Early detection of bark beetle infestation is a costly and time consuming task, as it can only be done reliable by personal screening the crowns for resin flow. Drone based detection systems prooved to be able to detect bark beetle infestation in the "red crown" stage. This is insufficient for a fast response under increasing infestation rates, as either the work load is very high or the tree is detected after the beetles have already multiplied and infested other trees. Therefore, a fast response method is needed to detect infested trees in the "green" stage. In this stage, only the hardly visible optical impression of the resin flow and the resin odor are detection cues.

A semi-conductor gas sensor containing three gas sensor elements was designed and integrated on a drone platform to detect monoterpene emissions over the crowns of a mixed deciduous/conifer forest. The sensor system was calibrated in the lab and tested under artificial lab and field conditions. The detection of alpha Pinen, the predominant mono terpene emitted by conifer resin, prooved to be successful. The detection of infested trees was prooven to be technical and chemo-ecologically possible. Heat Maps of stands with infested trees were generated in field trials and interpreted under additional information of the wind movement detected by a second drone.

This early detection concept can improve the fast response of foresters in order to protect conifer stands from bark beetle gradation. Further work is dedicated to the complete automation of the sensor drone flight and the Heat Map generation in order to transfer the measuring concept into the operational flow of forest management.