European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.

Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen ( tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests ( L., [L.] Karst., spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.Populus Pinus sylvestris Picea abies Betula

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.