Massive bark beetle outbreaks in needle-leaf forests during last decades have caused a gradual forest degradation of large spatial extent. Methods for mapping potential pest spread and for early stage detection of the infestations are highly demanded to prevent the outbreaks.
Optical remote sensing (RS) may spatially locate early disturbances by efficient detection of biochemical processes accompanying the early post-attack stress reaction of trees. RS may as well propose the areas with high risk of bark beetle attack by assessing the general physiological state of trees. Imaging spectroscopy combined with radiative transfer modelling at the appropriate scale has proven to be a suitable tool for state monitoring of coniferous forest stands. However, an appropriate parameterization and the assumptions of canopy reflectance models, especially concerning the influence of forest structure on up-scaling the leaf properties to canopy level, are still an issue of scientific questioning and investigation. The objective of this research is to use coupled radiative transfer models to develop an inversion routine for the retrieval of quantitative forest canopy biochemical parameters (water content and chlorophyll concentration) based on imaging spectroscopy data. The estimation of biochemicals will focus on monitoring Norway spruce physiological status for early detection of bark beetle infestations and for infestation risk assessment.