The local connection characteristics of convolutional neural network (CNN) are linked with the local spatial correlation of image pixels for water depth retrieval in this paper. The method has greater advantages and higher precision than traditional retrieval methods. Traditional remote sensing empirical models require manual extraction of retrieval factors and the process is complex. This paper proposes a model based on CNN which uses different remote sensing images in four spectral bands, red, green, blue, and near-infrared, to retrieving the water depth. In general, CNN is mostly used for image recognition and classification tasks, which can make full use of the local spatial correlation between pixels. The method in this paper exploits this feature of CNN for water depth retrieval, taking into consideration the nonlinear relationship between the radiance value and water depth value from adjacent and central pixels. In this paper, remote sensing image data, measured water depth data, and lidar sounding data are used as input data to build the model. Then, the retrieval error is analyzed and the parameters are adjusted to further optimize the model. Quantitative analysis and experimental results show that the CNN model achieves higher precision in the retrieval of shallow sea areas compared with the traditional method. The mean absolute error can reach within 0.8m. Finally, the model is shown to be highly portable and capable of retrieving water depth data with resolution equal to the resolution of the remote sensing image using only a small amount of input water depth data.

Geographic information describing coral reefs plays an important role in constructing electronic chart systems and protecting the ecological environment of the ocean. To derive geographic information of coral reefs more effectively, this paper proposes a methodology to detect coral reefs by combining airborne LiDAR bathymetry (ALB) bottom waveform and bathymetric feature data. A feature vector was established by deriving bottom waveform variables (the peak amplitude, pulsewidth, area, skewness, kurtosis, and backscatter cross section) and bathymetric variables (the depth standard deviation, slope, bathymetric position index, Gaussian curvature, mean curvature, and roughness). Using a support vector machine classifier, coral reefs were detected by distinguishing two classes (coral reefs and others) on the seafloor. To evaluate the classification performance of coral reefs, the developed method was applied to Yuanzhi Island, South China Sea surveys, and verified by field data (aerial digital camera images and underwater video images). The results showed that the classification overall accuracy of coral reefs can be greatly improved from 80.59%/90.31% when ALB bottom waveform or bathymetric variables features were used separately to 93.57% when using a combination of ALB bottom waveform and bathymetric features. In addition, the kappa coefficient can also be greatly improved from approximately 0.61/0.80 to 0.87. And the new proposed method performs better compared to the current classification method using ALB data to detect coral reefs with an overall accuracy of 90.92% and Kappa of 0.81. This highlights the potential of ALB data, combining waveform data and bathymetric data, for precisely detecting coral reefs in shallow water areas.

This contribution identifies the uncertainty sources influencing the component uncertainties for an airborne LiDAR bathymetry (ALB) measurement and presents the models for various component uncertainties (arising from the device, environment, and target for ALBs). Since various instrumental and environmental factors introduce vertical and horizontal uncertainties in ALB data, these uncertainties should be first analyzed and then precisely modeled to ensure the accuracy of the measurements. For this purpose, ten different effects that influence the accuracy of ALB data are systematically analyzed and modeled for four aspects in this article: the device aspect (laser pointing deflection, trajectory uncertainty, and boresight/lever arm offset), environmental aspect (atmospheric limitation, refraction on the sea surface, refraction in water, scattering in water, and water level fluctuation), target aspect (irregular bottom), and other aspect (accuracy of coordinate transformation model). In addition, the effect of the laser spot size is also discussed. To verify the presented uncertainty models, an ALB survey was operated around Yuanzhi Island in the South China Sea. For a water depth of 10 m, the theoretical overall root-sum-squares (RSS) for ten different effects of an ALB system (approximately 34 cm calculated using the total vertical uncertainty (TVU) models) is generally in accordance with the actual performance of the ALB data (approximately 39 cm) performance. The difference is mainly attributed to the limited accuracy of the ground truth data, and the difference between the water depth and laser ranging is reasonable. In this process, the topography data in the same region captured by a shipborne multibeam echo sounder (MBES) were used as the ground truth. The results indicated that for the typical ALB system, the laser pointing uncertainty and refraction uncertainty on the sea surface are primary uncertainty sources and should be corrected in a higher priority to meet the seafloor topographic accuracy demand of the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys (S-44). The proposed uncertainty models can be used not only to guide the actual measurement of an ALB system but also to provide the uncertainty correction reference for ALB data postprocessing.

Laser point cloud filtering is a fundamental step in various applications of light detection and ranging (LiDAR) data. The progressive triangulated irregular network (TIN) densification (PTD) filtering algorithm is a classic method and is widely used due to its robustness and effectiveness. However, the performance of the PTD filtering algorithm depends on the quality of the initial TIN-based digital terrain model (DTM). The filtering effect is also limited by the tuning of a number of parameters to cope with various terrains. Therefore, an improved PTD filtering algorithm based on a multiscale cylindrical neighborhood (PTD-MSCN) is proposed and implemented to enhance the filtering effect in complex terrains. In the PTD-MSCN algorithm, the multiscale cylindrical neighborhood is used to obtain and densify ground seed points to create a high-quality DTM. By linearly decreasing the radius of the cylindrical neighborhood and the distance threshold, the PTD-MSCN algorithm iteratively finds ground seed points and removes object points. To evaluate the performance of the proposed PTD-MSCN algorithm, it was applied to 15 benchmark LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission. The experimental results indicated that the average total error can be decreased from 5.31% when using the same parameter set to 3.32% when optimized. Compared with five other publicized PTD filtering algorithms, the proposed PTD-MSCN algorithm is not only superior in accuracy but also more robust.

The water level forecasting system represented by the hydrodynamic model relies too much on the input data and the forecast value of the boundary, therefore introducing uncertainty in the prediction results. Tide tables ignore the effect of the residual water level, which is usually significant. Therefore, to solve this problem, a water level forecasting method for the regional short-term (3 h) is proposed in this study. First, a simplified MIKE21 flow model (FM) was established to construct the regional major astronomical tides after subdividing the model residuals into stationary constituents (surplus astronomical tides, simulation deviation) and nonstationary constituents (residual water level). Harmonic analysis (HA) and long short-term memory (LSTM) were adopted to forecast these model residuals, respectively. Finally, according to different spatial background information, the prediction for each composition was corrected by the inverse distance weighting (IDW) algorithm and its improved IDW interpolation algorithm based on signal energy and the spatial distance (IDWSE) from adjacent observation stations to nonmeasured locations. The developed method was applied to Narragansett Bay in Rhode Island. Compared with the assimilation model, the root-mean-square error (RMSE) of the proposed method decreased from 12.3 to 5.0 cm, and R² increased from 0.932 to 0.988. The possibility of adding meteorological features into the LSTM network was further explored as an extension of the prediction of the residual water level. The results show that the accuracy was limited to a moderate level, which is related to the difficulty presented by using only wind features to completely characterize the regional dynamic energy equilibrium process.