Grassland‐use intensity maps for Switzerland based on satellite time series: Challenges and opportunities for ecological applications

Abstract

AbstractLand‐use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland‐use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule‐based algorithm to produce annual maps for 2018–2021 of grassland‐management events, that is, mowing and/or grazing, for Switzerland using Sentinel‐2 and Landsat 8 satellite data. 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 examined the relationships between the generated grassland‐use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam‐based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use‐intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land‐use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.