Data/hora: 20/06/2024 23:01:41

Biblioteca(s): | Embrapa Territorial.
Data corrente: | 29/04/2004
Data da última atualização: | 30/03/2015
Autoria: | LU, D.; MAUSEL, P.; BATISTELLA, M.; MORAN, E.
Afiliação: | 1-2 e 4: Indiana University; 3: Embrapa Monitoramento por Satélite.
Título: | Comparison of land-cover classification methods in the Brazilian Amazon Basin.
Ano de publicação: | 2004
Fonte/Imprenta: | Photogrammetric Engineering & Remote Sensing, v. 70, n. 6, p. 723-731, jun. 2004.
Idioma: | Inglês
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.
Palavras-Chave: | Amazonas; Amazonia brasileira; Mapeamento.
Thesagro: | Bacia Hidrográfica; Floresta Tropical Úmida; Satélite.
Categoria do assunto: | --
URL: | http://ainfo.cnptia.embrapa.br/digital/bitstream/item/105777/1/1146.pdf
Registro original: | Embrapa Territorial (CNPM)
Exemplares: | CNPM - (1146-1) - UPC - AP - PP - 04/001 - AP - 2004.001 - DIS