Detecting Root Rot Infected Norway Spruce Trees Using Multispectral and LiDAR UAV Data

Abstract. Root rot caused by fungi in the Heterobasidion spp. and Armillaria spp. is one of the most economically significant problems in the European forest industry. Early detection remains challenging due to the lack of external visual symptoms in infected trees. This study explores the potential of unmanned aerial vehicles (UAVs) equipped with multispectral and light detection and ranging (LiDAR) sensors for detecting spectral and structural differences between healthy and root rot-infected Norway spruce (Picea abies (L.) H. Karst.) trees growing in southern Sweden. Remote sensing data from a total of 110 trees, classified as healthy (n=59) or infected (n=51) based on post-harvest survey of stump decay, were analysed. Canopy multispectral reflectance values from red, green, red-edge and near-infrared (NIR) bands, as well as from the reflected intensity values from the point cloud of LiDAR data, were analysed based on pre-harvest remote sensing data. Statistical analysis revealed significant differences in spectral response between healthy and infected trees in both the NIR band from the multispectral data and the reflected intensity from the LiDAR point cloud. These results underscore the potential of UAV-based optical and LiDAR data for detecting forest pathogen damage, highlighting their value in supporting sustainable and effective forest management.