Continuous monitoring of forest canopy structure and phenology is pivotal for the assessment of ecosystem responses to environmental variability and changes. The present study evaluated the use of repeat digital trail cameras as a low-cost, flexible, and accessible in situ monitoring solution for quantifying daily canopy attributes, including effective leaf area index (Le) and canopy cover. A trial camera monitoring network (CrowNet) was established encompassing 20 forest stands in Italy, under different management and environmental conditions, resulting in over 44,000 daily images collected over three years. We demonstrated that taking the mean daily canopy attribute allowed to obtain smooth time series from trail cameras, from which phenological transition dates can be inferred. Daily canopy attributes were validated against manual digital cover photography measurement. To further explore the applicability of this monitoring solution, we performed a comparison between daily Le time series derived from a subset of trail cameras located in beech forests and data collected by multitemporal UAV LiDAR. Results demonstrated the close agreement between the two methods across the entire phenological period (start and end of season). We also illustrated use of continuous trail camera estimates to calibrate a vegetation index (NDVI) to infer leaf area and canopy cover from optical multi-temporal UAV data. We further investigated use of trail camera to detect species-specific differences in tree phenology from time series acquired in a mixed oak-hornbeam forest. We found different canopy structure and phenological transition dates in three broadleaved species (oak, ash, hornbeam), supporting the effectiveness of trail cameras for species-oriented phenology monitoring. We conclude that trail cameras provide a reliable solution for daily canopy monitoring, offering a significant cost-effective and flexible alternative to traditional field methods and providing potential to calibrate, validate or integrate remotely-sensed information. However, camera failures during adverse weather, and the need for more efficient image data quality checking procedures, still represent open challenges. Future improvements, such as weatherproof housing and automated pre-processing screening procedures, are therefore recommended for making trail camera fully operational in ground canopy and phenology monitoring. © 2025 Elsevier B.V.

Understanding the spatial heterogeneity of forest structure is crucial for comprehending ecosystem dynamics and promoting sustainable forest management. Unmanned aerial vehicle (UAV) LiDAR technology provides a promising method to capture detailed three-dimensional (3D) information about forest canopies, aiding in management and silvicultural practices. This study investigates the heterogeneity of forest structure in broadleaf forests using UAV LiDAR data, with a particular focus on tree crown features and their different information content compared to diameters. We explored a non-conventionally used method that emphasizes crown competition by employing a nearest neighbor selection technique based on metrics derived from UAV point cloud profiles at the tree level, rather than traditional DBH (diameter at breast height) spatial arrangement. About 300 vegetation elements within 10 plots collected in a managed Beech forest were used as reference data. We demonstrate that crown-based approaches, which are feasible with UAV LiDAR data at a reasonable cost and time, significantly enhances the understanding of forest heterogeneity, adding new information content for managers. Our findings underscore the utility of UAV LiDAR in characterizing the complexity and variability of forest structure at high resolution, offering valuable insights for carbon accounting and sustainable forest management. © 2024 by the authors.

Accurate estimates of aboveground biomass (AGB) are essential for forest policies to reduce carbon emissions. Unmanned aerial laser scanning (UAV-LS) offers unprecedented millimetric detail but is underutilized in monitoring broadleaf Mediterranean forests compared to coniferous ones. This study aims to design and evaluate a procedure for AGB estimates based on the predictive power of crown features. In the first step, we manually created Quantitative Structure Models (QSMs) for 320 trees using data from UAV laser scanning (UAV-LS), airborne laser scanning (ALS), and co-registered terrestrial laser scanning (TLS). This provided the most accurate non-destructive estimate of aboveground biomass (AGB) in the absence of destructive measurements. For each reference tree we also measured crown projection and crown volume to build two separated models relating AGB to such crown features. In the second phase, we evaluated the potential of UAV-LS for quantifying AGB in a pure European beech (Fagus sylvatica) forest and compared it with traditional ALS estimates, using fully automatic procedures. The two obtained tree-level AGB models were then tested using three datasets derived from 35 sampling plots over the same study area: (a) 1130 trees manually segmented (phase-2 reference); (b) trees automatically extracted from ALS data; and (c) trees automatically extracted from UAV-LS data. Results demonstrate that detailed UAV-LS data improve model sensitivity compared to ALS data (RMSE = 45.6 Mg ha⁻¹, RMSE% = 13.4%, R2 = 0.65, for the best ALS model; RMSE = 44.0 Mg ha⁻¹, RMSE% = 12.9%, R2 = 0.67, for the best UAV-LS model), allowing for the detection of AGB differences even in quite homogenous forest structures. Overall, this study demonstrates the combined use of both laser scanner data can foster non-destructive and more precise AGB estimation than the use of only one, in forested areas across hectare scales (1 to 100 ha). © 2025 by the authors.

