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| 5. |  | LIU, J.; HULL, V.; BATISTELLA, M.; DEFRIES, R.; DIETZ, T.; FU, F.; HERTEL, T. W.; IZAURRALDE, R. C.; LAMBIN, E. F.; LI, S.; MARTINELLI, L. A.; MCCONNELL, W. J.; MORAN, E. F.; NAYLOR, R.; OUYANG, Z.; POLENSKE, K. R.; REENBERG, A.; ROCHA, G. DE M.; SIMMONS, C. S.; VERBURG, P. H.; VITOUSEK, P. M.; ZHANG, F.; ZHU, C. Framing sustainability in a telecoupled world. Ecology and Society, v. 18, n. 2, jun. 2013. 19 p.| Biblioteca(s): Embrapa Territorial. |
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| 6. | | GÓMEZ, M. L.; HUANG, X.; ALVAREZ, D.; HE, W.; BAYSAL, C.; ZHU, C.; ARMARIO-NAJERA, V.; PERERA, A. B.; BENNASSER, P. C.; SABA-MAYORAL, A.; SOBRINO-MENGUAL, G.; VARGHEESE, A.; ABRANCHES, R.; ABREU, I. A.; BALAMURUGAN, S.; BOCK, R.; BUYEL, J. F.; CUNHA, N. B. da; DANIELL, H.; FALLER, R.; FOLGADO, A.; GOWTHAM, I.; HÄKKINEN, S. T.; KUMAR, S.; RAMALINGAM, S. K.; LACORTE, C. C.; LOMONOSSOFF, G. P.; LUÍS, I. M.; MA, J. K.-C.; MCDONALD, K. A.; MURAD, A. M.; NANDI, S.; O'KEEFE, B.; OKSMAN-CALDENTEY, K.-M.; PARTHIBAN, S.; PAUL, M. J.; PONNDORF, D.; RECH FILHO, E. L.; RODRIGUES, J. C. M.; RUF, S.; SCHILLBERG, S.; SCHWESTKA, J.; SHAH, P. S.; SINGH, R.; STOGER, E.; TWYMAN, R. M.; VARGHESE, I. P.; VIANNA, G. R.; WEBSTER, G.; WILBERS, R. H. P.; CAPELL, T.; CHRISTOU, P. Contributions of the international plant science community to the fight against human infectious diseases - part 1: epidemic and pandemic diseases. Plant Biotechnology Journal, v. 19, p. 1901-1920, 2021. Na publicação: Cristiano Lacorte; Andre Murad; Elibio Rech.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 7. | | HE, W.; BAYSAL, C.; GÓMEZ, M. L.; HUANG, X.; ALVAREZ, D.; ZHU, C.; ARMARIO-NAJERA, V.; PERERA, A. B.; BENNASER, P. C.; SABA-MAYORAL, A.; SOBRINO-MENGUAL, G.; VARGHEESE, A.; ABRANCHES, R.; ABREU, I. A.; BALAMURUGAN, S.; BOCK, R.; BUYEL, J. F.; CUNHA, N. B. da; DANIELL, H.; FALLER, R.; FOLGADO, A.; GOWTHAM, I.; HÄKKINEN, S. T.; KUMAR, S.; KUMAR, R. S.; LACORTE, C. C.; LOMONOSSOFF, G. P.; LUÍS, I. M.; MA, J. K.-C.; MCDONALD, K. A.; MURAD, A. M.; NANDI, S.; O’KEEF, B.; PARTHIBAN, S.; PAUL, M. J.; PONNDORF, D.; RECH FILHO, E. L.; RODRIGUES, J. C. M.; RUF, S.; SCHILLBERG, S.; SCHWESTKA, J.; SHAH, P. S.; SINGH, R.; STOGER, E.; TWYMAN, R. M.; VARGHESE, I. P.; VIANNA, G. R.; WEBSTER, G.; WILBERS, R. H. P.; CHRISTOU, P.; OKSMAN-CALDENTEY, K.-M.; CAPELL, T. Contributions of the international plant science community to the fight against infectious diseases in humans - part 2: Affordable drugs in edible plants for endemic and re-emerging diseases. Plant Biotechnology Journal, v. 19, p. 1921-1936, 2021. Na publicação: Cristiano Lacorte; Andre Murad; Elibio Rech.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
|
Data corrente: |
07/01/2016 |
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Data da última atualização: |
05/04/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ZHU, C.; LU, D.; VICTORIA, D. de C.; DUTRA, L. V. |
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Afiliação: |
CHANGMING ZHU, JIANGSU NORMAL UNIVERSITY/MICHIGAN STATE UNIVERSITY; DENGSHENG LU, MICHIGAN STATE UNIVERSITY; DANIEL DE CASTRO VICTORIA, CNPM; LUCIANO VIEIRA DUTRA, INPE. |
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Título: |
Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Remote Sensing, v. 8, n. 22, p. 1-14, 2016. |
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DOI: |
10.3390/rs8010022 |
