Due to the limitations of measurement equipment and the influence of factors such as the environment and target, measurement errors may occur during the data acquisition process of airborne LiDAR bathymetry (ALB). The refractive index of water is defined as the propagation ratio of the speed of light waves in a vacuum to that in water; this ratio influences not only the propagation speed of the laser pulse in water but also the propagation direction of the laser pulse entering water. Therefore, the influence of refractive index changes in water on the ALB errors needs to be analyzed. To this end, the principle of ALB is first briefly introduced. Then, the calculation method for the refractive index of water is described with Snell’s law and an empirical formula. Finally, the influence of refractive index changes on ALB errors is analyzed using the derived formula at the water–air interface and in the water column. The experimental results showed that in a constant elevation of 50 m for a bathymetric floor, the refractive index changes in water caused by temperature, salinity, and depth are less than 0.001. The maximum bathymetric error and maximum planimetric error caused by the refractive index changes at the water–air interface are 0.036 m and 0.015 m, respectively. The ALB errors caused by refractive index changes in the water column are relatively low, and the water column does not need to be layered to calculate the ALB errors. The influence of refractive index changes in water on the ALB error is minimal, accounting for only a small proportion of all bathymetric errors. Thus, it is necessary to determine whether the effect of the ALB error due to refractive index changes in water needs to be corrected based on the accuracy requirements of the data acquisition. This study and analysis can provide a reference basis for correcting ALB errors.

Complex terrains in coastal zones and shallow water areas around islands and reefs can be quickly detected via airborne LiDAR bathymetry (ALB) technology. Due to equipment placement deviations and measurement uncertainty, measurement deviations can occur in the overlapping areas of adjacent strips, so it is particularly necessary to register point clouds. To address areas with few seafloor structures and to overcome the challenges of extracting features in water areas with small terrain changes, a registration method for airborne LiDAR bathymetry seafloor point clouds based on the adaptive matching of corresponding points is proposed. First, the normal vector zenith angle, curvature change and omnidirectional variance in the seafloor points are calculated. Then, the corresponding points are adaptively matched according to the terrain feature similarity and distance constraints. Finally, the RANSAC algorithm and ICP algorithm are used for coarse registration and fine registration, respectively. The experimental results show that the method provides high accuracy and a uniform distribution of corresponding points. The root mean square error (RMSE) values when the registration method is applied to a flat area and coral reef area are 0.102 m and 0.041 m, respectively. Compared with that of the ICP alignment algorithm, the accuracy of the proposed method is improved by 0.114 m and 0.227 m, respectively, and compared with that of the NDT algorithm, the accuracy is improved by 0.316 m and 0.452 m, respectively; hence, the proposed method provides an effective solution for the registration of ALB data.

LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F₁-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas.

Airborne LiDAR bathymetry (ALB) can seamlessly measure land and shallow water areas. However, many factors lead to point cloud thickness and stratification of adjacent swaths, which are unfavorable for generating seafloor topographic maps. A refinement method based on neighborhood adaptive moving least squares (NA-MLS) is proposed considering the neighboring point distribution. First, a point cloud organization is established, and normal vectors are calculated using rasterization to reduce time consumption. Cylindrical neighborhood points are subsequently determined, and the coefficient matrix of the MLS is calculated. A condition number threshold is introduced to check the stability of the coefficient matrix, and the neighborhood size is adjusted adaptively. Finally, the points in adaptive neighborhoods are used to refine the seafloor points by MLS fitting. The experiments were conducted using open ALB data from NOAA. Compared with the raw ALB point clouds, the stratifications and small holes are eliminated by the proposed method, while the point quantity is reduced by 51.2%. The proposed method improves accuracy, with a mean maximum error decreased from 8.8 to 6.7 cm. Compared with traditional MLS, which combines rasterization (R-MLS) and MLS, which combines a k-dimensional tree (KD-MLS), the proposed method reduces the number of incorrect fitting points. Therefore, the proposed method effectively addresses the overfitting issue of MLS under uneven point density conditions, enhancing the accuracy of seafloor point clouds acquired via ALB systems.

Full-waveform airborne LiDAR bathymetry (ALB), which provides waveforms and point clouds, has become an essential technology for shallow water surveys. However, weak seafloor echoes are challenging to detect accurately because of waveform feature confusion caused by the complex measurement environments. To address this issue, waveform feature importances, feature histograms, and feature spaces of 14-dimensional waveform features are conducted to analyze the waveform feature confusion. Then, a random forest with optimized thresholds (RFOT) is proposed to detect normal seafloor echoes and weak seafloor echoes. Finally, waveform sharpening and condition screening are used to extract the seafloor echoes for overlapping waveforms in very shallow waters. The proposed method was verified with 14 swaths obtained by the Optech Aquarius system around Wuzhizhou Island. The results show that the energy features (area under curve, amplitude, etc.) can better discriminate the difference between weak seafloor echoes and noise than the shape features (RL area ratio, kurtosis, etc.). The number of seafloor echoes detected by the proposed method increased by 148.86% compared with the Aquarius system. The reference data prove that seafloor points detected by the proposed method are accurate and effective. Thus, this contribution effectively improves the bathymetric performance of the ALB system.