Abstract: In order to quickly and efficiently monitor the outbreaks of Dendrolimus superans, the 8th forest compartment of South Management Office Forest Farm in Heilongjiang Province was taken as the research area, while the Sentinel-2 remote sensing image in 2018 was used as data source to identify the Dendrolimus superans infestation area in the forest compartment. The original spectral features（8）, spectral index features（12） and the texture features（8） were extracted from the preprocessed image. Based on ANOVA and XGBoost classifiers, all features were dimensionally reduced and sorted by importance. The ensemble learning classification algorithm（Random Forest classifier and XGBoost classifier） was used to identify pest areas and compare their accuracy. The results showed that:（1） the XGBoost model with the top 14 important features was the most ideal for the identification of pest areas, and the overall accuracy reached to 95%（Kappa coefficient = 86%）, which were higher than the 93% of Random Forest（the top 10 features in order of importance）;（2） the top 14 feature names were: EVI1, Mean, MTCI, GNDVI, Variance, B4, B2, Homogeneity, B3, CRI1, EVI2, B8, B5 and CRE. This method can achieve efficient identification of Dendrolimus superans infestation areas, which can provide a basis for decision-making on pest control in northeast forest.