Pub Date : 2022-08-22DOI: 10.1080/01490419.2022.2116616
Bimalkumar Patel, R. Sarangi, Apurva Prajapati, Bhargav Devliya, Hitesh Patel
Abstract TSM is an essential parameter as it affects the biogeochemistry of the ocean. The high TSM range affects light penetration that’s related to the photosynthesis of primary producers. The aim is to develop a TSM algorithm in Gujarat coastal water using remote sensing reflectance (Rrs), to monitor TSM concentration from the satellite. Seawater sampling and HyperOCR radiometer data collection were carried out in the northeast Arabian Sea. The high suspended matter was observed near the Gulf of Khambhat due to industries and riverine fluxes. For an accurate TSM algorithm, we compared the developed algorithm to previous studies. The TSM algorithm has been developed using the Rrs681/Rrs490 band ratio that has the highest linear correlation (R2 = 0.977, MSE = 19.06). Rrs band ratios demonstrated better compared to single Rrs bands. Satellite images were generated by applying the developed algorithm with the input of Rrs681 and Rrs490 from OLCI. The developed algorithm has been validated successfully with in situ TSM data points, collected across the Daman, Porbandar, and Okha coastal waters. The study indicates that the developed algorithm can be more robust and valuable for various satellite-based synoptic mapping of TSM, including the future Indian Oceansat-3 OCM mission.
{"title":"Development of Total Suspended Matter (TSM) Algorithm and Validation over Gujarat Coastal Water, the Northeast Arabian Sea Using In Situ Datasets","authors":"Bimalkumar Patel, R. Sarangi, Apurva Prajapati, Bhargav Devliya, Hitesh Patel","doi":"10.1080/01490419.2022.2116616","DOIUrl":"https://doi.org/10.1080/01490419.2022.2116616","url":null,"abstract":"Abstract TSM is an essential parameter as it affects the biogeochemistry of the ocean. The high TSM range affects light penetration that’s related to the photosynthesis of primary producers. The aim is to develop a TSM algorithm in Gujarat coastal water using remote sensing reflectance (Rrs), to monitor TSM concentration from the satellite. Seawater sampling and HyperOCR radiometer data collection were carried out in the northeast Arabian Sea. The high suspended matter was observed near the Gulf of Khambhat due to industries and riverine fluxes. For an accurate TSM algorithm, we compared the developed algorithm to previous studies. The TSM algorithm has been developed using the Rrs681/Rrs490 band ratio that has the highest linear correlation (R2 = 0.977, MSE = 19.06). Rrs band ratios demonstrated better compared to single Rrs bands. Satellite images were generated by applying the developed algorithm with the input of Rrs681 and Rrs490 from OLCI. The developed algorithm has been validated successfully with in situ TSM data points, collected across the Daman, Porbandar, and Okha coastal waters. The study indicates that the developed algorithm can be more robust and valuable for various satellite-based synoptic mapping of TSM, including the future Indian Oceansat-3 OCM mission.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49527140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-22DOI: 10.1080/01490419.2022.2116615
Kourosh Shahryari Nia, M. Sharifi, S. Farzaneh
Abstract Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low.
