Digital elevation models (DEMs) are essential for quantitatively monitoring the current state and changes in the morphology of tidal flats and extracting terrain information, such as tidal channels. However, the unique environmental characteristics of tidal flats, where water and land coexist in shallow areas, make it challenging to apply traditional direct surveying methods or ship-based echo sounding. Additionally, remote sensing technologies such as airborne topographic light detection and ranging (LiDAR), drone photogrammetry, and satellite imagery are challenging to use in submerged areas. Airborne bathymetric LiDAR (ABL), which is capable of directly surveying the seabed through seawater, is a highly effective method for surveying tidal flats. However, the high turbidity and shallow water environment of tidal flats attenuate the return strength, resulting in a low signal-to-noise ratio and making airborne bathymetric LiDAR (ABL) full-waveforms extremely complex. In this study, we aim to improve the performance of ABL full-waveform processing to generate high-precision DEMs in such challenging environments. First, we analyze the characteristics of ABL waveforms under varying turbidity conditions and propose preprocessing and waveform decomposition techniques to improve seabed point extraction rates in tidal flat environments. To achieve this, experiments and validations are conducted using the data acquired by the Seahawk system along the west coast of Korea.