Semiautomatic Workflow for Accurate LiDAR-Derived DSM Retrieval in Aquatic Scenarios via Water Surface Mapping

High-resolution, 3-D water surface mapping in aquatic environments is critical for evaluating complex interactions between human activities and environmental dynamics. Despite the overall potential of LiDAR data to generate 3-D point clouds, providing the accurate and complete digital surface models (DSMs) at the interface between terrestrial and aquatic ecosystems is still a significantly challenging task. In fact, due to water's strong near-infrared absorption and its near specularity, LiDAR often results in weak or missing signal returns. In addition, direct linear interpolation for gap filling can introduce biases in the DSM reconstruction, especially near shorelines. This study proposes a four-step semiautomatic, open-source workflow for high-resolution DSM reconstruction in aquatic scenarios, using unsupervised machine learning for land-water classification based on optimized LiDAR-derived features. Mean water-level surface elevation, extracted from binary scene clustering, was used to fill DSM gaps over water via ad hoc gap filling. The accuracy of the resulting "water-filled" DSM (WFDSM) was evaluated across six diverse real-world aquatic scenarios with a range of challenging conditions (e.g., presence of aquatic vegetation, detached ponds, man-made structures, and land depressions) and compared against open-source products. Unsupervised clustering combining radiometric and geometric features achieved high classification accuracy (F-score > 0.97) for "water-level" targets, with negligible commission errors. Unlike standard products, WFDSM effectively handles variations in terrain and surface, maintaining low elevation biases (< 25 cm) even in areas with complex vegetation and fine-scale anthropogenic structures, thus demonstrating high suitability in both transitional and open-water areas.