Urban trees are subjected to stressful conditions caused by anthropogenic, biotic, and abiotic factors. These stressors can cause structural changes, increasing the risks of branch failure or even complete uprooting. To mitigate the risks to people’s safety, administrators must assess and evaluate the health and structural stability of trees. Risk analysis typically takes into account environmental vulnerability and tree characteristics, assessed at a specific point in time. However, although dynamic tests play a crucial role in risk assessment in urban environments, the high cost of the sensors significantly limits their widespread application across large tree populations. For this reason, the present study aims to evaluate the effectiveness of low-cost sensors in monitoring tree dynamics. A low-cost micro-electro-mechanical systems (MEMS) sensor is tested in the laboratory and the field using a pull-and-release test, and its performance is compared with that of seismic reference accelerometers. The collected data are analyzed and compared in terms of both the frequency and time domains. To obtain reliable measurements, the accelerations must be generated by substantial dynamic excitations, such as high wind events or abrupt changes in loading conditions. The results show that the MEMS sensor has lower accuracy and higher noise compared to the seismic sensor; however, the MEMS can still identify the main peaks in the frequency domain compared to the seismic sensor, provided that the input amplitude is sufficiently high.

Urban trees and forests provide essential ecosystem services, including carbon sequestration and climate regulation, but their capacity to deliver these benefits can be compromised by increasing disturbances associated with climate change. Tree stability assessment is therefore a key component of adaptive forest and urban green infrastructure management. Visual Tree Assessment (VTA) is typically the first step in risk analysis and is sometimes complemented by instrumental methods such as dynamic and static tests. Static pulling tests provide quantitative information on anchorage and mechanical stability, but their cost and logistical complexity generally limit their application to site-specific investigations.This study, carried out within the TREESURE project, evaluates the performance of a low-cost Micro-Electro-Mechanical Systems (MEMS) inclinometer for static tree tilt monitoring, with the aim of assessing its suitability for wider and longer-term deployments in support of resilience-oriented tree management. The approach combines a laboratory calibration against a geometric reference with field comparisons against a professional high-precision inclinometer commonly used in static pulling tests. In the laboratory, using a calibrated tilting beam and a 120 s averaging window, the MEMS sensor exhibited absolute errors on the order of a few hundredths of a degree, with maximum deviations of approximately 0.015°. In field conditions, comparisons were performed in the relative domain (baseline defined on the first stable plateau) along the longitudinal component, showing high concordance with the reference inclinometer.The results demonstrate that low-cost MEMS inclinometers can provide reliable measurements for static tree tilt monitoring. Owing to their battery-powered wireless operation and simplified data processing, such sensors offer potential for scalable and continuous monitoring of tree stability. This capability may support proactive management strategies aimed at reducing storm-related tree failures, enhancing tree longevity, and indirectly contributing to the preservation of forest and urban tree carbon stocks under increasing climate-induced disturbance regimes.

Continuous cover forest management (CCFM) plays a key role in sustaining forest multifunctionality by reconciling timber production with long-term carbon sequestration, biodiversity conservation, and landscape continuity. It represents a cornerstone of the closer-to-nature approach promoted by the European Forest Strategy for 2030 and related EU Guidelines, providing science-based solutions to enhance forest resilience to climate change while supporting bioeconomy and rural development. As forests are increasingly expected to contribute to climate change mitigation, they simultaneously face growing pressures from climatic extremes and environmental stressors, making the evaluation of management practices under changing climate conditions a critical research priority.In this study, carried out in the context of the EU project SMURF, we analyze the long-term application of group selection cutting within irregular black pine (Pinus nigra J.F. Arnold) stands of the Sila Plateau (Calabria, Southern Italy), a traditional silvicultural system widely adopted by private forest owners. Using ecosystem service modelling approaches, we assess variations in gross primary productivity (GPP) and net primary productivity (NPP) in relation to gap size, regeneration patterns, and climate parameters, with the aim of evaluating the effectiveness of this CCFM practice in supporting climate change mitigation.Results highlight how gap size variability allows the pursuit of multiple management objectives, ranging from the conservation of pure black pine stands—of high value for historical landscapes and habitat preservation—to the promotion of broadleaved species recruitment, which may enhance ecosystem resilience and wildfire resistance under future climate scenarios. Regeneration dynamics within gaps are analyzed to define operational silvicultural parameters that support successful natural regeneration while maintaining stable carbon sequestration rates. Overall, the study demonstrates that group selection cutting can effectively balance climate mitigation potential with economic income, contributing to science-based forest management strategies, forest resilience and adaptive capacity in Mediterranean mountain environments.

