Abstract:  Large-scale outbreaks of Dendrolimus superans infestation have led to severe losses of forest ecosystems and economies. It is of great significance to identify the area of Dendrolimus superans infestation quickly and accurately. In this study, knowledge graph technology was used to screen the sensitive features related to forest pests, and a random forest model with three different data combinations was constructed by comprehensive use of multi-source data to identify the Dendrolimus superans infestation occurrence area in the study area by remote sensing. The results are as follows: 1) The knowledge graph can be constructed and screened according to the graph construction process. In this study, the characteristics of forest insect pests were established by remote sensing, and the characteristics of Dendrolimus superans infestation in Northeast China were screened by remote sensing. 2) The combination of knowledge graph and remote sensing technology can provide practical features for constructing the pest identification model; 3) Compared with a single data source, the identification effect of Dendrolimus superans infestation based on multi-source data is better. In this study, the overall accuracy and Kappa coefficient of Sentinel-1A, Sentinel-2A and topographic data were 92.78% and 0.876 6, respectively. 

Key words:  , multi-source data, pest remote sensing identification, Dendrolimus superans, knowledge graph, random forest