An Automatic Algorithm to Extract Nearshore Bathymetric Photons Using Pre-Pruning Quadtree Isolation for ICESat-2 Data

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) equips with a novel photon-counting LiDAR system, which can generate underwater reflections in nearshore environments. However, due to the water reflection, scattering, and absorption, the distribution of bathymetric photons in the nearshore data varies with depth. The existing bathymetric photon extraction algorithms need more adaptability to seafloor topography. The changing density of bathymetric photons and the fluctuation of underwater topography make the noise removal of nearshore data full of challenges. This study proposed a bathymetric photon extraction algorithm using pre-pruning quadtree isolation (PQI). First, the pre-pruning step judges whether to stop the growth of quadtree in advance during quadtree isolation (QI) to avoid excessive division of noise photons. Second, the maximum inter-class variance algorithm (also called the Otsu method) obtains the best threshold of isolation depth (ID) and extracts bathymetric photons. The algorithm was tested on the Florida coast. The results show that the PQI algorithm can wholly and accurately extract bathymetric photons with different acquisition times from the data. The F1-score of the extracted results is 93.96%. This study provides an intelligent solution to processing bathymetric data in nearshore environments worldwide.