Using Cadastral Information to Support Forest Owner Aggregation in Small-Scale and Fragmented Forest Ownerships: A Network Analysis Approach in the Forest Sharing® Platform

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.