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Registro Completo |
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
07/10/2019 |
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Data da última atualização: |
07/10/2019 |
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Autoria: |
SOUZA, C. G.; ARANTES, T. B.; CARVALHO, L. M. T. de; AGUIAR, P. |
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Afiliação: |
Carolina Gusmão Souza, Universidade Estadual do Sudoeste da Bahia - UESB/Departamento de Ciências Exatas e Naturais; Tássia Borges Arantes, Universidade Federal de Lavras - UFLA/Departamento de Ciências Florestais; Luis Marcelo Tavares de Carvalho, Universidade Federal de Lavras - UFLA/Departamento de Ciências Florestais; Polyanne Aguiar, Universidade Federal de Lavras - UFLA/Departamento de Ciências Florestais. |
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Título: |
Multitemporal variables for the mapping of coffee cultivation areas. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, v. 54, e00017, 2019. |
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Idioma: |
Inglês |
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Notas: |
Título em português: Variáveis multitemporais para o mapeamento de áreas de cultivo de café. |
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Conteúdo: |
The objective of this work was to propose a new methodology for mapping coffee cropping areas that includes multitemporal data as input parameters in the classification process, by using the Landsat TM NDVI time series, together with an object-oriented classification approach. The algorithm BFAST was used to analyze coffee, pasture, and native vegetation temporal profiles, allied to a geographic object-based image analysis (GEOBIA) for mapping. The following multitemporal variables derived from the R package greenbrown were used for classification: mean, trend, and seasonality. The results showed that coffee, pasture, and native vegetation have different temporal behaviors, which corroborates the use of these data as input variables for mapping. The classifications using temporal variables, associated with spectral data, achieved high-global accuracy rates with 93% hit. When using only temporal data, ratings also showed a hit percentage above 80% accuracy. Data derived from Landsat TM time series are efficient for mapping coffee cropping areas, reducing confusion between targets and making the classification process more accurate, contributing to a correct characterization and mapping of objects derived from a RapidEye image, with a high spatial solution. |
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Palavras-Chave: |
BFAST; Classificação; MODIS; NDVI; Pacote greenbrown R; R package greenbrown. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus Nal: |
Classification; Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/202619/1/Multitemporal-variables-for-the-mapping.pdf
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Marc: |
LEADER 02100naa a2200277 a 4500
001 2112830
005 2019-10-07
008 2019 bl uuuu u00u1 u #d
100 1 $aSOUZA, C. G.
245 $aMultitemporal variables for the mapping of coffee cultivation areas.
260 $c2019
500 $aTítulo em português: Variáveis multitemporais para o mapeamento de áreas de cultivo de café.
520 $aThe objective of this work was to propose a new methodology for mapping coffee cropping areas that includes multitemporal data as input parameters in the classification process, by using the Landsat TM NDVI time series, together with an object-oriented classification approach. The algorithm BFAST was used to analyze coffee, pasture, and native vegetation temporal profiles, allied to a geographic object-based image analysis (GEOBIA) for mapping. The following multitemporal variables derived from the R package greenbrown were used for classification: mean, trend, and seasonality. The results showed that coffee, pasture, and native vegetation have different temporal behaviors, which corroborates the use of these data as input variables for mapping. The classifications using temporal variables, associated with spectral data, achieved high-global accuracy rates with 93% hit. When using only temporal data, ratings also showed a hit percentage above 80% accuracy. Data derived from Landsat TM time series are efficient for mapping coffee cropping areas, reducing confusion between targets and making the classification process more accurate, contributing to a correct characterization and mapping of objects derived from a RapidEye image, with a high spatial solution.
650 $aClassification
650 $aRemote sensing
650 $aSensoriamento Remoto
653 $aBFAST
653 $aClassificação
653 $aMODIS
653 $aNDVI
653 $aPacote greenbrown R
653 $aR package greenbrown
700 1 $aARANTES, T. B.
700 1 $aCARVALHO, L. M. T. de
700 1 $aAGUIAR, P.
773 $tPesquisa Agropecuária Brasileira$gv. 54, e00017, 2019.
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