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Airborne LiDAR bathymetry (ALB) has been shown to have the ability to retrieve water turbidity using the waveform parameters (i.e., slopes and amplitudes) of volume backscatter returns. However, directly and accurately extracting the parameters of volume backscatter returns from raw green-pulse waveforms in shallow waters is difficult because of the short waveform. This study proposes a new accurate and efficient method for the remote sensing of suspended sediment concentrations (SSCs) in shallow waters based on the waveform decomposition of ALB. The proposed method approaches raw ALB green-pulse waveforms through a synthetic waveform model that comprises a Gaussian function (for fitting the air-water interface returns), triangle function (for fitting the volume backscatter returns), and Weibull function (for fitting the bottom returns). Moreover, the volume backscatter returns are separated from the raw green-pulse waveforms by the triangle function. The separated volume backscatter returns are used as bases to calculate the waveform parameters (i.e., slopes and amplitudes). These waveform parameters and the measured SSCs are used to build two power SSC models (i.e., SSC (C)-Slope (K) and SSC (C)-Amplitude (A) models) at the measured SSC stations. Thereafter, the combined model is formed by the two established C-K and C-A models to retrieve SSCs. SSCs in the modeling water area are retrieved using the combined model. A complete process for retrieving SSCs using the proposed method is provided. The proposed method was applied to retrieve SSCs from an actual ALB measurement performed using the Optech Coastal Zone Mapping and Imaging LiDAR in a shallow and turbid water area. A mean bias of 0.05 mg/L and standard deviation of 3.8 mg/L were obtained in the experimental area using the combined model. [ABSTRACT FROM AUTHOR]

To obtain the mudflat topography when existing measuring systems and data processing methods are impracticable under special conditions, this paper presents a hovercraft-borne LiDAR (light detection and ranging) system and a novel comprehensive filtering method. The system is based on a hovercraft and equipped with a laser scanner and a POS (position and orientation system). The filtering method firstly segments the point cloud into different segments by combining the geometric and intensity information, then fitting the ground surface by cloth simulation method, and finally synthetically extracts the ground points with three constraints. These constraints are the distance of the point to the fitting surface, the normal difference between the point and the fitting surface, and the proportion of the possible ground points in the total points of each segment. The effectiveness of the measurement system and the development of the post-processing results were verified on the basis of field measurements, in which a total filtering error of 0.3% and the elevation accuracy of 6.4 cm were obtained. The proposed system and methods provide a new way for efficient and accurate topographic survey on mudflats. [ABSTRACT FROM AUTHOR]

Zhao, X.; Wang, X.; Zhao, J., and Zhou, F., 2021. An improved water-land discriminator using laser waveform amplitudes and point cloud elevations of airborne LIDAR. Journal of Coastal Research, 37(6), 1158–1172. Coconut Creek (Florida), ISSN 0749-0208. Laser waveforms or point clouds of airborne LIDAR are used to distinguish water and land. However, false alarms often occur in coastal areas with complex environments when laser waveforms are used. Elevations of three-dimensional (3D) point clouds can be used to help discriminate ocean and land but fail to identify inland waters. An improved water-land discriminator that uses laser waveform amplitudes and point cloud elevations is proposed in this study. First, 3D point cloud elevations derived using infrared (IR) laser are used as features to conduct ocean-land discrimination with fuzzy c-means clustering. Second, amplitudes of IR laser waveforms are used to identify inland waters from land derived by ocean-land discrimination. The proposed method is applied to data collected using Optech coastal zone mapping and imaging LIDAR (CZMIL). Land boundary derived from digital orthophoto map is used as a reference to evaluate different water-land discriminators. Results showed that consistency of the water-land interface derived using the improved water-land discriminator is higher with the reference interface than that derived by traditional waveform saturation, waveform clustering, and 3D point cloud methods. Overall accuracy of water-land points discriminated via waveform saturation, waveform clustering, 3D point cloud, and improved water-land discriminator is 93.80%, 93.84%, 95.66%, and 99.27%, respectively. High accuracy of the water-land interface and points indicates the effectiveness of the improved water-land discriminator for Optech CZMIL. [ABSTRACT FROM AUTHOR]

To obtain large-scale seabed surface image, this paper proposes a side-scan sonar (SSS) image mosaic method using couple feature points (CFPs) with constraint of track line positions. The SSS geocoded images are firstly used to form a coarsely mosaicked one and the overlapping areas between adjacent strip images can be determined based on geographic information. Inside the overlapping areas, the feature point (FP) detection and registration operation are adopted for both strips. According to the detected CFPs and track line positions, an adjustment model is established to accommodate complex local distortions as well as ensure the global stability. This proposed method effectively solves the problem of target ghosting or dislocation and no accumulated errors arise in the mosaicking process. Experimental results show that the finally mosaicked image correctly reflects the object distribution, which is meaningful for understanding and interpreting seabed topography. [ABSTRACT FROM AUTHOR]

Suspended sediment concentrations (SSCs) have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB). However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remote sensing of SSCs in calm waters by using the range biases of green surface points of ALB. The near water surface penetrations (NWSPs) of green lasers are calculated on the basis of the green and reference surface points. The range biases (ΔS) are calculated by using the corresponding NWSPs and beam-scanning angles. In situ measured SSCs (C) and range biases (ΔS) are used to establish an empirical C-ΔS model at SSC sampling stations. The SSCs in calm waters are retrieved by using the established C-ΔS model. The proposed method is applied to a practical ALB measurement performed by Optech Coastal Zone Mapping and Imaging LiDAR. The standard deviations of the SSCs retrieved by the 3D point-cloud method are less than 20 mg/L. [ABSTRACT FROM AUTHOR]