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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
31/07/2018 |
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Data da última atualização: |
31/07/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CHEN, Y.; LU, D.; MORAN, E.; BATISTELLA, M.; DUTRA, L. V.; SANCHES, I. D. A.; SILVA, R. F. B. da; HUANG, J.; LUIZ, A. J. B.; OLIVEIRA, M. A. F. de. |
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Afiliação: |
YAOLINAG CHEN, Zhejiang Agriculture and Forestry University; DENGSHENG LU, Zhejiang Agriculture and Forestry University; EMILIO MORAN, Michigan State University; MATEUS BATISTELLA, CNPTIA; 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. |
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Título: |
Mapping croplands, cropping patterns, and crop types using MODIS time-series data. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 69, p. 133-147, 2018. |
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DOI: |
https://doi.org/10.1016/j.jag.2018.03.00 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Crop types; Croplands; Cropping patterns; Decision tree classifier; MODIS NDVI. |
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Thesagro: |
Algodão; Milho; Sensoriamento Remoto; Sistema de Informação Geográfica; Soja. |
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Thesaurus Nal: |
Brazil. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02902naa a2200373 a 4500
001 2093725
005 2018-07-31
008 2018 bl --- 0-- 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 $aBrazil
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 $aMODIS NDVI
700 1 $aLU, D.
700 1 $aMORAN, E.
700 1 $aBATISTELLA, M.
700 1 $aDUTRA, L. V.
700 1 $aSANCHES, I. D. A.
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, 2018.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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| Registros recuperados : 2 | |
| 1. |  | McCAY, T. S.; BROWN, G. G.; CALLAHAM JUNIOR, Mac A.; CHANG, C-H; DAVOLOS, A.; DOBSON, A.; GORRES, J. H.; HERRICK, B. M.; JAMES, S. W.; JOHNSTON, M. R.; McHUGH, D.; MINTEER, T.; MOORE, J.-D.; NOURI-AIIN, M.; NOVO, M.; ORTIZ-PACHAR, J.; PINDER, R. A.; RICHARDSON, J. B.; SNYDER, B. A.; SZLAVECZ, K. Tools for monitoring and study of peregrine pheretimoid earthworms (Megascolecidae). Pedobiologia - Journal of Soil Ecology, v. 83, 150669, 2020. 12 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Florestas. |
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| 2. | | CHANG. C.-H.; BARTZ, M. L. C.; BROWN, G. G.; CALLAHAM JUNIOR, Mac A.; CAMERON, E. K.; DÁVALOS, A.; DOBSON, A.; GORRES, J. H.; HERRICK, B. M.; IKEDA, H.; JAMES, S. W.; JOHNSTON, M. R.; McCAY, T. S.; McHUGH, D.; MINAMIYA, Y.; NOURI-AIIN, M.; NOVO, M.; ORTIZ-PACHAR, J.; PINDER, R. A.; RANSOM, T.; RICHARDSON, J. B.; SNYDER, B. A.; SZLAVECZ, K. The second wave of earthworm invasions in North America: biology, environmental impacts, management and control of invasive jumping worms. Biological Invasions, v. 23, n. 11, p. 3291-3322, Nov. 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 2 | |
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