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.

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.

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.

Accurate and frequently updated tree volume estimates are required for poplar plantations, which are characterized by fast growth rate and short rotation. In this study, we tested the potential of terrestrial laser scanning (TLS) as a reliable method for developing nondestructive tree volume allometries in poplar plantations. The trial was conducted in Italy, where 4- to 10-year-old hybrid plantations were sampled to develop tree crown volume allometry in leaf-on conditions, tree stem volume, and height-diameter allometries in leaf-off conditions. We tested one-entry models based on diameter and two-entry models based on both diameter and height. Model performance was assessed by residual analysis. Results indicate that TLS can provide accurate models of tree stem and crown volume, with percentage of root-mean-square error of about 20 percent and 15 percent, respectively. The inclusion of height does not bring relevant improvement in the models, so that only diameter can be used to predict tree stem and crown volume. The TLS-measured stem volume estimates agreed with an available formula derived from harvesting. We concluded that TLS is a reliable method for developing nondestructive volume allometries in poplar plantations and holds great potential to enhance conventional tree inventory and monitoring. © The Author(s) 2020. Published by Oxford University Press on behalf of the Society of American Foresters. All rights reserved.

Mast seeding, the variable and intermittent production of seeds, has cascading effects on ecosystem functioning. This study explores its influence on the brown bear populations in the Italian Alps, focusing on beechnuts (Fagus sylvatica L.), the primary food source for bears in the region. Using historical data and field sampling, we estimated and mapped the annual seed biomass from 2007 to 2021 for the province of Trento. The energy content of beechnuts was assessed through high heating values, providing the caloric resources available. Data on beechnuts production, records of damages and GPS data from 16 Eurasian brown bears were integrated to perform a temporal and spatial analysis at home range and at landscape level. Standardised damages to beehives and livestock decreased during mast years, suggesting that bears met their trophic needs through natural food sources. In fact, bears used more agricultural areas and less beech forest during years of beech crop failure. At landscape level, agriculture and pasture areas close to beech forests and distant from cities showed a higher risk of damage, providing a tool to anticipate management actions. This work provides insights on the ecological dynamics and conservation implications of brown bears in the study area by mapping the spatial and temporal aspects of mast seeding and bear-related damages. © 2025 The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley & Sons Ltd.

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).

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.

Key message: We provided long-term stand and canopy structural data from permanent monitoring plots representative of some most diffuse temperate and Mediterranean forests, under different coppice management regimes. Periodic inventories were performed in the surveyed plots since the 1970s. Annual litterfall production and its partitioning (leaf, woody, reproductive parts) and optical canopy measurements using the LAI-2000 Plant Canopy Analyzer were performed every year in fully equipped plots since the 1990s. These data can be used for evaluating the influence of coppice management in the stand and canopy structure, the parametrization of radiative transfer models that require accurate ground truth data, and the calibration of high to medium resolution remotely sensed data. Dataset access is at https://doi.org/10.17632/z8zm3ytkcx.2. Associated metadata is available at https://agroenvgeo.data.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/2bd2d77f-3cf8-43da-b1b5-9f8196dc017f . © 2019, INRA and Springer-Verlag France SAS, part of Springer Nature.

Monitoring vegetation structure and functioning is critical for modelling terrestrial ecosystems and energy cycles. Leaf area index (LAI) is an important structural property of vegetation used in many land-surface, climate, and forest monitoring applications. Remote sensing provides a unique way to obtain estimates of leaf area index at spatially extensive areas. However, the analysis and extraction of quantitative information from remotely-sensed data require accurate cross-calibration with in situ forest measurements, which are generally spatially-and temporally-limited, thereby limiting the ability to compare the seasonal dynamic patterns between field and remotely-sensed time series. This is particularly relevant in temperate broadleaved forests, which are characterized by high level of complexity, which can complicate the retrieval of vegetation attributes from remotely-sensed data. In this study, we performed a long-term comparison of MODIS LAI products with continuous in situ leaf area index measurements collected monthly in temperate and Mediterranean forests from 2000 to 2016. Results indicated that LAI showed a good correlation between satellite and ground data for most of the stands, and the pattern in seasonal changes were highly overlapping between the time-series. We conclude that MODIS LAI data are suitable for phenological application and for up-scaling LAI from the stand level to larger scales. © 2019, Italian Society of Remote Sensing. All rights reserved.