Abstract: Using MODIS-MOD09Q1 remote sensing data, three easily obtainable and responsive indicators, namely Normalized Difference Vegetation Index （NDVI）, Ratio Vegetation Index （RVI）, and Near Infrared Reflectance （NIR）, were used to classify the changes in vegetation index for the degree of damage in disaster areas, and a comprehensive pest index （PCI） model was constructed to achieve rapid extraction of information from the Eeannis jacobssoni disaster area. On this basis, with the help of temperature and precipitation data, combined with GIS spatial overlay analysis method, the climate characteristics suitable for pest growth were revealed. The results showed that using the comprehensive pest index could accurately extract the severity information of pest disaster areas, with an overall accuracy and Kappa coefficient of 85.00% and 0.81, respectively;Eeannis jacobssoni was suitable for climates with less precipitation in winter and spring, more precipitation in summer, and temperatures that should not be too high, which was consistent with its biological characteristics. This climate was similar to the Greater Khingan Mountains forest area, with a high risk of invasion, and should be highly valued by the Chinese forestry department.

Key words: Eeannis jacobssoni, MODIS-MOD09Q1, disaster area data extraction, adaptive climate characteristics