|
|
Registro Completo |
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
22/11/2002 |
Data da última atualização: |
22/11/2002 |
Autoria: |
LIEBEREI, R.; REISDORFF, C.; MACHADO, A. D. (ed.). |
Título: |
Interdisciplinary research on the conservation and sustainable use of the Amazonian Rain Forest and its information requirements. |
Ano de publicação: |
1996 |
Fonte/Imprenta: |
Hamburg : Universitat Hamburg; Brasília, DF: CNPq, 1996. |
Páginas: |
323 p. |
ISBN: |
3-00-000909-4 |
Idioma: |
Inglês |
Notas: |
Report on the workshop held in Brasilia, Brazil, November 20-22, 1995. |
Conteúdo: |
Organization of research for the development of the amazon region; How research can contribute to the sustainable use of the amazon; Technology transfer to the private sector; European research activities on sustainable management of the amazon region; State-of-the-art of information sources in brazilian amazon and amazonian information systems; Perspectives and trends in global information management; The transfer and application of research results: how to link the science-business with the development-business; Introductory statements of the working group I: socio-economic implication; Amazonia: conflict and violence a threat to sustainable development; Legal aspects concerning the conservation and sustainable use of amazonian forest; Social and economicimplications of recent strategies for amazonia: a critical assessment; Land tenure, forms of production and environment in the amazon region; Conservation and sustainable development in amazonia: the programme on south-south co-operation on environmentally sound socio-economic development in the humid tropics; Carbon balance and tropical ecosystems, problems of measurement and scaling up; LBA - the large-scale biosphere-atmosphere experiment in amazonia; Deforestation and use of soil as pasture: climatic impacts; Biodiversity and economic botany; Sustainable land use systems for the amazon region; Pastures on amazonian forestlands: a review of environmental and economic performance; Agroforestry.
|
Palavras-Chave: |
Brasil. |
Thesagro: |
Biodiversidade; Ecologia; Floresta Tropical Úmida; Meio Ambiente; Natureza; Preservação da Natureza; Proteção Ambiental; Recurso Natural. |
Thesaurus Nal: |
Amazonia. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02371nam a2200289 a 4500 001 1330585 005 2002-11-22 008 1996 bl uuuu u01u1 u #d 020 $a3-00-000909-4 100 1 $aLIEBEREI, R. 245 $aInterdisciplinary research on the conservation and sustainable use of the Amazonian Rain Forest and its information requirements. 260 $aHamburg : Universitat Hamburg; Brasília, DF: CNPq$c1996 300 $a323 p. 500 $aReport on the workshop held in Brasilia, Brazil, November 20-22, 1995. 520 $aOrganization of research for the development of the amazon region; How research can contribute to the sustainable use of the amazon; Technology transfer to the private sector; European research activities on sustainable management of the amazon region; State-of-the-art of information sources in brazilian amazon and amazonian information systems; Perspectives and trends in global information management; The transfer and application of research results: how to link the science-business with the development-business; Introductory statements of the working group I: socio-economic implication; Amazonia: conflict and violence a threat to sustainable development; Legal aspects concerning the conservation and sustainable use of amazonian forest; Social and economicimplications of recent strategies for amazonia: a critical assessment; Land tenure, forms of production and environment in the amazon region; Conservation and sustainable development in amazonia: the programme on south-south co-operation on environmentally sound socio-economic development in the humid tropics; Carbon balance and tropical ecosystems, problems of measurement and scaling up; LBA - the large-scale biosphere-atmosphere experiment in amazonia; Deforestation and use of soil as pasture: climatic impacts; Biodiversity and economic botany; Sustainable land use systems for the amazon region; Pastures on amazonian forestlands: a review of environmental and economic performance; Agroforestry. 650 $aAmazonia 650 $aBiodiversidade 650 $aEcologia 650 $aFloresta Tropical Úmida 650 $aMeio Ambiente 650 $aNatureza 650 $aPreservação da Natureza 650 $aProteção Ambiental 650 $aRecurso Natural 653 $aBrasil 700 1 $aREISDORFF, C. 700 1 $aMACHADO, A. D.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Solos (CNPS) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
Registro Completo
Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
25/01/2016 |
Data da última atualização: |
04/01/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
SCHULTZ, B.; IMMITZER, M.; FORMAGGIO, A. R.; SANCHES, I. D. A.; LUIZ, A. J. B.; ATZBERGER, C. |
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. |
Título: |
Self-guided segmentation and classification of multi-temporal landsat 8 images for crop type mapping in southeastern Brazil. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Remote Sensing, Basel, v. 7, n. 11, p. 14482-14508, 2015. |
ISBN: |
http://dx.doi.org/10.3390/rs71114482 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Crop mapping; Mapeamento agrícola; Multi-resolution segmentation; OBIA; OLI; Random forest; Segmentação multirresolução. |
Thesagro: |
Sensoriamento remoto. |
Thesaurus NAL: |
Brazil; Remote sensing. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/137582/1/2015AP38.pdf
|
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.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Meio Ambiente (CNPMA) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|