Airborne bathymetric LiDAR (ABL) acquires waveform data with better accuracy and resolution and greater user control over data processing than discrete returns. The ABL waveform is a mixture of reflections from the water surface and bottom, water column backscattering, and noise, and it can be separated into individual components through waveform decomposition. Because the point density and positional accuracy of the point cloud are dependent on waveform decomposition, an effective decomposition technique is required to improve ABL measurement. In this study, a new progressive waveform decomposition technique based on Gaussian mixture models was proposed for universal applicability to various types of ABL waveforms and to maximize the observation of seafloor points. The proposed progressive Gaussian decomposition (PGD) estimates potential peaks that are not detected during the initial peak detection and progressively decomposes the waveform until the Gaussian mixture model sufficiently represents the individual waveforms. Its performance is improved by utilizing a termination criterion based on the time difference between the originally detected and estimated peaks of the approximated model. The PGD can be universally applied to various waveforms regardless of water depth or underwater environment. To evaluate the proposed approach, it was applied to the waveform data acquired from the Seahawk sensor developed in Korea. In validating the PGD through comparative evaluation with the conventional Gaussian decomposition method, the root mean square error was found to decrease by approximately 70%. In terms of point cloud extractability, the PGD extracted 14–18% more seafloor points than the Seahawk’s data processing software.

Inundation of roads by heavy rainfall has attracted more attention than traffic accidents, traffic congestion, and construction because it simultaneously causes travel delays and threatens driver safety. For these reasons, in this paper, we propose an inundation hazard index (IHI) of road links, which shows the possibility of inundation of road links caused by rainfall. To generate the index, we have used two key data sources, namely the digital elevation model (DEM) and past rainfall records of when inundation has occurred. IHI is derived by statistically analyzing the relationships between the normalized relative height of the road links calculated from DEM within the watershed and past rainfall records. After analyzing the practical applicability of the proposed index with a commercial car navigation system through a set of tests, we confirmed that the proposed IHI could be implemented to choose safer routes, with reduced chances of encountering roads having inundation risks.s

As elongated indentations or valleys in a wetland caused by tidal currents, tidal creeks act as drainage pathways and promote tidal flat evolution. Determining their geometric information is essential for topographical research of tidal flats. The airborne light detection and ranging (LiDAR) system has been the most efficient surveying technique in tidal topography because it can directly acquire precise geo-referenced point clouds for wide areas. Existing tidal creek extraction methods using airborne LiDAR data have limitations such as excessive user intervention, lack of adaptability to various shapes and sizes of tidal creeks, and decreased precision due to conversion to the digital elevation model. This study aims to overcome these limitations and effectively extract various types of tidal creeks by utilizing ground filtering which is a technique to filter off-ground objects (such as buildings, trees, etc.) in land LiDAR surveys. To derive a suitable method for tidal creek extraction, three verified ground filtering techniques, adaptive triangulated irregular network, gLiDAR, and cloth simulation filtering (CSF), were selected and tested using LiDAR point data. We modified the application procedure and optimized their parameters to enable tidal creek extraction. Our results confirmed that CSF can extract various tidal creeks with minimal user intervention. Finally, we calculated their depths and generated a tidal creek map.

The absence of accurate point classification limits the effective use of airborne bathymetric LiDAR (ABL) data for coastal zone mapping. In this study, we propose a classification approach using a custom waveform decomposition technique with the pseudo-waveform generated from ABL point cloud data. Initially, the input point clouds were organized into a 2D grid. Next, the points that fall into a grid cell were organized into a histogram using Z-values to generate the pseudo-waveform. Subsequently, the pseudo-waveform was decomposed into water bottom, column, surface, and noise components using a custom multiple Gaussian curve fitting method. The proposed approach was evaluated with datasets acquired in Florida, USA, using a Riegl VQ-880-G ABL system. With an optimized parameter set, the proposed approach achieved F1 score of 98.944% for the classification of water bottom and an overall accuracy of 91.234% for all the classes. Further, the proposed approach was evaluated with datasets acquired in South Korea using a Seahawk system and compared against MBES data, demonstrating that the water bottom was successfully classified with a vertical error of 0.049 ± 0.167 m.

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.