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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 nondestructive 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 estimated 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ecologically and economically sustainable planning of forest resources requires tools capable of providing estimates with adequate accuracy on volume, biomass and woody increments. Interest in these attributes has increased since the United Nations Framework Convention on Climate Change has been given a further boost by the birth of the carbon credit market in the early 2000s. However, the data collection necessary to formulate allometric models for estimating wood volume is challenging, both due to the considerable amount of data required and because the necessary destructive measurements are very laborious. Furthermore, given the great structural, managerial and environmental diversity that characterizes the Italian forests, the sample size for the development of allometric models must necessarily be large. Over the years, all these aspects have led to a progressive abandonment of measurements in the forests for the production of volume tables. Recent applications of the terrestrial laser scanner (TLS) for collecting dimensional information on trees have demonstrated their effectiveness. In this study we present the work carried out in the autumn/winter 2022-2023 for the creation of new volume tables for the black pine forests in Vallombrosa (FI -- Central Italy), based on data collected with a TLS. The study involved the same pine forests studied in 1969 for the production of volume tables in Vallombrosa. After showing the methods and analysis needed to obtain the volume tables, the paper discusses the results in comparison with those produced in 1969. [ABSTRACT FROM AUTHOR]

The forestry sector in Italy is facing issues related to the need to make the most of environmental, territorial and socio-economic opportunities. The research is called upon to translate technological advances into practical applications, even in the field of geomatics and information and communication technologies. The exploitation of precision technologies can foster innovation and the improvement of management processes as well as the development of new products useful for forest owners, entrepreneurs, forest technicians and citizens, with benefits for the quality of forest production, the reduction of production costs and the reduction of environmental and social impacts. This note provides a brief overview of precision technologies applied to forest farm-scale monitoring, silviculture management, logging, poplar farming, forest nursery and forest product traceability. [ABSTRACT FROM AUTHOR]

Forest understory may be strongly affected by silvicultural practices such as thinning, which simultaneously modulates the overstory canopy cover and influences the availability of light. However, the understory layer is rarely considered in management decisions, partly because methods to estimate understory leaf area index are poorly developed. In this study we used two different restricted view angle photographic methods to estimate overstory plant area index LO (zenith cover photography), understory leaf area index LU (nadir cover photography) and their related canopy attributes (foliage clumping, foliage cover, crown cover, crown porosity). These measurements were performed in beech stands under different management regime. Results from photography indicated that not only overstory but also understory canopy attributes were significantly influenced by forest management. In addition, a significant negative correlation was found between LO and LU. We conclude that the photographic methods are effective for monitoring (overstory and understory) canopy status in managed stands, on account of their rapid and not destructive procedures, which allows large scale implementation of the methods.

A spatially explicit approach for stand-scale economic valuation of current and future potential of cork forests with respect to ecosystem production is developed and presented. The approach, which relies in large part on the mensuration of stand top height and number of trees as main drivers, has been tested on the pure cork forests of Sardinia (Italy). The test was conducted to assess the effects of alternative silvicultural options on cork and fodder production, carbon sequestration, and water yield. Under current conditions, the surveyed pure cork oak forest stands in Sardinia are characterized, on average, by an annual economic production of 93 euro ha-1 yr-1 as concerns cork, 37 euro ha-1 yr-1 as concerns carbon sequestration and 261 euro ha-1 yr-1 as concerns water yield. The value of cork production on an 11-year cycle equals 1023 euro ha-1 on average. The total economic production values among the tested silvicultural alternatives have proven to be characterized by relatively small differences, due to the trade-offs among the considered goods and services. Therefore, at least under conditions similar to those surveyed, managers may safely rely on different stand density options, without any relevant detrimental effect on total economic production. The tested spatial visualization of the economic values of goods and services production can be useful in supporting forest management planning, e.g., to identify priority areas in order to maximize ecosystem production for local communities. The approach proposed here and tested to this end proves to be readily applicable to other cork contexts with similar characteristics under Mediterranean conditions. [ABSTRACT FROM AUTHOR]

The AlForLab project, a Public-Private Laboratory which is part of the Cluster MEA (Materials Energy Environment) addressed to the Calabria Region (southern Italy), has gained great benefit by using LiDAR data acquired in the frame of a national remote sensing programme of the Ministry of the Environment and Protection of Land and Sea. This kind of LiDAR data, distributed in raster format and publicly available for research and non-profit purposes, have proved to be a suitable tool to support forest management. Their usage, however, has required the recognition and correction of non-forest elements included in the Digital Surface Model (DSM), like electric powerlines, wind turbines, sub-vertical rocks and viaducts. Such outliers, if remaining into the Canopy height Model (CHM), can generate potential errors in application of LiDAR-based prediction models. This paper proposes some semi-automatic pre-processing procedures, directly applicable on raster data, in order to obtain a CHM without non-forest elements. The methods described here have been developed in open-source environment (R and QGIS). The correction procedures carried out were tested in three municipalities having forest area between 1700 and 5400 ha, and characterized by different types of outliers. The performances of the methods were evaluated by comparing the estimated forest volume obtained before and after their application. Although low total volume changes were observed on the entire study areas (about 0.5%, corresponding to 1500 to 7200 m3), more significant effects, tens to hundreds cubic meters per hectare of overestimation, can occur in stands or forest compartments with a high presence of outliers. In conclusion, the proposed methods have proved to be suitable to achieve a reliable CHM for forest applications. [ABSTRACT FROM AUTHOR]

In this work, a spatial decision support system dedicated to forest management in Calabria (CFOR) is presented. The main aim of the tool is to assess wood production under various harvesting options. All functionalities have been developed by Python and R languages for QGIS. Main inputs are digital models, mainly derived from Lidar, and vector shapefiles. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article., (© 2020 Published by Elsevier Inc.)