High-quality pellets are typically produced from coniferous sawdust. However, achieving comparable quality from alternative feedstocks, such as broadleaf wood, often necessitates pre-treatments or additives. Yet, within the framework of small-scale pellet production, local forest enterprises may lack the resources for such treatments and usually produce pellets from the whole trees, including branches, leaves and tops. This can have an impact on the quality of the pellets obtained in this manner. To be classified as high-quality pellets (A1 class), the specific features of the pellet must be higher or fall below the thresholds specified in the EN ISO 17225 standard. In this study, we developed an alternative statistical approach to evaluate pellet quality in comparison to the constant thresholds reported in the technical standard. We applied such an approach to evaluate the quality of pellets produced from the broadleaved species common in the Mediterranean forestry, including European beech (Fagus sylvatica L.), Turkey oak (Quercus cerris L.), Eucalyptus (clone Eucalyptus camaldulensis x C. bicostata), and Poplar clone AF6. In particular, we focused on three variables that are generally the most troublesome for the production of high-quality pellets from the broadleaved species, namely bulk density, ash content, and lower heating value. We found that the beech pellets showed satisfactory bulk density (average effect size of −1.2, with no statistical difference in comparison to the standard’s threshold) and ash content (average effect size of about −5 and significantly lower than the standard’s threshold), but the heating value was significantly lower than the threshold required by the standard (average effect size of about −3). Conversely, other investigated species exhibited notable deficiencies, with turkey oak pellets displaying acceptable heating values. We found a significant improvement in ash content and heating value with increasing stem age within the same species thus suggesting that material derived from thinning interventions might be preferable over coppice-derived biomass for high-quality pellet production. We suggest that future research on the topic should focus on investigating pellets produced from blends of beech and turkey oak biomass. We further recommend a wider application of the proposed statistical approach, considering that it is clear and easy to interpret, and allows for a statistical comparison of the obtained values against the requirements of the technical standard.

The application of modern technologies to increase the overall sustainability of forest operations is known as precision forest harvesting. Precision forest harvesting can be a very powerful tool; however, it requires modern forest machinery, which is expensive. Given that most of the forest operators in the Mediterranean area are small-scale businesses, they do not have the resources to purchase costly equipment; thus, the application of precision forest harvesting is affected. Bearing this in mind, in this study, we aimed to test the accuracy of the GNSS receiver on which an innovative Information and Communication Technology (ICT) system developed to monitor small-scale forest operations is based. We tested the GNSS’s accuracy by comparing the extraction routes recorded during coppicing interventions in two forest sites located in Central Italy with those obtained with a more high-performing GNSS receiver. We also used linear mixed-effects models (LMMs) to investigate the effects on the GNSS positioning error of topographic features, such as the slope, elevation, aspect and Topographic Position Index (TPI). We found that the average positioning error was about 2 m, with a maximum error of about 5 m. The LMMs showed that the investigated topographic features did not significantly affect the positioning error and that the GNSS accuracy was strongly related to the specific study area that we used as a random effect in the model (marginal coefficient of determination was about 0.13 and conditional coefficient of determination grew to about 0.59). As a consequence of the negligible canopy cover after coppicing, the tested GNSS receiver achieved satisfactory results. It could therefore be used as a visualising tool for a pre-planned extraction route network, allowing the operator to follow it on the GNSS receiver screen. However, these results are preliminary and should be further tested in more experimental sites and various operational conditions.

Sustainable forest operations require operational planning tools that effectively integrate productivity, environmental conservation, and social acceptability, particularly within complex and environmentally sensitive forest systems. In Mediterranean small-scale forestry, harvesting decisions are frequently guided by expert judgment rather than by systematic and transparent planning frameworks. This reliance on subjective decision making can result in heterogeneous management practices and, in some cases, suboptimal operational outcomes. This study aims to validate a GIS-based Analytic Hierarchy Process (GIS–AHP) decision support system for the selection of harvesting and wood systems in the chestnut coppices of central Italy and to assess the robustness of its recommendations when expert judgments are provided by different stakeholder groups. The methodology integrates spatial data and multi-criteria analysis to evaluate the suitability of three extraction systems (forwarder, cable skidder, and cable yarder) and three wood systems (Cut-To-Length, Whole-Tree Harvesting, and Tree-Length) across 162 Forest Management Units (1332.5 ha), using weights elicited from four stakeholder categories (researchers, technicians, forest owners, and workers; n = 144). Results show statistically significant differences in mean suitability values among stakeholder groups for all systems; however, convergence at the operational decision level is high. The cable skidder is recommended over 94%–100% of the area depending on the stakeholder category, with full agreement among all groups in 87.7% of the Forest Management Units. For wood systems, Whole-Tree Harvesting is selected over 96.1% of the analysed area, with agreement in 95.1% of the Forest Management Units. Divergences are therefore limited and attributable to differences in AHP weighting structures. Overall, the findings demonstrate that the GIS–AHP approach provides stable and transferable recommendations despite variability in expert perspectives, supporting its applicability as a transparent and robust decision support tool for operational planning in chestnut coppices and similar Mediterranean forest systems.

Accurate detection of machinery-induced strip roads after forest operations is fundamental for assessing soil disturbance and supporting sustainable forest management. However, in Mediterranean pine forests where canopy openings after boom-corridor thinning are moderate, the effectiveness of different remote sensing techniques remains uncertain. Previous studies have shown that LiDAR-based methods can reliably detect logging trails in different forest stands, but their direct transfer to structurally simpler, even-aged Mediterranean stands has not been validated. This study addresses this gap by testing whether UAV-derived RGB imagery can achieve comparable accuracy to LiDAR-based methods under the canopy conditions of boom-corridor thinning. We compared four approaches for detecting strip roads in a black pine (Pinus nigra Arn.) plantation on Mount Amiata (Tuscany, Italy): one based on high-resolution UAV RGB imagery and three based on LiDAR data, namely Hillshading (Hill), Local Relief Model (LRM), and Relative Density Model (RDM). The RDM method was specifically adapted to Mediterranean conditions by redefining its return-density height interval (1–30 cm) to better capture areas of bare soil typical of recently trafficked strip roads. Accuracy was evaluated against a GNSS-derived control map using nine performance metrics and a balanced subsampling framework with bootstrapped confidence intervals and ANOVA-based statistical comparisons. Results confirmed that UAV-RGB imagery provides reliable detection of strip roads under moderate canopy openings (accuracy = 0.64, Kappa = 0.27), while the parameter-tuned RDM achieved the highest accuracy and recall (accuracy = 0.75, Kappa = 0.49). This study demonstrates that RGB-based mapping can serve as a cost-effective solution for operational monitoring, while a properly tuned RDM provides the most robust performance when computational resources are sufficient to work on large point clouds. By adapting the RDM to Mediterranean forest conditions and validating the effectiveness of low-cost UAV-RGB surveys, this study bridges a key methodological gap in post-harvest disturbance mapping, offering forest managers practical, scalable tools to monitor soil impacts and support sustainable mechanized harvesting.