Salt marshes provide diverse habitats for a wide range of creatures and play a key defensive and buffering role in resisting extreme marine hazards for coastal communities. Accurately obtaining the terrains of salt marshes is crucial for the comprehensive management and conservation of coastal resources and ecology. However, dense vegetation coverage, periodic tide inundation, and pervasive ditch distribution create challenges for measuring or estimating salt marsh terrains. These environmental factors make most existing techniques and methods ineffective in terms of data acquisition resolution, accuracy, and efficiency. Drone multi-line light detection and ranging (LiDAR) has offered a fire-new perspective in the 3D point cloud data acquisition and potentially exhibited great superiority in accurately deriving salt marsh terrains. The prerequisite for terrain characterization from drone multi-line LiDAR data is point cloud filtering, which means that ground points must be discriminated from the non-ground points. Existing filtering methods typically rely on either LiDAR geometric or intensity features. These methods may not perform well in salt marshes with dense, diverse, and complex vegetation. This study proposes a new filtering method for drone multi-line LiDAR point clouds in salt marshes based on the artificial neural network (ANN) machine learning model. First, a series of spatial–spectral features at the individual (e.g., elevation, distance, and intensity) and neighborhood (e.g., eigenvalues, linearity, and sphericity) scales are derived from the original data. Then, the derived spatial–spectral features are selected to remove the related and redundant ones for optimizing the performance of the ANN model. Finally, the reserved features are integrated as input variables in the ANN model to characterize their nonlinear relationships with the point categories (ground or non-ground) at different perspectives. A case study of two typical salt marshes at the mouth of the Yangtze River, using a drone 6-line LiDAR, demonstrates the effectiveness and generalization of the proposed filtering method. The average G-mean and AUC achieved were 0.9441 and 0.9450, respectively, outperforming traditional geometric information-based methods and other advanced machine learning methods, as well as the deep learning model (RandLA-Net). Additionally, the integration of spatial–spectral features at individual–neighborhood scales results in better filtering outcomes than using either single-type or single-scale features. The proposed method offers an innovative strategy for drone LiDAR point cloud filtering and salt marsh terrain derivation under the novel solution of deeply integrating geometric and radiometric data.

Terrestrial laser scanning (TLS) can acquire high-precision and high-resolution 3D point clouds of the scanned targets, and the common technical challenge is how to accurately and efficiently process the massive unorganized point clouds. Density is one of the most fundamental and significant quantities that can be derived for each point by counting the number of neighbors in a given 3D space. It contains key information of the target geometric features and thus plays an important role in TLS data processing (e.g., classification and feature extraction). However, density usually varies remarkably for different scenes with varied scan geometry (e.g., distance and incidence angle), scan resolution (e.g., horizontal and vertical angular resolutions), and object geometry (e.g., slope, size, and spatial attitude), thereby greatly hindering its full capability in reflecting the geometric discrepancies of different targets. The density must be corrected before reliably used as a proxy for target geometric features. In this study, a generalized rigorous model is proposed to correct the density of single-scan TLS point clouds based on the mathematically deduced relation between density and related influencing factors. The superiority of the proposed model over existing models is that it is rigorously developed based on the instrumental scanning principles and target spatial geometry and can be used for different instruments, scanning scenes, targets, and scanning parameters. The proposed model is verified by a series of indoor quantitative control experiments and three natural scenes with totally different instruments and scanning conditions. The results show that the proposed model can accurately simulate the density variation at different scanning geometries with an average correlation coefficient of 0.9957 between the calculated and actual densities. The effects of all influencing factors on density are significantly removed in complex natural scenes by the proposed model where the coefficient of variation of the density from a homogeneous surface after correction is reduced by 72.31%, on the average. The proposed model exhibits good performance in terms of the feasibility, effectiveness, and generality and can derive a corrected density value that is a proxy of target geometric size. Additionally, the proposed model can be conducted on each scan individually before the co-registration in the actual TLS campaign with a number of single scans, which has tremendous application value in facilitating TLS data interpretation.

Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.

Mobile laser scanning (MLS) systems enable rapid acquisition of high-definition 3D point clouds for urban digitization, topographic mapping, and infrastructure inspection. Despite the critical role of point cloud density in quantifying data fidelity and object discriminability, its inherent spatiotemporal variability — arising from the nonlinear interplay of scanning geometry, platform dynamics, and surface topology — has remained inadequately addressed in current metrological frameworks. This study establishes a rigorous mathematical model that quantifies MLS density variations through the interdependent variables: density search radius, scanning distance, angular resolution, platform velocity, pulse repetition frequency, and three angles defining the spatial orientation of the local infinitesimal plane at the target point. Building upon this formulation, we propose the first MLS point cloud density correction method to mitigate heterogeneity caused by varying influencing factors and to derive a new corrected density value for each point that serves as an indicator of target geometry attribute. Experiments conducted across different platforms and environments demonstrate that the proposed method effectively eliminates inhomogeneity in density. The correction procedure achieves an average 61% decrease in the density coefficient of variation over homogeneous surfaces. The proposed method exhibits strong performance regarding feasibility and generality, offering significant application value in enhancing MLS data interpretation and understanding spatial distribution patterns of point clouds under various circumstances.