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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Siteplutao.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentificadorJ8LNKAN8RW/3D53ESE
Repositóriodpi.inpe.br/plutao/2012/11.28.15.38
Última Atualização2013:01.17.14.02.29 (UTC) marciana
Repositório de Metadadosdpi.inpe.br/plutao/2012/11.28.15.38.01
Última Atualização dos Metadados2022:04.11.18.00.43 (UTC) marciana
Chave SecundáriaINPE--PRE/
DOI10.3390/rs4092492
ISSN2072-4292
Rótulolattes: 8408207746528834 1 BernardesAdMoAdGiRu:2012:MoBiBe
Chave de CitaçãoBernardesMorAdaGiaRud:2012:MoBiBe
TítuloMonitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery
Ano2012
Data de Acesso30 abr. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho881 KiB
2. Contextualização
Autor1 Bernardes, Tiago
2 Moreira, Maurício Alves
3 Adami, Marcos
4 Giarolla, Angélica
5 Rudorff, Bernardo Friedrich Theodor
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JHT4
3
4 8JMKD3MGP5W/3C9JGHP
5 8JMKD3MGP5W/3C9JGKP
Grupo1 DSR-OBT-INPE-MCTI-GOV-BR
2 DSR-OBT-INPE-MCTI-GOV-BR
3 DSR-OBT-INPE-MCTI-GOV-BR
4 DSR-OBT-INPE-MCTI-GOV-BR
5 DSR-OBT-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 tiago.bernardes@cemaden.gov.br
2 mauricio@dsr.inpe.br
Endereço de e-Mailtiago.bernardes@cemaden.gov.br
RevistaRemote Sensing
Volume4
Número9
Páginas2492-2509
Histórico (UTC)2012-11-28 23:06:26 :: lattes -> marciana :: 2012
2013-01-17 14:02:29 :: marciana -> administrator :: 2012
2016-06-04 01:08:12 :: administrator -> marciana :: 2012
2016-10-11 00:24:12 :: marciana -> administrator :: 2012
2018-06-05 00:02:03 :: administrator -> marciana :: 2012
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveFoliar biomass
Growing season
High yield
Landsat images
Leaf biomass
Minas Gerais
Minimum value
Previous year
Pure pixel
Reference map
Remote sensing imagery
Vegetation index
Wavelet filtering
Pixels
Radiometers
Remote sensing
Vegetation
Satellite imagery
ResumoCoffee is the second most valuable traded commodity worldwide. Brazil is the worlds largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield. Keywords: remote sensing; coffee yield; vegetation indices; wavelet filtering.
ÁreaSRE
Arranjourlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Monitoring Biennial Bearing...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreementnão têm arquivos
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/J8LNKAN8RW/3D53ESE
URL dos dados zipadoshttp://urlib.net/zip/J8LNKAN8RW/3D53ESE
Idiomaen
Arquivo Alvoremotesensing-04-02492.pdf
Grupo de Usuáriosadministrator
lattes
marciana
Visibilidadeshown
Política de Arquivamentoallowpublisher allowfinaldraft
Permissão de Leituraallow from all
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
Lista de Itens Citandosid.inpe.br/mtc-m21/2012/07.13.14.56.26 1
sid.inpe.br/mtc-m21/2012/07.13.14.41 1
DivulgaçãoWEBSCI; PORTALCAPES; COMPENDEX.
Acervo Hospedeirodpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
e-Mail (login)marciana
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