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Idioma: |
Inglês |
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Conteúdo: |
Mapping cropland distribution over large areas has attracted great attention in recent years, however, traditional pixel-based classification approaches produce high uncertainty in cropland area statistics. This study proposes a new approach to map fractional cropland distribution in Mato Grosso, Brazil using time series MODIS enhanced vegetation index (EVI) and Landsat Thematic Mapper (TM) data. The major steps include: (1) remove noise and clouds/shadows contamination using the Savizky?Gloay filter and temporal resampling algorithm based on the time series MODIS EVI data; (2) identify the best periods to extract croplands through crop phenology analysis; (3) develop a seasonal dynamic index (SDI) from the time series MODIS EVI data based on three key stages: sowing, growing, and harvest; and (4) develop a regression model to estimate cropland fraction based on the relationship between SDI and Landsat-derived fractional cropland data. The root mean squared error of 0.14 was obtained based on the analysis of randomly selected 500 sample plots. This research shows that the proposed approach is promising for rapidly mapping fractional cropland distribution in Mato Grosso, Brazil. |
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Palavras-Chave: |
Crop phenology analysis; Fractional cropland distribution; Mato Grosso; MODIS EVI; Seasonal dynamic index. |
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Thesaurus NAL: |
Landsat. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/136760/1/4634.pdf
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|
Marc: |
LEADER 01959naa a2200241 a 4500
001 2033197
005 2016-04-05
008 2016 bl uuuu u00u1 u #d
024 7 $a10.3390/rs8010022$2DOI
100 1 $aZHU, C.
245 $aMapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data.$h[electronic resource]
260 $c2016
520 $aMapping cropland distribution over large areas has attracted great attention in recent years, however, traditional pixel-based classification approaches produce high uncertainty in cropland area statistics. This study proposes a new approach to map fractional cropland distribution in Mato Grosso, Brazil using time series MODIS enhanced vegetation index (EVI) and Landsat Thematic Mapper (TM) data. The major steps include: (1) remove noise and clouds/shadows contamination using the Savizky?Gloay filter and temporal resampling algorithm based on the time series MODIS EVI data; (2) identify the best periods to extract croplands through crop phenology analysis; (3) develop a seasonal dynamic index (SDI) from the time series MODIS EVI data based on three key stages: sowing, growing, and harvest; and (4) develop a regression model to estimate cropland fraction based on the relationship between SDI and Landsat-derived fractional cropland data. The root mean squared error of 0.14 was obtained based on the analysis of randomly selected 500 sample plots. This research shows that the proposed approach is promising for rapidly mapping fractional cropland distribution in Mato Grosso, Brazil.
650 $aLandsat
653 $aCrop phenology analysis
653 $aFractional cropland distribution
653 $aMato Grosso
653 $aMODIS EVI
653 $aSeasonal dynamic index
700 1 $aLU, D.
700 1 $aVICTORIA, D. de C.
700 1 $aDUTRA, L. V.
773 $tRemote Sensing$gv. 8, n. 22, p. 1-14, 2016.
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