Abstract
The detection of invasive species is a crucial step for environmental management. However, traditional methods often require intensive labor in the field, which tends to be time-consuming and costly. In contrast, drones can cover large areas quickly and efficiently, creating a cost-effective alternative. However, the use of drones to detect or monitor invasive trees in coastal ecosystems is still unusual. For this reason, we carried out a trial to verify whether drones can effectively detect invasive alien pine trees at reduced costs in comparison with traditional detection methods. A drone was used to survey an area invaded by pines in an open coastal ecosystem in southern Brazil. We estimated total cost and time for detecting and controlling pines with and without the use of a drone. When using a drone to locate pines and conduct targeted control activities, the cost was reduced to approximately one-third compared with traditional methods. The time needed to detect and control invasive trees was more than seven times less compared with traditional active search and control. Our study demonstrates the potential of drone use for early detection, monitoring and control of invasive plant species in coastal and other open ecosystems. The use of drones can provide a cost-effective and efficient way to detect invasive plant species, allowing for targeted control actions.
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All data generated during this study are included in this published article and its supplementary information files.
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All authors contributed to the study conception and design. Drone data collection were performed by Rafael Barbizan Sühs. Pine control actions were performed by all authors. The first draft of the manuscript was written by Rafael Barbizan Sühs, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Barbizan Sühs, R., Ziller, S.R. & Dechoum, M. Is the use of drones cost-effective and efficient in detecting invasive alien trees? A case study from a subtropical coastal ecosystem. Biol Invasions 26, 357–363 (2024). https://doi.org/10.1007/s10530-023-03190-5
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DOI: https://doi.org/10.1007/s10530-023-03190-5