Regional Mountain Conference

Early detection of bark beetle infestation is critical for effective forest management, particularly in mountain forests where steep terrain and limited accessibility often constrain timely interventions. Visual symptoms at canopy level typically appear weeks after host colonization, when control measures are often no longer effective. This study presents a methodological framework that combines drone-based methods with scent detection dogs to improve the detection of early and mostly symptom-free infestation stages of the European spruce bark beetle (Ips typographus).

Multispectral drone imagery is used to spatially prioritize potentially infested trees, while trained detection dogs are deployed for targeted field validation based on pheromone cues. The approach aims to bridge the temporal gap between beetle colonization and visible crown symptoms, while reducing the effort required for extensive ground surveys. Particular emphasis is placed on testing the approach under realistic alpine conditions, including steep terrain, heterogeneous stand structure, and variable meteorological settings.

This contribution focuses on study design, field protocols, and decision workflows relevant for operational forest protection. By systematically integrating remote sensing and biological detection, the proposed framework seeks to support timely decision-making, resource prioritization, and adaptive management in mountain forests facing increasing disturbance pressure under climate change.