Airborne bathymetric Light Detection and Ranging (LiDAR) systems have attracted attention as efficient surveying tools that acquire high-resolution and high-precision coastal topographic data more cost-effectively than traditional shipborne acoustic sounding or field surveys. Since laser pulses are refracted at the air-water interface, for the accurate registration of seafloor points, it is crucial to distinguish whether each return signal is from land or water at the waveform stage, before generating the point cloud. Conventional land-water discrimination techniques often rely on near-infrared (NIR) channel data for water-surface detection or water-body identification. However, NIR signal reliability is often compromised by specular reflection from water surfaces, and many recently developed sensors employ only a single green laser wavelength owing to system miniaturization and weight reduction. This situation underscores the necessity for land-water discrimination techniques that use only single green-channel waveform information. In this study, we analyzed various waveform features extracted from individual waveforms acquired with the Seahawk airborne bathymetric LiDAR system across coastal areas with varying water depths and turbidities. These waveforms were decomposed into Gaussian components, from which features were extracted and used in machine learning classifiers to evaluate their versatility and effectiveness for land-water discrimination under diverse coastal conditions. Four tree-based machine learning models-decision tree, random forest, XGBoost, and LightGBM-were evaluated using a stratified cross-validation scheme for performance assessment. All models achieved a high validation accuracy of approximately 0.99, demonstrating discriminative capability based on waveform features. In comparative evaluations considering both test accuracy and computational efficiency, LightGBM showed the most balanced performance, indicating its suitability as a general-purpose model for waveform-based land-water discrimination.

Tidal flats play a vital role in coastal ecosystems by supporting biodiversity, mitigating natural hazards, and functioning as blue carbon reservoirs. However, monitoring their geomorphological changes remains challenging due to high turbidity, shallow depths, and tidal variability. Conventional approaches—such as satellite remote sensing, acoustic sounding, and topographic LiDAR—face limitations in resolution, accessibility, or coverage of submerged areas. Airborne bathymetric LiDAR (ABL), which uses green laser pulses to detect reflections from both the water surface and seabed, has emerged as a promising alternative. Unlike traditional discrete-return data, full waveform analysis offers greater accuracy, resolution, and reliability, enabling more flexible point cloud generation and extraction of additional signal parameters. A critical step in ABL processing is waveform decomposition, which separates complex returns into individual components. Conventional methods typically assume fixed models with three returns (water surface, water column, bottom), which perform adequately in clear waters but deteriorate under shallow and turbid conditions. To address these limitations, we propose an adaptive progressive Gaussian decomposition (APGD) tailored to tidal flat environments. APGD introduces adaptive signal range selection and termination criteria to suppress noise, better accommodate asymmetric echoes, and incorporates a water-layer classification module. Validation with datasets from Korea’s west coast tidal flats acquired by the Seahawk ABL system demonstrates that APGD outperforms both the vendor software and the conventional PGD, yielding higher reliability in bottom detection and improved bathymetric completeness. At the two test sites with different turbidity conditions, APGD achieved seabed coverage ratios of 66.7–70.4% and bottom-classification accuracies of 97.3% and 96.7%. Depth accuracy assessments further confirmed that APGD reduced mean depth errors compared with PGD, effectively minimizing systematic bias in bathymetric estimation. These results demonstrate APGD as a practical and effective tool for enhancing tidal flat monitoring and management.

Kim, H.; Lee, J.; Kim, J., and Hur, H., 2023. Machine learning based water labeling using waveform features of airborne bathymetric LiDAR. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 225-229. Charlotte (North Carolina), ISSN 0749-0208. Airborne Bathymetric LiDAR (ABL) utilizes water-penetrating green lasers to detect the water surface and bottom and to measure depth. An ABL survey can efficiently cover large areas and is suited to shallow waters, a risky area in ship-based multibeam echosounder surveys. The received ABL signal is a mixture of surface and bottom reflections and underwater backscattering. To obtain valid bathymetric data, the superimposed waveform must be decomposed into its individual components and classified to what level each component corresponds. In this study, we propose a classification algorithm based on machine learning for effective water labeling of ABL waveforms. It first decomposes each waveform into its Gaussian components and then extracts waveform features such as amplitude, width, and return number from each component. A Support Vector Machine (SVM), a machine learning technique, is applied with the extracted waveform features to assign them water surface, water column, and bottom labels. In addition, we extracted various types of waveform features, configured them in different combinations, and identified effective waveform features for water labeling. The proposed approach was evaluated on waveform data acquired from the shallow coastal waters using the Seahawk ABL system developed by the Ministry of Maritime Affairs and Fisheries of Korea. Accordingly, a waveform feature combination suitable for water labeling of Seahawk data using SVM was derived.

