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| 2. |  | EVANS, T. L.; COSTA, M.; TOMAS, W. M.; CAMILO, A. R. A sar fine and medium spatial resolution approach for mapping the brazilian Pantanal. Geografia, Rio Claro, v.38, n.espepcial, p.25-43, agosto, 2013. 19 p.| Biblioteca(s): Embrapa Pantanal. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pantanal. Para informações adicionais entre em contato com cpap.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Pantanal. |
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Data corrente: |
17/07/2014 |
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Data da última atualização: |
11/02/2015 |
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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: |
EVANS, T. L.; COSTA, M.; TOMAS, W. M.; CAMILO, A. R. |
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Afiliação: |
University of Victoria; University of Victoria; WALFRIDO MORAES TOMAS, CPAP. |
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Título: |
Large-scale habitat mapping of the Brazilian Pantanal wetland: A synthetic aperture radar approach. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Remote Sensing of Environment, v. 155, p. 89-108, 2014. |
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ISSN: |
0034-4257 |
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Idioma: |
Inglês |
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Conteúdo: |
This study uses 50 m spatial resolution, dual-season, HH and HV L-band ALOS/PALSAR, and HH and HV C-band RADARSAT-2 data, as well as a comprehensive set of ground reference points, to map the diverse habitats of the hydrologically variant subregions of the Pantanal by using a hierarchical object based image analysis approach. First, mean and standard deviation values of image object training sites were evaluated, and used as the basis for forming preliminary land cover class thresholds for each subregion. Then, acombination of additional refined thresholds, hierarchical rules, and a supervised nearest neighbor algorithm (eCognition Feature Space Optimization) employing several features as primary inputs (mean, standard deviation, seasonal change detection, brightness, maximum difference, area, roundness, brightness, compactness, shape index, and length/ width)wasutilized,resultinginthede finitionandclassificationoftenhabitatclasses:Forest/Woodland,Riparian Forest, Open Wood Savanna, Open Wood Savanna subject to prolonged flooding, Open Grass Savanna, Agriculture, Swampy Grassland, Swampy Mixed Savanna, Vazantes, and Water. This classification was achieved with an overall accuracy of 80% for the entire Pantanal. The produced habitat spatial distribution maps will provide vital information for determining refuge zones for terrestrial species, and connectivity of aquatic habitats during the dry season, as well as providing crucial baseline data to aid in monitoring changes in the region, and to help de fine conservation strategies for habitat in this wetland. MenosThis study uses 50 m spatial resolution, dual-season, HH and HV L-band ALOS/PALSAR, and HH and HV C-band RADARSAT-2 data, as well as a comprehensive set of ground reference points, to map the diverse habitats of the hydrologically variant subregions of the Pantanal by using a hierarchical object based image analysis approach. First, mean and standard deviation values of image object training sites were evaluated, and used as the basis for forming preliminary land cover class thresholds for each subregion. Then, acombination of additional refined thresholds, hierarchical rules, and a supervised nearest neighbor algorithm (eCognition Feature Space Optimization) employing several features as primary inputs (mean, standard deviation, seasonal change detection, brightness, maximum difference, area, roundness, brightness, compactness, shape index, and length/ width)wasutilized,resultinginthede finitionandclassificationoftenhabitatclasses:Forest/Woodland,Riparian Forest, Open Wood Savanna, Open Wood Savanna subject to prolonged flooding, Open Grass Savanna, Agriculture, Swampy Grassland, Swampy Mixed Savanna, Vazantes, and Water. This classification was achieved with an overall accuracy of 80% for the entire Pantanal. The produced habitat spatial distribution maps will provide vital information for determining refuge zones for terrestrial species, and connectivity of aquatic habitats during the dry season, as well as providing crucial baseline data to aid in monitoring changes in t... Mostrar Tudo |
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Palavras-Chave: |
ALOS/PALSAR; ENVISAT/ASAR; Habitat mapping; K&C Initiative; RADARSAT-2. |
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Thesaurus NAL: |
Pantanal; wetlands. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02267naa a2200253 a 4500
001 1990916
005 2015-02-11
008 2014 bl uuuu u00u1 u #d
022 $a0034-4257
100 1 $aEVANS, T. L.
245 $aLarge-scale habitat mapping of the Brazilian Pantanal wetland$bA synthetic aperture radar approach.
260 $c2014
520 $aThis study uses 50 m spatial resolution, dual-season, HH and HV L-band ALOS/PALSAR, and HH and HV C-band RADARSAT-2 data, as well as a comprehensive set of ground reference points, to map the diverse habitats of the hydrologically variant subregions of the Pantanal by using a hierarchical object based image analysis approach. First, mean and standard deviation values of image object training sites were evaluated, and used as the basis for forming preliminary land cover class thresholds for each subregion. Then, acombination of additional refined thresholds, hierarchical rules, and a supervised nearest neighbor algorithm (eCognition Feature Space Optimization) employing several features as primary inputs (mean, standard deviation, seasonal change detection, brightness, maximum difference, area, roundness, brightness, compactness, shape index, and length/ width)wasutilized,resultinginthede finitionandclassificationoftenhabitatclasses:Forest/Woodland,Riparian Forest, Open Wood Savanna, Open Wood Savanna subject to prolonged flooding, Open Grass Savanna, Agriculture, Swampy Grassland, Swampy Mixed Savanna, Vazantes, and Water. This classification was achieved with an overall accuracy of 80% for the entire Pantanal. The produced habitat spatial distribution maps will provide vital information for determining refuge zones for terrestrial species, and connectivity of aquatic habitats during the dry season, as well as providing crucial baseline data to aid in monitoring changes in the region, and to help de fine conservation strategies for habitat in this wetland.
650 $aPantanal
650 $awetlands
653 $aALOS/PALSAR
653 $aENVISAT/ASAR
653 $aHabitat mapping
653 $aK&C Initiative
653 $aRADARSAT-2
700 1 $aCOSTA, M.
700 1 $aTOMAS, W. M.
700 1 $aCAMILO, A. R.
773 $tRemote Sensing of Environment$gv. 155, p. 89-108, 2014.
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Registro original: |
Embrapa Pantanal (CPAP) |
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