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Registro Completo |
Biblioteca(s): |
Embrapa Semiárido. |
Data corrente: |
20/07/2016 |
Data da última atualização: |
07/03/2017 |
Tipo da produção científica: |
Comunicado Técnico/Recomendações Técnicas |
Autoria: |
FREITAS, S. T. de; BARBOSA, M. A. G.; SOUZA, F. de F.; NASSUR, R. de C. M. R. |
Afiliação: |
SERGIO TONETTO DE FREITAS, CPATSA; MARIA ANGELICA GUIMARAES BARBOSA, CPATSA; FLAVIO DE FRANCA SOUZA, CPATSA; RITA DE CÁSSIA MIRELA RESENDE NASSUR, Professora da UNEB, Juazeiro - BA. |
Título: |
Colheita e armazenamento de acerola destinada o consumo in natura. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
Petrolina: Embrapa Semiárido, 2016. |
Páginas: |
Não paginado. |
Descrição Física: |
il. |
Série: |
(Embrapa Semiárido. Instruções Técnicas, 126). |
ISSN: |
1809-0001 |
Idioma: |
Português |
Conteúdo: |
Neste trabalho, são apresentadas informações sobre a colheita, acondicionamento, transporte, controle de podridão, embalagem e armazenamento de acerolas destinadas ao consumo in natura produzidas no Submédio do Vale do São Francisco, tendo em vista as boas práticas de manejo durante esses processos. As recomendações apresentadas estão embasadas em resultados de trabalhos realizados na Embrapa Semiárido. |
Palavras-Chave: |
Cereja das Antilhas; Malphigia; Vale do São Francisco. |
Thesagro: |
Acerola; Pós-Colheita. |
Thesaurus Nal: |
Malpighia emarginata. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/145644/1/INT126.pdf
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Marc: |
LEADER 01174nam a2200253 a 4500 001 2049284 005 2017-03-07 008 2016 bl uuuu u0uu1 u #d 022 $a1809-0001 100 1 $aFREITAS, S. T. de 245 $aColheita e armazenamento de acerola destinada o consumo in natura.$h[electronic resource] 260 $aPetrolina: Embrapa Semiárido$c2016 300 $aNão paginado.$cil. 490 $a(Embrapa Semiárido. Instruções Técnicas, 126). 520 $aNeste trabalho, são apresentadas informações sobre a colheita, acondicionamento, transporte, controle de podridão, embalagem e armazenamento de acerolas destinadas ao consumo in natura produzidas no Submédio do Vale do São Francisco, tendo em vista as boas práticas de manejo durante esses processos. As recomendações apresentadas estão embasadas em resultados de trabalhos realizados na Embrapa Semiárido. 650 $aMalpighia emarginata 650 $aAcerola 650 $aPós-Colheita 653 $aCereja das Antilhas 653 $aMalphigia 653 $aVale do São Francisco 700 1 $aBARBOSA, M. A. G. 700 1 $aSOUZA, F. de F. 700 1 $aNASSUR, R. de C. M. R.
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Registro original: |
Embrapa Semiárido (CPATSA) |
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Registro Completo
Biblioteca(s): |
Embrapa Roraima. |
Data corrente: |
17/05/2022 |
Data da última atualização: |
17/05/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
PRUDENTE, V. H. R.; SKAKUN, S.; OLDONI, L. V.; XAUD, H. A. M.; XAUD, M. R.; ADAMI, M.; SANCHES, I. D. A. |
Afiliação: |
HARON ABRAHIM MAGALHAES XAUD, CPAF-RR; MARISTELA RAMALHO XAUD, CPAF-RR. |
Título: |
Multisensor approach to land use and land cover mapping in Brazilian Amazon. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 189, p. 95-109, 2022. |
ISSN: |
0924-2716/ |
DOI: |
https://doi.org/10.1016/j.isprsjprs.2022.04.025 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Multilayer Perceptron; Random Forest; Roraima state; Sentinel images. |
Categoria do assunto: |
-- |
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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