Accurate bathymetric data is essential for marine, coastal ecosystems, and related studies. In the past decades, a lot of studies were investigated to obtain bathymetric data in shallow waters using ...satellite remotely sensed data. Satellite multispectral imagery has been widely used to estimate shallow water depths based on empirical models and physics-based models. However, the in-situ water depth information is essential (as the priori) to use the empirical model in a specific area, which limits its application, especially for remote reefs. In this study, the bathymetric maps in shallow waters were produced based on empirical models with only satellite remotely sensed data (i.e., the new ICESat-2 bathymetric points and Sentinel-2 multispectral imagery). The bathymetric points from the spaceborne ICESat-2 lidar were used in place of the in-situ auxiliary bathymetric points to train the classical empirical models (i.e., the linear model and the band ratio model). The bathymetric points were firstly extracted from noisy ICESat-2 raw data photons by an improved point cloud processing algorithm, and then were corrected for bathymetric errors (which were caused by the refraction effect in the water column, the refraction effect on the water surface, and the fluctuation effect on the water surface). With the trained empirical models and Sentinel-2 multispectral images, the bathymetric maps were produced for Yongle Atoll, in the South China Sea and the lagoon near Acklins Island and Long Cay, to the southeast of Bahama with four-date Sentinel-2 images. The bathymetry performance (including the accuracy and consistency of multi-date data) was evaluated and compared with the in-situ measurements. The results indicate that the bathymetric accuracy is well, and the RMSE is lower or close to 10% of the maximum depth for the two models with four-date images in two study areas. The consistency of multi-date data is well with the mean R2 of 0.97. The main novelties of this study are that the accuracy bathymetric points can be obtained from the ICESat-2 raw data using the proposed signal processing and error correction method, and using the ICESat-2 bathymetric points, the satellite multispectral imagery based on empirical models is no longer limited by local priori measurements, which were essential in previous studies. Hence, In the future, with the help of free and open-access satellite data (i.e., ICESat-2 data and Sentinel-2 imagery), this approach can be extended to a larger scale to obtain bathymetric maps in the shallow water of coastal areas, surroundings of islands and reefs, and inland waters.
•Estimating bathymetric topography with only satellite remotely sensed data.•Using new ICESat-2 lidar points and Sentinel-2 multispectral imageries.•Proposing signal detection and bathymetric error correction method for ICESat-2.•Training empirical models by ICESat-2 bathymetric points to estimate water depths.•Drawing and validating bathymetry in two study areas with multi-date datasets.
Water depth can be measured using airborne LiDAR bathymetry (ALB). However, when the green laser beam passes through the air-water interface, the sea surface slope greatly affects the laser ...propagation path, significantly influencing the accuracy of the measured seafloor topography. To reduce its influence, a refraction correction method at the air-water interface based on the sea surface profile and ray tracing is proposed. First, the 3-D sea surface profile is fit based on the least-squares criteria and the wave spectrum, using the laser point data reflected by the sea surface. Then, on the basis of the sea surface slope, the geolocation biases of the laser points are corrected by tracing every laser transmission path at the air-water interface. The developed method is used to correct the ALB data collected in the South China Sea, and verified by the topography data captured by a ship-borne multibeam echo sounder. Before the refraction correction, the mean absolute error (MAE) is 14.2 cm, and the root-mean-square error (RMSE) is 17.5 cm. After the refraction correction, the MAE and RMSE decrease to 7.2 and 8.3 cm, respectively. The developed method can effectively improve the bathymetric accuracy of the ALB data.
