Mapping pasture management in the Brazilian Amazon from dense Landsat time series

We detected burned pastures with area adjusted user accuracies of 80-98%, burned secondary regrowth with 63-80% and tilled pastures with 74-78%. Our approach was able to discriminate management events even when they succeeded very fast. This way, the mapping approach with clear observation sequences allows us to contextualize management events directly from dense time series of high resolution satellite data. This opens new pathways to disentangle how management practices between smallholders and agribusinesses vary in the Brazilian Amazon. With sensor constellations from Sentinel-2 and Landsat data becoming a unified source for much denser time series soon, our method bears great potential to better understand and map pasture dynamics in the Amazon.