Abstract In boreal forests, old deciduous trees, particularly European Aspen ( Populus tremula L.), play a crucial role in supporting biodiversity by providing unique habitats for cavity‐nesting birds, insects, and mammals. Despite their ecological importance, the low economic value and sparse distribution of aspen limit knowledge of their spatial and temporal distribution, hindering effective forest management and conservation. Similarly, standing dead trees are vital for biodiversity, offering habitats for numerous species. Accurate identification of tree species and standing dead trees is essential for forest mapping and biodiversity monitoring. Unmanned aerial vehicles (UAVs) have proven effective for detailed forest assessments, offering imagery with ultra‐high spatial resolution at relatively low costs. Their flexibility and customizable sensor payloads enable rapid data acquisition in challenging forest regions, making them a cost‐efficient alternative to manned aircraft. This study assessed the accuracy of different UAV‐based sensors and their combinations in classifying Scots pine ( Pinus sylvestris L.), Norway spruce ( Picea abies (L.) Karst.), birches ( Betula pendula Roth and Betula pubescens Ehrh.), European aspen, and standing dead trees. Spectral and structural features from true‐color (RGB) and multispectral (MSP) photogrammetric point clouds, as well as LiDAR data, were used as predictors. A total of 1,205 field‐measured trees (approx. 250 per class) were analyzed, with 70% used for training and 30% for validation. Our results showed that the LiDAR + MSP approach achieved the highest accuracy (78%) and kappa value (0.72), effectively leveraging LiDAR's structural detail and MSP's spectral richness. Among single sensors, MSP performed best (75% accuracy), while RGB and LiDAR achieved 71% and 60%, respectively. These findings highlight that while single‐sensor datasets can perform well, fusing spectral and structural data is essential for maximizing classification accuracy. UAV‐based multi‐sensor approaches offer significant potential for advancing assessments of biodiversity indicators and sustainable forest management.