Towards scalable wide area UAS monitoring of forest disturbance using hydrogen powered airships

Abstract

ABSTRACT Uncrewed Aircraft Systems (UAS) have been recognized as powerful tools for providing monitoring data supporting mitigating and adapting to various abiotic and biotic forest disturbances. However, their suitability and commercial viability have been significantly limited by the necessity for operation primarily within visual line of sight (VLOS). Efforts are currently ongoing to establish frameworks for beyond visual line of sight (BVLOS) UAS operation. This study represents the first successful application of hydrogen-powered BVLOS airship UAS technology for monitoring forest disturbances. The test area covered 1.3 km2 of conserved forest area in North Karelia, Finland, seriously affected by bark beetles and other disturbance agents. Monitoring flights were conducted in spring, summer, and autumn in BVLOS setting operated from a command centre situated approximately 75 km distance from the test area. Deep learning models were applied to study the tree health in the disturbance area, with the special focus on examining the scalability of machine learning models. Multispectral imagery outperformed RGB imagery in classifying trees as healthy, infested, and dead spruce trees, as well as non-spruce tree classes. With multispectral imagery, the F1-scores were 0.88–0.94, 0.80, 0.92–0.96, and 0.75–0.82 for healthy, infested, and dead spruce trees, and non-spruces, respectively. Transfer learning and fine-tuning, which adapted previously trained models to new study areas, improved results compared to training models from scratch and thus contributed to the development of scalable UAS remote sensing methods in mitigating forest disturbances on a larger scale.