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| Registros recuperados : 24 | |
| 5. |  | LUZ, N. B. da; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; FRANCISCON, L. Classificação de imagens RapidEye baseada em objetos e análise da ecologia de paisagens como suporte ao componente geoespacial do Inventário Florestal Nacional. In: SIMPÓSIO NACIONAL DE INVENTÁRIO FLORESTAL, 2., 2013, Curitiba. Anais. Brasília, DF: Serviço Florestal Brasileiro, 2013. p. 139. Resumo.| Biblioteca(s): Embrapa Florestas. |
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| 6. | | OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; LUZ, N. B. da; MATTOS, P. P. de. National system of permanent plots: proposal for a methodological model. In: IUFRO World Congress, 22., 2005, Brisbane. Forests in the balance: linking tradition and technology: program & abstracts. [Vienna]: IUFRO, 2005. 1 CD-ROM. Também publicado no The International Forestry Review, Oxford, v. 7, n. 5, p.196, Aug. 2005. Resumo.| Biblioteca(s): Embrapa Florestas. |
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| 8. | | OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; CIESLA, W. M.; JOHNSON, E.; RHEA, R.; PENTEADO JUNIOR, J.; LUZ, N. B. da. Aerial sketchmapping for monitoring forest conditions in Southern Brazil. In: MONITORING SCIENCE AND TECHNOLOGY SYMPOSIUM, Denver, 2004. Proceedings... Fort Collins: USDA, Forest Service, Rocky Mountain Research Station, 2005. 1 CD-ROM. (Proceedings RMRS-P37CD).| Biblioteca(s): Embrapa Florestas. |
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| 9. | | LUZ, N. B. da; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; FRANCISCON, L.; MESQUITA JÚNIOR, H. N. de; FREITAS, J. V. de. Classificação híbrida de imagens Landsat-8 e RapidEye para o mapeamento do uso e cobertura da terra nas Unidades Amostrais de Paisagem do Inventário Florestal Nacional do Brasil. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 7222-7230. Disponível online.| Biblioteca(s): Embrapa Florestas. |
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| 10. | | LUZ, N. B. da; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; MATTOS, P. P. de; FRANCISCON, L.; FREITAS, J. V. de. Classificação de imagens orientada a objetos como suporte ao componente geoespacial do Inventário Florestal Nacional Brasileiro. In: SIMPÓSIO NACIONAL DE INVENTÁRIO FLORESTAL, 3., 2014, Manaus. Anais... Brasília, DF: Serviço Florestal Brasileiro, 2014. p. 126. Disponível online. Resumo.| Biblioteca(s): Embrapa Florestas. |
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| 11. | | HOLLER, W. A.; ROSOT, M. A. D.; GARRASTAZU, M. C.; FIGUEIRA, I. F. R.; LUZ, N. B. da; MARAN, J. C.; FRANCISCON, L.; OLIVEIRA, Y. M. M. de. Dinâmica de uso e cobertura da terra e análise de tendência de mudanças para o município de Caçador, SC. Ciência e Natura, Santa Maria v. 40, e63, 2018. 22 p.| Biblioteca(s): Embrapa Florestas. |
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| 12. | | COSTA, C. R. da; LUZ, N. B. da; ARAKI, H.; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; KRUEGER, C. P. Análise da exatidão cartográfica das imagens Rapideye adotadas no Inventário Florestal Nacional do Brasil (IFN-BR). In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 3289-3296. Disponível online.| Biblioteca(s): Embrapa Florestas. |
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| 13. | | LUZ, N. B. da; GARRASTAZU, M. C.; ROSOT, M. A. D.; MARAN, J. C.; OLIVEIRA, Y. M. M. de; FRANCISCON, L.; CARDOSO, D. J.; FREITAS, J. V. de. Brazilian National Forest Inventory: a landscape scale approach to monitoring and assessing forested landscapes. Pesquisa Florestal Brasileira, Colombo, v. 38, e201701493, 2018. 14 p. Artigo de revisão.| Biblioteca(s): Embrapa Florestas. |
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| 14. | | OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; CIESSLA, W. M.; JOHNSON, E. W.; RHEA, R.; PENTEADO JUNIOR, J. F.; LUZ, N. B. da. O mapeamento aéreo expedito para o monitoramento florestal no sul do Brasil. In: DISPERATI, A. A.; SANTOS, J. R. dos (Ed.). Aplicações de geotecnologias na Engenharia Florestal. Curitiba: Copiadora Gabardo, 2004. p. 12-24.| Biblioteca(s): Embrapa Florestas. |
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| 17. | | LUZ, N. B. da; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; MESQUITA JÚNIOR, H. N. de; FREITAS, J. V. de; COSTA, C. R. da. Developments in forest monitoring under the Brazilian National Forest Inventory: multi-source and hybrid image classification approaches. In: WORLD FORESTRY CONGRESS, 14., 2015, Durban. Forests and people: investing in a sustainable future. Rome: FAO, 2015. 8 p.| Biblioteca(s): Embrapa Florestas. |
