Land cover change maps for Mato Grosso State in Brazil: 2001-2017 (Version 2), links to files (original) (raw)

2018

Abstract

This data set includes yearly maps of land cover classification for the state of Mato Grosso, Brasil, from 2001 to 2017, based on MODIS image time series (collection 6) at 250 meter spatial resolution (product MOD13Q1).Ground samples consisting of 1,892 time series with known labels are used as training data for a support vector machine classifier. We used the radial basis function kernel, with cost C=1 and gamma = 0.01086957. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of Land cover change maps for Mato Grosso State in BrazilMato Grosso, Brazil's agricultural frontier. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion.Quality assessment using a 5-fold cross-validation of the training samples indicates an overall accuracy of 96% and the following user's and producer's accuracy for the land cover classes:Cerrado: UA - 98% PA - 99%Fallow_Cotton UA - 96% PA - 93%Forest UA - 99% PA - 98%Pasture UA - 97% PA - 98%Soy-Corn UA- 91% PA - 93%Soy-Cotton UA - 97% PA - 97%Soy-Fallow UA - 98% PA - 98%Soy-Millet UA- 90% PA - 89%Soy-Sunflower UA - 77% PA - 65%---The correlation coefficients between the agricultural areas classified by our method and the estimates by IBGE (Brazil's Census Bureau) for the harvests from 2001 to 2017, were equal to 0.98. At state level the soybean, cotton, corn and sunflower areas had a correlation equal 0.97, 0.85, 0.98 and 0.80.The areas classified as forest were compared with the Hansen et al. (2013) mapping for the year 2000. In order to separate the forest areas, we examined the areas with more than 25% tree cover on the Hansen et al. (2013, doi:10.1126/science.1244693) map. We found that 99% of the pixels classified as forest match the pixels indicated by Hansen et al. (2013) as having more than 25% tree cover. When we joined the cerrado and forest classes, 84% of the pixels match the pixels by Hansen et al. (2013, doi [...]

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