OPEN ACCESS | Published on : 28-Feb-2026 | Pages: 1-13 | Doi : 10.37446/edibook162024 /1-13
Forest diseases pose a significant threat to global forest ecosystems, exposing biodiversity, carbon storage capacity, and valuable timber resources. Pathogens such as Phytophthora ramorum (sudden oak death), Austropuccinia psidii (myrtle rust), Hymenoscyphus fraxineus (ash dieback), and Cryphonectria parasitica (chestnut blight) are spreading rapidly, exacerbated by climate change and human activities. Conventional monitoring methods, such as ground surveys, are typically labor-intensive, time-consuming, and insufficient for early-stage detection of these diseases. In contrast, Unmanned Aerial Vehicles (UAVs) equipped with advanced remote sensing technologies offer a high-resolution, scalable, and cost-effective approach to forest health evaluations. This chapter synthesizes current research on UAV-based forest disease monitoring, with a particular focus on the integration of multispectral, hyperspectral, thermal, and LiDAR sensors for early pathogen detection and management. UAVs have demonstrated their ability to successfully identify a range of forest diseases through machine learning-driven spectral analysis, 3D canopy mapping, and thermal anomaly detection. UAVs provide distinct advantages over traditional methods, including rapid data acquisition, real-time monitoring, and access to remote and difficult-to-reach terrains. Fusing UAV data with satellite imagery and artificial intelligence (AI) algorithms further enhances predictive modelling, enabling more proactive and efficient disease management. Despite these advancements, challenges such as limited flight endurance, sensor calibration inconsistencies, and regulatory restrictions hinder their widespread use. Future research should prioritize standardizing monitoring protocols, improving sensor sensitivity for pre-symptomatic disease detection, and developing autonomous UAV systems for large-scale forest monitoring. Integrating UAV technology into forest management frameworks holds the potential to revolutionize disease surveillance, supporting global conservation efforts to preserve forest ecosystems in the face of increasing climate and anthropogenic pressures.
UAVs, Forest disease monitoring, Remote sensing, Machine learning, Early detection
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