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| Registros recuperados : 64 | |
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| 22. | | ADAMI, M.; VENTURIERI, A.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; GOMES, A. R. Lulc change on Rondonia, western Brazilian Amazon, by Terraclass project. In: INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM; CANADIAN SYMPOSIUM ON REMOTE SENSING, 35., 2014, Québec. Energy and our Changing Planet: final program. [S.l.]: IEEE, 2014. Não paginado. IGARSS 2014.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 23. | | ADAMI, M.; VENTURIERI, A.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; GOMES, A. R. Lulc change on Rondonia, western brazilian amazon, by Terraclass project. In: INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM; CANADIAN SYMPOSIUM ON REMOTE SENSING, 35., 2014, Québec. Energy and our Changing Planet. [S.l.]: IEEE, 2014. IGARSS 2014.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 25. | | GUSSO, A.; FORMAGGIO, A. R.; RIZZI, R.; ADAMI, M.; RUDORFF, B. F. T. Soybean crop area estimation by Modis/Evi data. Pesquisa Agropecuaria Brasileira, Brasília, DF, v. 47, n. 3, p. 425-435, mar. 2012. Título em português: Estimativa de áreas de cultivo de soja por meio de dados Modis/Evi.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 26. | | PADOVANI, C. R.; SHIMABUKURO, Y. E.; FREITAS, R. M.; ADAMI, M.; VETTORAZZI, C. A. Spatial analysis of Pantanal wetland flood dynamics determined from modis images: a case study . In: INTECOL INTERNATIONAL WETLANDS CONFERENCE, 8., Cuiabá, 2008. Big wetlands, big concerns: abstracts. [Sl.: s.n], 2008. p.160| Biblioteca(s): Embrapa Pantanal. |
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| 27. | | BERNADES, T.; ADAMI, M.; FARMAGGIO, A. R.; MOREIRA, M. A.; FRANÇA, D. de A.; NOVAES, M. R. de. Imagens mono e multitemporais Modis para estimativa da área com soja no Estado de Mato Grosso. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 46, n. 11, p. 1530-1537, nov. 2011 Título em inglês: Mono and multitemporal Modis imagery for soybean area estimate in Mato Grosso State, Brazil.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 28. | | SANTOS, M. N. dos; PINAGÉ, E. R.; LONGO, M.; ARAUJO, L. S. de; ADAMI, M.; MORTON, D.; KELLER, M. Lidar-based assessment of forest edge effects across a degraded landscape in the Brazilian Amazon. In: CONFERENCE ON LIDAR APPLICATIONS FOR ASSESSING AND MANAGING FOREST ECOSYSTEMS, 14., 2015. La Grande Motte, France. Proceedings of SilviLaser... La Grande Motte, France: IGN, 2015. p. 81-83.| Biblioteca(s): Embrapa Territorial. |
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| 31. | | MARTORANO, L. G.; SOTTA, E. D.; MARTORANO, P.; ADAMI, M.; BELTRÃO, N.; LISBOA, L.; OLIVEIRA, A.; NASCIMENTO, N. Effects of agricultural expansion on abundance of species in Amazon rainforest. In: INTERNATIONAL ECOSUMMIT, 5., 2016, Montpellier. Ecological sustainability: Engineering Change. [S.l.]: Elsevier, 2016.| Biblioteca(s): Embrapa Amapá. |
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| 35. | | SOUSA, L. M. de; KATO, O. R.; ADAMI, M.; SOUZA, A. A. A.; RAMOS, W. F.; SILVA, I. dos S. e. Analise multitemporal do desmatamento no município de Tomé-Açú entre 1985 a 2018. Pesquisa Florestal Brasileira, Colombo, v. 42, e201902053, 2022. 11 p.| Biblioteca(s): Embrapa Florestas. |
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| 39. | | GOERGEN, Ç L. C. de G.; KILCA, R. de V.; NARVAES, I. da S.; SILVA, M. N.; SILVA, E. A.; PEREIRA, R. S. P.; ADAMI, M. Distinção de espécies de eucalipto de diferentes idades por meio de imagens TM/Landsat 5. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 1, p. 53-60, jan. 2016 Título em inglês: Distinction of eucalyptus species of different ages using Landsat 5 TM images.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 40. | | ALMEIDA, C. A. de; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; ADAMI, M.; VENTURIERI, A.; DINIZ, C. G.; DESSAY, N.; DURIEUX, L.; GOMES, A. R. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amazonica, Manaus, v. 46, n. 3, p. 291-302, July/Sept. 2016.| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Amazônia Oriental. |
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| Registros recuperados : 64 | |
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Registro Completo
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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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