Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning
This work addresses the challenge of early detection for forest management and policy development, though it appears incremental as it adapts existing deep learning architectures to a specific domain problem.
The researchers tackled the problem of early detection of bark beetle-infested trees in forests by developing a deep learning method that classifies different stages of bark beetle attacks at the individual tree level using UAV images, achieving an average accuracy of 98.95% and outperforming the baseline by approximately 10%.
Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world. For the development of effective forest policies and management plans, the early detection of infested trees is essential. Despite the visual symptoms of bark beetle infestation, this task remains challenging, considering overlapping tree crowns and non-homogeneity in crown foliage discolouration. In this work, a deep learning based method is proposed to effectively classify different stages of bark beetle attacks at the individual tree level. The proposed method uses RetinaNet architecture (exploiting a robust feature extraction backbone pre-trained for tree crown detection) to train a shallow subnetwork for classifying the different attack stages of images captured by unmanned aerial vehicles (UAVs). Moreover, various data augmentation strategies are examined to address the class imbalance problem, and consequently, the affine transformation is selected to be the most effective one for this purpose. Experimental evaluations demonstrate the effectiveness of the proposed method by achieving an average accuracy of 98.95%, considerably outperforming the baseline method by approximately 10%.