Leveraging Sentinel-2 time series for bark beetle-induced forest dieback inventory

The bark beetle is one of the most critical, biotic disturbance agents causing tree dieback in several coniferous forest ecosystems around Europe. Forest dieback inventory plays a crucial role to study the effect of this biotic forest disturbance and improve forest management strategies. In this study, we explore the performance of remote sensing methods used to perform the inventory mapping of bark beetle-induced forest dieback. Specifically, we analyse the performance of classification models trained with Random Forest and XGBoost, as well as semantic segmentation models trained with U-Net by accounting for both spectral bands of Sentinel-2 images and some developed spectral vegetation indices. In addition, we investigate the effect of accounting for temporal knowledge on the performance of remote sensing methods. To this aim, we consider a dataset of Sentinel-2 time series acquired from May to October 2018 in non-overlapping forest scenes from the Northeast of France. The selected scenes host bark beetle infestation hotspots of different sizes, which originate from the mass reproduction of the bark beetle in the 2018 infestation. The results of this study show that the Random Forest model trained taking into account the temporal patterns in both spectral bands and vegetation indices achieves the highest accuracy in the study inventory task. Finally, we use an eXplainable Artificial Intelligence technique to explain the effect of temporal knowledge on the Random Forest inventory decisions.