Time Series Analysis of Tree Health for Early Forest Damage Detection Using UAV Data

The early detection of forest damage is critical for minimizing economic losses and improving climate-smart forestry practices. This study applies time series analysis to monitor tree health and detect early signs of damage in the research park Svartberget in northern Sweden. High-resolution data in red (R), green (G), and blue (B) bands (RGB), and multispectral data in G, R, red edge (RE), and near-infrared (NIR) bands were collected at varying intervals from 31 May to 9 November 2024, aligned with the phenological stages of the boreal forest vegetation season, using a unmanned aerial vehicle (UAV) at a flight altitude of 80 m above ground level. A total of 756 trees were manually digitized and classified into three categories: damaged (30 trees), birch (33 trees), and coniferous (693 trees). This classification enabled spectral trajectory analysis to evaluate changes in tree vitality. By identifying trends in single bands and spectral indices sensitive to tree health, this study aims to develop a replicable methodology for monitoring forest vitality. The results will contribute to broader efforts in mapping early forest damage and advancing climate-smart forestry practices. Future work will also explore data acquisition at higher flight altitudes (100 m and 120 m), incorporate LiDAR and hyperspectral data, and apply additional vegetation indices and spectral ratios to enhance the detectability of forest health.