Lee, J.; Kim, J.; Kim, H., and Wie, G., 2023. Automatic analysis of cadastral registration status of coastal land using open spatial information databases. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 260-264. Charlotte (North Carolina), ISSN 0749-0208. Coastal land adjacent to the coastline can be effectively managed by accurate topological identification of the coastline, a current-status survey, and registration on the cadastral map. However, traditional field surveys have limitations owing to poor site accessibility and large tidal differences, resulting in many unregistered lands around the coastline. This is particularly challenging for public lands that lack detailed registrations in coastal areas. To address this issue, the cadastral registration status of land was studied using automatic spatial analysis techniques applied to continuous cadastral and natural coastline maps, which constitute the latest open spatial information databases. Further, a qualitative analysis was performed using aerial ortho-photos to identify areas in which the boundaries of natural coastline and cadastral maps are mismatched. The results of this study allowed quantitative and qualitative analyses of the areas and current statuses of unregistered lands along the western coast of Jeollanam-do, Korea. Overall, this approach provides a more accurate and efficient way to manage coastal land by providing data on marine resources, marine environment, and marine pollution.

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.

Seabed segmentation from airborne bathymetric Light Detection and Ranging (LiDAR) point cloud data presents unique challenges, primarily due to variations in the z-axis resulting from differences in water depth and seabed topography. To address these complexities, we introduced an improved version of PointNet specifically designed for seabed segmentation using Airborne Bathymetric LiDAR (ABL) point cloud data. The proposed method integrates a window-based attention mechanism to capture spatial relationships in both horizontal and vertical dimensions while incorporating orthogonal regularization to preserve geometric integrity. The model's performance was assessed using various normalization methods and window sizes, demonstrating its effectiveness in accurately identifying seabed regions. Experimental results indicate that while the proposed network generally improves segmentation accuracy, its performance is sensitive to the choice of normalization and window parameters. This study represents a meaningful advancement in applying deep learning techniques to bathymetric LiDAR data, offering a robust framework for seabed segmentation.

The Arctic region is often assessed as having high uncertainty and risk in construction projects owing to its extreme environmental conditions. However, it is emerging as a new frontier for future energy development, given its abundant resources. We aim to establish a risk management framework by identifying country risk factors that arise from spatial differences when Korean construction companies, with extensive experience in Middle Eastern and Asian construction projects, enter the less familiar Arctic market. Through a comprehensive literature review, 21 country risk factors were identified. Subsequently, a survey was conducted among Korean experts involved in overseas construction projects to assess these risks. The results indicate that in the Middle East, "political" and "legal" risks are perceived as relatively severe, attributed to the instability of political institutions and legal frameworks. In contrast, in the Arctic, "environmental" and "cultural" risks are identified as the most critical, primarily owing to the extreme climate and stringent environmental protection regulations. In this study, we utilized human sensing methodologies through expert surveys to capture spatially informed risk data. This study contributes to understanding the regional characteristics of country risks driven by spatial differences and provides foundational data for Korean construction companies in formulating strategies for international project entry. However, the study is limited by a lack of comprehensive data on the Arctic region, necessitating future research to develop adaptive strategies through long-term data collection on environmental changes.

