Understory is a key component of forest biodiversity. The structure of the forest stand and the horizontal composition of the canopy play a major role on the light regime of the understory, which in turn affects the abundance and the diversity of the understory plant community. Reliable assessments of canopy structural attributes are essential for forest research and biodiversity monitoring programs, as well as to study the relationship between canopy and understory plant communities. Canopy photography is a widely used method but it is still not clear which photographic techniques is better suited to capture canopy attributes at stand-level that can be relevant in forest biodiversity studies. For this purpose, we collected canopy structure and understory plant diversity data on 51 forest sites in the north-eastern Italian Alps, encompassing a diversity of forest types from low-elevation deciduous, to mixed montane stands to subalpine coniferous forests. Canopy images were acquired using both digital cover (DCP) and hemispherical (DHP) photography, and analysed canopy structural attributes. These attributes were then compared to tree species composition data to evaluate whether they were appropriate to differentiate between forest types. Additionally, we tested what canopy attributes derived from DCP and DHP best explained the species composition of vascular plants growing in the understory. We found that hemispherical canopy photography was most suitable to capture differences in forest types, which was best expressed by variables such as leaf inclination angle and canopy openness. On our sites, DHP-based canopy attributes were also able to better distinguish between different conifer forests. Leaf clumping was the most important attribute for determining plant species distribution of the understory, indicating that diverse gap structures create different microclimate conditions enhancing diverse plant species with different ecological strategies. This study supports the reliability of canopy photography to derive meaningful indicators in forest and biodiversity research, but also provide insights for increasing understory diversity in managed forests of high conservation value. © 2025

The dataset reports data collected in 38 square (50 x 50m) 0.25 ha plots representative of poplar plantations in Lombardy Region (Northern Italy), which were used to calibrate optical information derived from unmanned aerial vehicle (UAV) and satellite (Sentinel-2) sensors. In each plot, the diameter at breast height was measured using a caliper; height, stem and crown volume of each tree were then derived from diameter using allometric equations developed in an independent study. Additional canopy attributes (foliage and crown cover, crown porosity, leaf area index) were derived in each plot from 12-20 optical images collected using digital cover photography (DCP). The collected data allows characterizing the assessment of structure of these plantations, along with their variation over the rotation time. Canopy and crown data also enable the evaluation of optimal rotation and tree spacing, as well as the relationship between stand and canopy structure. The raw datasets consist of 2,591 records (trees) associated with inventory measurements and 616 records (images) associated with optical canopy measurements. An R code was also provided to calculate plot-level attributes from raw data. Dataset and associated metadata are freely available at http://dx.doi.org/10.17632/ycr7w5pvkt.1. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.