{"title":"Tidal Level Prediction Using Combined Methods of Harmonic Analysis and Deep Neural Networks in Southern Coastline of Iran","authors":"Kourosh Shahryari Nia, M. Sharifi, S. Farzaneh","doi":"10.1080/01490419.2022.2116615","DOIUrl":"https://doi.org/10.1080/01490419.2022.2116615","url":null,"abstract":"Abstract Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47986084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-12DOI: 10.1080/01490419.2022.2113578
Xianping Qin, Yuanxi Yang, Bijiao Sun
Abstract The ranges derived from acoustic measurements between seafloor stations are relatively more accurate compared with those derived from the sea surface vessel transducer to the seafloor transponders, because measurements through mixed water layers will be affected by complex acoustic range errors. Coordinates of seafloor stations can be improved by the direct-path acoustic ranging. Systematic errors in acoustic rangings, however, will significantly deteriorate the accuracy of vertical coordinates. In order to mitigate the effects of these systematic errors (e.g., acoustic ray bending and sound speed variation errors in acoustic measurements on the seafloor station location parameters), the observation model needs to be finely constructed. First, a new observation model with acoustic ray bending and sound speed bias parameters is established. Then, using a seafloor geodetic network with four moored stations at a depth of about 3000 m in the South China Sea, the significance of the acoustic ray bending parameter is tested. The results show that (1) the acoustic ray bending parameter is significant at the 90% confidence level, which means that the acoustic ray bending error in the seafloor geodetic network is not negligible; (2) by estimating the coefficient of acoustic ray bending, the influence of the acoustic ray bending error on the vertical coordinate components can be significantly mitigated; our model improves the accuracy of the seafloor stations’ position with differences in the horizontal coordinate components less than 0.1 cm between the two-dimensional adjustment and three-dimensional adjustment, and also improves the vertical coordinate component to uncertainty less than 3.0 cm; (3) the relative movement between the moored stations is less than 50 cm, and the horizontal movement is larger than the vertical movement.
{"title":"A Robust Method to Estimate the Coordinates of Seafloor Stations by Direct-Path Ranging","authors":"Xianping Qin, Yuanxi Yang, Bijiao Sun","doi":"10.1080/01490419.2022.2113578","DOIUrl":"https://doi.org/10.1080/01490419.2022.2113578","url":null,"abstract":"Abstract The ranges derived from acoustic measurements between seafloor stations are relatively more accurate compared with those derived from the sea surface vessel transducer to the seafloor transponders, because measurements through mixed water layers will be affected by complex acoustic range errors. Coordinates of seafloor stations can be improved by the direct-path acoustic ranging. Systematic errors in acoustic rangings, however, will significantly deteriorate the accuracy of vertical coordinates. In order to mitigate the effects of these systematic errors (e.g., acoustic ray bending and sound speed variation errors in acoustic measurements on the seafloor station location parameters), the observation model needs to be finely constructed. First, a new observation model with acoustic ray bending and sound speed bias parameters is established. Then, using a seafloor geodetic network with four moored stations at a depth of about 3000 m in the South China Sea, the significance of the acoustic ray bending parameter is tested. The results show that (1) the acoustic ray bending parameter is significant at the 90% confidence level, which means that the acoustic ray bending error in the seafloor geodetic network is not negligible; (2) by estimating the coefficient of acoustic ray bending, the influence of the acoustic ray bending error on the vertical coordinate components can be significantly mitigated; our model improves the accuracy of the seafloor stations’ position with differences in the horizontal coordinate components less than 0.1 cm between the two-dimensional adjustment and three-dimensional adjustment, and also improves the vertical coordinate component to uncertainty less than 3.0 cm; (3) the relative movement between the moored stations is less than 50 cm, and the horizontal movement is larger than the vertical movement.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49644818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-08DOI: 10.1080/01490419.2022.2089412
I. Bij de Vaate, Ericka Martin, D. C. Slobbe, M. Naeije, M. Verlaan
Abstract In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean.
