Climate change is increasing pest insects’ ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the mapping of trees that were in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicles (UAV)-based imagery collected in spring and fall in four study areas in Helsinki, Finland. We used the Random Forest machine learning to classify trees based on their symptoms during both occasions. Our approach achieved an overall classification accuracy of 78.2% and 84.5% for healthy, declined and dead trees for spring and fall datasets, respectively. The results suggest that fall or the end of summer provides the most accurate tree vitality classification results. We also investigated the transferability of Random Forest classifiers between different areas, resulting in overall classification accuracies ranging from 59.3% to 84.7%. The findings of this study indicate that multispectral UAV-based imagery is capable of classifying tree decline in Norway spruce trees during a bark beetle infestation.