Algorithm for Detection of Water Surface Height in UAV-Borne Photon-Counting LiDAR.

Unmanned aerial vehicle (UAV)-borne laser scanning systems using photon-counting technology are applied to high-resolution water surface mapping with high efficiency. Affected by vast noise photons in raw data, the detection of surface photons from a weak reflective target like water still faces challenges in low signal-to-noise ratio (SNR) application scenarios. Noise filtering of raw data and surface detection from possible signals are two essential steps for water surface detection. In this letter, a water surface height retrieval algorithm is investigated for characterizing terrain and surface height. The proposed algorithm implements multilevel filtering to minimize noise photons and subsequently extracts the topmost boundary points as water surface photons using a modified alpha-shape to derive the water level elevation. Noise filtering results show that the multilevel filtering approach is effective in preserving signal photons integrity at low SNR. Moreover, the accuracy assessment further substantiates the robustness of the methodology in calm waters, and the root mean square error (RMSE) for the estimated water surface height was 0.02 m compared with percentile heights. Our algorithm provides an efficient solution for high-resolution water surface mapping in UAV-borne photon-counting LiDAR (PCL).