Large-Area UAS-Based Forest Health Monitoring Utilizing a Hydrogen-Powered Airship and Multispectral Imaging

Abstract

Climate change is threatening forest ecosystems worldwide by inducing various abiotic and biotic disturbances. In Europe, the European spruce bark beetle (Ips typographus L.) poses a significant threat, causing serious mortality in mature Norway spruce (Picea abies H. Karst.) stands. Rapidly evolving remote sensing technologies offer valuable tools for monitoring forest health, enabling timely management operations. This study presents a novel approach for large-area forest health monitoring using Uncrewed Aircraft Systems (UAS) and multispectral imaging. The research focuses on a hydrogen-powered Beyond Visual Line of Sight (BVLOS) airship for efficient monitoring of disturbances caused by I. typographus. A specific challenge is training machine learning models capable of covering wide areas. Our objective was to study the potential of deep learning models, including transfer learning and fine-tuning techniques, in developing the scalability and accuracy of UAS-based monitoring for detecting individual spruce trees and classifying their health. The approach was empirically evaluated in a study site in North Karelia, Finland. A multispectral image dataset was collected over a 1.3 km2 test area in May 2023 in a BVLOS setting operated from a command centre 75 km away. The results indicated that employing transfer learning significantly improved classification accuracy compared to training models from scratch, showing potential for implementing scalable machine learning methods for large-area UAS surveys. The best model yielded F1-scores of 0.936 for healthy, 0.955 for dead, and 0.817 for non-spruce classes. Furthermore, the results indicated that BVLOS airships offered high accuracy while reducing emissions and labour associated with UAS monitoring.