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Registro Completo |
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
25/04/2001 |
Data da última atualização: |
25/04/2001 |
Autoria: |
NEVES, E.; AZEVEDO, C. P. de A.; GASPAROTTO, L.; DUNISCH, O.; BAUCH, J. |
Afiliação: |
NEVES, E., pesquisador da Embrapa Florestas. |
Título: |
Biomass production and nutrition aspects plantatio trees in Amazonia. |
Ano de publicação: |
1998 |
Fonte/Imprenta: |
In: LIEBEREI, R.; BIANCHI, H.; VOB, K., ed. Proceedings of the third SHIFT-Workshop, Manaus march 15-19, 1998. Bonn: Bundesministerium fur Bildung und Forschung, 1998. p.413-418. |
Idioma: |
Português |
Categoria do assunto: |
-- |
Marc: |
LEADER 00572naa a2200157 a 4500 001 1301837 005 2001-04-25 008 1998 bl uuuu u00u1 u #d 100 1 $aNEVES, E. 245 $aBiomass production and nutrition aspects plantatio trees in Amazonia. 260 $c1998 700 1 $aAZEVEDO, C. P. de A. 700 1 $aGASPAROTTO, L. 700 1 $aDUNISCH, O. 700 1 $aBAUCH, J. 773 $tIn: LIEBEREI, R.; BIANCHI, H.; VOB, K., ed. Proceedings of the third SHIFT-Workshop, Manaus march 15-19, 1998. Bonn: Bundesministerium fur Bildung und Forschung, 1998. p.413-418.
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Registro original: |
Embrapa Florestas (CNPF) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
19/11/2021 |
Data da última atualização: |
26/04/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
OLDONI, L. V.; MERCANTE, E.; ANTUNES, J. F. G.; CATTANI, C. E. V.; SILVA JUNIOR, C. A. da; CAON, I. L.; PRUDENTE, V. H. R. |
Afiliação: |
LUCAS VOLOCHEN OLDONI, INPE; ERIVELTO MERCANTE, UNIOESTE; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; CARLOS EDUARDO VIZZOTTO CATTANI, UNIOESTE; CARLOS ANTONIO DA SILVA JUNIOR, UNEMAT; IVÃ LUIZ CAON, UNIOESTE; VICTOR HUGO ROHDEN PRUDENTE, INPE. |
Título: |
Extraction of crop information through the spatiotemporal fusion of OLI and MODIS images. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Geocarto International, 2021. |
DOI: |
https://doi.org/10.1080/10106049.2021.2000648 |
Idioma: |
Inglês |
Conteúdo: |
ABSTRACT. Spatiotemporal data fusion algorithms have been developed tofuse satellite imagery from sensors with different spatial and tempoporal resolutions and generate predicted imagery. In this study, we compare the predictions of three spatiotemporal data fusion algorithms in blending Landsat-8/OLI and Terra-Aqua/MODIS images for mapping soybean and corn under five classification scenarios. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and Flexible Spatiotemporal Data Fusion (FSDAF) algorithms were compared to generate images for the 2016/2017 summer crop-year. Classifications including phenological metrics extracted from FSDAF- and STARFM-predicted EVI time series had overalls accuracies higher than the other scenarios, 93.11% and 91.33%, respectively. The results show that phenological metrics extracted from predicted images are an interesting alternative to overcome cloud cover frequency limitations for soybean and corn mapping in tropical areas. |
Palavras-Chave: |
Algoritmos de fusão de dados espaço-temporal; ESTARM; FSDAF; Séries temporais; STARFM; Time series. |
Thesagro: |
Fenologia; Sensoriamento Remoto. |
Thesaurus NAL: |
Phenology; Remote sensing. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02008naa a2200325 a 4500 001 2136350 005 2022-04-26 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1080/10106049.2021.2000648$2DOI 100 1 $aOLDONI, L. V. 245 $aExtraction of crop information through the spatiotemporal fusion of OLI and MODIS images.$h[electronic resource] 260 $c2021 520 $aABSTRACT. Spatiotemporal data fusion algorithms have been developed tofuse satellite imagery from sensors with different spatial and tempoporal resolutions and generate predicted imagery. In this study, we compare the predictions of three spatiotemporal data fusion algorithms in blending Landsat-8/OLI and Terra-Aqua/MODIS images for mapping soybean and corn under five classification scenarios. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and Flexible Spatiotemporal Data Fusion (FSDAF) algorithms were compared to generate images for the 2016/2017 summer crop-year. Classifications including phenological metrics extracted from FSDAF- and STARFM-predicted EVI time series had overalls accuracies higher than the other scenarios, 93.11% and 91.33%, respectively. The results show that phenological metrics extracted from predicted images are an interesting alternative to overcome cloud cover frequency limitations for soybean and corn mapping in tropical areas. 650 $aPhenology 650 $aRemote sensing 650 $aFenologia 650 $aSensoriamento Remoto 653 $aAlgoritmos de fusão de dados espaço-temporal 653 $aESTARM 653 $aFSDAF 653 $aSéries temporais 653 $aSTARFM 653 $aTime series 700 1 $aMERCANTE, E. 700 1 $aANTUNES, J. F. G. 700 1 $aCATTANI, C. E. V. 700 1 $aSILVA JUNIOR, C. A. da 700 1 $aCAON, I. L. 700 1 $aPRUDENTE, V. H. R. 773 $tGeocarto International, 2021.
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