Título:	Comparison of land-cover classification methods in the Brazilian Amazon Basin.
Autoria:	LU, D. MAUSEL, P. BATISTELLA, M. MORAN, E.
Afiliação:	1-2 e 4: Indiana University; 3: Embrapa Monitoramento por Satélite.
Ano de publicação:	2004
Referência:	Photogrammetric Engineering & Remote Sensing, v. 70, n. 6, p. 723-731, jun. 2004.
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.
Thesagro:	Bacia Hidrográfica Floresta Tropical Úmida Satélite
Palavras-chave:	Mapeamento Amazonia brasileira Amazonas
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPM)