In Switzerland, the decreasing significance of agriculture has led to prominent processes of land abandonment in mountainous areas where the maintenance of open land relies on human intervention. At the same time, urbanisation in Switzerland is increasing at a rapid rate at the expense of other land use types, particularly open land agriculture. In spite of these observed trends, the extent and location of anticipated land-use changes for the coming 20 years remain unknown, as does the impact on landscape services. This research defines 5 scenarios of future land-use for Switzerland along axes of Globalisation to Regionalisation and Market-driven developments to high policy intervention. Using the Dyna-CLUE land use modelling framework we incorporate socio-economic and bio-geographical variables to model scenarios of land change for 2035. By identifying locations for key land use transitions which occur across several scenarios, we find that unless large scale policy interventions are made, large areas of the Swiss Plateau and Alpine valley bottoms face strong urbanisation and much of the mountainous pasture agriculture continues to face risk of abandonment

Grassland use intensity is a topic of growing interest worldwide, as grasslands are integral in supporting biodiversity, food production, and regulating of the global carbon cycle. Data available for characterizing grasslands management are largely descriptive and collected from laborious field campaigns or questionnaires. The recent launch of the Sentinel-2 earth monitoring constellation provides new possibilities for high temporal and spatial resolution remote sensing data covering large areas. This study aims to evaluate the potential of a time series of Sentinel-2 data for mapping of mowing frequency in the region of Canton Aargau, Switzerland. We tested two cloud masking processes and three spatial mapping units (pixels, parcel polygons and shrunken parcel polygons), and investigated how missing data influence the ability to accurately detect and map grassland management activity. We found that more than 40% of the study area was mown before 15 June, while the remaining part was either mown later, or was not mown at all. The highest accuracy for detection of mowing events was achieved using additional clouds masking and size reduction of parcels, which allowed correct detection of 77% of mowing events. Additionally, we found that using only standard cloud masking leads to significant overestimation of mowing events, and that the detection based on sparse time series does not fully correspond to key events in the grass growth season.