Windstorms are a major disturbance factor for European forests. The 2018 Vaia storm, felled large volumes of timber in Italy causing serious ecological and financial losses. Remote sensing is fundamental for primary assessment of damages and post‐emergency phase. An explicit estimation of the timber loss caused by Vaia using satellite remote sensing was not yet undertaken. In this investigation, three different estimates of timber loss were compared in two study sites in the Alpine area: pre‐existing local growing stock volume maps based on lidar data, a recent national‐level forest volume map, and an novel estimation of AGB values based on active and passive remote sensing. The compared datasets resemble the type of information that a forest manager might potentially find or produce. The results show a significant disagreement in the different biomass estimates, related to the methods used to produce them, the study areas characteristics, and the size of the damaged areas. These sources of uncertainty highlight the difficulty of estimating timber loss, unless a unified national or regional European strategy to improve preparedness to forest hazards is defined. Considering the frequent impacts on forest resources that occurred in the last years in the European Union, remote sensing‐based surveys targeting forests is urgent, particularly for the many European countries that still lack reliable forest stocks data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Vegetation structure is one of the key factors in forest ecosystems. Especially understory structure has major implications for wildlife habitat selection, reproduction, and survival. Structural indices traditionally used to characterize understory vegetation are based on field vegetation surveys describing general features such as leaf area index (LAI), canopy cover or vegetation height, hiding much of the three-dimensional vegetation structure complexity. The application of terrestrial laser scanning (TLS) in forest ecological and management applications is becoming more effective. In this study, we use TLS data to quantify spatial attributes of forest subcanopy in four different forest strata ranging from 0.5 m to 10 m from the ground. We collected data in 12 plots of mature European beech (Fagus sylvatica L.) forests and 12 plots of mature black pine (Pinus nigra subsp. laricio Maire) forests, located in the Sila National Park, Italy. We propose a TLS-based approach to estimate a fine-scale vegetation density using the Plant Density Index (PDI) and to test the PDI at different height classes. We found a significant relationship between the PDI and the number of trees belonging to the dominant layer, using the Spearman correlation coefficient (r = 0.83, p<inf>val</inf> = 0.001). Basing on PDI values, a cluster analysis of the four subcanopy strata was carried out for deriving clusters of structurally homogeneous forest plots. Results identified three clusters in terms of the vegetation features in the horizontal height classes: the first cluster primarily includes Beech forests characterized by plots with the highest tree densities; the second one includes both Beech and Pine forests characterized by dense ground vegetation and shrubs and an intermediate tree density; the third group is represented by Pine forests with massive presence of vegetation lower strata and moderate tree density. Then, PCA allowed identifying the relationship between the considered subcanopy layers and forest plots. © 2021 Elsevier B.V.

Vegetation structure is a key determinant of species distribution and diversity. Compared to traditional methods, the use of Terrestrial Laser Scanning (TLS) has allowed massive amounts of point cloud data collected for quantifying three-dimensional habitat properties at increasing spatial and temporal scales. We used TLS to characterize the forest plots across a broad range of forest structural diversity, located in the Sila National Park, South Italy. The dataset reports data collected in 24 15-m-radius circular plots, 12 of which were dominated by beech (Fagus sylvatica L.) and 12, by black pine (Pinus nigra subsp. laricio). In detail, this work provides dataset of i) plot-level attributes calculated from raw data, such as the number of trees, ii) tree-level data, comprising a total of 1709 trees, with information related to field-based forest inventory such as the diameter at breast height (DBH), and iii) plot-level information related to the time for conducting both traditional field- and TLS-based forest inventories. Compared to traditional methods, the use of TLS allows a very high-resolution quantification of the 3D forest structural properties, also reducing the time for conducting forest inventories. © 2020

Accurate estimates of crown cover (CC) are central for a wide range of forestry studies. As direct measurements do not exist to retrieve this variable in the field, CC is conventionally determined from optical measurements as the complement of gap fraction close to the zenith. As an alternative to passive optical measurements, active sensors like terrestrial Light Detection And Ranging (LiDAR) allows for characterizing the 3D canopy structure with unprecedented detail. We evaluated the reliability of terrestrial LiDAR (TLS) to estimate CC using a voxel-based approach. Specifically, we tested how different voxel sizes (ranging from 5-20 cm) and voxel densities (1-9 points/dm³) influenced the retrieval of CC. Results were compared against benchmark values obtained from digital cover photography (DCP). The trial was performed in hybrid poplar plantations in Northern Italy. Results indicate that TLS can be used for obtaining accurate estimates of CC, but the choice of voxel size and point density is critical for achieving such accuracy. In hybrid poplars, the best performance was obtained using voxel size of 10 cm and point density of 8 points/dm³. The combined ability of measuring and mapping CC also holds great potential to use TLS for calibrating and upscaling results using coarser-scale remotely sensed products. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

