Tommaso Tognetti
Tommaso Tognetti has a total of 23 co-authors. Here are the top co-authors by number of shared publications:
- Francesca Giannetti
Department of Agriculture, Food, Environment and Forestry, University of Florence, 50145 Florence, Italy
3 shared publications - Hervè Atsè Corti
Bluebiloba Startup Innovativa SRL, 50126 Florence, Italy
3 shared publications - Ilaria Incollu
Department of Agriculture, Food, Environment and Forestry, University of Florence, 50145 Florence, Italy
3 shared publications - Yamuna Giambastiani
Bluebiloba Startup Innovativa s.r.l., Via C. Salutati 78, 50126 Florence, Italy
3 shared publications - Alessandro Errico
Bluebiloba Startup Innovativa s.r.l., Via C. Salutati 78, 50126 Florence, Italy
2 shared publications - Andrea Laschi
Department of Agricultural, Food and Forest Sciences—SAAF, University of Palermo, Viale Delle Scienze ed 4, 90128 Palermo, Italy
2 shared publications
Latest Publications
- Open Access
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.
Open AccessUsing 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.
- Open Access
- 2 July 2026
- Published by MDPI AG in Preprints.org
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 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.
- 2 July 2026
- Published by MDPI AG in Preprints.org
Open AccessUsing 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 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.
- Open Access
Preliminary Metrological Characterization of Low-Cost MEMS Inclinometer for Tree Stability Assessment: From Laboratory to Field
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., , RMSE –, Deming slope –). 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.Open AccessPreliminary Metrological Characterization of Low-Cost MEMS Inclinometer for Tree Stability Assessment: From Laboratory to Field
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., , RMSE –, Deming slope –). 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.