Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1033197
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dc.contributor.authorZHU, C.pt_BR
dc.contributor.authorLU, D.pt_BR
dc.contributor.authorVICTORIA, D. de C.pt_BR
dc.contributor.authorDUTRA, L. V.pt_BR
dc.date.accessioned2016-01-07T11:11:11Zpt_BR
dc.date.available2016-01-07T11:11:11Zpt_BR
dc.date.created2016-01-07pt_BR
dc.date.issued2016pt_BR
dc.identifier.citationRemote Sensing, v. 8, n. 22, p. 1-14, 2016.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1033197pt_BR
dc.descriptionMapping 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.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectSeasonal dynamic indexpt_BR
dc.subjectCrop phenology analysispt_BR
dc.subjectFractional cropland distributionpt_BR
dc.subjectMato Grossopt_BR
dc.subjectMODIS EVIpt_BR
dc.titleMapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2016-04-05T11:11:11Zpt_BR
dc.subject.nalthesaurusLandsatpt_BR
riaa.ainfo.id1033197pt_BR
riaa.ainfo.lastupdate2016-04-05pt_BR
dc.identifier.doi10.3390/rs8010022pt_BR
dc.contributor.institutionCHANGMING ZHU, JIANGSU NORMAL UNIVERSITY/MICHIGAN STATE UNIVERSITY; DENGSHENG LU, MICHIGAN STATE UNIVERSITY; DANIEL DE CASTRO VICTORIA, CNPM; LUCIANO VIEIRA DUTRA, INPE.pt_BR
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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