{"title":"Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods","authors":"I. Bij de Vaate, Ericka Martin, D. C. Slobbe, M. Naeije, M. Verlaan","doi":"10.1080/01490419.2022.2089412","DOIUrl":"https://doi.org/10.1080/01490419.2022.2089412","url":null,"abstract":"Abstract In the Arctic Ocean, obtaining water levels from satellite altimetry is hampered by the presence of sea ice. Hence, water level retrieval requires accurate detection of fractures in the sea ice (leads). This paper describes a thorough assessment of various surface type classification methods, including a thresholding method, nine supervised-, and two unsupervised machine learning methods, applied to Sentinel-3 Synthetic Aperture Radar Altimeter data. For the first time, the simultaneously sensed images from the Ocean and Land Color Instrument, onboard Sentinel-3, were used for training and validation of the classifiers. This product allows to identify leads that are at least 300 meters wide. Applied to data from winter months, the supervised Adaptive Boosting, Artificial Neural Network, Naïve-Bayes, and Linear Discriminant classifiers showed robust results with overall accuracies of up to 92%. The unsupervised Kmedoids classifier produced excellent results with accuracies up to 92.74% and is an attractive classifier when ground truth data is limited. All classifiers perform poorly on summer data, rendering surface classifications that are solely based on altimetry data from summer months unsuitable. Finally, the Adaptive Boosting, Artificial Neural Network, and Bootstrap Aggregation classifiers obtain the highest accuracies when the altimetry observations include measurements from the open ocean.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43118086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-05DOI: 10.1080/01490419.2022.2091696
Y. Lumban-Gaol, K. Ohori, R. Peters
Abstract Satellite-Derived Bathymetry (SDB) can be calculated using analytical or empirical approaches. Analytical approaches require several water properties and assumptions, which might not be known. Empirical approaches rely on the linear relationship between reflectances and in-situ depths, but the relationship may not be entirely linear due to bottom type variation, water column effect, and noise. Machine learning approaches have been used to address nonlinearity, but those treat pixels independently, while adjacent pixels are spatially correlated in depth. Convolutional Neural Networks (CNN) can detect this characteristic of the local connectivity. Therefore, this paper conducts a study of SDB using CNN and compares the accuracies between different areas and different amounts of training data, i.e., single and multi-temporal images. Furthermore, this paper discusses the accuracies of SDB when a pre-trained CNN model from one or a combination of multiple locations is applied to a new location. The results show that the accuracy of SDB using the CNN method outperforms existing works with other methods. Multi-temporal images enhance the variety in the training data and improve the CNN accuracy. SDB computation using the pre-trained model shows several limitations at particular depths or when water conditions differ.
{"title":"Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks","authors":"Y. Lumban-Gaol, K. Ohori, R. Peters","doi":"10.1080/01490419.2022.2091696","DOIUrl":"https://doi.org/10.1080/01490419.2022.2091696","url":null,"abstract":"Abstract Satellite-Derived Bathymetry (SDB) can be calculated using analytical or empirical approaches. Analytical approaches require several water properties and assumptions, which might not be known. Empirical approaches rely on the linear relationship between reflectances and in-situ depths, but the relationship may not be entirely linear due to bottom type variation, water column effect, and noise. Machine learning approaches have been used to address nonlinearity, but those treat pixels independently, while adjacent pixels are spatially correlated in depth. Convolutional Neural Networks (CNN) can detect this characteristic of the local connectivity. Therefore, this paper conducts a study of SDB using CNN and compares the accuracies between different areas and different amounts of training data, i.e., single and multi-temporal images. Furthermore, this paper discusses the accuracies of SDB when a pre-trained CNN model from one or a combination of multiple locations is applied to a new location. The results show that the accuracy of SDB using the CNN method outperforms existing works with other methods. Multi-temporal images enhance the variety in the training data and improve the CNN accuracy. SDB computation using the pre-trained model shows several limitations at particular depths or when water conditions differ.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45353937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-19DOI: 10.1080/01490419.2022.2091695
Ruichen Zhang, Guojun Zhai, S. Bian, Houpu Li, B. Ji
Abstract High-accuracy seabed surface modelling provides multi-source high-precision fundamental geographic datasets for marine visual computing, seabed topography detection, marine biology, marine engineering and other fields. Proposed in this paper is a high-precision seabed surface model, which combines B-spline functions and Fourier-series, referred to as the Spline-Fourier-series (S-FS) method. Firstly, the mathematical relationship between the B-spline functions and Fourier-series in the modelling process is explored in depth, deducing the non-recursive basis functions of the Spline-Fourier-series model and the specific representation of the two dimensional Spline-Fourier-series model. Furthermore, using a publicly available Large-area bathymetric dataset, extensive experiments are conducted for comparisons with traditional methods (nearest-neighbor, bilinear, bicubic) and traditional Fourier-series, which generally shows the S-FS method has higher accuracy, better convergence and stronger robustness. Finally, based on its mathematically theoretical model, three characteristics (dimensionality reduction, multi-resolution expression and multi-scale visualization) of the S-FS method for constructing high-precision seabed surface are analyzed visually and deeply. Compared with B-spline function, the basic functions of the S-FS method inherit its prioritized compactly-supported performance and do not need to be recursively calculated anymore, thereby further showing its feasibility and extensibility in the field of high-precision seabed surface modelling.