Accurate acquisition of information on seabed sediment distributions plays an important role in the construction of basic marine geographic databases. Although a multibeam echo-sounding system (MBES) ...can satisfy large-scale seafloor mapping with high precision and high resolution, the development of a consistent, stable, repeatable and validated seabed sediment classification method based on swath acoustic data is still in its infancy. To achieve accurate prediction and mapping of geographic seabed sediment information, this paper developed a deep learning model based on feature optimization. First, faced with high-dimensional features extracted from multibeam bathymetry and backscatter intensity measurement data, a fuzzy ranking (FR) feature optimization method was proposed. By combining the physical properties of actual sediment samples, the multidimensional features derived from terrain and intensity data are ranked and optimally selected according to the mean square error to eliminate redundant and irrelevant features. Second, the deep belief network (DBN) deep learning method was used to build a supervised seabed sediment classification model. The optimized features and actual sediment samples participate in model training, which further enhances the prediction ability of acoustic data to seabed sediments. Finally, to evaluate the performance of the DBN model, this experiment used large-scale multibeam survey data and ground-truth data (acquired by grabbers, core samplers, dredges, etc.) in the southern Irish Sea to achieve accurate prediction of 10 sediment types (slightly gravelly muddy sand, slightly gravelly sand, gravelly mud, gravelly muddy sand, gravelly sand, muddy sand, muddy sandy gravel, sand, sandy gravel and sandy mud). The experiment results show that by using the optimal feature combination based on FR, the overall classification accuracy and Kappa coefficient reached 86.20% and 0.834, respectively, which are significantly improved compared to the evaluation metrics of other feature selection methods. In addition, compared with the current five typical supervised classification methods (i.e., the random forests, BP neural network, support vector machine, maximum likelihood and decision trees methods), the proposed DBN classification model achieves a better performance, highlighting its application potential in seabed sediment detection and mapping.
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•The new DBN sediment classification model based on fuzzy ranking feature optimization achieves accurate prediction of sediments with 10 types.•The proposed fuzzy ranking method selects acoustic features with close relation to the physical properties of ground-truth sediments.•A newly extracted feature of seafloor terrain morphology can be used as an effective feature to characterize and mapping seabed sediment.•The combination of bathymetry and backscatter intensity features is more effective for characterizing and mapping the seabed sediments.•Deep learning framework based on DBN improves model training speed and stability of supervised seabed sediment classification.
The near-trench coseismic rupture behaviour of the 2011 Tohoku-Oki earthquake remains poorly understood due to the scarcity of near-field observations. Differential bathymetry offers a unique ...approach to studying offshore coseismic seafloor deformation but has a limited horizontal resolution. Here we use differential bathymetry estimates with improved horizontal resolutions to investigate near-trench coseismic slip behaviours in the 2011 Tohoku-Oki earthquake. In the main rupture region, a velocity-strengthening behaviour in the shallow fault is observed. By contrast, the seafloor uplift decreases towards the trench, but the trend inverts near the backstop interface outcrop, revealing significant off-fault deformation features. Amongst various competing off-fault effects observed, we suggest that inelastic deformation plays a predominant role in near-trench tsunami excitation. Large trench-bleaching rupture is also observed immediately north of 39°, delimiting the northern extent of the main rupture region. Overall, striking spatial heterogeneity of the shallow rupture behaviour is revealed for the region.
Seabed sediment classification has significance for the utilization of marine resources and marine scientific research. Currently, the multibeam echo sounder (MBES) is increasingly becoming the tool ...of choice for large-scale seabed sediment classification. To further explore the technology of seabed sediment classification, this paper proposes a new classification method. In addition to backscatter mosaic, the method also integrates three other different types of features, including texture features of backscatter mosaic, MBES bathymetry features, and backscatter angular response (AR) features, which are given different weights in the classification process. First, geographically weighted regression (GWR) analysis is performed between different types of features and seabed sediment types, and the normalized coefficient of determination (R2) is employed as the weight coefficient for the different types of features. Second, the backscatter mosaic is combined with features from different types to predict the seabed sediment types using a deep neural network (DNN) classifier. Third, the classification residuals of the features from these three different types are acquired through the above classification results. Last, the classification residuals of features from different types are added to the classification results of the backscatter mosaic according to the weights, thereby achieving seabed sediment classification based on MBES multifeatures with different weights. The results show that the overall classification accuracy of the seabed sediments can be significantly improved from 88.98%/85.14% to 93.43% when using the DNN classification model based on MBES multifeatures with different weights compared with the other two models (DNN classification model based on MBES multifeatures with equal weights and DNN classification model based on principal component analysis (PCA) dimensionality reduction). The kappa coefficient can also be significantly improved from approximately 0.85/0.80 to 0.91. Via analysis, the proposed method can reasonably assign the weights of the different features and take advantage of integrating MBES multifeatures for seabed sediment classification. This approach also provides an important reference for future research on seabed sediment classification.
