To reduce the size and cost of an integrated infrared (IR) and green airborne LiDAR bathymetry (ALB) system, and improve the accuracy of the green ALB system, this study proposes a method to accurately determine water surface and water bottom heights using a single green laser corrected by the near water surface penetration (NWSP) model. The factors that influence the NWSP of green laser are likewise analyzed. In addition, an NWSP modeling method is proposed to determine the relationship between NWSP and the suspended sediment concentration (SSC) of the surface layer, scanning angle of a laser beam and sensor height. The water surface and water bottom height models are deduced by considering NWSP and using only green laser based on the measurement principle of the IR laser and green laser, as well as employing the relationship between NWSP and the time delay of the surface return of the green laser. Lastly, these methods and models are applied to a practical ALB measurement. Standard deviations of 3.0, 5.3, and 1.3 cm are obtained by the NWSP, water-surface height, and water-bottom height models, respectively. Several beneficial conclusions and recommendations are drawn through the experiments and discussions.

Airborne LiDAR bathymetry (ALB) is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused by pulse stretching induces depth bias, which is the largest error source in ALB depth measurements. The traditional depth bias model is often applied to reduce the depth bias, but it is insufficient when used with various ALB system parameters and ocean environments. Therefore, an accurate model that considers all of the influencing factors must be established. In this study, an improved depth bias model is developed through stepwise regression in consideration of the water depth, laser beam scanning angle, sensor height, and suspended sediment concentration. The proposed improved model and a traditional one are used in an experiment. The results show that the systematic deviation of depth bias corrected by the traditional and improved models is reduced significantly. Standard deviations of 0.086 and 0.055 m are obtained with the traditional and improved models, respectively. The accuracy of the ALB-derived depth corrected by the improved model is better than that corrected by the traditional model.

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.

Ocean–land waveform classification (OLWC) is crucial in airborne LiDAR bathymetry (ALB) data processing and can be used for ocean–land discrimination and waterline extraction. However, the accuracy of OLWC for single-wavelength ALB systems is low given the nature of the green laser waveform in complex environments. Thus, in this article, a deep learning-based OLWC method called the multichannel voting convolutional neural network (MVCNN) is proposed based on the comprehensive utilization of multichannel green laser waveforms. First, multiple green laser waveforms collected in deep and shallow channels are input into a multichannel input module. Second, a one-dimensional (1-D) convolutional neural network (CNN) structure is proposed to handle each green channel waveform. Finally, a multichannel voting module is introduced to perform majority voting on the predicted categories derived by each 1-D CNN model and output the final waveform category (i.e., ocean or land waveforms). The proposed MVCNN is evaluated using the raw green laser waveforms collected by Optech coastal zone mapping and imaging LiDAR (CZMIL). Results show that the overall accuracy, kappa coefficient, and standard deviation of the overall accuracy for the OLWC utilizing green laser waveforms based on MVCNN can reach 99.41%, 0.9800, and 0.03%, respectively. Results further show that the classification accuracy of the MVCNN is improved gradually with the increase in the number of laser channels. The multichannel voting module can select the correct waveform category from the deep and shallow channels. The proposed MVCNN is highly accurate and robust, and it is slightly affected by aquaculture rafts and the merging effect of green laser waveform in very shallow waters. Thus, the use of MVCNN in OLWC for single-wavelength ALB systems is recommended. In addition, this article explores the relationships between green deep and shallow channel waveforms based on the analysis of CZMIL waveform data.

Wavelet threshold denoising with different threshold selection rules (TSRs) were used to reduce random noise (RN) in bathymetric laser full-waveforms. A nonreasonable threshold used for denoising can result in over-smoothing or under-smoothing of the signal and easily remove details of weak bottom return (BR). A unique and optimal TSR for all bathymetric full-waveforms of waters with different depths or turbidities is unavailable. Hence, an adaptive threshold selection (ATS) is proposed to improve the performance of RN reduction by adaptively selecting a threshold for each full-waveform based on the prominence of BR-to-noise ratio. The proposed method is applied to reduce the RN in raw green laser full-waveforms collected via Optech coastal zone mapping and imaging LIght Detection And Ranging (LiDAR). Compared with other traditional methods, the ATS improves the ratio of detectable BR by 5.64% and achieves a root mean squared error (RMSE) closer to the real RN level. Therefore, ATS can effectively remove the RN, enhance the prominence, and ensure the fidelity of weak BR.

The discrimination of water–land waveforms is a critical step in the processing of airborne topobathy LiDAR data. Waveform features, such as the amplitudes of the infrared (IR) laser waveforms of airborne LiDAR, have been used in identifying water–land interfaces in coastal waters through waveform clustering. However, water–land discrimination using other IR waveform features, such as full width at half maximum, area, width, and combinations of different features, has not been evaluated and compared with other methods. Furthermore, false alarms often occur when water–land discrimination in coastal areas is conducted using IR laser waveforms because of environmental factors. This study provides an optimal feature for water–land discrimination using an IR laser by comparing the performance of different waveform features and proposes a dual-clustering method integrating K-means and density-based spatial clustering applications with noise algorithms to improve the accuracy of water–land discrimination through the clustering of waveform features and positions of IR laser spot centers. The proposed method is used for practical measurement with Optech Coastal Zone Mapping and Imaging LiDAR. Results show that waveform amplitude is the optimal feature for water–land discrimination using IR laser waveforms among the researched features. The proposed dual-clustering method can correct mislabeled water or land waveforms and reduce the number of mislabeled waveforms by 48% with respect to the number obtained through traditional K-means clustering. Water–land discrimination using IR waveform amplitude and the proposed dual-clustering method can reach an overall accuracy of 99.730%. The amplitudes of IR laser waveform and the proposed dual-clustering method are recommended for water–land discrimination in coastal and inland waters because of the high accuracy, resolution, and automation of the methods.