A Method for Land–Water Boundary Extraction from UAV-Borne Bathymetric LiDAR Point Clouds

Accurate and timely extraction of land–water boundaries is essential for water resources management, flood monitoring, and wetland conservation. This study proposes a method for extracting land–water boundaries from unmanned aerial vehicle (UAV)-borne bathymetric light detection and ranging (LiDAR) point clouds. First, land point clouds are projected onto a reference plane constructed by fitting water surface points. Next, an alpha-shape algorithm extracts the boundary of these projected points. A combined algorithm of periodic curvature adaptive exponential tension spline interpolation and Savitzky–Golay (SG) filtering is then applied for secondary smoothing to generate the land boundary curve. Finally, curve segments adjacent to water surface points are identified as the land–water boundary. Experimental results show that, compared with manual extraction, the method yielded a root mean square error (RMSE) of 0.118 m at Danjiangkou Reservoir (main test area) and 0.184 m against independent real-time kinematic (RTK) measurements at Suzhou Reservoir. This demonstrates that UAV-borne bathymetric LiDAR meets the requirements for refined boundary extraction, and the proposed secondary smoothing algorithm successfully balances accuracy and smoothness to accurately reflect natural boundary morphology.