Currently,remote sensing data sources for biomass estimation include optical,Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR).The scarcity of field plot data constrains the improvement of model inversion accuracy when using remote sensing techniques to invert regional biomass.Here,we utilize Unmanned Aerial Vehicle (UAV) LiDAR point cloud data,combined with field surveys,to expand the sample size,aiming to verify the feasibility of using airborne radar as a substitute of field survey,and establish an inversion model for aboveground vegetation biomass using optical image features (vegetation index data and texture feature data).The results show that,the biomass estimates derived from tree canopy width data acquired by LiDAR exhibit high accuracy,with an overall error percentage of 1.74％ compared to field-measured biomass results;the aboveground biomass density in Nanjing's Longwang Hill scenic area is estimated to be 96.78 Mg·hm^-2using LiDAR data and 107.94 Mg·hm^-2 using optical image feature model;validation data from the campus adjacent to Longwang Hill show 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,demonstrating the effectiveness of the proposed method.Therefore,the integration of UAV LiDAR and canopy amplitude biomass model effectively expands the sample size,and the resulting image characteristic biomass model performs well,which provides a feasible approach for large-scale biomass inversion.