Accurate estimates of canopy cover (CC) are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used to estimate CC from the complement of gap fraction measurements obtained with restricted-view sensors. In this short note we evaluated the influence of the image pixel resolution (ground sampling distance; GSD) on CC estimation in poplar plantations obtained from field (cover photography; GSD < 1 cm), unmanned aerial (UAV; GSD <10 cm) and satellite (Sentinel-2; GSD = 10 m) imagery. The trial was conducted in poplar tree plantations in Northern Italy, with varying age and canopy cover. Results indicated that the coarser resolution available from satellite data is suitable to obtain estimates of canopy cover, as compared with field measurements obtained from cover photography; therefore, S2 is recommended for larger scale monitoring and routine assessment of canopy cover in poplar plantations. The higher resolution of UAV compared with Sentinel-2 allows finer assessment of canopy structure, which could also be used for calibrating metrics obtained from coarser-scale remote sensing products, avoiding the need of ground measurements. © 2021 Centro di Ricerca per la Selvicoltura, Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria. All rights reserved.

Although coppice forests represent a significant part of the European forest area, especially across southern Countries, they received little attention within the Sustainable Forest Management (SFM) processes and scenarios, whose guidelines have been mainly designed to high forests and national scale. In order to obtain “tailored” information on the degree of sustainability of coppices on the scale of the stand, we evaluated (i) whether the main coppice management options result in different responses of the SFM indicators, and (ii) the degree to which the considered SFM indicators were appropriate in their application at stand level. The study considered three different management options (Traditional Coppice TC, coppice under Natural Evolution NE, and coppice under Conversion to high forest by means of periodical thinning CO). In each of the 43 plots considered in the study, which covered three different European Forest Types, we applied a set of eighteen “consolidated” SFM indicators, covering all the six SFM Criteria (FOREST EUROPE, 2020) and, additionally, tested other sixteen novel indicators shaped for agamic forests and/or applicable at stand level. Results confirmed that several consolidated indicators related to resources status (Growing stock and Carbon stock), health (Defoliation and Forest damage), and socio-economic functions (Net revenue, Energy and Accessibility) were highly appropriate for evaluating the sustainability of coppice at stand level. In addition, some novel indicators related to resources status (Total above ground tree biomass), health (Stand growth) and protective functions (Overstorey cover and Understorey cover) proved to be highly appropriate and able to support the information obtained by the consolidated ones. As a consequence, a subset of consolidated SFM indicators, complemented with the most appropriate novel ones, may represent a valid option to support the evaluation of coppice sustainability at stand level. An integrated analysis of the SFM indicators showed that NE and CO display significant higher environmental performances as compared with TC. In addition, CO has positive effects also on socio-economic issues, while TC -which is an important cultural heritage and a silvicultural option that may help to keep local communities engaged in forestry – combines high wood harvesting rates with dense understory cover. Overall, each of the three management options showed specific sustainability values; as a consequence, their coexistence at a local scale and in accordance with the specific environmental conditions and the social-economic context, is greatly recommended since it may fulfill a wider array of sustainability issues. © 2021

Assessing forest canopy dynamics is crucial for understanding the response of vegetation to environmental variability and change. While digital repeat photography is gaining increased attention for obtaining field phenology observations, colour indices derived from this method are often affected by leaf colour and actual canopy structure, complicating the physical interpretation of results. In addition, repeated photography requires power, storage capacity and remote data transfer, which are often limited in forest conditions. As an alternative, we tested a simple, cheap and fast solution to derive daily canopy structure observation from digital camera traps (CTs). Formerly deployed for wildlife monitoring, CTs are low-cost digital cameras designed for outdoor conditions and have low battery consumption, enable repeat acquisition, and often feature remote data transfer protocols. The trial was performed in a deciduous oak stand, where continuous images were acquired over a 1-year period using the time-lapse feature of the CT. Daily time series of canopy structure attributes were derived from the collected images using simple and automated procedures. Results were validated against reference manual cover photography measurements. The daily time series of foliage cover and leaf area index were then used to derive phenological transition dates, which were compared against phenological observations obtained from satellite Sentinel-2 data. Results indicated that field and satellite data provided comparable accuracy in determining the start of season (SOS). Larger discrepancies were found in determining the end of season (EOS), which can be attributed to the low number of good quality autumn images available from the satellite data. We concluded that CT is a robust method, which is ideally suited for routine, continuous field monitoring of canopy attributes and phenology. While this method can be used for evaluating remote sensing observations, the combination of CTs with satellite data holds great potential for greater spatiotemporal coverage, from field to landscape scales. © 2021

