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Registro Completo |
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
12/06/2015 |
Data da última atualização: |
09/05/2016 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
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. |
Afiliação: |
Naíssa Batista da Luz, ONU/FAO; YEDA MARIA MALHEIROS DE OLIVEIRA, CNPF; MARIA AUGUSTA DOETZER ROSOT, CNPF; MARILICE CORDEIRO GARRASTAZU, CNPF; LUZIANE FRANCISCON, CNPF; Humberto Navarro de Mesquita Júnior, Serviço Florestal Brasileiro; Joberto Veloso de Freitas, Serviço Florestal Brasileiro. |
Título: |
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. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. |
Páginas: |
p. 7222-7230. |
Descrição Física: |
Disponível online. |
Idioma: |
Português |
Conteúdo: |
In response to the growing demand for reliable information on forest and tree resources as well as for land use/land cover (LULC) maps at larger scales, the Brazilian National Forest Inventory (NFI-BR) is now being conducted. Besides the traditional approaches related to forest assessment, the NFI-BR includes a geospatial component to provide such information at landscape scale. Using a sampling grid of 20 km × 20 km, field registry sample units were established, and 100 km2 landscape sample units (LSU) were located on a 40 km × 40 km grid. LULC maps are being prepared for each LSU using RapidEye and Landsat-8 imagery. Different remote sensing techniques are being tested to characterize LULC in order to identify patterns in different themes using spatial analysis, such as forest fragmentation, state of conservation, production and forest health. The mapping approach uses a hybrid approach, here understood as the combination of automatic unsupervised pixel-by-pixel classification and object based image classification. Attributes from image objects such as spectral characteristics, texture, and context are also involved in process tree classification, as well as ancillary data such as roads, water bodies and digital terrain models. LULC maps are the basis for analyzing landscape-scale forest fragmentation analysis as well as for evaluating compliance of permanent preservation areas under recently approved environmental legislation. |
Palavras-Chave: |
Ancillary data; Automatic image classification; Brasil; Classificação automática de imagens; Classificação orientada a objetos; Imagem de satélite; Inventário Florestal Nacional; Object-based classification. |
Thesagro: |
Sensoriamento Remoto. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/142855/1/2015-Marilice-Classificacao-hibrida-de-imagens-Landsat-8-e-RapidEye.pdf
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Marc: |
LEADER 02615nam a2200301 a 4500 001 2017535 005 2016-05-09 008 2015 bl uuuu u00u1 u #d 100 1 $aLUZ, N. B. da 245 $aClassificação híbrida de imagens Landsat-8 e RapidEye para o mapeamento do uso e cobertura da terra nas Unidades Amostrais de Paisagem do Inventário Florestal Nacional do Brasil.$h[electronic resource] 260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE$c2015 300 $ap. 7222-7230.$cDisponível online. 520 $aIn response to the growing demand for reliable information on forest and tree resources as well as for land use/land cover (LULC) maps at larger scales, the Brazilian National Forest Inventory (NFI-BR) is now being conducted. Besides the traditional approaches related to forest assessment, the NFI-BR includes a geospatial component to provide such information at landscape scale. Using a sampling grid of 20 km × 20 km, field registry sample units were established, and 100 km2 landscape sample units (LSU) were located on a 40 km × 40 km grid. LULC maps are being prepared for each LSU using RapidEye and Landsat-8 imagery. Different remote sensing techniques are being tested to characterize LULC in order to identify patterns in different themes using spatial analysis, such as forest fragmentation, state of conservation, production and forest health. The mapping approach uses a hybrid approach, here understood as the combination of automatic unsupervised pixel-by-pixel classification and object based image classification. Attributes from image objects such as spectral characteristics, texture, and context are also involved in process tree classification, as well as ancillary data such as roads, water bodies and digital terrain models. LULC maps are the basis for analyzing landscape-scale forest fragmentation analysis as well as for evaluating compliance of permanent preservation areas under recently approved environmental legislation. 650 $aSensoriamento Remoto 653 $aAncillary data 653 $aAutomatic image classification 653 $aBrasil 653 $aClassificação automática de imagens 653 $aClassificação orientada a objetos 653 $aImagem de satélite 653 $aInventário Florestal Nacional 653 $aObject-based classification 700 1 $aOLIVEIRA, Y. M. M. de 700 1 $aROSOT, M. A. D. 700 1 $aGARRASTAZU, M. C. 700 1 $aFRANCISCON, L. 700 1 $aMESQUITA JÚNIOR, H. N. de 700 1 $aFREITAS, J. V. de
