Speaker
Description
Selecting the most appropriate harvesting system is a critical decision in forest operations. In practice, this choice is often based primarily on the experience of forest technicians or logging contractors. Increasingly, however, there is a need for more objective and transparent approaches that can support decisions in complex operational contexts. In recent years, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has emerged as a promising framework for harvesting system selection. This decision is inherently multi-criteria, because operational, topographic, pedological, infrastructural, and stand-related factors all influence system suitability to varying degrees. At the same time, advances in remote sensing and the growing availability of high-resolution spatial datasets, such as Digital Terrain Models (DTM), Digital Surface Models (DSM), and soil trafficability maps, have expanded planning potential beyond the scale of individual stands and toward broader, property-level applications.
Despite this progress, GIS–MCDA planning tools are still not fully integrated into everyday forestry practice, particularly in regions where small-scale and fragmented ownership remains dominant, such as the Italian Apennines. A key challenge in developing a reliable Decision Support System (DSS) for this context is the degree of agreement among stakeholders. Forest technicians, researchers, owners, and logging operators all contribute to harvesting system decisions, but their priorities and perspectives do not always coincide.
To address this issue, we developed a GIS-MCDA-based DSS to identify the most suitable harvesting system at the Forest Management Unit (FMU) level across an entire forest property of approximately 1,000 ha. For each FMU, the DSS selects the most suitable option among three alternatives: forwarder, skidder, and all-terrain cable yarder. The system relies on six criteria that can currently be derived entirely from spatial and remote sensing data, including road network density, distance to the nearest road segment, soil bearing capacity (estimated through soil trafficability mapping), terrain slope and roughness (derived from high-resolution DTM), and harvestable biomass (estimated from DSM data, based on the prescribed silvicultural treatment).
The relative importance of the criteria was determined using the Analytic Hierarchy Process (AHP) through interviews with more than 100 experts representing four stakeholder categories: researchers, forest technicians, forest owners, and logging contractors. The DSS combines spatially explicit criterion values with stakeholder-specific weights to generate suitability maps for each harvesting system. These maps are then overlaid with FMU boundaries to identify the highest-suitability system for each unit. The model was run separately using the weight sets obtained from each stakeholder group, enabling a comparative assessment of agreement and divergence in system selection across the forest supply chain.
Results are currently being finalized and will be presented for the first time at the conference.
| Keywords | GIS; LiDAR; MCDA; DTM |
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