The European bark beetle (Ips typographus, L.) is a potentially severe invasive species in the UK and North America. It is resulting in a high degree of fragmentation, forest productivity, and phenology. Understanding its biology, as well as developing early detection based on its behavior, is an important aspect of its successful management and eradication. Bark beetle infestation causes changes biochemical and biophysical characteristics such as chlorophyll water and nitrogen content. This study showcases the potential of the Canopy Chlorophyll Content (CCC) product derived from remote sensing datasets to detect early bark beetle infestation in Bavarian forest national park. We generated time series CCC maps from RapidEye and Sentinel-2 images of the study area through Radiative transfer model inversion. The CCC products were then classified into infested and healthy using CCC mean and variance collected in 2015 and 2016 from infested and healthy Norway spruce trees in the Park. Reference data obtained from processing and interpretation of high resolution (0.1m) color aerial photographs were used to validate the accuracy of the infestation maps. Our results demonstrated that CCC products as derived from remote sensing data were a rigorous proxy to early detect bark beetle infestation. Validation of the infestation maps revealed > 70% classification accuracy throughout the time-space. Hence, CCC products play a significant role to understand the dynamics of the infestation and improve the management of bark beetle outbreaks in forest ecosystem. Despite these promising results, other plant traits such as dry matter content and Nitrogen content will need to be investigated as additional predictors, which may considerably improve the accuracy of early detection of bark beetle infestation using remote sensing derived products
