Integrating Remote Sensing and Ground-Based Inventories to Map and Validate Old-Growthness across Italian Forests

Mature and Old-Growth-like Forests (MOGF) are critical for biodiversity conservation and climate regulation, yet they remain rare and fragmented across Europe. Th is study aims to ecologically validate a highresolution (30 m) predictive map of MOGF probability across Italy by integrating it with independent groundbased data from the 2015 National Forest Inventory (INFC2015). Utilizing a multi-source approach, we analyzed 6993 inventory sampling plots across diverse forest types and environmental gradients. Statistical analysis confirmed that higher MOGF probabilities strongly correlate with indicators of advanced stand development, including older age classes (> 120 years), multilayered vertical structures, and high canopy cover (> 80%). MOGF probability is also signifi cantly higher within strictly protected areas (e.g., National Park Zone A) and topographically remote sites characterized by high altitudes and steep slopes. Quantitative correlations also link MOGF probability to an increase in above-ground biomass and deadwood. While the study successfully validates the spatial patterns of forest maturity, limitations include the current lack of national-scale 3D LiDAR data for deadwood modeling and the lack of microhabitat indicators in the INFC2015. However, the anticipated inclusion of these metrics in future national frameworks will further refi ne the monitoring of these ecosystems.