In the last decade, thousands of hectares of forests have been lost in the Czech Republic, primarily related to European spruce bark beetle (Ips typographus L.), while more than 50% of the remaining Czech forests are in great danger, thus posing severe threats to the resilience, stability, and functionality of those forests. The role of remote sensing in monitoring dynamic structural changes caused by pests is essential to understand and sustainably manage these forests. This study hypothesized a possible correlation between tree health status and multisource time series remote sensing data using different processed layers to predict the potential spread of attack by European spruce bark beetle in healthy trees. For this purpose, we used WorldView-2, Pléiades 1B, and SPOT-6 images for the period of April to September from 2018 to 2020; unmanned aerial vehicle (UAV) imagery data were also collected for use as a reference data source. Our results revealed that spectral resolution is crucial for the early detection of infestation. We observed a significant difference in the reflectance of different health statuses, which can lead to the early detection of infestation as much as two years in advance. More specifically, several bands from two different satellites in 2018 perfectly predicted the health status classes from 2020. This method could be used to evaluate health status classes in the early stage of infestation over large forested areas, which would provide a better understanding of the current situation and information for decision making and planning for the future.
Investigating the Correlation between Multisource Remote Sensing Data for Predicting Potential Spread of Ips typographus L. Spots in Healthy Trees
