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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
14/03/2011 |
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Data da última atualização: |
11/11/2022 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
ESPINHEIRO, R. F.; DIAS, H. L. T.; LIMA, A. S.; ALBUQUERQUE, N. I. de; GUIMARÃES, D. A.; OHASHI, O. M.; KAHWAGE, P. R.; GARCIA, A. R. |
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Afiliação: |
UFPA; UFPA; UFPA; NATALIA INAGAKI DE ALBUQUERQUE, CPATU; UFPA; UFPA; PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA ANIMAL; ALEXANDRE ROSSETTO GARCIA, CPATU. |
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Título: |
Microbiota bacteriana do trato gastrointestinal de caititus (Tayassu tajacu) criados em cativeiro. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
In: CONGRESO INTERNACIONAL SOBRE MANEJO DE FAUNA SILVESTRE EN LA AMAZONIA Y AMERICA LATINA, 9., 2010, Santa Cruz. Libro de resumenes. [Casilla: Museo de Historia Natural Noel Kempff Mercado: Fundación Amigos del Museo Noel Kempff Mercado], 2010. |
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Páginas: |
p. 141. |
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Idioma: |
Português |
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Notas: |
IX CIMFAUNA. |
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Palavras-Chave: |
Bactérias; Caititus; Sistema gastrointestinal. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00924nam a2200241 a 4500
001 1880627
005 2022-11-11
008 2010 bl uuuu u00u1 u #d
100 1 $aESPINHEIRO, R. F.
245 $aMicrobiota bacteriana do trato gastrointestinal de caititus (Tayassu tajacu) criados em cativeiro.$h[electronic resource]
260 $aIn: CONGRESO INTERNACIONAL SOBRE MANEJO DE FAUNA SILVESTRE EN LA AMAZONIA Y AMERICA LATINA, 9., 2010, Santa Cruz. Libro de resumenes. [Casilla: Museo de Historia Natural Noel Kempff Mercado: Fundación Amigos del Museo Noel Kempff Mercado]$c2010
300 $ap. 141.
500 $aIX CIMFAUNA.
653 $aBactérias
653 $aCaititus
653 $aSistema gastrointestinal
700 1 $aDIAS, H. L. T.
700 1 $aLIMA, A. S.
700 1 $aALBUQUERQUE, N. I. de
700 1 $aGUIMARÃES, D. A.
700 1 $aOHASHI, O. M.
700 1 $aKAHWAGE, P. R.
700 1 $aGARCIA, A. R.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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Biblioteca(s): |
Embrapa Roraima. |
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Data corrente: |
17/05/2022 |
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Data da última atualização: |
17/05/2022 |
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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: |
PRUDENTE, V. H. R.; SKAKUN, S.; OLDONI, L. V.; XAUD, H. A. M.; XAUD, M. R.; ADAMI, M.; SANCHES, I. D. A. |
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Afiliação: |
HARON ABRAHIM MAGALHAES XAUD, CPAF-RR; MARISTELA RAMALHO XAUD, CPAF-RR. |
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Título: |
Multisensor approach to land use and land cover mapping in Brazilian Amazon. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 189, p. 95-109, 2022. |
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ISSN: |
0924-2716/ |
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DOI: |
https://doi.org/10.1016/j.isprsjprs.2022.04.025 |
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Idioma: |
Inglês |
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Conteúdo: |
Remote sensing has an important role in the Land Use and Land Cover (LULC) mapping process worldwide. Combining spaceborne optical and microwave data is essential for accurate classification in areas with frequent cloud cover, such as tropical regions. In this study, we investigate the possible improvements, when SAR data is incorporated into the classification process along with optical data. We used MSI/Sentinel-2 and SAR/Sentinel-1 to provide LULC mapping in the Roraima State, Brazil, in 2019. This State is located in a tropical area, where the cloud cover is frequent over the year. Cloud cover becomes substantial, especially during the May-August period when crops are grown. Twenty-nine scenarios involving a combination of optical- and SAR-based features, as well as times of data acquisition, were considered in this study. Our results showed that optical or SAR data used individually are not enough to provide accurate LULC mapping. The best results in terms of overall accuracy (OA) were achieved using metrics of multi-temporal surface reflectance and vegetation index (VI) for optical imagery, and values of backscatter coefficient in different polarizations and their ratios yielding an OA of 86.41 ± 1.74%. Analysis of three periods of data (January to April, May to August, and September