Tree attribute assessment in urban greenwood using ground-based LiDAR and multiseasonal aerial photography data

Advances in LiDAR and unmanned aerial vehicle technology have made high-resolution data available, which can be used for individual tree detection assessing attributes. The accuracy of these assessments is still not clear stands with high species diversity as well leaf-off leaf-on conditions. aim this study was to assess the quality top heights extracted from photogrammetric point clouds canopy height models ground-based mixed coniferous forest depending on phenological stage. has been carried out Botanical Garden Petrozavodsk State University (Republic Karelia, Russia). Four flight missions (in 2019–2021) using Phantom 4 Pro quadcopter were conducted arboretum (> 200 species) during periods leafless, leaf biomass growth, full foliage autumn colouration. A single laser scanning performed a Leica BLK 360. Multiseasonal ultra-high resolution orthophoto mosaics (1.1–2.8 cm/pixel), (average density 4200 points/m2), (11 600 points/m2) obtained. Further analysis three sites differing composition, site area. Tree tops automatically detected their estimated R environment software. We found that most trees (78.9%) correctly by algorithms based collected also number false positive (FP) negative (FN) cases increased decreasing green deciduous trees. Compared an average value, 9.4% cone-shaped crowns (Abies sibirica, A. balsamea, fraseri, Picea abies, P. pungens, omorika, Pseudotsuga menziesii, Larix sibirica) regardless density, decreased 10% ellipsoidal-shaped (e.g. Thuja occidentalis, genus Pinus) or broad-leaved density. lowest value (F = 0.49) leafless period. High values 0.84) obtained colouration indicates general. For growth period, 0.69) results. matched measured (R2 0.99). highest crowns. increments different between 2019 2021 clouds. annual increment Pinus sibirica (52 cm), menziesii (32 cm). Overall, our results shown potential use mapping estimating attributes multi-species arboretums urban parks, natural forests.