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| 18. | | OLIVEIRA, Y. M. M. de; GARRASTAZU, M. C.; ROSOT, M. A. D.; LUZ, N. B. da; ABRANTES, M. A.; BOGNOLA, I. A.; FREITAS, J. V. de; MATTOS, P. P. de; VIBRANS, A. C.; FRANCISCON, L.; GOMIDE, G. Detection of Pinus sp. and Hovenia dulcis as invasive species in native forests of South Brazil using National Forest Inventory (NFI-BR) data. In: WORLD FORESTRY CONGRESS, 14., 2015, Durban. Forests and people: investing in a sustainable future. Roma: FAO, 2015. Poster.| Biblioteca(s): Embrapa Florestas. |
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| 19. | | LUZ, N. B. da; MARAN, J. C.; GARRASTAZU, M. C.; ROSOT, M. A. D.; FRANCISCON, L.; HOLLER, W. A.; GAIAD, N. P.; OLIVEIRA, Y. M. M. de; FREITAS, J. V. de. Manual de análise de paisagem: volume 1: procedimentos para a execução do mapeamento de uso e cobertura da terra. Colombo: Embrapa Florestas, 2018. 92 p. (Embrapa Florestas. Documentos, 316).| Biblioteca(s): Embrapa Florestas. |
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| 20. | | ROSOT, M. A. D.; MARAN, J. C.; GARRASTAZU, M. C.; OLIVEIRA, Y. M. M. de; FRANCISCON, L.; CLERICI, N.; VOGT, P.; LUZ, N. B. da; FREITAS, J. V. de. Priorizatization analysis of riparian forest corridors in the Brazilian National Forest Inventory. Pesquisa Florestal Brasileira, Colombo, v. 39, (nesp), e201902043, 2019. p. 603. Edição especial dos resumos do IUFRO World Congress, 25., 2019, Curitiba.| Biblioteca(s): Embrapa Florestas. |
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| Registros recuperados : 24 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
04/02/2016 |
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Data da última atualização: |
02/07/2018 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
LUZ, N. B. da; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; MESQUITA JÚNIOR, H. N. de; FREITAS, J. V. de; COSTA, C. R. da. |
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Afiliação: |
NAÍSSA BATISTA DA LUZ, FAO; YEDA MARIA MALHEIROS DE OLIVEIRA, CNPF; MARIA AUGUSTA DOETZER ROSOT, CNPF; MARILICE CORDEIRO GARRASTAZU, CNPF; HUMBERTO NAVARRO DE MESQUITA JÚNIOR, SERVIÇO FLORESTAL BRASILEIRO; JOBERTO VELOSO DE FREITAS, SERVIÇO FLORESTAL BRASILEIRO; CLAUBER ROGERIO DA COSTA, UNIVERSIDADE FEDERAL DO PARANA. |
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Título: |
Developments in forest monitoring under the Brazilian National Forest Inventory: multi-source and hybrid image classification approaches. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: WORLD FORESTRY CONGRESS, 14., 2015, Durban. Forests and people: investing in a sustainable future. Rome: FAO, 2015. |
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Páginas: |
8 p. |
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Idioma: |
Inglês |
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Conteúdo: |
Information on forest and tree resources as well as land use and land cover (LULC) maps are a growing demand which Brazilian National Forest Inventory (NFI-BR) is designed to meet through field and remote sensing surveys. Field data collection comprises biophysical variables for forest and environment condition assessment, as well as socioeconomic variables for characterization of how people living nearby forests use and perceive the forest resources. The landscape level, based on remote sensing survey and spatial analysis, focuses on variables such as forest fragmentation, changes in forest cover and land use, and the condition of forest along rivers and water bodies. Multi-temporal Landsat-8 (L-8) and RapidEye (RE) high resolution imagery and ancillary data are the sources of information for an intricate hybrid image classification approach. Object-oriented analysis coupled with pixel based multi-data classification is providing reliable information on forest, trees and LULC monitoring. Global forest cover data, Landsat-8 TOA reflectance as well as derived 32-day vegetation index composites along the year are being processed in a cloud computing environment, providing pixel-based 30m pre-classification results. These results and ancillary map information (i.e., urban areas, roads, rivers and water bodies) are included in an object-based approach based on RE 5m spatial resolution imagery to produce landscape sample units (LSU) LULC Maps. The described hybrid image classification technique takes advantage of