Accurate estimates of canopy structure like canopy cover (CC), Leaf Area Index (LAI), crown volume (Vcr), as well as tree and stand structure like stem volume (V_st) and basal area (G), are considered essential measures to manage poplar plantations effectively as they are correlated with the growth rate and the detection of possible stress. This research exploits the possibility of developing a precision forestry application using an unmanned aerial vehicle (UAV), terrestrial digital camera and traditional field measurements to monitor poplar plantation variables. We set up the procedure using explanatory variables from the Grey Level Co-occurrence Matrix textural metrics (Entropy, Variance, Dissimilarity and Contrast) calculated based on UAV multispectral imagery. Our results show that the GCLM texture derived by multispectral ortomosaic provides adequate explanatory variables to predict poplar plantation characteristics related to plants' canopy and stand structure. The evaluation of the models targeting the different poplar plantation variables (i.e. Vcr, G_ha, Vst_ha, CC and LAI) with the four GLCM explanatory variables (i.e. Entropy, Variance, Dissimilarity and Contrast) consistently higher or equal resulted to R² ≥0.86. © 2024, Editura Silvica. All rights reserved.

Stand structure and tree spatial patterns are key elements to understand natural dynamics and competition processes in forest ecosystems. We performed repeated, mapped tree inventory measures (x, y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure and diversity in 1 ha oak-hornbeam planted forest with pedunculate oak (Quercus robur L.), established in 2003 also for the conservation of a variety of oak genotypes. Two inventories were carried out in 2009 and 2019. The use of repeated and mapped tree measures allows to investigate the changes in spatial pattern processes through time in this forest. © 2024 Istituto Sperimentale per la Selvicoltura. All rights reserved.