1 remote sensing image was used to extract the corn planting area one week after the pest outbreak，combined with the Ｒenormalized Difference Vegetation Index ( ＲDVI) ，which is sensitive to changes in dense and sparse vegetation，and the average ＲDVI value and standard deviation were used to reflect the growth changes of corn after being affected by pests． Furthermore，by setting standardized thresholds，the decision tree method was used to classify the degree of corn pest infestation． The results show that the overall accuracy of dividing corn damage based on ＲDVI can reach 75%，indicating that ＲDVI can be used as an indicator for monitoring corn damage． In addition，by analyzing the habitat factors in the Kailu area，it was found that high temperature and drought weather conditions and a large number of pest sources are important reasons for the serious damage to corn in this area．