Detecting Bark Beetle Infestation at the Green Attack Phase Using Multi-Scale Physiological Indicators

Bark beetle outbreaks pose a significant threat to European forest ecosystems, with early detection of their green attack phase being critical for implementing timely countermeasures. While traditional remote sensing approaches often focus on proxies representing vegetation structure, we aim to introduce a novel approach by emphasizing physiological proxies that respond near-instantaneously to stress. By bridging scales from leaf to tree, landscape, and satellite levels, the BeatTheBeetle project aims to develop a comprehensive framework for detecting early signs of bark beetle infestation.

In this contribution, we will present results from an intensive field campaign conducted at spruce trees in the Pitztal valley (Tyrol, western Austria) to characterize leaf-level physiological responses. Measurements included leaf gas exchange, active and passive chlorophyll fluorescence, and visible and near-infrared reflectance. Preliminary results present a comparison between leaf gas exchange data, leaf-level imaging spectroscopy, and initial observations from an uncrewed aerial vehicle (UAV) flight.

Our findings highlight the potential of physiological proxies in advancing remote sensing techniques for early bark beetle detection. They represent an important step towards integrating multi-scale physiological indicators into remote sensing workflows and pave the way for further work exploring the scalability of these proxies across other platforms, ranging from UAVs to the satellite scale, to enable large-scale forest health monitoring.