Potential of Spectral-Spatial Analysis to Map Forest Tree Dieback Due to Bark Beetle Hotspots in Sentinel-2 Images

Forest tree dieback inventory plays a crucial role to improve forest management strategies. In this study, we explore the performance of a spectral-spatial machine learning approach used to analyse Sentinel-2 images to detect forest tree dieback events due to bark beetle infestation. We analyse the performance of classification models trained with Random Forest, XGBoost and Multi-Layer Perceptron, as well as semantic segmentation models trained with U-Net by accounting for both spectral and spatial information contained in the remote sensing data. We consider a set of Sentinel-2 images acquired in non-overlapping forest scenes from a region located in the Northeast of France. The selected scenes host bark beetle infestation hotspots originated from the mass reproduction of the bark beetle in the 2018 infestation. Results show that the U-Net model, trained accounting for spectral and spectral-spatial data, achieves the best performance. However, the simpler Random Forest model achieves competitive results with respect to the more complex one, namely U-Net.