  Remote sensing satellite data offer the unique possibility to map land use
land cover transformations by providing spatially explicit information.
However, detection of short-term processes and land use patterns of high
spatial-temporal variability is a challenging task. We present a novel
framework using multi-temporal TerraSAR-X data and machine learning techniques,
namely Discriminative Markov Random Fields with spatio-temporal priors, and
Import Vector Machines, in order to advance the mapping of land cover
characterized by short-term changes. Our study region covers a current
deforestation frontier in the Brazilian state Par\'{a} with land cover
dominated by primary forests, different types of pasture land and secondary
vegetation, and land use dominated by short-term processes such as
slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X
imagery acquired over the course of the 2014 dry season, as well as optical
data (RapidEye, Landsat) for reference. Results show that land use land cover
is reliably mapped, resulting in spatially adjusted overall accuracies of up to
$79\%$ in a five class setting, yet limitations for the differentiation of
different pasture types remain. The proposed method is applicable on
multi-temporal data sets, and constitutes a feasible approach to map land use
land cover in regions that are affected by high-frequent temporal changes.
