Canopy chlorophyll content retrieved from time series remote sensing data as a proxy for detecting bark beetle infestation

The European spruce bark beetle (Ips typographus, L.) is an invasive species resulting in a high degree of fragmentation, forest productivity, and phenology. Understanding its biology and its early detection based on its behaviour is essential for its successful management and eradication. This study demonstrates the potential of the canopy chlorophyll content (CCC) product retrieved from remote sensing datasets to detect early bark beetle infestations in the Bavarian Forest National Park. Time series CCC maps were generated through radiative transfer model inversion of images from RapidEye and Sentinel-2 (2011–2018). The CCC products were then classified into stressed and healthy classes using calculated CCC mean and variance values obtained for infected and healthy Norway spruce trees in 2016. The location of infected plots obtained from the interoperation of resolution (0.1 m) aerial photographs was used as reference data to validate the accuracy of the infestation maps generated from CCC. Validation of the infestation maps indicated a classification accuracy of up to 78%. Our results demonstrated that CCC products derived from satellite remote sensing data were a rigorous proxy for early detection of bark beetle infestation. Hence, CCC products may play a significant role in understanding the dynamics of the Infestation and improving the management of bark beetle outbreaks in forest ecosystems. Inclusion of other remotely sensed plant traits as additional parameters in the model, such as dry matter and nitrogen, may further improve the accuracy of early detection of bark beetle infestation using satellite remote sensing.