A Remote Sensing Detection Method for Ceracris Kiangsu Tsai in Moso Bamboo Forests Integrating SMOTE Algorithm and Multi-Model Ensemble

Ceracris kiangsu Tsai (C.kiangsu) is one of the main leaf-feeding pests in Moso bamboo forests. An in-depth exploration of its response mechanism is crucial for achieving large-scale, precise detection and maintaining the healthy development of Moso bamboo forests. However, existing research on C.kiangsu pest detection is relatively limited, with traditional forest pest models often constrained by unbalanced samples for generalization. This study integrates field survey data with Sentinel-2 MSI data to explore the remote sensing response mechanism of the pest; proposes a collaborative detection method for C.kiangsu infestations in Moso bamboo forests that combines the SMOTE algorithm with a multi-model ensemble, optimizes model parameters via the Bayesian algorithm, and simultaneously uses SHAP values to deeply analyze model interpretability and excavate pest detection indicators. The results showed that: 1) Leaf LCC, LWC, and LDMC exhibit excellent responsiveness to C.kiangsu pest infestations (p < 0.01), with vegetation indices outperforming moisture indices and texture features. 2) The optimal Stacking ensemble learning hybrid model achieves OA of 82.22% and Kappa of 0.7625, improving by 3.5% and 0.0468 over the best single LightGBM model. 3) SHAP analysis reveals that the vegetation index RVI is the core indicator for pest detection, ranking among the top three in feature importance in each base model. The contribution to the meta-model is LightGBM > ET > SVM > XGBoost. This study successfully breaks through the accuracy bottleneck of traditional single models, providing a solid scientific basis for Moso bamboo pest management and practical significance for its sustainable development.