Abstract
Eurasian Curlew populations are declining in Europe despite conservation efforts. Mowing practices may attract Curlews to areas with a higher chance of survival, but this potential cannot be assessed due to limited documentation on mowing dates. This study developed a remote sensing method for mowing event detection by applying cloud masking, outlier detection via Isolation Forest, and data smoothing on satellite images to create a Normalised Difference Vegetation Index (NDVI) time series. GPS data from the LBV Society for the Protection of Birds and Nature in Bavaria was used to examine changes in Curlews’ field use under mown and unmown conditions in their breeding areas. The developed approach detected 80 % of mowing events in trained data and 84 % in validation data with a ± three-day precision. Curlews visited fields significantly less often under unmown conditions and their field use increased substantially shortly after mowing events. Their reaction was stronger later in the season and is likely related to non-territorial behaviour. Fields under regulated mowing contracts showed more intensive Curlew activity than those conventionally managed. The workflow introduced for identifying mowing events through optical satellite imagery was designed with an emphasis on model robustness and on being accessible and reproducible for conservation practitioners and researchers. This simplified method successfully provided insights into factors influencing Curlews’ use of grassland during their stay in their breeding areas. Mowing practices significantly impact their habitat choices, suggesting their use as an innovative conservation approach to recover Curlew populations.
Original language | English |
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Article number | 109299 |
Journal | Agriculture, Ecosystems and Environment |
Volume | 378 |
DOIs | |
State | Published - 1 Feb 2025 |
Keywords
- Breeding area
- Generalised Linear Mixed Model
- Isolation Forest
- NDVI
- Numenius tandar
- conservation strategies
- grassland management
- remote sensing