•Integrating three different types of feature is more effective for seabed sediment classification.•The GWR model effectively evaluate the importance of the MBES multifeatures.•The DNN-based seabed classification model achieves prediction of sediments with three types and a shipwreck.
Achieving coastal and shallow-water bathymetry is essential for understanding the marine environment and for coastal management. Bathymetric data in shallow sea areas can currently be obtained using ...SDB (satellite-derived bathymetry) with multispectral satellites based on depth inversion models. In situ bathymetric data are crucial for validating empirical models but are currently limited in remote and unapproachable areas. In this paper, instead of using the measured water depth data, ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) ATL03 bathymetric points at different acquisition dates and multispectral imagery from Sentinel-2/GeoEye-1 were used to train and evaluate water depth inversion empirical models in two study regions: Shanhu Island in the South China Sea, and Heron Island in the Great Barrier Reef (GBR) in Australia. However, different sediment types also influenced the SDB results. Therefore, three types of sediments (sand, reef, and coral/algae) were analyzed for Heron Island, and four types of sediments (sand, reef, rubble and coral/algae) were analyzed for Shanhu Island. The results show that accuracy generally improved when sediment classification information was considered in both study areas. For Heron Island, the sand sediments showed the best performance in both models compared to the other sediments, with mean R2 and RMSE values of 0.90 and 1.52 m, respectively, representing a 5.6% improvement of the latter metric. For Shanhu Island, the rubble sediments showed the best accuracy in both models, and the average R2 and RMSE values were 0.97 and 0.65 m, respectively, indicating an RMSE improvement of 15.5%. Finally, bathymetric maps were generated in two regions based on the sediment classification results.
The occlusion of buildings in urban environments leads to the intermittent reception of satellite signals, which limits the utilization of observations. This subsequently results in a decline of the ...positioning and attitude accuracy of Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated system (GNSS/INS). This study implements a smooth post-processing strategy based on a tightly coupled differential GNSS/INS. Specifically, this strategy used the INS-estimated position to reinitialize integer ambiguity. The GNSS raw observations were input into the Kalman filter to update the measurement. The Rauch-Tung-Striebel smoothing (RTSS) algorithm was used to process the observations of the entire period. This study analyzed the performance of loosely coupled and tightly coupled systems in an urban environment and the improvement of the RTSS algorithm on the navigation solution from the perspective of fully mining the observations. The experimental results of the simulation data and real data show that, compared with the traditional tightly coupled processing strategy which does not use INS-aided integer ambiguity resolution and RTSS algorithm, the strategy in this study sufficiently utilized INS observations and GNSS observations to effectively improve the accuracy of positioning and attitude and ensure the continuity of navigation results in an obstructed environment.
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 article. 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 article proposes a model based on CNN, which uses different remote sensing images in four spectral bands, red, green, blue, and near-infrared, to retrieve 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 article 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 article, 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 accuracy of the CNN model in shallow sea areas retrieval is improved by more than 50%. The mean absolute error can reach within 0.8 m. Finally, the model is shown to be highly portable and capable of retrieving water depth data with resolution equal to the spatial resolution of the remote sensing image using only a small amount of input water depth data.
The shallow-water temperature profile is typically parameterized using a few empirical orthogonal function (EOF) coefficients. However, when the experimental area is poorly known or highly variable, ...the adaptability of the EOFs will be significantly reduced. In this study, a new set of basis functions, generated by combining the internal-wave eigenmodes with the average temperature gradient, is developed for characterizing the temperature perturbations. Temperature profiles recorded by a thermistor chain in the South China Sea in 2015 are processed and analyzed. Compared to the EOFs, the new set of basis functions has higher reconstruction accuracy and adaptability; it is also more stable in ocean regions that have internal waves.
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