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- W2080197746 abstract "A spectral linear-mixing model using Landsat ETM+ imagery was undertaken to estimate fraction images of green vegetation, soil and shade in an indigenous land area in the state of Mato Grosso in the central-western region of Brazil. The fraction images were used to classify different types of land use and vegetation cover. The fraction images were classified by the following two methods: (a) application of a segmentation based on the region-growing technique; and (b) grouping of the regions segmented using the per-region unsupervised classifier named ISOSEG. Adopting a 75% threshold, ISOSEG generated 44 clusters that were grouped into eight land-use and vegetation-cover classes. The mapping achieved an average accuracy of 83%, showing that the methodology is efficient in mapping areas of great land-use and vegetation-cover diversity, such as that found in the Brazilian cerrado (savanna)." @default.
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- W2080197746 date "2009-04-01" @default.
- W2080197746 modified "2023-10-05" @default.
- W2080197746 title "Land-use and vegetation-cover mapping of an indigenous land area in the state of Mato Grosso (Brazil) based on spectral linear mixing model, segmentation and region classification" @default.
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- W2080197746 doi "https://doi.org/10.1080/10106040802488534" @default.
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