Grassland-use intensity maps for Switzerland

A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework. The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)). The webcam-based reference data used for verification was subsequently added on 14.02.2024.本研究采用基于规则的算法[(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795)，基于Sentinel-2与Landsat 8卫星时间序列数据，生成了瑞士2018至2021年的草地管理事件年度分布图，涵盖刈割与/或放牧两类管理活动。所有卫星影像均通过[FORCE](https://force-eo.readthedocs.io)框架完成处理。 所生成的地图空间分辨率为10 m × 10 m，可提供瑞士全境草地管理事件的发生次数与时间节点信息。最终地图的永久草地掩膜采用多种土地利用图层实现，该方案参考[Huber et al. (2022)](https://doi.org/10.1002/rse2.298)的方法，但将其中的作物掩膜替换为各州的农业用地数据。 本研究基于独立参考数据对管理事件的检测效果开展评估，该参考数据来源于瑞士境内广泛分布的公开网络摄像头每日时间序列影像。此外，我们结合全国性生物多样性数据，对生成的管理强度指标的生态学相关性进行了检验（详见[Weber et al., 2023](https://doi.org/10.1002/rse2.372)）。 用于验证的基于网络摄像头的参考数据于2024年2月14日补充加入本数据集。