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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
005 2018-07-02
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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Embrapa Florestas (CNPF) |
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