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| Registros recuperados : 12 | |
| 10. |  | KUX, H. J. H.; SANTOS, A. P. dos; SAUSEN, T. M.; BUENO, A. M. T. R.; SOUZA, L. F. de; NUNES, J. S. D. Analise da inter-relacao entre o uso do solo e a compartimentacao geomorfologica atraves de dados do LANDSAT, em parte do Estado do Mato Grosso do Sul. In: SIMPOSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 2., 1982, Brasilia, DF. Anais. Brasilia: CNPq / Sao Jose dos Campos: INPE, 1982. v.3. p.735-746.| Biblioteca(s): Embrapa Cerrados. |
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| 11. | | SOARES, J. V.; KUX, H. J. H.; PALME, U. W.; CHEN, S. C.; BANON, G. F.; SCHIFINO, A. C.; SA, I. B.; CHOUDHURY, E. N.; CORDEIRO, G. G. Microwave remote sensing data from a spaceborne platform as a tool to monitore the hydrological cycle of a floodplain area ("Varzea") at Northeast Brazil (test site: Petrolina, Pernambuco State, Brazil). Sao Jose dos Campos: INPE, 1988. 21 p. (INPE- 4741 - RPE/579)| Biblioteca(s): Embrapa Semiárido. |
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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: |
27/03/1996 |
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Data da última atualização: |
06/04/2017 |
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Autoria: |
SILVA, J. dos S. V. da; KUX, H. J. H. |
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Afiliação: |
EMBRAPA. Centro de Pesquisa Agropecuaria do Pantanal (Corumba, MS); Instituto Nacional de Pesquisas Espaciais (Sao Jose dos Campos, SP). |
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Título: |
Remote sensing techniques to the detection and mapping of flooding dynamics within the Pantanal, Mato Grosso do Sul State, Brazil: preleminary results. |
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Ano de publicação: |
1991 |
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Fonte/Imprenta: |
In: INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, 24., 1991, Rio de Janeiro. Sumario... Rio de janeiro: Erim, 1991. p.49-50. |
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Idioma: |
Inglês |
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Conteúdo: |
The Pantanal, an extensive floodplain region in central-western Brazil, is annually flooded. A relative precise knowledge of the dynamics of flooding of great immediate interest for cattle-raising activities and wildlife management and of the areal extent of the flooded areas can be obtained from the digital classification of TM/Landsat scenes. The objective o this study is to evaluate the performance of classification algorithms (Maximum Likelihood, K-means, Single cell, Slicing) in order to discriminate four thematic classes (open water, flooded areas, wet areas and dry areas) by using TM bands 3, 4 and 5 from two well-defined seasons during the hydrologic year: the dry and flood seasons. The test site is located in the southern Pantanal (coordinates: S 19o28'49", W Gr 57o09o09o, and S 19o37'29", W Gr 57o18o19o). The native vegetation consists mainly of grass, herbs, woods, and bushes. The soils have a high clay content. The images (size 512x470 pixels) were transformed into reflectance frames and registered. Following the classifiers mentioned above were applied. For the dry and flood seasons five and seven thematic images were generated respectively. From the analysis of several thematic classifications, the classifier on Maximum Likelihood showed for the scene of the dry season, the most consistent results, followed by the Density Slicing classifer. The algorithms Single cell and K-means presented the lowest performance. As for the flood scene, Maximum Likelihood indicated results that were the closest on... MenosThe Pantanal, an extensive floodplain region in central-western Brazil, is annually flooded. A relative precise knowledge of the dynamics of flooding of great immediate interest for cattle-raising activities and wildlife management and of the areal extent of the flooded areas can be obtained from the digital classification of TM/Landsat scenes. The objective o this study is to evaluate the performance of classification algorithms (Maximum Likelihood, K-means, Single cell, Slicing) in order to discriminate four thematic classes (open water, flooded areas, wet areas and dry areas) by using TM bands 3, 4 and 5 from two well-defined seasons during the hydrologic year: the dry and flood seasons. The test site is located in the southern Pantanal (coordinates: S 19o28'49", W Gr 57o09o09o, and S 19o37'29", W Gr 57o18o19o). The native vegetation consists mainly of grass, herbs, woods, and bushes. The soils have a high clay content. The images (size 512x470 pixels) were transformed into reflectance frames and registered. Following the classifiers mentioned above were applied. For the dry and flood seasons five and seven thematic images were generated respectively. From the analysis of several thematic classifications, the classifier on Maximum Likelihood showed for the scene of the dry season, the most consistent results, followed by the Density Slicing classifer. The algorithms Single cell and K-means presented the lowest performance. As for the flood scene, Maximum Likelihood indic... Mostrar Tudo |
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Palavras-Chave: |
Flooding; Hidrology; Mapeamento; Mapping; Techniques; Tecnica. |
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Thesagro: |
Hidrologia; Inundação; Sensoriamento Remoto. |
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Thesaurus NAL: |
Pantanal; remote sensing. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02390naa a2200265 a 4500
001 1788033
005 2017-04-06
008 1991 bl --- 0-- u #d
100 1 $aSILVA, J. dos S. V. da
245 $aRemote sensing techniques to the detection and mapping of flooding dynamics within the Pantanal, Mato Grosso do Sul State, Brazil$bpreleminary results.
260 $c1991
520 $aThe Pantanal, an extensive floodplain region in central-western Brazil, is annually flooded. A relative precise knowledge of the dynamics of flooding of great immediate interest for cattle-raising activities and wildlife management and of the areal extent of the flooded areas can be obtained from the digital classification of TM/Landsat scenes. The objective o this study is to evaluate the performance of classification algorithms (Maximum Likelihood, K-means, Single cell, Slicing) in order to discriminate four thematic classes (open water, flooded areas, wet areas and dry areas) by using TM bands 3, 4 and 5 from two well-defined seasons during the hydrologic year: the dry and flood seasons. The test site is located in the southern Pantanal (coordinates: S 19o28'49", W Gr 57o09o09o, and S 19o37'29", W Gr 57o18o19o). The native vegetation consists mainly of grass, herbs, woods, and bushes. The soils have a high clay content. The images (size 512x470 pixels) were transformed into reflectance frames and registered. Following the classifiers mentioned above were applied. For the dry and flood seasons five and seven thematic images were generated respectively. From the analysis of several thematic classifications, the classifier on Maximum Likelihood showed for the scene of the dry season, the most consistent results, followed by the Density Slicing classifer. The algorithms Single cell and K-means presented the lowest performance. As for the flood scene, Maximum Likelihood indicated results that were the closest on...
650 $aPantanal
650 $aremote sensing
650 $aHidrologia
650 $aInundação
650 $aSensoriamento Remoto
653 $aFlooding
653 $aHidrology
653 $aMapeamento
653 $aMapping
653 $aTechniques
653 $aTecnica
700 1 $aKUX, H. J. H.
773 $tIn: INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, 24., 1991, Rio de Janeiro. Sumario... Rio de janeiro: Erim, 1991. p.49-50.
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