An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images

Recchia, V., Andresini, G., Appice, A., Fontana, G., Malerba, D. (2025). An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images. In Discovery Science. DS 2024. Lecture Notes in Computer Science, vol 15244. DOI: 10.1007/978-3-031-78980-9_12

Abstract: Forests play a key role in maintaining the balance of ecosystems, regulating climate, conserving biodiversity, and supporting various ecological processes. However, insect outbreaks, particularly bark beetle outbreaks, pose a significant threat to European spruce forest health by causing an increase in forest tree mortality. Therefore, developing accurate forest disturbance inventory strategies is crucial to quantifying and promptly mitigating outbreak diseases and boosting effective environmental management. In this paper, we propose a deep learning-based approach, named AVALON, that implements a CNN to detect tree dieback events in Sentinel-2 images of forest areas. To this aim, each pixel of a Sentinel-2 image is transformed into an imagery representation that sees the pixel within its surrounding pixel neighbourhood. We incorporate an attention mechanism into the CNN architecture to gain accuracy and achieve useful insights from the explanations of the spatial arrangement of model decisions. We assess the effectiveness of the proposed approach in two case studies regarding forest scenes in the Northeast of France and the Czech Republic, which were monitored using Sentinel-2 satellite in October 2018 and September 2020, respectively. Both case studies host bark beetle outbreaks in the considered periods.