Abstract: Taking Pinus yunnanensis in the Heilongtan area of Shilin County, which is often harmed by Tomicus yunnanensis, as the research object, the canopy dieback rate of 120 P. yunnanensis was manually investigated and their locations were recorded. A UAV equipped with a hyperspectral imager was used to collect the hyperspectral data of P. yunnanensis in the study area, and the canopy spectral reflectance was extracted according to the sample location, the canopy spectrum was analyzed, and 16 spectral features were extracted using the wavelet transform method. Based on the sample canopy withering rate and spectral feature data, the BP neural network method was used to train the classification diagnosis model, which was used as the classification diagnosis model for the damage degree of T. yunnanensis to P. yunnanensis in the study area. UAV-mounted LiDAR system was used to collect LiDAR data of P. yunnanensis in the study area. According to the normalized cutting method, the individual canopy of P. yunnanensis was segmented and extracted, and the segmentation and extraction results were fused with hyperspectral data to extract the canopy spectrum of individual P. yunnanensis. Finally, the trained diagnostic model was used to classify and diagnose the damage degree of P. yunnanensis in the study area by T. yunnanensis and visualize the results. The results showed that a total of 11029 P. yunnanensis strains were divided and extracted. The classification results were 10142 healthy strains, 490 mildly damaged strains, 266 moderately damaged strains, and 131 severely damaged strains. 120 samples were selected to verify the accuracy of the classification results.The overall classification accuracy was 90.83%, and the classification accuracy was high.