Accurate estimates of seed production are central for understanding mast seeding mechanisms at tree and forest scales, and for designing sustainable management strategies. As trees are long-lived organisms, a long-term perspective is required to understand how reproduction acts during the life cycle of a tree. However, long-term series of seed production are challenging to obtain, as the available seed count procedures strictly rely on field methods, which are cost- and time-consuming, inherently limiting their widespread use at extensive spatial and temporal scales. In this study, we proposed a simple, rapid and flexible field method based on counting the seed in mobile ground quadrats (GQ), which was tested in beech forests. Quadrat measurements were first validated against reference measurements obtained from litter traps (LT) in three permanent plots. Results indicated that GQ provides robust and reliable estimates of seeds, which are not affected by seed predation occurring at the forest floor. Additional quadrat measurements were performed to evaluate the influence of sampling schemes (random, regular, systematic) on the estimation of mean seed production at the plot scale. One hundred quadrats were collected in 0.25 ha beech plots and considered as a reference for evaluating the different sampling schemes and sampling sizes. Measurements were performed in October (three plots), which represented the peak of seed fall, and November (two plots). Results indicate that about 25 randomly located measurements allowed to characterize plot-level mean seed production with an acceptable error below 20%, regardless of the different mean seed production observed between the studied plots and the sampling periods. If the 25 sampling points are arranged in a grid, the obtained mean estimates are within the confidence interval of the reference plot-level values. © 2021 Elsevier B.V.

• Key message: Litter trap is considered the most effective method to quantify seed production, but it is expensive and time-consuming. Counting fallen seeds using a quadrat placed on the ground yields comparable estimates to the litter traps. Ground quadrat estimates derived from either visual counting in the field or image counting from quadrat photographs are comparable, with the latter being also robust in terms of user sensitivity. • Context: Accurate estimates of forest seed production are central for a wide range of ecological studies. As reference methods such as litter traps (LT) are cost- and time-consuming, there is a need of fast, reliable, and low-cost tools to quantify this variable in the field. • Aims: To test two indirect methods, which consist of counting the seeds fallen in quadrats. • Methods: The trial was performed in three broadleaved (beech, chestnut, and Turkey oak) tree species. Seeds are either manually counted in quadrats placed at the ground (GQ) or from images acquired in the same quadrats (IQ) and then compared against LT measurements. • Results: GQ and IQ provide fast and reliable estimates of seeds in both oak and chestnut. In particular, IQ is robust in terms of user sensitivity and potentially enables automation in the process of seed monitoring. A null-mast year in beech hindered validation of quadrats in beech. • Conclusion: Quadrat counting is a powerful tool to estimate forest seed production. We recommend using quadrats and LT to cross-calibrate the two methods in case of estimating seed biomass. Quadrats could then be used more routinely on account of their faster and simpler procedure to obtain measurements at more spatially extensive scales. © 2021, The Author(s).

Masting is a complex mechanism which is mainly driven by a combination of internal plant resources and climatic conditions. While the driving role of climate in masting is being intensively studied, the interplay among climate, seed production, vegetation growth and phenology still needs further investigation. The objectives of this study were to identify the climatic determinants of different levels of seed production and of NDVI-based vegetation growth and phenology in European beech, and to evaluate if exists a trade-off between these two plant processes. To answer these questions, we used a 25-year-long dataset of beech seed production. We exploited the concept of ecological niche assuming that a mast year can be modeled like a species with variable preferences for different resources, which are the underlying annual climatic conditions; we performed an Ecological Niche Factor Analysis (ENFA), a presence-only modeling tool conventionally used in zoology and botany, and used seasonal (spring, summer, autumn) Standardized Precipitation-Evaporation Index (SPEI) observations, considering the current year (y−0), and up to one (y−1) and two (y−2) years before the masting event. For analyzing the role of vegetation growth and phenology, we used seasonal Normalized Difference Vegetation Index (NDVI) values and associated NDVI-based phenological metrics derived from Landsat imagery. Results indicated the driving role of climate for masting, especially in VHSP years. A moist summer and dry spring at y−2 and a dry summer at y−1 represented the main driving climatic conditions for masting; while a moist spring during the observation year represented the key condition for triggering higher intensities of seed production. Summer NDVI at y−0 and y−1 represented the variables discriminating best between masting and non-masting years and resulted as driven by opposite summer climatic conditions than seed production, thus indicating a trade-off between seed production and vegetation phenology. We concluded that reproduction and vegetation growth act as two different climate-dependent plant responses in beech, in a way that certain conditions through the years promote mast seeding and the opposite conditions favor vegetation growth. The understanding of climate-growth-masting relationships represents indispensable knowledge for providing a holistic view of masting mechanisms and developing adaptive forest management strategies in this species. © 2020