multi-temporal Landsat-8 data, valuable ancillary information and high resolution RE data to produce good quality LULC maps for the landscape sample units of NFI-BR. MenosInformation on forest and tree resources as well as land use and land cover (LULC) maps are a growing demand which Brazilian National Forest Inventory (NFI-BR) is designed to meet through field and remote sensing surveys. Field data collection comprises biophysical variables for forest and environment condition assessment, as well as socioeconomic variables for characterization of how people living nearby forests use and perceive the forest resources. The landscape level, based on remote sensing survey and spatial analysis, focuses on variables such as forest fragmentation, changes in forest cover and land use, and the condition of forest along rivers and water bodies. Multi-temporal Landsat-8 (L-8) and RapidEye (RE) high resolution imagery and ancillary data are the sources of information for an intricate hybrid image classification approach. Object-oriented analysis coupled with pixel based multi-data classification is providing reliable information on forest, trees and LULC monitoring. Global forest cover data, Landsat-8 TOA reflectance as well as derived 32-day vegetation index composites along the year are being processed in a cloud computing environment, providing pixel-based 30m pre-classification results. These results and ancillary map information (i.e., urban areas, roads, rivers and water bodies) are included in an object-based approach based on RE 5m spatial resolution imagery to produce landscape sample units (LSU) LULC Maps. The described hybrid image classific... Mostrar Tudo |
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Palavras-Chave: |
Análise de imagem baseada em objeto; Cloud computing; Computação na nuvem; Landscape; Object-based image analysis; Paisagem; RapidEye. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/138475/1/2015-Yeda-WFC-Development.pdf
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Marc: |
LEADER 02657nam a2200277 a 4500
001 2036151
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008 2015 bl uuuu u00u1 u #d
100 1 $aLUZ, N. B. da
245 $aDevelopments in forest monitoring under the Brazilian National Forest Inventory$bmulti-source and hybrid image classification approaches.$h[electronic resource]
260 $aIn: WORLD FORESTRY CONGRESS, 14., 2015, Durban. Forests and people: investing in a sustainable future. Rome: FAO$c2015
300 $a8 p.
520 $aInformation on forest and tree resources as well as land use and land cover (LULC) maps are a growing demand which Brazilian National Forest Inventory (NFI-BR) is designed to meet through field and remote sensing surveys. Field data collection comprises biophysical variables for forest and environment condition assessment, as well as socioeconomic variables for characterization of how people living nearby forests use and perceive the forest resources. The landscape level, based on remote sensing survey and spatial analysis, focuses on variables such as forest fragmentation, changes in forest cover and land use, and the condition of forest along rivers and water bodies. Multi-temporal Landsat-8 (L-8) and RapidEye (RE) high resolution imagery and ancillary data are the sources of information for an intricate hybrid image classification approach. Object-oriented analysis coupled with pixel based multi-data classification is providing reliable information on forest, trees and LULC monitoring. Global forest cover data, Landsat-8 TOA reflectance as well as derived 32-day vegetation index composites along the year are being processed in a cloud computing environment, providing pixel-based 30m pre-classification results. These results and ancillary map information (i.e., urban areas, roads, rivers and water bodies) are included in an object-based approach based on RE 5m spatial resolution imagery to produce landscape sample units (LSU) LULC Maps. The described hybrid image classification technique takes advantage of multi-temporal Landsat-8 data, valuable ancillary information and high resolution RE data to produce good quality LULC maps for the landscape sample units of NFI-BR.
653 $aAnálise de imagem baseada em objeto
653 $aCloud computing
653 $aComputação na nuvem
653 $aLandscape
653 $aObject-based image analysis
653 $aPaisagem
653 $aRapidEye
700 1 $aOLIVEIRA, Y. M. M. de
700 1 $aROSOT, M. A. D.
700 1 $aGARRASTAZU, M. C.
700 1 $aMESQUITA JÚNIOR, H. N. de
700 1 $aFREITAS, J. V. de
700 1 $aCOSTA, C. R. da
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