Discrimination of Larch Needle Pest Severity Based on Sentinel-2 Super-Resolution and Spectral Derivatives—A Case Study of Erannis jacobsoni Djak

In recent years, Jas’s Larch Inchworm (Erannis jacobsoni Djak, EJD) outbreaks have frequently occurred in forested areas of Mongolia, causing significant damage to forest ecosystems, and rapid and effective monitoring methods are urgently needed. This study focuses on a typical region of EJD infestation in the larch forests located in Binder, Khentii, Mongolia. Initial super-resolution enhancement was performed on Sentinel-2 images, followed by the calculation of vegetation indices and first-order spectral derivatives. The Kruskal–Wallis H test (KW test), Dunn’s multiple comparison test (Dunn’s test), and the RF-RFECV algorithm were then employed to identify sensitive features. Using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) machine learning algorithms, along with field survey data and UAV remote sensing data, multiple models were developed to assess the severity of EJD infestation and the corresponding spatial distribution characteristics. Seven sensitive combined features were obtained from high-quality super-resolution Sentinel-2 images. Then, a high-precision monitoring model was constructed, and it was revealed that the areas prone to EJD infestation are located at elevations of 1171–1234 m, on gentle slopes, and in semi-shady or semi-sunny areas. The super-resolution processing of Sentinel-2 satellite data can effectively refine monitoring results. The combination of the first-order spectral derivatives and vegetation indices can improve the monitoring accuracy and the discrimination of light and moderate damage. D8a and NDVIswir can be used as important indicators for assessing the severity of EJD infestation. EJD has an adaptive preference for certain environments, and environmental factors directly or indirectly affect the diffusion and distribution of EJD.