{"title":"Analytical Method for High-Precision Seabed Surface Modelling Combining B-Spline Functions and Fourier Series","authors":"Ruichen Zhang, Guojun Zhai, S. Bian, Houpu Li, B. Ji","doi":"10.1080/01490419.2022.2091695","DOIUrl":"https://doi.org/10.1080/01490419.2022.2091695","url":null,"abstract":"Abstract High-accuracy seabed surface modelling provides multi-source high-precision fundamental geographic datasets for marine visual computing, seabed topography detection, marine biology, marine engineering and other fields. Proposed in this paper is a high-precision seabed surface model, which combines B-spline functions and Fourier-series, referred to as the Spline-Fourier-series (S-FS) method. Firstly, the mathematical relationship between the B-spline functions and Fourier-series in the modelling process is explored in depth, deducing the non-recursive basis functions of the Spline-Fourier-series model and the specific representation of the two dimensional Spline-Fourier-series model. Furthermore, using a publicly available Large-area bathymetric dataset, extensive experiments are conducted for comparisons with traditional methods (nearest-neighbor, bilinear, bicubic) and traditional Fourier-series, which generally shows the S-FS method has higher accuracy, better convergence and stronger robustness. Finally, based on its mathematically theoretical model, three characteristics (dimensionality reduction, multi-resolution expression and multi-scale visualization) of the S-FS method for constructing high-precision seabed surface are analyzed visually and deeply. Compared with B-spline function, the basic functions of the S-FS method inherit its prioritized compactly-supported performance and do not need to be recursively calculated anymore, thereby further showing its feasibility and extensibility in the field of high-precision seabed surface modelling.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46376325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-02DOI: 10.1080/01490419.2022.2082603
Rohini Selvaraj, Sannasiraj S. A., Sundar Vallam
Abstract
Propagation of tropical cyclones and their landfall along the coast affect the livelihood of the coastal community with loss of life, and Bay of Bengal is particularly vulnerable as past disasters have shown. The present study investigates the effects of tropical cyclones namely Phailin, Hudhud and Vardah during its landfall along the East Coast of India. Numerical modelling of storm surges primarily depends on the wind characteristics, for which, the performance of the simulated storm surge from cyclone wind and pressure fields of ECMWF is examined with Telemac-2D. The quality of the wind field is enhanced by applying available wind modification techniques, such as the parametric cyclone wind model superposed with ECMWF wind field, and the direct modification of ECMWF wind field. The superposed wind speed is found in good agreement with the measured wind data. The hydrodynamic simulation was then performed for the cyclonic events for the computation of the storm surge. The predictions agree well with the observed surges for the simulations performed with modified wind fields. The error reduced from 15 cm to 6 cm and model skill improved by 3% leading to a correlation coefficient of 0.98.