Currently, the settlement of soft ground is measured using instruments operated by on-site workers. However, this method is expensive and inefficient in terms of data consistency, cost-effectiveness, and utility. On the other hand, surveying using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) is used in various fields. However, studies on its utility in soft ground are insufficient. Therefore, in this study, we examined the optimal method for creating digital elevation models (DEMs) for estimating the settlement of soft ground using UAV LiDAR survey data. This method involved selecting a coastal construction site as the study area and acquiring data through UAV LiDAR surveying. The acquired data were used to create DEMs through preprocessing and postprocessing. Settlement measurements obtained from on-site instruments and settlement estimates derived from DEMs created using various interpolation methods and grid sizes were compared and analyzed. Additionally, the utility of the created time-series DEMs in the settlement estimation of soft ground was evaluated. We proposed the optimal method for creating DEMs for estimating the settlement of soft ground and suggested methods to utilize the proposed time-series DEMs. Our research results show that the use of UAV LiDAR survey data can lead to the economical and efficient settlement estimation of soft ground

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.

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.

Lee, J.; Kim, J.; Hur, H., and Wie, G., 2023. Coastal erosion monitoring using SEAHAWK Airborne Bathymetric LIDAR data on the east coast of Korea. Journal of Coastal Research, 39(2), 366–376. Charlotte (North Carolina), ISSN 0749-0208. Coastal erosion is accelerating along the east coast of Korea because of natural and anthropogenic forces. A current constraint in understanding and modeling these changes within large coastal areas is the multitemporal bathymetric data or recursive observations. This is because the main survey techniques for coastal erosion monitoring depend on field and echo-sounding surveys. However, the recent availability of Airborne Bathymetric LIDAR (ABL) systems enables topography mapping over large coastal areas. In particular, the development of the SEAHAWK system, a Korean ABL system, provides more data on the east coast of Korea. The ABL system enables assessing temporal changes along the large coasts without other conventional survey data. However, there are issues with the accuracy of the technique relative to quantitative uncertainties and ability to resolve nearshore spatial patterns of erosion and deposition indicative of geomorphologic change. This study validates the ABL data for coastal erosion monitoring by using the multi-temporal data acquired in 2013 and 2020 on the east coast of Korea and analyzes the results to reveal the quantitative and qualitative effectiveness of this technique.

As coastal erosion of the east coast is accelerating, the need for scientific and quantitative coastal erosion monitoring technology for a wide area increases. The traditional method for observing changes in the coast was precision monitoring based on field surveys, but it can only be applied to a small area. The airborne bathymetric Light Detection And Ranging (LiDAR) system is a technology that enables economical surveying of coastal and seabed topography in a wide area. In particular, it has the advantage of constructing topographical data for the intertidal zone, which is a major area of interest for coastal erosion monitoring. In this study, time series analysis of coastal seabed topography acquired in Aug, 2021 and Mar. 2022 on the littoral cell GW36 in Gangwon was performed using the Seahawk Airborne Bathymetric LiDAR (ABL) system. We quantitatively monitored the topographical changes by measuring the baseline length, shoreline and Digital Terrain Model (DTM) changes. Through this, the effectiveness of the ABL surveying technique was confirmed in coastal erosion monitoring.

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.

The waveform data of the Airborne Bathymetric LiDAR (ABL; LiDAR: Light Detection And Ranging) system provides data with improved accuracy, resolution, and reliability compared to the discrete-return data, and increases the user's control over data processing. Furthermore, we are able to extract additional information about the return signal. Waveform decomposition is a technique that separates each echo from the received waveform with a mixture of water surface and seabed reflections, waterbody backscattering, and various noises. In this study, a new waveform decomposition technique based on a Gaussian model was developed to improve the point extraction performance from the ABL waveform data. In the existing waveform decomposition techniques, the number of decomposed echoes and decomposition performance depend on the peak detection results because they use waveform peaks as initial values. However, in the study, we improved the approximation accuracy of the decomposition model by adding the estimated potential peak candidates to the initial peaks. As a result of an experiment using waveform data obtained from the East Coast from the Seahawk system, the precision of the decomposition model was improved by about 37% based on evaluating RMSE compared to the Gaussian decomposition method.

