China’s extensive grasslands are ecologically and economically vital but are increasingly degraded by grasshopper outbreaks. Traditional monitoring approaches are too limited for large-scale management. This study developed an advanced monitoring framework for the Xilingol League by integrating multi-source remote sensing, a degree-day model, and machine learning (ML). Field survey data from 2018 to 2023 were combined with 29 environmental variables aligned to grasshopper life stages. Four ML algorithms—Random Forest (RF), XGBoost, Multilayer Perceptron (MLP), and Logistic Regression (LR)—were evaluated for predictive performance. RF consistently outperformed other models, achieving the highest accuracy and robustness. Spatial autocorrelation analysis (Global Moran’s I) confirmed that grasshopper distributions were persistently clustered across all years, highlighting non-random outbreak patterns. Suitability mapping showed highly suitable habitats concentrated in East Ujumqin, West Ujumqin, and Xilinhot, with pronounced interannual variability, including a peak in 2022. Variable importance analysis identified soil type and vegetation type as dominant universal drivers, while precipitation, soil texture, and humidity exerted region-specific effects. These findings demonstrate that coupling biologically informed indicators with integrated learning provides ecologically interpretable and scalable predictions of outbreak risk. The framework offers a robust basis for early warning and targeted management, advancing sustainable pest control and grassland conservation.
Raza et al. (2025) studied this question.