Abstract
Agricultural activities cause rapid changes in vegetation development at local and regional scales. Those modifications affect the small-scale behavior of animals, like the foraging ground usage of breeding white storks. Only recently, a novel approach, that enables to quantify the relationship between mowing and harvesting activities and a prolonged foraging time of storks by combining remote sensing time series with GPS telemetry, has been proposed. This study examines the stability of this approach. We investigate two potential influencing factors: different vegetation indices and time lags over which vegetation dynamics were retrieved. Mostly independent from the vegetation index and time lag, we observed that storks spent large proportions of foraging time in areas characterized by a recent drop in vegetation indices, indicative for a preferred usage after harvesting and mowing events. This suggest that the proposed approach is relatively stable and hence, provides a reasonable basis to investigate the effects of anthropogenic vegetation alterations on animal behavior at small spatiotemporal scales.
Original language | English |
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Title of host publication | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4799-4802 |
Number of pages | 4 |
ISBN (Electronic) | 9781728163741 |
DOIs | |
State | Published - 26 Sep 2020 |
Event | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: 26 Sep 2020 → 2 Oct 2020 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Conference
Conference | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 |
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Country/Territory | United States |
City | Virtual, Waikoloa |
Period | 26/09/20 → 2/10/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Anthropocene
- habitat usage
- remote sensing
- telemetry data
- vegetation dynamics