Taper equations are indispensable tools for characterizing the stem profile of trees, providing valuable insights for forest management, timber inventory, and optimal assortments allocation. The recent progress in Terrestrial Laser Scanning (TLS) has revolutionized forest inventory practices by enabling non-destructive data collection. In this study, four taper models from three different model categories were established based on point cloud data of 219 Pinus nigra trees. The taper equations fitted with TLS data were used to predict the diameter at specific stem heights and the total stem volume. The results show that among fitted models, the Max and Burkhart segmented model calibrated by the means of a mixed-effects approach provided the best estimate of the diameter at different heights and the total stem volume evaluated for different diameter at breast height (DBH) classes. In numerical terms, this model es-timated the diameter and the volume with a respective overall error of 0.781 cm and 0.021 m³. The predicted profile also shows that above a relative height of 0.7, the diameter error tends to increase due to the low reliability of data collected beyond the base of the crown primarily caused by interference from branches and leaves. Nevertheless, this study shows that TLS technology presents a compelling opportunity and a promising non-destructive alternative for generating taper profiles and estimating tree volume. © SISEF.

The distribution of species is primarily driven by the availability of trophic resources. In a given forest type, insects trophically related to the dominant tree are expected to be evenly distributed due to the abundance of their foodplant. However, their distribution is also influenced by complex relationships with abiotic and biotic parameters such as available space, predatory pressure, and morphometric traits. In this study, we investigated how the three-dimensional structure of space below the canopy may affect the composition of nocturnal lepidoptera communities. To synthesise the complexity of the dispersal behaviour of these insects, we evaluated easily measurable traits such as wingspan and the presence of tympanic organs, both connected to their mobility and thus potentially influenced by the structure of the available flight space. The study was conducted in the Sila National Park (Italy), where 12 sampling sites were selected in pine forests and an additional 12 in beech forests. Forest spatial structure was investigated using a portable terrestrial laser scanner. Moths were sampled monthly using light traps from May to October in both 2019 and 2020. Among measured forest traits, we observed that the space above three meters from the ground is the only factor influencing community composition. Larger species with tympanic organs prefer environments with less space below tree canopies. Our findings could be the starting point for future studies that investigate a potential defence strategy of moths against bats, as tympanate and larger species not only actively avoid chiropter predation but could also choose denser forests because of a lower bat activity. Moths' distribution and community composition thus appear to be significantly shaped by the spatial structure of forests. © 2024 Elsevier B.V.

Accurate co-registration of terrestrial and aerial point clouds can provide a high-resolution description of tree components across large forest areas. However, a semi-automatic approach for co-registering point clouds is still needed, given the challenges in geospatial data processing, particularly in complex topographical conditions. The main objective of this study is to present the application of a novel procedure for the co-registration of point clouds obtained from terrestrial and UAV surveys in Mediterranean forests. The proposed methodology proves to be promising and will constitute the basis for experimentation on a larger scale. © 2024 Istituto Sperimentale per la Selvicoltura. All rights reserved.

The understory is an essential ecological and structural component of forest ecosystems. The lack of efficient, accurate, and objective methods for evaluating and quantifying the spatial spread of understory characteristics over large areas is a challenge for forest planning and management, with specific regard to biodiversity and habitat governance. In this study, we used terrestrial and airborne laser scanning (TLS and ALS) data to characterize understory in a European beech and black pine forest in Italy. First, we linked understory structural features derived from traditional field measurements with TLS metrics, then, we related such metrics to the ones derived from ALS. Results indicate that (i) the upper understory density (5–10 m above ground) is significantly associated with two ALS metrics, specifically the mean height of points belonging to the lower third of the ALS point cloud within the voxel (HM<inf>1/3</inf>) and the corresponding standard deviation (SD<inf>1/3</inf>), while (ii) for the lower understory layer (2–5 m above ground), the most related metric is HM<inf>1/3</inf> alone. As an example application, we have produced a map of forest understory for each layer, extending over the entire study region covered by ALS data, based on the developed spatial prediction models. With this study, we also demonstrated the power of hand-held mobile-TLS as a fast and high-resolution tool for measuring forest structural attributes and obtaining relevant ecological data. © 2023 by the authors.