Accurately describing forest surface fuel load is significant for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, as well as assessing potential fire hazards. In this study, the Light Detection and Ranging (LiDAR) data was used to estimate surface fuel load, due to its ability to provide three-dimensional information to quantify forest structural characteristics with high spatial accuracies. Firstly, the multilayered eucalypt forest vegetation was stratified by identifying the cut point of the mixture distribution of LiDAR point density through a non-parametric fitting strategy as well as derivative functions. Secondly, the LiDAR indices of heights, intensity, topography, and canopy density were extracted. Thirdly, these LiDAR indices, forest type and previous fire disturbances were then used to develop two predictive models to estimate surface fuel load through multiple regression analysis. Model 1 was developed based on LiDAR indices, which produced a R2 value of 0.63. Model 2 (R2 = 0.8) was derived from LiDAR indices, forest type and previous fire disturbances. The accurate and consistent spatial variation in surface fuel load derived from both models could be used to assist fire authorities in guiding fire hazard-reduction burns and fire suppressions in the Upper Yarra Reservoir area, Victoria, Australia.