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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
06/07/2020 |
Data da última atualização: |
07/07/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SANTOS, M. A. S.; ASSAD, E. D.; GURGEL, A. C.; OMAR, N. |
Afiliação: |
MARCIO A. S. SANTOS, Mackenzie Presbyterian University; EDUARDO DELGADO ASSAD, CNPTIA; ANGELO C. GURGEL, FGV; NIZAM OMAR, Mackenzie Presbyterian University. |
Título: |
Similarity metrics enforcement in seasonal agriculture areas classification. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Remote Sensing, v. 12, n. 11, p. 1-14, 2020. |
DOI: |
https://doi.org/10.3390/rs12111791 |
Idioma: |
Inglês |
Notas: |
Article 1791. |
Conteúdo: |
Abstract. Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country´s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas. |
Palavras-Chave: |
Aprendizado de máquina; Dinâmica de uso da terra; Land use dynamics; Time series similarity metrics. |
Thesagro: |
Agricultura; Sensoriamento Remoto; Uso da Terra. |
Thesaurus Nal: |
Agriculture; Land use; Remote sensing; Time series analysis. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214380/1/AP-Similarity-metrics-2020.pdf
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Marc: |
LEADER 02359naa a2200313 a 4500 001 2123627 005 2020-07-07 008 2020 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3390/rs12111791$2DOI 100 1 $aSANTOS, M. A. S. 245 $aSimilarity metrics enforcement in seasonal agriculture areas classification.$h[electronic resource] 260 $c2020 500 $aArticle 1791. 520 $aAbstract. Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country´s diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas. 650 $aAgriculture 650 $aLand use 650 $aRemote sensing 650 $aTime series analysis 650 $aAgricultura 650 $aSensoriamento Remoto 650 $aUso da Terra 653 $aAprendizado de máquina 653 $aDinâmica de uso da terra 653 $aLand use dynamics 653 $aTime series similarity metrics 700 1 $aASSAD, E. D. 700 1 $aGURGEL, A. C. 700 1 $aOMAR, N. 773 $tRemote Sensing$gv. 12, n. 11, p. 1-14, 2020.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Roraima. Para informações adicionais entre em contato com cpafrr.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Roraima. |
Data corrente: |
02/02/2015 |
Data da última atualização: |
19/02/2015 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
BETTIOL NETO, J. E.; PIO, R.; CHAGAS, E. A.; CHALFUN, N. N. J. |
Afiliação: |
EDVAN ALVES CHAGAS, CPAF-RR. |
Título: |
Cultivo da Ameixa. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
In: PIO, R. Cultivo de fruteiras de clima temperado em regiões subtropicais e tropicais. Lavras : Ed. UFLA, 2014. |
Idioma: |
Português |
Thesagro: |
Ameixa. |
Thesaurus NAL: |
Rosaceae. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00481naa a2200169 a 4500 001 2007418 005 2015-02-19 008 2014 bl --- 0-- u #d 100 1 $aBETTIOL NETO, J. E. 245 $aCultivo da Ameixa. 260 $c2014 650 $aRosaceae 650 $aAmeixa 700 1 $aPIO, R. 700 1 $aCHAGAS, E. A. 700 1 $aCHALFUN, N. N. J. 773 $tIn: PIO, R. Cultivo de fruteiras de clima temperado em regiões subtropicais e tropicais. Lavras : Ed. UFLA, 2014.
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