|
|
Registros recuperados : 5 | |
1. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | PRUDENTE, V. H. R.; SKAKUN, S.; OLDONI, L. V.; XAUD, H. A. M.; XAUD, M. R.; ADAMI, M.; SANCHES, I. D. A. Multisensor approach to land use and land cover mapping in Brazilian Amazon. ISPRS Journal of Photogrammetry and Remote Sensing, v. 189, p. 95-109, 2022. Biblioteca(s): Embrapa Roraima. |
| ![Visualizar detalhes do registro](/consulta/web/img/visualizar.png) ![Acesso ao objeto digital](/consulta/web/img/pdf.png) ![Imprime registro no formato completo](/consulta/web/img/print.png) |
3. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | NOGUEIRA, S. F.; SILVA, G. B. S. da; ADAMI, M.; SANTOS, P. M.; PEZZOPANE, J. R. M.; NUÑEZ, D.; GREGO, C. R.; TEIXEIRA, A. H.; SKAKUN, S.; ROLISOLA, I. Estimating forage mass of a crop-livestock system (icl) using satellite images and climate data. In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. Proceedings reference... Brasília, DF: Embrapa, 2021. p. 804-809. Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
| ![Visualizar detalhes do registro](/consulta/web/img/visualizar.png) ![Imprime registro no formato completo](/consulta/web/img/print.png) |
4. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | NOGUEIRA, S. F.; SILVA, G. B. S. da; ADAMI, M.; SANTOS, P. M.; PEZZOPANE, J. R. M.; NUÑEZ, D.; GREGO, C. R.; TEIXEIRA, A. H.; SKAKUN, S.; ROLISOLA, I. Estimating forage mass of a crop-livestock system (icl) using satellite images and climate data. In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS: 100% DIGITAL, 2., 2021. WCCLF 2021 proceedings. Campo Grande, MS: Embrapa Gado de Corte, 2021. p.804-809. Biblioteca(s): Embrapa Meio Ambiente. |
| ![Visualizar detalhes do registro](/consulta/web/img/visualizar.png) ![Acesso ao objeto digital](/consulta/web/img/pdf.png) ![Imprime registro no formato completo](/consulta/web/img/print.png) |
5. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | WALDNER, F.; BELLEMANS, N.; HOCHMAN, Z.; NEWBY, T.; ABELLEYRA, D. de; VERÓN, S. R.; BARTALEV, S.; LAVRENIUK, M.; KUSSUL, N.; LE MAIRE, G.; SIMÕES, M.; SKAKUN, S.; DEFOURNY, P. Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed. International Journal of Applied Earth Observation and Geoinformation, v. 80, p. 82-93, Aug. 2019. Biblioteca(s): Embrapa Solos. |
| ![Visualizar detalhes do registro](/consulta/web/img/visualizar.png) ![Acesso restrito ao objeto digital](/consulta/web/img/lock.png) ![Imprime registro no formato completo](/consulta/web/img/print.png) |
Registros recuperados : 5 | |
|
|
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
|
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.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Roraima (CPAF-RR) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|