Abstract
A current hindrance to the scientific use of available bathymetric lidar point clouds is the frequent lack of accurate and thorough segmentation of seafloor points. Furthermore, scientific end-users typically lack access to waveforms, trajectories, and other upstream data, and also do not have the time or expertise to perform extensive manual point cloud editing. To address these needs, this study seeks to develop and test a novel clustering approach to seafloor segmentation that solely uses georeferenced point clouds. The proposed approach does not make any assumptions regarding the statistical distribution of points in the input point cloud. Instead, the approach organizes the point cloud into an inverse histogram and finds a gap that best separates the seafloor using the proposed peak-detection method. The proposed approach is evaluated with datasets acquired in Florida with a Riegl VQ-880-G bathymetric LiDAR system. The parameters are optimized through a sensitivity analysis with a point-wise comparison between the extracted seafloor and ground truth. With optimized parameters, the proposed approach achieved F1-scores of 98.14–98.77%, which outperforms three popular existing methods. Further, we compared seafloor points with Reson 8125 MBES hydrographic survey data. The results indicate that seafloor points were detected successfully with vertical errors of −0.190 ± 0.132 m and −0.185 ± 0.119 m (μ ± σ) for two test datasets.
Highlights
The volume of bathymetric lidar point clouds available to coastal scientists, engineers, and coastal zone managers is increasing rapidly
A current impediment to effective use of these bathymetric lidar point clouds is that many lack accurate point classification [2,3]
There are a number of reasons why a given bathymetric lidar point cloud may lack accurate point classification
Summary
The volume of bathymetric lidar point clouds available to coastal scientists, engineers, and coastal zone managers is increasing rapidly. A current impediment to effective use of these bathymetric lidar point clouds is that many lack accurate point classification (the segmentation of points into water surface, water column noise, and seafloor points) [2,3]. Accurate segmentation of seafloor (or lakebed or riverbed, depending on the area) points, often referred to as “bathymetric bottom”, is critically important to a number of processing and analysis tasks, including hydrodynamic modeling, benthic habitat mapping, and sediment transport studies [4,5,6,7]. There are a number of reasons why a given bathymetric lidar point cloud may lack accurate point classification. More often (and especially in the case of airborne lidar) point classification has been performed, but sometimes only through rough, automated routines, which can result in a number of errors of both omission and commission in segmenting bathymetric bottom points.
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