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Assessing Differentiation Between Pasture and Croplands Using Remote Sensing Image Time Series Metrics

Pasture and croplands comprise two different types of land use, which are very common in Brazil. Mapping these areas using remote sensing techniques is a challenge when using a single date image due to their similarity in spectral response. Time series might aid in discrimination of these areas once it explores the temporal behavior of surface patterns. In this work we explore time series obtained from remote sensing images to separate pasturelands from croplands in Brazilian Cerrado biome, using metrics derived from a data cube. We used Landsat 8 imagery as data source to compose a time series of six bands from OLI sensor (2 to 7) for the year of 2018. Random Forest algorithm was elected to execute the classification obtaining global accuracy of 80% and 0.58 of Kappa.