| Summary: | Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by <i>Tomicus</i> spp. (the pine shoot beetle, PSB) to Yunnan pine (<i>Pinus yunnanensis</i> Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with <i>R</i><sub>690</sub> and <i>R</i><sub>798</sub> as variables had the highest accuracy (<i>R</i><sup>2</sup> = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB.
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