Multi-Platform LiDAR Comparative Assessment for Above-Ground Biomass and Carbon Estimation in Mediterranean Woody Crops

Reliable aboveground biomass (AGB) estimates for woody crops are required for carbon accounting and MRV; however, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed orchards. We benchmarked four LiDAR modalities across three Mediterranean woody-crop sites in Córdoba (Spain), IFAPA, Doña María, and Villaseca using open national airborne laser scanning (PNOA/ALS), Riegl ALS, unmanned laser scanning (ULS), and mobile laser scanning (MLS). The field inventory used 58 fixed-area plots (20×50 m; 0.1 ha) collected in December 2024-January 2025 (1,867 trees) and species-specific allometries based on D2r to derive tree and plot AGB; carbon was computed using wood carbon fractions (0.445 olive; 0.457 almond) and CO2e via IPCC conversion. Plot-level LiDAR metrics (e.g., mean height, p95, maximum height, and cover proxies) were extracted from normalized point clouds and modeled with Random Forest, XGBoost, and an ensemble under an 80/20 train-test split. Mean field AGB differed among sites (33.89, 30.94 and 12.76 Mg ha−1 for Villaseca, Doña María, and IFAPA). In the provided summaries, XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). The results support cross-platform LiDAR for orchard AGB mapping and identify conditions under which open national LiDAR can enable scalable MRV. In addition, we evaluated TreeQSM-based quantitative structure models (QSMs) as an independent tree-level 3D reconstruction approach and examined their site-dependent agreement with field inventory estimates.