This study aimed to verify the validity of linear, power function and exponential models to estimate stand variables in Galician Atlantic forests of Pinus radiata D. Don using Light Detection and Ranging (LiDAR). These forests differ in structure and species composition from boreal forests, in which this methodology is fully developed. The models tested use LiDAR-derived canopy height and intensity distribution metrics as explanatory variables to predict the following stand attributes: mean height, dominant height, stand basal area, stand volume, stand crown biomass, stand stem biomass and stand aboveground biomass. Exponential models performed best in most cases, with goodness-of-fit statistics similar to those reported in the international literature for boreal forests. The coefficient of determination ranged from 0.44 (for stand crown biomass) to 0.87 (for dominant height), and the root mean square error/mean·100 ranged from 8.2 per cent (for dominant height) to 31.6 per cent (for stand stem biomass). Model precision did not essentially vary after reducing 94 per cent of the original point cloud, i.e. when laser pulse density was reduced from 8 pulses m⁻² to only 0.5 pulses m⁻².

Crown fire initiation and spread are key elements in gauging fire behaviour potential in conifer forests. Crown fire initiation and spread models implemented in widely used fire behaviour simulation systems such as FARSITE and FlamMap require accurate spatially explicit estimation of canopy fuel complex characteristics. In the present study, we evaluated the potential use of very low-density airborne LiDAR (light detection and ranging) data (0.5 first returns m–2) – which is freely available for most of the Spanish territory – to estimate canopy fuel characteristics in Pinus radiata D. Don stands in north-western Spain. Regression analysis indicated strong relationships (R2 = 0.82–0.98) between LiDAR-derived metrics and field-based fuel estimates for stand height, canopy fuel load, and average and effective canopy base height Average and effective canopy bulk density (R2 = 0.59–0.70) were estimated indirectly from a set of previously modelled forest variables. The LiDAR-based models developed can be used to elaborate geo-referenced raster files to describe fuel characteristics. These files can be generated periodically, whenever new freely available airborne LiDAR data are released by the Spanish National Plan of Aerial Orthophotography, and can be used as inputs in fire behaviour simulation systems.