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| Registros recuperados : 6 | |
| Registros recuperados : 6 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
03/07/2020 |
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Data da última atualização: |
03/07/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
C - 0 |
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Autoria: |
CECHIM JÚNIOR, C.; JOHANN, J. A.; ANTUNES, J. F. G.; DEPPE, F. |
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Afiliação: |
CLÓVIS CECHIM JÚNIOR, Unioeste; JERRY ADRIANI JOHANN, Unioeste; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; FLÁVIO DEPPE, SIMEPAR. |
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Título: |
Sugarcane mapping in Paraná State Brazil using MODIS EVI images. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
International Journal of Advanced Remote Sensing and GIS, v. 9, n. 1, p. 3205-3221, 2020. |
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DOI: |
https://doi.org/10.23953/cloud.ijarsg.451 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract Sugarcane cultivated in Brazil deserves attention because it makes the Country the world's largest producer of sugar and ethanol. The aim of this work was to develop and evaluate a methodology for sugarcane mapping in Paraná State, Brazil using temporal series of the MODIS EVI, for 2010/2011 to 2013/2014 crop seasons. The methodology included supervised classification Fuzzy ARTMAP, taking as input variables such as terms of harmonics amplitude and phase, and phenological metrics of culture. Area estimates indicated a moderate and strong correlation (rs), ranging from 0.62 to 0.71 comparing with IBGE official data and from 0.79 to 0.87 with the Canasat data. To assess mapping accuracy, Canasat vector maps were used as reference to build the confusion matrix. The method developed based on Fuzzy ARTMAP proved efficient to map and estimate the acreage of sugarcane in the State of Paraná, due to digital processing techniques used in homogeneous samples, selection of phenological seasonal metrics, and decomposition of images in accordance with harmonics and supervised training. These together minimized the neural network forecast errors. Results indicate that the methodology is appropriate for sugarcane mapping. |
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Palavras-Chave: |
Annual agriculture; Índice de vegetação; Mapeamento de cana-de-açúcar; Timeseries. |
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Thesagro: |
Agricultura; Cana de Açúcar; Sensoriamento Remoto. |
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Thesaurus NAL: |
Agriculture; Remote sensing; Sugarcane; Time series analysis; Vegetation index. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214369/1/AP-Sugarcane-mapping-2020.pdf
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Marc: |
LEADER 02196naa a2200313 a 4500
001 2123618
005 2020-07-03
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.23953/cloud.ijarsg.451$2DOI
100 1 $aCECHIM JÚNIOR, C.
245 $aSugarcane mapping in Paraná State Brazil using MODIS EVI images.$h[electronic resource]
260 $c2020
520 $aAbstract Sugarcane cultivated in Brazil deserves attention because it makes the Country the world's largest producer of sugar and ethanol. The aim of this work was to develop and evaluate a methodology for sugarcane mapping in Paraná State, Brazil using temporal series of the MODIS EVI, for 2010/2011 to 2013/2014 crop seasons. The methodology included supervised classification Fuzzy ARTMAP, taking as input variables such as terms of harmonics amplitude and phase, and phenological metrics of culture. Area estimates indicated a moderate and strong correlation (rs), ranging from 0.62 to 0.71 comparing with IBGE official data and from 0.79 to 0.87 with the Canasat data. To assess mapping accuracy, Canasat vector maps were used as reference to build the confusion matrix. The method developed based on Fuzzy ARTMAP proved efficient to map and estimate the acreage of sugarcane in the State of Paraná, due to digital processing techniques used in homogeneous samples, selection of phenological seasonal metrics, and decomposition of images in accordance with harmonics and supervised training. These together minimized the neural network forecast errors. Results indicate that the methodology is appropriate for sugarcane mapping.
650 $aAgriculture
650 $aRemote sensing
650 $aSugarcane
650 $aTime series analysis
650 $aVegetation index
650 $aAgricultura
650 $aCana de Açúcar
650 $aSensoriamento Remoto
653 $aAnnual agriculture
653 $aÍndice de vegetação
653 $aMapeamento de cana-de-açúcar
653 $aTimeseries
700 1 $aJOHANN, J. A.
700 1 $aANTUNES, J. F. G.
700 1 $aDEPPE, F.
773 $tInternational Journal of Advanced Remote Sensing and GIS$gv. 9, n. 1, p. 3205-3221, 2020.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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