The distribution of combustibles in forest is one of the important factors that affect the occurrence and spread of forest fires. The purpose of this study is to combine the traditional forest survey data with point cloud data from light laser detection and ranging(LiDAR), slope and meteorological factors so as to evaluate forest fire potentials with fuel characteristic classification system(FCCS). Pu’er City of Yunnan Province was selected as the research area in this paper. An object-oriented based segmentation was performed based on the crown height model(CHM) which was produced by the airborne LiDAR data, and the overlay analysis of the provincial level inventory data of forest resources of the research area was used to determine the division unit and vegetation type according to the flammability of vegetation, which was divided into coniferous forest, broad-leaved forest, shrub and bamboo forest. On such a basis, stratified random sampling was used to form the validation dataset. Then the authors extracted the LiDAR variables and applied the multivariate stepwise regression method to analyzing the extracted variables with the reference data set to obtain the forest parameters of different vegetation types. In the end, the forest parameters together with the meteorological factors were used as inputs to the forest fire classification model (FCCS), and the fire potential of each segmentation unit was calculated by the model. Finally, the authors compiled maps of potential fire behavior, crown fire, effective combustibles and comprehensive fire hazard result. The results showed that the overall fire potential level of combustible materials in the research area is relatively low, which is consistent with the actual situation in the study area; the vertical structure of the forest is closely related to the forest fire risk potentials. Accurate estimation of forest parameters plays a very important role in the mapping of combustibles.