia do Solo; VI Reunião Brasileira de Biologia do Solo, Bonito/MS, 17 a 22 de setembro de 2006.
650 $aArroz
650 $aMicrorganismo
650 $aNitrogênio
650 $aPlanta
653 $aN
700 1 $aSIMÕES ARAÚJO, J. L.
700 1 $aBALDANI, J. I.
700 1 $aBALDANI, V. L. D.
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Registro original: |
Embrapa Agrobiologia (CNPAB) |
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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
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
22/11/2019 |
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Data da última atualização: |
11/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
WERNER, J. P. S.; OLIVEIRA, S. R. de M.; ESQUERDO, J. C. D. M. |
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Afiliação: |
Feagri/Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA. |
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Título: |
Mapping cotton fields using data mining and MODIS time-series. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
International Journal of Remote Sensing, v. 41, n. 7, p. 2457-2476, 2020. |
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DOI: |
10.1080/01431161.2019.1693072 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT. Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012-2013 to 2015-2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R2) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches. MenosABSTRACT. Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012-2013 to 2015-2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and us... Mostrar Tudo |
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Palavras-Chave: |
Árvore de decisão; Crop monitoring; Data mining; Decision tree; Índice de vegetação; Mineração de dados; Monitoramento de culturas; Sensor MODIS; Séries temporais. |
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Thesagro: |
Algodão; Sensoriamento Remoto. |
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Thesaurus NAL: |
Remote sensing; Time series analysis; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 03227naa a2200325 a 4500
001 2114954
005 2020-12-11
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1080/01431161.2019.1693072$2DOI
100 1 $aWERNER, J. P. S.
245 $aMapping cotton fields using data mining and MODIS time-series.$h[electronic resource]
260 $c2020
520 $aABSTRACT. Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012-2013 to 2015-2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R2) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches.
650 $aRemote sensing
650 $aTime series analysis
650 $aVegetation index
650 $aAlgodão
650 $aSensoriamento Remoto
653 $aÁrvore de decisão
653 $aCrop monitoring
653 $aData mining
653 $aDecision tree
653 $aÍndice de vegetação
653 $aMineração de dados
653 $aMonitoramento de culturas
653 $aSensor MODIS
653 $aSéries temporais
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aESQUERDO, J. C. D. M.
773 $tInternational Journal of Remote Sensing$gv. 41, n. 7, p. 2457-2476, 2020.
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