Integrating Remote Sensing, Machine Learning, and Degree-Day Models for Predicting Grasshopper Habitat Suitability in Temperate Grasslands
Ahmed R. Huang W. Dong Y. Dildar Z. Ashraf H.A. Rahman Z.U. Rysbekova A.
December 2025Multidisciplinary Digital Publishing Institute (MDPI)
Remote Sensing
2025#17Issue 24
Highlights: What are the main findings? The Random Forest model outperformed other machine learning algorithms, providing the most accurate and robust prediction of grasshopper habitat suitability in the Xilingol grasslands. Grasshopper distributions showed consistently clustered patterns, with high-suitability habitats concentrated in East Ujumqin, West Ujumqin, and Xilinhot, and driven universally by soil and vegetation types. What are the implications of the main findings? The integrated framework offers a scalable, early-warning tool for proactive pest management, enabling resource allocation to persistent, high-risk outbreak zones. The identification of region-specific drivers (e.g., precipitation, humidity) underscores the need for locally tailored control strategies within a broader monitoring system. 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.
degree-day model , grasshopper , grassland ecosystems , habitat suitability modeling , machine learning , remote sensing , spatial autocorrelation
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State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
University of Chinese Academy of Sciences, Beijing, 100049, China
School of Astronautics, Beihang University of Aeronautics and Astronautics, Beijing, 102206, China
Technology Implementation and Commercialization Department, Kazakh Research Institute of Plant Protection and Quarantine, Almaty, 050070, Kazakhstan
State Key Laboratory of Remote Sensing and Digital Earth
University of Chinese Academy of Sciences
School of Astronautics
Technology Implementation and Commercialization Department
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