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September 10, 2025Open Access

Spatial Analysis of Bathymetric Data from UAV Photogrammetry and ALS LiDAR: Shallow-Water Depth Estimation and Shoreline Extraction

OSOktawia SpechtGdynia Maritime University

Puntos clave

Effective depth estimation down to 1 m was achieved through support vector regression on bathymetric data, emphasizing its utility in shallow-water surveys.
UAV photogrammetry yielded a standard deviation of 0.11 m and accuracy of 0.22 m, while ALS LiDAR provided an average point density of 63.26 pts/m2.
Only 44.5% of the grid cells met the NOAA point density threshold, pointing to challenges in data consistency with UAV methods.
The modified shoreline extraction method from ALS data resulted in mean positional accuracy of 1.24 m, supporting reliable shoreline mapping efforts.

Resumen

The shoreline and seabed topography are key components of the coastal zone and are essential for hydrographic surveys, shoreline process modelling, and coastal infrastructure management. The development of unmanned aerial vehicles (UAVs) and optoelectronic sensors, such as photogrammetric cameras and airborne laser scanning (ALS) using light detection and ranging (LiDAR) technology, has enabled the acquisition of high-resolution bathymetric data with greater accuracy and efficiency than traditional methods using echo sounders on manned vessels. This article presents a spatial analysis of bathymetric data obtained from UAV photogrammetry and ALS LiDAR, focusing on shallow-water depth estimation and shoreline extraction. The study area is Lake Kłodno, an inland waterbody with moderate ecological status. Aerial imagery from the photogrammetric camera was used to model the lake bottom in shallow areas, while the LiDAR point cloud acquired through ALS was used to determine the shoreline. Spatial analysis of support vector regression (SVR)-based bathymetric data showed effective depth estimation down to 1 m, with a reported standard deviation of 0.11 m and accuracy of 0.22 m at the 95% confidence, as reported in previous studies. However, only 44.5% of 1 × 1 m grid cells met the minimum point density threshold recommended by the National Oceanic and Atmospheric Administration (NOAA) (≥5 pts/m2), while 43.7% contained no data. In contrast, ALS LiDAR provided higher and more consistent shoreline coverage, with an average density of 63.26 pts/m2, despite 27.6% of grid cells being empty. The modified shoreline extraction method applied to the ALS data achieved a mean positional accuracy of 1.24 m and 3.36 m at the 95% confidence level. The results show that UAV photogrammetry and ALS laser scanning possess distinct yet complementary strengths, making their combined use beneficial for producing more accurate and reliable maps of shallow waters and shorelines.

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