Abstract: Using Sentinel–2A multi-spectral image as the data source, the spatial pattern of pest damage at the southern foot of Changbai Mountain was quantitatively obtained by coupling the insect mouth density using spatial distribution of injured tree species extracted using a convolutional neural network model and leaf foliation rate by the difference of the leaf area index reversed by the PROSAIL model at multiple time points. Results show that: the overall accuracy of 7 LAI inversion in 2018–2020 was above 88%; the optimal reference phase of red pine was in June 2019, R² is 0.82 and other species in June 2018; linear function, R² is 0.755; larch pest area of 6174 hm², and spruce damage area ratio of 65.19%. The reference phase of the leaf loss rate is June of the year before the disaster; the relationship between the pest density and the leaf loss rate is linear; the spatial pattern of different tree species is different, and the proportion of evergreen trees is generally higher than that of deciduous tree species.