01790naa a2200229 a 450000100080000000500110000800800410001910000110006024500830007126000090015452011440016365000240130765000290133165000140136065300130137465300240138765300150141170000150142670000190144170000140146077300860147410170392015-03-30       2004    bl uuuu     u00u1 u    #d1 aLU, D.  aComparison of land-cover classification methods in the Brazilian Amazon Basin.  c2004  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.  aBacia Hidrográfica  aFloresta Tropical Úmida  aSatélite  aAmazonas  aAmazonia brasileira  aMapeamento1 aMAUSEL, P.1 aBATISTELLA, M.1 aMORAN, E.  tPhotogrammetric Engineering & Remote Sensinggv. 70, n. 6, p. 723-731, jun. 2004.