{"title":"Hydrodynamic Modelling of Storm Surge with Modified Wind Fields along the East Coast of India","authors":"Rohini Selvaraj, Sannasiraj S. A., Sundar Vallam","doi":"10.1080/01490419.2022.2082603","DOIUrl":"https://doi.org/10.1080/01490419.2022.2082603","url":null,"abstract":"<p><b>Abstract</b></p><p>Propagation of tropical cyclones and their landfall along the coast affect the livelihood of the coastal community with loss of life, and Bay of Bengal is particularly vulnerable as past disasters have shown. The present study investigates the effects of tropical cyclones namely Phailin, Hudhud and Vardah during its landfall along the East Coast of India. Numerical modelling of storm surges primarily depends on the wind characteristics, for which, the performance of the simulated storm surge from cyclone wind and pressure fields of ECMWF is examined with Telemac-2D. The quality of the wind field is enhanced by applying available wind modification techniques, such as the parametric cyclone wind model superposed with ECMWF wind field, and the direct modification of ECMWF wind field. The superposed wind speed is found in good agreement with the measured wind data. The hydrodynamic simulation was then performed for the cyclonic events for the computation of the storm surge. The predictions agree well with the observed surges for the simulations performed with modified wind fields. The error reduced from 15 cm to 6 cm and model skill improved by 3% leading to a correlation coefficient of 0.98.</p>","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-18DOI: 10.1080/01490419.2022.2079778
Jianhu Zhao, Wenbiao Liang, Jinye Ma, Meiqin Liu, Yuqing Li
Abstract Aiming at the problem that lack of the measured sound velocity profile (SVP) leads to the unreliable underwater positioning solution, this paper proposed an efficient underwater positioning method by the self-constraint conditions of water depth and sound velocity gradient. To construct the depth constraint condition, the sound propagation distance error model is deduced by acoustic ray tracing, and the sound vertical propagation error model related to the incident angle and sound velocity error is given firstly. By fitting the vertical propagation error model, the reference depth is solved, and the vertical propagation distances between the transducer and the underwater control points of all observation epochs are gotten. Then with the solved vertical distance of each epoch and the sound velocity gradient from neighbor SVPs as the constraint conditions, the SVP is retrieved by the simulated annealing (SA) algorithm. With the retrieved SVP, the underwater positioning can be performed when the measured SVP is absent. The proposed method was verified by an experiment in the 3000 m depth water area of the South China Sea. The results achieved 2.07 m/s of standard deviation of the SVP inversion, centimeter-level horizontal positioning accuracy and 0.54 m of vertical positioning accuracy under the circumstance of lack of SVP measurement.
{"title":"A Self-Constraint Underwater Positioning Method without the Assistance of Measured Sound Velocity Profile","authors":"Jianhu Zhao, Wenbiao Liang, Jinye Ma, Meiqin Liu, Yuqing Li","doi":"10.1080/01490419.2022.2079778","DOIUrl":"https://doi.org/10.1080/01490419.2022.2079778","url":null,"abstract":"Abstract Aiming at the problem that lack of the measured sound velocity profile (SVP) leads to the unreliable underwater positioning solution, this paper proposed an efficient underwater positioning method by the self-constraint conditions of water depth and sound velocity gradient. To construct the depth constraint condition, the sound propagation distance error model is deduced by acoustic ray tracing, and the sound vertical propagation error model related to the incident angle and sound velocity error is given firstly. By fitting the vertical propagation error model, the reference depth is solved, and the vertical propagation distances between the transducer and the underwater control points of all observation epochs are gotten. Then with the solved vertical distance of each epoch and the sound velocity gradient from neighbor SVPs as the constraint conditions, the SVP is retrieved by the simulated annealing (SA) algorithm. With the retrieved SVP, the underwater positioning can be performed when the measured SVP is absent. The proposed method was verified by an experiment in the 3000 m depth water area of the South China Sea. The results achieved 2.07 m/s of standard deviation of the SVP inversion, centimeter-level horizontal positioning accuracy and 0.54 m of vertical positioning accuracy under the circumstance of lack of SVP measurement.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48982416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-09DOI: 10.1080/01490419.2022.2075499
Willian Ney Cassol, S. Daniel, É. Guilbert, N. Debese
Abstract The estimation of the uncertainty related to bathymetric data is essential in determining the quality of the data acquisition. This estimation is based on the covariance propagation considering the classical sounding georeferencing model. The estimation of the uncertainty using the Total Propagated Uncertainty (TPU) model is well described in the literature. Developing on this model, this study introduces an analysis of the morphological influence of the seafloor on the uncertainty value of the sounded points. Advancing the comprehension of the influence of the seafloor on the uncertainty value of the bathymetric data would improve the processing and interpretation of the seafloor surface as well as the structures present on the seafloor.
