Research on Grassland Classification Method in Water Conservation Areas of the Qinghai–Tibet Plateau Based on Multi-Source Data Fusion

The Qinghai–Tibet Plateau is a crucial ecological security barrier in China and Asia. Its grassland ecosystem has high ecological service value. Scientific assessments and classifications of grasslands are crucial for determining the value of grassland resources and implementing refined management. Traditional grassland classification methods have used expert knowledge and linear models, which are subjective and cannot describe complex nonlinear relationships. We conducted a case study in Hongyuan County, Sichuan Province, in the water conservation area of the Qinghai–Tibet Plateau, using multi-source data including Landsat 8 (15 m/30 m), MOD15A2 (500 m), ALOS imagery (12.5 m), and 435 field survey samples, combined with machine learning models such as convolutional neural network (CNN), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), histogram gradient boosting (HistGradientBoosting), and random forest (RF). The objective was to develop a novel grassland classification method that integrates multi-source remote sensing data with machine learning algorithms. Based on the evaluation metrics of SHAP values, mean annual precipitation (MAP, 0.675), >0 °C Accumulated Temperature (AT, 0.591), and aspect (ASPECT, 0.548) were the most critical factors influencing alpine grasslands, revealing a driving mechanism characterized by climate dominance, topographic regulation, soil support, and vegetation response. The XGBoost model demonstrated the best performance (with an accuracy of 0.829, Precision of 0.818, Recall of 0.829, weighted F1-score of 0.820, and an AUC value of 0.870). The pixel-by-pixel absolute difference calculation between the model-predicted and the actual classification results showed that regions with no discrepancy (absolute value = 0) accounted for 75.82%, those with a minor discrepancy (absolute value = 1) accounted for 23.63%, and regions with a major discrepancy (absolute value = 2) accounted for only 0.54%. This study has established a replicable paradigm for the precise management and conservation of alpine grassland resources. Through the synergistic application of deep learning and machine learning, it generated superior baseline data, quantitatively uncovered a grassland differentiation mechanism dominated by hydrothermal factors and fine-tuned by topography in the complex Qinghai–Tibet Plateau, and delivered high-precision spatial distribution maps of grassland classes.