Forest ownership fragmentation represents a major constraint to sustainable forest management in many European countries, particularly in Italy, where private forests are often divided into small and spatially dispersed holdings. This study evaluates the potential of cadastral data to support forest-owner aggregation through graph-based spatial network analysis implemented within the Forest Sharing^® platform. Its novelty lies in the operational integration of voluntary cadastral parcel data with a comparative network framework that quantifies how parcel representation and spatial thresholds alter the identification of candidate multi-owner management units. The analysis included 29,549 cadastral forest parcels voluntarily registered by 910 private forest owners across Italy. Parcels were treated as network nodes, and three spatial relationship models were compared: strict topological adjacency, minimum polygon-to-polygon distance, and distance between internal representative points. Distance-based networks were evaluated at 50-m increments from 50 to 1000 m, while cluster eligibility was defined by a minimum area of 30 ha and at least two owners. Strict adjacency identified six eligible clusters covering 465.7 ha. The minimum polygon-distance method identified 31 eligible clusters and 7297.2 ha at 100 m, increasing to 44 clusters and 14,204.8 ha at 1000 m. The point-on-surface method was substantially more conservative at short thresholds but progressively converged with the polygon method; edge recall increased from 0.479 at 100 m to 0.916 at 1000 m, while precision remained equal to 1.0. Median ownership complexity remained limited to two or three owners per eligible cluster, although the largest components included substantially more owners at high thresholds. The results demonstrate that parcel geometry and threshold selection materially influence estimated aggregation opportunities and support the use of polygon-based proximity combined with sensitivity analysis as a transparent screening framework for collective forest management.

Urban trees provide important benefits but can also pose safety risks when stability is reduced. Visual Tree Assessment (VTA) is typically the first step in risk analysis and is sometimes complemented by instrumental methods such as dynamic and static tests. Static pulling tests provide quantitative information on anchorage, but their cost and logistics limit use to site-specific applications. This study evaluates a low-cost Micro-Electro-Mechanical Systems (MEMS) inclinometer for quasi-static inclination measurements during a static pulling test, combining a laboratory calibration against a geometric reference with field comparisons against a professional high-precision inclinometer commonly used in static pulling tests. In the laboratory, using a calibrated tilting beam and a 120 s averaging window, the MEMS sensor yielded absolute errors on the order of a few hundredths of a degree (up to ≈0.015°) compared to the geometric expectation. In the field, comparisons were performed in the relative domain (baseline on the first stable plateau) along the longitudinal component, showing high concordance with the reference high-precision inclinometer commonly used in arboricultural pulling tests (e.g., $r\ge 0.99$, RMSE $\approx 0.04$–$0.07°$, Deming slope $\approx 1.02$–$1.05$). These results support the feasibility of low-cost MEMS for static tilt assessment. Given battery-powered wireless operation and simple processing, they indicate a potential for wider deployments in repeated or scheduled quasi-static assessments (e.g., during controlled pulling tests), complementing professional instrumentation.

Forest ownership fragmentation represents a major constraint to sustainable forest management in many European countries, particularly in Italy, where private forests are often divided into small and spatially dispersed holdings. This study evaluates the potential of cadastral data to support forest owner aggregation through a graph-based spatial network analysis implemented within the Forest Sharing® platform. The analysis was conducted on 29,549 cadastral forest parcels voluntarily registered by 910 private forest owners across Italy. Parcels were represented as nodes in a spatial network and connected using four aggregation-distance thresholds (100, 250, 500, and 1000 m). Connected components were interpreted as potential forest management clusters. Only clusters including at least 30 ha of forest area and a minimum of two owners were retained. The proposed methodology successfully identified aggregable forest management units under all scenarios. Increasing aggregation distances substantially increased both the number of eligible clusters and the total forest area available for collective management. The number of clusters increased from 5 at the 100 m threshold to 48 at 500 m, while the aggregable forest area increased from approximately 700 ha to more than 12,000 ha at the 1000 m threshold. Most clusters involve two and five owners, indicating that substantial aggregation opportunities can be achieved without excessive ownership complexity. The study highlights the potential of cadastral information and spatial network analysis to support forest owner associations, collective management initiatives, and forest policy measures aimed at overcoming ownership fragmentation.