{"title":"An Empirical Study of the Influence of Seafloor Morphology on the Uncertainty of Bathymetric Data","authors":"Willian Ney Cassol, S. Daniel, É. Guilbert, N. Debese","doi":"10.1080/01490419.2022.2075499","DOIUrl":"https://doi.org/10.1080/01490419.2022.2075499","url":null,"abstract":"Abstract The estimation of the uncertainty related to bathymetric data is essential in determining the quality of the data acquisition. This estimation is based on the covariance propagation considering the classical sounding georeferencing model. The estimation of the uncertainty using the Total Propagated Uncertainty (TPU) model is well described in the literature. Developing on this model, this study introduces an analysis of the morphological influence of the seafloor on the uncertainty value of the sounded points. Advancing the comprehension of the influence of the seafloor on the uncertainty value of the bathymetric data would improve the processing and interpretation of the seafloor surface as well as the structures present on the seafloor.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44806664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-11DOI: 10.1080/01490419.2022.2064572
Tyler Susa
Abstract Accurate charting of nearshore bathymetry is critical to the safe and dependable use of coastal waterways frequented by the trading, fishing, tourism, and ocean energy industries. The accessibility of satellite imagery and the availability of various satellite-derived bathymetry (SDB) techniques have provided a cost-effective alternative to traditional in-situ bathymetric surveys. Furthermore, improved algorithms and the advancement of machine learning models have provided opportunity for higher quality bathymetric derivations. However, to date the relative accuracy and performance between traditional physics-based techniques, improved physics-based methods, and machine learning ensemble models have not been adequately quantified. In this study, nearshore bathymetry is derived from Sentinel-2 satellite imagery near La Parguera, Puerto Rico utilizing a traditional band-ratio algorithm, a band-ratio switching method, a random forest machine learning model, and the XGBoost machine learning model. The machine learning models returned comparable results and were markedly more accurate relative to other techniques; however, both machine learning models required an extensive training dataset. All models were constrained by environmental influences and image spatial resolution, which were assessed to be the limiting factors for routine use of satellite-derived bathymetry as a reliable method for hydrographic surveying.
{"title":"Satellite Derived Bathymetry with Sentinel-2 Imagery: Comparing Traditional Techniques with Advanced Methods and Machine Learning Ensemble Models","authors":"Tyler Susa","doi":"10.1080/01490419.2022.2064572","DOIUrl":"https://doi.org/10.1080/01490419.2022.2064572","url":null,"abstract":"Abstract Accurate charting of nearshore bathymetry is critical to the safe and dependable use of coastal waterways frequented by the trading, fishing, tourism, and ocean energy industries. The accessibility of satellite imagery and the availability of various satellite-derived bathymetry (SDB) techniques have provided a cost-effective alternative to traditional in-situ bathymetric surveys. Furthermore, improved algorithms and the advancement of machine learning models have provided opportunity for higher quality bathymetric derivations. However, to date the relative accuracy and performance between traditional physics-based techniques, improved physics-based methods, and machine learning ensemble models have not been adequately quantified. In this study, nearshore bathymetry is derived from Sentinel-2 satellite imagery near La Parguera, Puerto Rico utilizing a traditional band-ratio algorithm, a band-ratio switching method, a random forest machine learning model, and the XGBoost machine learning model. The machine learning models returned comparable results and were markedly more accurate relative to other techniques; however, both machine learning models required an extensive training dataset. All models were constrained by environmental influences and image spatial resolution, which were assessed to be the limiting factors for routine use of satellite-derived bathymetry as a reliable method for hydrographic surveying.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48388000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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