Estimating the temporal heterogeneity of mowing events on grassland for haymilk-production using Sentinel-2 and greenness-index

The temporal and spatial heterogeneity of mowing on intensively used grasslands can be an important factor influencing the survival of insects or small animals after harvest. High heterogeneity contributes to the preservation of biodiversity on these grasslands. In our study we use satellite image time-series to analyze the temporal distribution of mowing events on grassland patches of Austrian farms. Our approach compares the standard deviation and the mean values of a greenness-index in Sentinel-2 time-series, and does not require to determine the date of mowing. A high standard deviation of the greenness-index demonstrates that the area of interest (AOI) includes grassland patches with tall and short (un-mowed and mowed) grass at a certain timepoint. If large parts of the AOI are mown at once, this results in a low standard deviation and a low mean value of the greenness-index. We were able to show that the haymilk farms in our study applied a more heterogeneous temporal mowing schedule on their grasslands compared to conventional farms. The data and information were obtained from the Austrian Sentinel-2 semantic EO data cube (Sen2Cube.at), which contains multi-spectral imagery data from Sentinel-2 satellites as well as derived semantic (categorical) and greenness-index layers since 2015.