Early detection of bark beetle attacks: Integrating Segment Anything Model (SAM) zero-shot segmentation and spectral indices for tree health assessment

Sammanfattning

Forests offer vital ecosystem services but face threats from various stressors, including climate change and insect infestations. The European spruce bark beetle (Ips typographus L.) poses significant risks to Norway Spruce (Picea abies). Early detection of bark beetle infestations is crucial for damage control but challenging with traditional methods. This study aims to utilize modern remote sensing technologies, particularly high-resolution Unmanned Aerial Vehicle (UAV) imagery, combined with the Segment Anything Model (SAM) to segment individual spruce trees and detect early signs of bark beetle attacks. Field studies were conducted in Mulatorp, a nature reserve in southeast Sweden, capturing UAV images over several months. The SAM, a state-of-the-art deep learning model for image segmentation, was used to segment individual spruce trees from RGB UAV data. The study aimed to assess SAM's zero-shot capabilities, refine its segmentation parameters, and compare its outputs with manually validated data. Additionally, the study sought to develop a time-series of vegetation indices to detect early signs of bark beetle infestations. Results indicated that SAM's box prompts yielded better segmentation accuracy than point prompts, though the model often merged canopies and missed some trees. Despite the high spatial resolution of UAV imagery, SAM detected only 37% of all trees and 33% of Norway spruce trees, with an IoU of 0.55 for spruce trees. The Green Chromatic Coordinate (GCC) was identified as the most effective vegetation index for early detection, showing significant differences between healthy and attacked trees as early as June. The findings suggest that while SAM has potential for remote sensing applications, its current zero-shot capabilities are insufficient for precise tree segmentation without further refinement and training. The study highlights the importance of integrating advanced segmentation models with UAV imagery for effective forest health monitoring and early intervention in bark beetle infestations. Future research should focus on enhancing SAM’s segmentation accuracy and expanding field-validated datasets to improve early detection frameworks.