ABSTRACT Kim, H.; Lee, J., and Kim, Y., 2021. Tidal creek mapping from airborne LiDAR data using multi-resolution cloth simulation filtering. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 86–90. Coconut Creek (Florida), ISSN 0749-0208. Tidal creeks are transitional waterways promoting the evolution and expansion of tidal flats. The precise and objective delineation of tidal creeks is critical for monitoring the characteristics, formation, and evolution of tidal flats. Airborne light detection and ranging (LiDAR) data are the most widely used source of tidal topography information, since they can provide precise terrain information over wide areas. However, existing tidal creek extraction methods using airborne LiDAR data have limitations, such as the necessity of excessive user intervention and a lack of adaptability to the various shapes and widths of tidal creeks. The morphological irregularities, complexity, and diverse widths (from a few centimeters to several kilometers) of tidal creeks, complicates their automatic or manual extraction from LiDAR data. In this study, we propose an effective and practical method for the mapping of tidal creeks with a wide range of widths. Here, cloth simulation filtering (CSF), a verified ground filtering technique used to filter off-ground objects from point cloud in land LiDAR surveys, was adopted and modified. By sequentially filtering the creek points through a hypothetical cloth with an increasingly larger grid resolution, the proposed multi-resolution CSF can extract huge tidal creeks without compromising the details of narrow creeks. This sequential filtering is based on local thresholds calculated using the creek points extracted during the previous filtering and does not require empirical parameter adjustments. The results of an experimental evaluation based on airborne LiDAR data collected over the west coast of South Korea indicate that the accuracy of the proposed method is high (Kappa > 0.8) and superior to that of results obtained through an user parameter.

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.

해상 준설성과의 과학적 평가를 위해서는 준설선의 유도 위치, 준설수심 및 준설 토사량 등의 준설공정을 실시간으로 모니터링하면서 작업공정을 관리하고 평가할 수 있는 시스템의 구축이 필요하다. 본 연구에서는 기상조건과 거리에 상관없이 상시측량이 가능한 GPS 측량방법을 응용하는 정밀위치측량과 항행의 두가지 기법에 수심측정기법 등을 병용하는 해상측량시스템을 개발하고자 하였다. Beacon DGPS를 기반으로 하는 선박위치측정, GPS/Gyro 통합장비에 의한 선박방향 측정, 준설심도와 붐대위치 측정, 조위에 따른 준설심도 보정 등의 기능을 갖춘 해상준설선의 유도 및 위치관리시스템을 구축하고 준설선의 작업 현황을 실시간으로 모니터링하면서 정확히 작업을 유도할 수 있는 운용프로그램을 개발하였다. 본 연구에서 개발된 시스템은 해상준설 또는 해상건설산업에서 원가 절감에 크게 기여할 수 있을 것으로 기대된다. In order to perform scientific evaluation of dredge results, it is needed to construct the system which is able to manage and evaluate the work process by monitoring in real-time the dredge process such as dredge ship position, dredge depth and dredge volume. This research aims to develop the hydrographic dredge surveying system adding water depth measurement method to both precise positioning and navigation methods using GPS, which allows a high rate of measurement and long distances between the control point and dredging points, operate in all weather conditions, and does not require line of sight to points. We constructed Beacon DGPS-based hydrographic dredger guidance and position management system and developed the operation program which makes the dredge operation perform as monitoring work situation in real-time. It is expected that this developed system will be able to contributes to reducing ultimately the cost in hydrographic dredging or hydrographic construction industries.

We propose a method for automatic registration of high-resolution satellite imagery using LIDAR intensity data. We first generate a reference image using LIDAR intensity data as an alternative to ground control points, which allows us to conduct the GCP collection process automatically. Next, the proposed automatic matching is applied to the target and reference images. In the matching process, image chips are used as registration primitives and their edge information are employed as primary information for similarity measurements. Based on these principles, the overall registration procedure was developed to be automatic and straightforward. To test the feasibility of the developed method, we designed an experiment using multimodal and multitemporal real datasets: a stereo pair of IKONOS-2 images, one Quickbird image and LIDAR data. The experimental results show that the accuracy is acceptable for practical applications. The results of this study should pave the way for the development of an automatic and generally applicable registration method of high-resolution satellite imagery using LIDAR intensity data.