There are various remote sensing data sources for biomass estimation, including optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR). When using remote sensing techniques for regional biomass estimation, the scarcity of field plot data constrains the improvement of model inversion accuracy. In this study, unmanned aerial vehicle LiDAR point cloud data combined with field survey was used to expand the sample size, trying to verify the feasibility of using airborne radar instead of field survey, and establishing an inversion model of vegetation aboveground biomass with optical images features(Vegetation index data and texture feature data). The results show that: 1) The biomass estimated based on the tree crown width data obtained from LiDAR has a high level of accuracy, with an overall error percentage of 1.74% compared to the biomass results obtained from field measurements.; 2) the aboveground biomass density estimated from the LiDAR data is 96.78 Mg·hm-2, the biomass density from the optical image feature model is 107.94 Mg·hm-2 in the Longwang Hill; 3) The validation data from the campus adjacent to Longwang Mountain shows that the model-inverted aboveground biomass density is 92.6 Mg·hm-2, while the result based on LiDAR data is 104.11 Mg·hm-2. It can be observed that this method demonstrates good effectiveness. Therefore, the sample size can be expanded by unmanned aerial vehicle LiDAR and crown amplitude biomass model, and the resulting image characteristic biomass model has good results and provides a feasible method for large-scale biomass inversion.