The present study proposes and exemplifies a methodology to estimate the carbon stock from aerial biomass.Essentially, the method combines field data from forest inventory to obtain site specific measurements of carbonstock, and airborne LiDAR (ALS) data to derive proxy variables of forest structure. We then model and patializecarbon predictions. First, we filtered forest inventory plots to retrieve those representing ‘pure’ forest stands, i.e. dominated by asingle species (above 80% of individuals belong to the same species). Next, carbon stock was calculated frombiomass estimations based on dasometric measurements in the plots. A set of ALS-related variables were thencalculated extracting ALS data on plot sites. Finally, specific Random Forest models (one per species) were fittedto establish the relationship between aerial carbon (response) and ALS data (predictors). The models were appliedto the entire study region of Catalonia, Spain. Information on species presence (retrieved from the Spanish ForestMap as canopy cover) was used in the process so that each pixel in the region was assigned a weighted sum ofaerial carbon according to the species coverage.The procedure was applied in Catalonia (Northeast of Spain), a region dominated by Mediterranean-type forests.We used the 4th National Forest Inventory (2016) and ALS data from the National Plan of Aerial Orthophotogra-phy (0.5 points m-2; 2016-17). The most frequent species were Pinus halepensis (N=804), Pinus nigra (N=275),Pinus sylvestris (N=501), Quercus suber (N=118), Quercus ilex (N=393) and Quercus pubescens (N=118), thus inthis example only these species were retained from inventory plots. ALS data consisted of Canopy cover, CanopyRelief Ratio, height mean, height standard deviation, height kurtosis, height skewness, 99th percentile, slopeaspect, elevation and slope. Three models were fitted for each species apiece, randomly resampling the originaldata to apply a k-fold (k=3) cross-validation (CV) procedure. The adjusted R2 values obtained from CV rangedfrom 0.82 in Quercus suber to above 0.9 in Pinus communities (Pinus halepensis 0.9, Pinus nigra 0.91, Pinussylvestris 0.93). Other Quercus species obtained R2 around 0.87 (Quercus ilex 0.86, Quercus pubescens 0.87).