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Embrapa Florestas (CNPF) |
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![](/consulta/web/img/deny.png) | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
04/03/2015 |
Data da última atualização: |
04/03/2015 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
LIU, Y.; BRUIJN, I.; JACK, A. L. H.; DRYNAN, K.; BERG, A. H. van den; THOEN, E.; SANDOVAL-SIERRA, V.; SKAAR, I.; WEST, P. van; DIÉGUEZ-URIBEONDO, J.; VOORT, M. van der; MENDES, R.; MAZOLLA, M.; RAAIJMAKERS, J. M. |
Afiliação: |
YIYING LIU, Netherlands Institute of Ecology; IRENE DE BRUIJN, Netherlands Institute of Ecology; ALLISON L H JACK, Wageningen University; KEITH DRYNAN, Landcatch, Hendrix Genetics; ALBERT H VAN DEN BERG, University of Aberdeen; EVEN THOEN, Norwegian Veterinary Institute; VALDIMIR SANDOVAL-SIERRA, Real Jardin Botanico CSIC; IDA SKAAR, University of Aberdeen; PIETER VAN WEST, University of Aberdeen; JAVIER DIÉGUEZ-URIBEONDO, Real Jardin Botanico CSIC; MENNO VAN DER VOORT, Wageningen University; RODRIGO MENDES, CNPMA; MARK MAZOLLA, USDA-ARS; JOS M RAAIJMAKERS, Netherlands Institute of Ecology. |
Título: |
Deciphering microbial landscapes of fish eggs to mitigate emerging diseases. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
The ISME Journal, London, v. 8, p. 2002?2014, 2014. |
Idioma: |
Inglês |
Conteúdo: |
Abstract: Animals and plants are increasingly suffering from diseases caused by fungi and oomycetes. These emerging pathogens are now recognized as a global threat to biodiversity and food security. Among oomycetes, Saprolegnia species cause significant declines in fish and amphibian populations. Fish eggs have an immature adaptive immune system and depend on nonspecific innate defences to ward off pathogens. Here, meta-taxonomic analyses revealed that Atlantic salmon eggs are home to diverse fungal, oomycete and bacterial communities. Although virulent Saprolegnia isolates were found in all salmon egg samples, a low incidence of Saprolegniosis was strongly correlated with a high richness and abundance of specific commensal Actinobacteria, with the genus Frondihabitans (Microbacteriaceae) effectively inhibiting attachment of Saprolegniato salmon eggs. These results highlight that fundamental insights into microbial landscapes of fish eggs may provide new sustainable means to mitigate emerging diseases. |
Palavras-Chave: |
Emerging pathogens. |
Thesagro: |
Doença animal; População microbiana; Salmão. |
Thesaurus NAL: |
Actinobacteria; Fish diseases; Microbiome; Salmo salar; salmon; Saprolegniosis. |
Categoria do assunto: |
H Saúde e Patologia |
Marc: |
LEADER 02085naa a2200397 a 4500 001 2010549 005 2015-03-04 008 2014 bl uuuu u00u1 u #d 100 1 $aLIU, Y. 245 $aDeciphering microbial landscapes of fish eggs to mitigate emerging diseases.$h[electronic resource] 260 $c2014 520 $aAbstract: Animals and plants are increasingly suffering from diseases caused by fungi and oomycetes. These emerging pathogens are now recognized as a global threat to biodiversity and food security. Among oomycetes, Saprolegnia species cause significant declines in fish and amphibian populations. Fish eggs have an immature adaptive immune system and depend on nonspecific innate defences to ward off pathogens. Here, meta-taxonomic analyses revealed that Atlantic salmon eggs are home to diverse fungal, oomycete and bacterial communities. Although virulent Saprolegnia isolates were found in all salmon egg samples, a low incidence of Saprolegniosis was strongly correlated with a high richness and abundance of specific commensal Actinobacteria, with the genus Frondihabitans (Microbacteriaceae) effectively inhibiting attachment of Saprolegniato salmon eggs. These results highlight that fundamental insights into microbial landscapes of fish eggs may provide new sustainable means to mitigate emerging diseases. 650 $aActinobacteria 650 $aFish diseases 650 $aMicrobiome 650 $aSalmo salar 650 $asalmon 650 $aSaprolegniosis 650 $aDoença animal 650 $aPopulação microbiana 650 $aSalmão 653 $aEmerging pathogens 700 1 $aBRUIJN, I. 700 1 $aJACK, A. L. H. 700 1 $aDRYNAN, K. 700 1 $aBERG, A. H. van den 700 1 $aTHOEN, E. 700 1 $aSANDOVAL-SIERRA, V. 700 1 $aSKAAR, I. 700 1 $aWEST, P. van 700 1 $aDIÉGUEZ-URIBEONDO, J. 700 1 $aVOORT, M. van der 700 1 $aMENDES, R. 700 1 $aMAZOLLA, M. 700 1 $aRAAIJMAKERS, J. M. 773 $tThe ISME Journal, London$gv. 8, p. 2002?2014, 2014.
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