Multisensor Modeling of Old-Growthness in Mediterranean Pine Forests: New Insights from High-Density Airborne Laser Scanning and Satellite Integration

Old-growth forests (OGFs) are conservation priorities under the EU Biodiversity Strategy for 2030, but identifying relict stands in Mediterranean Pinus nigra (black pine) forests is challenging due to structural complexity. We evaluated a multisensor remote sensing framework to estimate an Old-Growth Index (OGI) in Cazorla Natural Park (Spain) using high-density Airborne Laser Scanning (ALS) data from the Spanish National Plan for Aerial Orthophotography (PNOA-LiDAR, 2024), RGB–NIR orthophotographs, and multispectral imagery (Sentinel-2 and WorldView-3). Four predictor sets (ALS metrics only; ALS plus canopy cover fraction (CCF); ALS + CCF + Sentinel-2; and ALS + CCF + WorldView-3) were evaluated across Multiple Linear Regression, Random Forest, and Support Vector Regression (SVR) using repeated nested cross-validation. SVR combined with the predictor set ALS + CCF + WorldView-3 achieved the highest nested R2 (0.79), outperforming Random Forest and Multiple Linear Regression. However, models relying solely on ALS (R2 = 0.71) or ALS with CCF (R2 = 0.77) yielded statistically comparable performance. Furthermore, integrating Sentinel-2 (R2 = 0.61–0.68) provided no clear gain over ALS-based models. These findings demonstrate that open-access national LiDAR programs provide sufficient 3D structural information to map stand maturity without requiring commercial satellite imagery. Continuous spatial predictions of OGI offer valuable baseline maps to support conservation planning and OGF monitoring in Mediterranean pine ecosystems.