A cross-scale synergistic framework integrating UAV LiDAR and sentinel-2 imagery for fine-grained vegetation mapping in coastal wetlands

Accurate identification of vegetation associations in coastal muddy tidal-flat wetlands is essential for understanding wetland ecosystem structure, function, and dynamics. However, spectral similarity and structural complexity limit conventional satellite-based mapping. Integrating UAV LiDAR with satellite imagery offers new opportunities for improving classification accuracy and enabling scalable wetland monitoring. In this study, we develop a UAV LiDAR-based point cloud classification method, CMFF-RandLA-Net, and construct a synergistic point cloud–satellite framework for multi-scale vegetation mapping. Multi-source features, including BRDF-corrected intensity, VDVI, and normalized scan angle, were incorporated to enhance feature representation, together with a feature fusion strategy to improve classification robustness. In the coastal wetlands of Yancheng, China, the proposed model achieved an overall accuracy (OA) of 95.53% in point cloud classification, with F1-scores above 0.80 for the dominant species Spartina alterniflora and Phragmites australis. The high-precision point cloud-derived ground truth was then used to train satellite classifiers for Sentinel-2 imagery. Among four evaluated algorithms (XGBoost, Random Forest, LightGBM, and Linear SVM), XGBoost performed best (OA = 92.85%, Kappa = 0.8381) and uniquely identified minority classes, enabling regional-scale vegetation mapping. The proposed framework bridges fine-scale UAV LiDAR classification and satellite-based regional mapping, providing a practical approach for coastal wetland monitoring.