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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
14/01/2014 |
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Data da última atualização: |
14/01/2014 |
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Autoria: |
BORGES, C. C.; MORESCHI, J. C. |
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Título: |
Potencialidade do uso de cruzetas de madeira tratada no Brasil. |
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Ano de publicação: |
2013 |
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Fonte/Imprenta: |
Floresta, Curitiba, v. 43, n. 2, p. 313-326, abr./jun. 2013. |
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Idioma: |
Português |
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Conteúdo: |
Neste trabalho, são apresentados dados relativos à potencialidade do uso da madeira de Corymbia citriodora, Eucalyptus cloeziana e Eucalyptus dunnii como cruzetas de madeira para redes de distribuição de energia elétrica. Os resultados apresentam o rendimento do processo produtivo para cada espécie, com análise dos ensaios mecânicos de flexão, estudo de penetração em tratamento com o preservativo arseniato de cobre cromatado (CCA) e discussão relativa a considerações econômicas e funcionais do material madeira. A espécie Corymbia citriodora apresentou o melhor rendimento, de 75%, seguido pelo Eucalyptus cloeziana, com 42%. A espécie Eucalyptus dunnii foi desclassificada, por apresentar um rendimento de apenas 7%. Os ensaios mecânicos demonstraram que tanto a espécie Corymbia citriodora quanto a Eucalyptus cloeziana atendem aos requisitos de resistência da norma técnica NBR 8458. No tratamento preservativo das cruzetas, observou-se penetração total de CCA no alburno da espécie Corymbia citriodora, o que, em conjunto com o cerne naturalmente resistente e o rendimento produtivo, classifica essa espécie como a mais indicada entre as estudadas. Com base nos resultados obtidos, conclui-se que o uso de cruzetas de madeira é viável no Brasil, além de menos impactante ao meio ambiente, por se tratar de um recurso natural renovável. |
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Palavras-Chave: |
Crossarms; Cruzetas; Madeira tratada; Resistência à flexão; Treated wood. |
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Thesaurus Nal: |
bending strength. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01937naa a2200205 a 4500
001 1975999
005 2014-01-14
008 2013 bl uuuu u00u1 u #d
100 1 $aBORGES, C. C.
245 $aPotencialidade do uso de cruzetas de madeira tratada no Brasil.
260 $c2013
520 $aNeste trabalho, são apresentados dados relativos à potencialidade do uso da madeira de Corymbia citriodora, Eucalyptus cloeziana e Eucalyptus dunnii como cruzetas de madeira para redes de distribuição de energia elétrica. Os resultados apresentam o rendimento do processo produtivo para cada espécie, com análise dos ensaios mecânicos de flexão, estudo de penetração em tratamento com o preservativo arseniato de cobre cromatado (CCA) e discussão relativa a considerações econômicas e funcionais do material madeira. A espécie Corymbia citriodora apresentou o melhor rendimento, de 75%, seguido pelo Eucalyptus cloeziana, com 42%. A espécie Eucalyptus dunnii foi desclassificada, por apresentar um rendimento de apenas 7%. Os ensaios mecânicos demonstraram que tanto a espécie Corymbia citriodora quanto a Eucalyptus cloeziana atendem aos requisitos de resistência da norma técnica NBR 8458. No tratamento preservativo das cruzetas, observou-se penetração total de CCA no alburno da espécie Corymbia citriodora, o que, em conjunto com o cerne naturalmente resistente e o rendimento produtivo, classifica essa espécie como a mais indicada entre as estudadas. Com base nos resultados obtidos, conclui-se que o uso de cruzetas de madeira é viável no Brasil, além de menos impactante ao meio ambiente, por se tratar de um recurso natural renovável.
650 $abending strength
653 $aCrossarms
653 $aCruzetas
653 $aMadeira tratada
653 $aResistência à flexão
653 $aTreated wood
700 1 $aMORESCHI, J. C.
773 $tFloresta, Curitiba$gv. 43, n. 2, p. 313-326, abr./jun. 2013.
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Registro original: |
Embrapa Florestas (CNPF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
25/01/2016 |
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Data da última atualização: |
04/01/2023 |
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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: |
SCHULTZ, B.; IMMITZER, M.; FORMAGGIO, A. R.; SANCHES, I. D. A.; LUIZ, A. J. B.; ATZBERGER, C. |
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Afiliação: |
BRUNO SCHULTZ, INPE; MARCUS IMMITZER, University of Natural Resources and Life Sciences, Viena; ANTONIO ROBERTO FORMAGGIO, INPE; IEDA DEL'ARCO SANCHES, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; CLEMENT ATZBERGER, University of Natural Resources and Life Sciences, Viena. |
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Título: |
Self-guided segmentation and classification of multi-temporal landsat 8 images for crop type mapping in southeastern Brazil. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Remote Sensing, Basel, v. 7, n. 11, p. 14482-14508, 2015. |
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ISBN: |
http://dx.doi.org/10.3390/rs71114482 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ? 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map. MenosAbstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ? 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanu... Mostrar Tudo |
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Palavras-Chave: |
Crop mapping; Mapeamento agrícola; Multi-resolution segmentation; OBIA; OLI; Random forest; Segmentação multirresolução. |
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Thesagro: |
Sensoriamento remoto. |
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Thesaurus NAL: |
Brazil; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/137582/1/2015AP38.pdf
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Marc: |
LEADER 02967naa a2200301 a 4500
001 2034915
005 2023-01-04
008 2015 bl uuuu u00u1 u #d
100 1 $aSCHULTZ, B.
245 $aSelf-guided segmentation and classification of multi-temporal landsat 8 images for crop type mapping in southeastern Brazil.$h[electronic resource]
260 $c2015
520 $aAbstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ? 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.
650 $aBrazil
650 $aRemote sensing
650 $aSensoriamento remoto
653 $aCrop mapping
653 $aMapeamento agrícola
653 $aMulti-resolution segmentation
653 $aOBIA
653 $aOLI
653 $aRandom forest
653 $aSegmentação multirresolução
700 1 $aIMMITZER, M.
700 1 $aFORMAGGIO, A. R.
700 1 $aSANCHES, I. D. A.
700 1 $aLUIZ, A. J. B.
700 1 $aATZBERGER, C.
773 $tRemote Sensing, Basel$gv. 7, n. 11, p. 14482-14508, 2015.
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Embrapa Meio Ambiente (CNPMA) |
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