Abstract: ark beetle outbreaks pose a severe threat to spruce forests, with the European spruce bark beetle (Ips typographus L.) being the dominant pest in the Czech Republic. Although multispectral imagery using visible (VIS) and near-infrared (NIR) wavelengths has been employed for early detection, it primarily captures visible infestation symptoms rather than the underlying physiological stress, which can, however, be detected using thermal measurements, highlighting the benefit of integrating or complementing multispectral with thermal imagery. We compared a time series of Unmanned Aerial Vehicle (UAV) based thermal and multispectral imagery over a 650—ha coniferous stand in Central Bohemia, acquired at key phases of infestation (a) a year before infestation (August 2020); (b) closely before bark beetle infestation (April 2021); (c) in the initial phase of the green-attack (May 2021); and (d) in green-attack stage, early detection (June 2021), based on the species phenological model and field survey. Comparisons of canopy temperature and Normalised Difference Vegetation Index (NDVI) showed that thermal imagery successfully discriminated between healthy and infested trees seven weeks after beetle attack (green-attack), whereas NDVI differences remained negligible. These results confirm that UAV thermal imaging outperforms multispectral data for the early, individual-tree detection of bark beetle infestation.

Key words: Stress detectability, Drone, Thermal signature, Time-series analysis, NDVI, Biotic disturbance