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 "Bosco Fontana" natural reserve includes the last remaining mixed floodplain forest in northern Italy and one of the most endangered ecosystems in Europe. Its effective management is hindered by the complexity of interactions of mixed-tree species and the influence of environmental factors on understory plant diversity. In this study we analyzed the patterns of natural evolution in semi-natural floodplain forest stands at Bosco Fontana with the aim of better understanding its current natural processes and dynamics. Stand structure, taxonomic and functional diversity, species composition, and leaf area index (LAI) of overstory and understory layers were surveyed in permanent plots over two inventory years (1995, 2005). The influence of environmental factors on understory plant diversity was assessed using Ellenberg’s indices for light, soil moisture, soil nutrient and soil reaction. Results indicated that overstory species composition varies according to the soil moisture, with hornbeam prevailing in xeric sites and deciduous oak species in mesic sites. Xeric sites showed high functional dispersion in both drought and shade tolerant traits, while it was significantly lower in both overstory and understory in the moist site. Functional dispersion of drought tolerance in the overstory and understory layers was positively correlated, while species richness was negatively correlated between the two layers. Diversity in the understory was mainly correlated with soil conditions. Understory LAI was positively correlated with overstory LAI in xeric and mesic plots, while no correlations were found in the moist plot. Overall, our results suggest that site conditions (soil conditions and water availability) are the major drivers of understory and overstory dynamics in the study forest. Hence, local site conditions and the understory should be carefully considered in the management of mixed floodplain forests. © SISEF.

Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA) and to the CIE L^*a^*b^* colour space to obtain estimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography). The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution. We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications. © 2015 Elsevier B.V.

Key message: Foliage clumping can be estimated from logarithm averaging method in LAI-2000. The spatial scaling of clumping effects considered by the instrument is dependent on the sensor’s azimuthal view. Accurate estimates of foliage clumping index (Ω) are required to improve the retrieval of leaf area index (L) from optical instruments like LAI-2000/2200 Plant Canopy Analyzer (PCA) and digital hemispherical photography (DHP). The logarithm averaging method is often used to approximate L because clumping effects are considered at scales larger than the sensor’s field of view. However, the spatial scaling considered for logarithm averaging typically differs between PCA and DHP, resulting in different estimates of foliage clumping. Based on simulation, we demonstrated that applying restricting azimuth view caps (e.g., 45° or 10°) allows reliable estimation of Ω and more accurate estimation of L from PCA. Simulated Ω and L values were comparable to those measured using the PCA, DHP and litter traps. Linear averaging of the gap fractions across readings at a plot or site yields a concurrent estimate of effective leaf area index (L<inf>e</inf>), thus enabling the calculation of L<inf>e</inf>, L, and Ω from a single instrument fitted with view caps. Users need to be aware that the method they use for averaging gap fractions determines whether they are measuring L<inf>e</inf> or L, and PCA users need to be aware that they are applying increasingly large corrections for foliage clumping as they use more restrictive view caps, a fact that they can use to their advantage to improve estimates of L. © 2014, Springer-Verlag Berlin Heidelberg.