to December) used for classification allowed us to identify the optimal period for distinguishing specific classes. When comparing our LULC map with a LULC product derived within the MapBiomas project we observed that our method performed better to map annual and perennial crops and water classes. Our methodology provides a more accurate LULC for the Roraima State, and the proposed technique can be applied to benefit other regions that are affected by persistent cloud cover. MenosRemote sensing has an important role in the Land Use and Land Cover (LULC) mapping process worldwide. Combining spaceborne optical and microwave data is essential for accurate classification in areas with frequent cloud cover, such as tropical regions. In this study, we investigate the possible improvements, when SAR data is incorporated into the classification process along with optical data. We used MSI/Sentinel-2 and SAR/Sentinel-1 to provide LULC mapping in the Roraima State, Brazil, in 2019. This State is located in a tropical area, where the cloud cover is frequent over the year. Cloud cover becomes substantial, especially during the May-August period when crops are grown. Twenty-nine scenarios involving a combination of optical- and SAR-based features, as well as times of data acquisition, were considered in this study. Our results showed that optical or SAR data used individually are not enough to provide accurate LULC mapping. The best results in terms of overall accuracy (OA) were achieved using metrics of multi-temporal surface reflectance and vegetation index (VI) for optical imagery, and values of backscatter coefficient in different polarizations and their ratios yielding an OA of 86.41 ± 1.74%. Analysis of three periods of data (January to April, May to August, and September to December) used for classification allowed us to identify the optimal period for distinguishing specific classes. When comparing our LULC map with a LULC product derived within the MapBi... Mostrar Tudo |
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Palavras-Chave: |
Multilayer Perceptron; Random Forest; Roraima state; Sentinel images. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143151/1/1-s2.0-S0924271622001289-main.pdf
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Marc: |
LEADER 02597naa a2200265 a 4500
001 2143151
005 2022-05-17
008 2022 bl uuuu u00u1 u #d
022 $a0924-2716/
024 7 $ahttps://doi.org/10.1016/j.isprsjprs.2022.04.025$2DOI
100 1 $aPRUDENTE, V. H. R.
245 $aMultisensor approach to land use and land cover mapping in Brazilian Amazon.$h[electronic resource]
260 $c2022
520 $aRemote sensing has an important role in the Land Use and Land Cover (LULC) mapping process worldwide. Combining spaceborne optical and microwave data is essential for accurate classification in areas with frequent cloud cover, such as tropical regions. In this study, we investigate the possible improvements, when SAR data is incorporated into the classification process along with optical data. We used MSI/Sentinel-2 and SAR/Sentinel-1 to provide LULC mapping in the Roraima State, Brazil, in 2019. This State is located in a tropical area, where the cloud cover is frequent over the year. Cloud cover becomes substantial, especially during the May-August period when crops are grown. Twenty-nine scenarios involving a combination of optical- and SAR-based features, as well as times of data acquisition, were considered in this study. Our results showed that optical or SAR data used individually are not enough to provide accurate LULC mapping. The best results in terms of overall accuracy (OA) were achieved using metrics of multi-temporal surface reflectance and vegetation index (VI) for optical imagery, and values of backscatter coefficient in different polarizations and their ratios yielding an OA of 86.41 ± 1.74%. Analysis of three periods of data (January to April, May to August, and September to December) used for classification allowed us to identify the optimal period for distinguishing specific classes. When comparing our LULC map with a LULC product derived within the MapBiomas project we observed that our method performed better to map annual and perennial crops and water classes. Our methodology provides a more accurate LULC for the Roraima State, and the proposed technique can be applied to benefit other regions that are affected by persistent cloud cover.
653 $aMultilayer Perceptron
653 $aRandom Forest
653 $aRoraima state
653 $aSentinel images
700 1 $aSKAKUN, S.
700 1 $aOLDONI, L. V.
700 1 $aXAUD, H. A. M.
700 1 $aXAUD, M. R.
700 1 $aADAMI, M.
700 1 $aSANCHES, I. D. A.
773 $tISPRS Journal of Photogrammetry and Remote Sensing$gv. 189, p. 95-109, 2022.
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