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.

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.

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 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.