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| Registros recuperados : 5 | |
| Registros recuperados : 5 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
29/04/2004 |
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Data da última atualização: |
30/03/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
Internacional - B |
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Autoria: |
LU, D.; MAUSEL, P.; BATISTELLA, M.; MORAN, E. |
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Afiliação: |
1-2 e 4: Indiana University; 3: Embrapa Monitoramento por Satélite. |
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Título: |
Comparison of land-cover classification methods in the Brazilian Amazon Basin. |
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Ano de publicação: |
2004 |
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Fonte/Imprenta: |
Photogrammetric Engineering & Remote Sensing, v. 70, n. 6, p. 723-731, jun. 2004. |
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Idioma: |
Inglês |
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Conteúdo: |
Four distinctly different classifiers were used to analyze multispectral data. Which of these classifiers is most suitable for a specific study area is not always clear. This paper provides a comparison of minimum-distance classifier (MDC), maximumlikelihood classifier (MLC), extraction and classification of homogeneous objects (ECHO), and decision-tree classifier based on linear spectral mixture analysis (DTC-LSMA). Each of the classifiers used both Landsat Thematic Mapper data and identical field-based training sample datasets in a western Brazilian Amazon study area. Seven land-cover classes? mature forest, advanced secondary succession, initial secondary succession, pasture lands, agricultural lands, bare lands, and water?were classified. Classification results indicate that the DTC-LSMA and ECHO classifiers were more accurate than were the MDC and MLC. The overall accuracy of the DTCLSMA approach was 86 percent with a 0.82 kappa coefficient and ECHO had an accuracy of 83 percent with a 0.79 kappa coefficient. The accuracy of the other classifiers ranged from 77 to 80 percent with kappa coefficients from 0.72 to 0.75. |
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Palavras-Chave: |
Amazonas; Amazonia brasileira; Mapeamento. |
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Thesagro: |
Bacia Hidrográfica; Floresta Tropical Úmida; Satélite. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/105777/1/1146.pdf
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Marc: |
LEADER 01790naa a2200229 a 4500
001 1017039
005 2015-03-30
008 2004 bl uuuu u00u1 u #d
100 1 $aLU, D.
245 $aComparison of land-cover classification methods in the Brazilian Amazon Basin.
260 $c2004
520 $aFour distinctly different classifiers were used to analyze multispectral data. Which of these classifiers is most suitable for a specific study area is not always clear. This paper provides a comparison of minimum-distance classifier (MDC), maximumlikelihood classifier (MLC), extraction and classification of homogeneous objects (ECHO), and decision-tree classifier based on linear spectral mixture analysis (DTC-LSMA). Each of the classifiers used both Landsat Thematic Mapper data and identical field-based training sample datasets in a western Brazilian Amazon study area. Seven land-cover classes? mature forest, advanced secondary succession, initial secondary succession, pasture lands, agricultural lands, bare lands, and water?were classified. Classification results indicate that the DTC-LSMA and ECHO classifiers were more accurate than were the MDC and MLC. The overall accuracy of the DTCLSMA approach was 86 percent with a 0.82 kappa coefficient and ECHO had an accuracy of 83 percent with a 0.79 kappa coefficient. The accuracy of the other classifiers ranged from 77 to 80 percent with kappa coefficients from 0.72 to 0.75.
650 $aBacia Hidrográfica
650 $aFloresta Tropical Úmida
650 $aSatélite
653 $aAmazonas
653 $aAmazonia brasileira
653 $aMapeamento
700 1 $aMAUSEL, P.
700 1 $aBATISTELLA, M.
700 1 $aMORAN, E.
773 $tPhotogrammetric Engineering & Remote Sensing$gv. 70, n. 6, p. 723-731, jun. 2004.
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Embrapa Territorial (CNPM) |
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