Mineração de dados de sensoriamento remoto para detecção e classificação de áreas de pastagem na Amazônia Legal

Most of deforested areas in the Brazilian Amazon are occupied by pasture lands. The main cause of pasture degradation in this region is related to the condition of vegetation cover because of the fast regrowth and the competition with invasive plants. The aim of this study is to semi-automatically detect and classify patterns of pasture lands in the Legal Amazon, using time series of remote sensing images and data mining techniques, according to the conditions of the vegetation cover. The study site is the path/row 001/67 from Landsat 8 satellite. 28 images of surface reflectance, from 2013 to 2015, were used to construct the time series. Two classification methods were used: per pixel and object based. The following features were extracted from each image: vegetation indexes, fractions from the Spectral Linear Unmixing Model and components from the Tasseled Cap Transformation. The first step of the classification consisted in identifying pasture pattern, distinguishing class Pasture from Vegetation and Others. Later on, the pasture areas were reclassified into Clear Pasture (herbaceous pasture) and Dirty Pasture (shrubby pasture). In order to better evaluate the results, a classification procedure involving all classes was performed. The classification was validated by visual interpretation of a high spatial resolution image (RapidEye). The best accuracy was obtained on the object based approach, where it reached around 90%. Considering the per-pixel approach, it was difficult to identify some pasture due to the great amount of mixed elements in the images, like patterns of grass, tree, bush and others.