There is a growing interest in stand fuel inventories through the use of light detection and ranging (LiDAR) technology among fire researchers. In this study, canopy fuel load of Turkish red pine (Pinus brutia Ten.) stands was estimated based on point-cloud data collected from five sample plots using a mobile laser scanner (MLS). For this, stand fuel characteristics (i.e., dbh and number of trees) and fuel load models were used in combination. The estimation results were stand fuel characteristics were compared against the ground truth obtained through traditional field measurements. Preliminary results indicated that MLS was able to capture dbh and the number of trees information at the plot level. Thus, it is possible to estimate canopy fuel loads in Turkish red pine relying on species-specific models that utilize MLS data as input. Further research is needed for automating the point-cloud data analysis workflow and more accurate characterization of surface fuel in Mediterranean forests.