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51. | | FARIAS, J. R. B.; CASTRO, C. de. Aptidão agrícola do girassol - zoneamentos. In: REUNIÃO NACIONAL DE PESQUISA DE GIRASSOL, 16.; SIMPÓSIO NACIONAL SOBRE A CULTURA DO GIRASSOL, 4., 2005, Londrina. Ata... Londrina: Embrapa Soja, 2005. p. 73-79. (Embrapa Soja. Documentos, 267). Organizado por Regina Maria Villas Bôas de Campos Leite, Cesar de Castro, Simone Ery Grosskopf. Biblioteca(s): Embrapa Soja. |
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Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
21/09/2018 |
Data da última atualização: |
07/01/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
CHEN, Y.; LU, D.; MORAN, E.; BATISTELLA, M.; DUTRA, L. V.; DEL'ARCO SANCHES, I.; SILVA, R. F. B. da; HUANG, J.; LUIZ, A. J. B.; OLIVEIRA, M. A. F. de. |
Afiliação: |
YAOLINAG CHEN, Zhejiang Agriculture and Forestry University, Zhejiang University, Michigan State University; DENGSHENG LU, Zhejiang Agriculture and Forestry University, Michigan State University; EMILIO MORAN, Michigan State University; MATEUS BATISTELLA, CNPTIA, Unicamp; LUCIANO VIEIRA DUTRA, INPE; IARA DEL´ARCO SANCHES, INPE; RAMON FELIPE BICUDO DA SILVA, Unicamp; JINGFENG HUANG, Zhejiang University; ALFREDO JOSE BARRETO LUIZ, CNPMA; MARIA ANTONIA FALCAO DE OLIVEIRA, INPE. |
Título: |
Mapping croplands, cropping patterns, and crop types using MODIS time-series data. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 69, p. 133-147, July 2018. |
DOI: |
https://doi.org/10.1016/j.jag.2018.03.00 |
Idioma: |
Inglês |
Conteúdo: |
Abstract: The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types. MenosAbstract: The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop t... Mostrar Tudo |
Palavras-Chave: |
Crop types; Croplands; Cropping patterns; Decision tree classifier; Índice de vegetação; MODIS NDVI; Séries temporais. |
Thesagro: |
Algodão; Milho; Sensoriamento Remoto; Sistema de Informação Geográfica; Soja. |
Thesaurus NAL: |
Geographic information systems; Normalized difference vegetation index; Remote sensing; Time series analysis. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 03130naa a2200433 a 4500 001 2096162 005 2020-01-07 008 2018 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1016/j.jag.2018.03.00$2DOI 100 1 $aCHEN, Y. 245 $aMapping croplands, cropping patterns, and crop types using MODIS time-series data.$h[electronic resource] 260 $c2018 520 $aAbstract: The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types. 650 $aGeographic information systems 650 $aNormalized difference vegetation index 650 $aRemote sensing 650 $aTime series analysis 650 $aAlgodão 650 $aMilho 650 $aSensoriamento Remoto 650 $aSistema de Informação Geográfica 650 $aSoja 653 $aCrop types 653 $aCroplands 653 $aCropping patterns 653 $aDecision tree classifier 653 $aÍndice de vegetação 653 $aMODIS NDVI 653 $aSéries temporais 700 1 $aLU, D. 700 1 $aMORAN, E. 700 1 $aBATISTELLA, M. 700 1 $aDUTRA, L. V. 700 1 $aDEL'ARCO SANCHES, I. 700 1 $aSILVA, R. F. B. da 700 1 $aHUANG, J. 700 1 $aLUIZ, A. J. B. 700 1 $aOLIVEIRA, M. A. F. de 773 $tInternational Journal of Applied Earth Observation and Geoinformation$gv. 69, p. 133-147, July 2018.
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