Pub Date : 2023-05-27DOI: 10.1080/01490419.2023.2213838
Kaidi Jin, Hongzhou Chai, Chuhan Su, Minzhi Xiang, Mingchen Shi
Abstract Doppler velocity logger (DVL) error parameters can significantly influence the navigation accuracy of DVL/strapdown inertial navigation system (SINS) integration for unmanned underwater vehicles (UUV). To improve the navigation accuracy of UUV, this study proposes a two-stage DVL calibration method aided by global navigation satellite system (GNSS) measurements. First, utilizing the velocity of GNSS/SINS integrated navigation, the scale factor error of DVL is calculated by the moduli of velocities in UUV body frame and DVL instrument frame. Then, using the measurements throughout the calibration process, the calibration problem of the installation angle is converted to a nonlinear constraint optimization problem by describing the angle as a unit quaternion. Moreover, an easy-to-implement quaternion estimation algorithm is chosen to solve the problem and obtain the optimal quaternion. Simulation and sea trial indicate that the proposed method can rapidly and accurately estimate the DVL error parameters in different scenarios, and the position accuracy of the DVL/SINS system is improved.
{"title":"A GNSS-aided DVL calibration method based on quaternion estimation for underwater vehicles","authors":"Kaidi Jin, Hongzhou Chai, Chuhan Su, Minzhi Xiang, Mingchen Shi","doi":"10.1080/01490419.2023.2213838","DOIUrl":"https://doi.org/10.1080/01490419.2023.2213838","url":null,"abstract":"Abstract Doppler velocity logger (DVL) error parameters can significantly influence the navigation accuracy of DVL/strapdown inertial navigation system (SINS) integration for unmanned underwater vehicles (UUV). To improve the navigation accuracy of UUV, this study proposes a two-stage DVL calibration method aided by global navigation satellite system (GNSS) measurements. First, utilizing the velocity of GNSS/SINS integrated navigation, the scale factor error of DVL is calculated by the moduli of velocities in UUV body frame and DVL instrument frame. Then, using the measurements throughout the calibration process, the calibration problem of the installation angle is converted to a nonlinear constraint optimization problem by describing the angle as a unit quaternion. Moreover, an easy-to-implement quaternion estimation algorithm is chosen to solve the problem and obtain the optimal quaternion. Simulation and sea trial indicate that the proposed method can rapidly and accurately estimate the DVL error parameters in different scenarios, and the position accuracy of the DVL/SINS system is improved.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"403 - 425"},"PeriodicalIF":1.6,"publicationDate":"2023-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47903907","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 : 2023-05-23DOI: 10.1080/01490419.2023.2213840
Yanhong Wang, Yilan Chen, Yikai Feng, Zhipeng Dong, Xiaoyu Liu
Abstract Satellite-derived bathymetry is an economic and effective method of obtaining large-scale, high-resolution bathymetric information. At present, bathymetric inversion models require existing bathymetric data as a necessary condition, but these may be difficult to obtain around many small islands. Under the condition of sparse measurement data, many common methods have low levels of accuracy. This paper proposes a method of transferring a bathymetric model for an island that has in situ data to an island that has little in situ data. First, in situ data are used to establish a high-precision satellite bathymetry model for a given island. Second, this model is transplanted to other islands. The addition of just 2–3 data points to correct the model results can reduce the mean relative error to 6.62% in the range of 0–20 m. This is close to the accuracy of establishing the model using a large number of measurement data (mean relative error of 5.25%).
{"title":"Multispectral Satellite-Derived Bathymetry Based on Sparse Prior Measured Data","authors":"Yanhong Wang, Yilan Chen, Yikai Feng, Zhipeng Dong, Xiaoyu Liu","doi":"10.1080/01490419.2023.2213840","DOIUrl":"https://doi.org/10.1080/01490419.2023.2213840","url":null,"abstract":"Abstract Satellite-derived bathymetry is an economic and effective method of obtaining large-scale, high-resolution bathymetric information. At present, bathymetric inversion models require existing bathymetric data as a necessary condition, but these may be difficult to obtain around many small islands. Under the condition of sparse measurement data, many common methods have low levels of accuracy. This paper proposes a method of transferring a bathymetric model for an island that has in situ data to an island that has little in situ data. First, in situ data are used to establish a high-precision satellite bathymetry model for a given island. Second, this model is transplanted to other islands. The addition of just 2–3 data points to correct the model results can reduce the mean relative error to 6.62% in the range of 0–20 m. This is close to the accuracy of establishing the model using a large number of measurement data (mean relative error of 5.25%).","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"426 - 440"},"PeriodicalIF":1.6,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46702067","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}
Abstract In recent years, two types of macroalgae, namely, Ulva prolifera and Sargassum horneri, have appeared occasionally together in the Yellow Sea and the East China Sea. Remote sensing enables timely and cost-effective observation of macroalgae across large areas. In the available studies, the recognition and classification of the two macroalgae are primarily based on spectral difference analysis. In this study, the spectral features, indices and textural feature parameters of the macroalgae targets were extracted and a preliminary multi-feature dataset was constructed based on Sentinel-2 images. Feature selection was performed using SHAP-based importance analysis and Bhattacharyya distance. From this, a multi-feature dataset was created and used as an input to a deep semantic segmentation network of improved ResUnet. The experiments of intelligent recognition and classification of U. prolifera and S. horneri were carried out using the proposed multi-feature-based ResUnet algorithm, with specific F1-scores of 96.7% and 96.8%, respectively. The proposed multi-feature-based ResUnet algorithm can obtain efficient and high-accuracy results for the recognition and classification of marine floating macroalgae. It achieves accurate remote sensing monitoring of the two types of marine floating macroalgae and has significant theoretical research significance and practical application value.
{"title":"Distinguishing Ulva prolifera and Sargassum horneri by using multi-feature-based ResUnet algorithm","authors":"Jinyu Li, Shengjia Zhang, Chao Zhang, Hong-chun Zhu","doi":"10.1080/01490419.2023.2197265","DOIUrl":"https://doi.org/10.1080/01490419.2023.2197265","url":null,"abstract":"Abstract In recent years, two types of macroalgae, namely, Ulva prolifera and Sargassum horneri, have appeared occasionally together in the Yellow Sea and the East China Sea. Remote sensing enables timely and cost-effective observation of macroalgae across large areas. In the available studies, the recognition and classification of the two macroalgae are primarily based on spectral difference analysis. In this study, the spectral features, indices and textural feature parameters of the macroalgae targets were extracted and a preliminary multi-feature dataset was constructed based on Sentinel-2 images. Feature selection was performed using SHAP-based importance analysis and Bhattacharyya distance. From this, a multi-feature dataset was created and used as an input to a deep semantic segmentation network of improved ResUnet. The experiments of intelligent recognition and classification of U. prolifera and S. horneri were carried out using the proposed multi-feature-based ResUnet algorithm, with specific F1-scores of 96.7% and 96.8%, respectively. The proposed multi-feature-based ResUnet algorithm can obtain efficient and high-accuracy results for the recognition and classification of marine floating macroalgae. It achieves accurate remote sensing monitoring of the two types of marine floating macroalgae and has significant theoretical research significance and practical application value.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"376 - 401"},"PeriodicalIF":1.6,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43063267","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 : 2023-04-27DOI: 10.1080/01490419.2023.2200043
V. Noujas, R. S. Kankara, B. Rajan
Abstract Episodic extreme waves caused by cyclones can have catastrophic consequences for coastal zones, including drastic beach morphology changes. The present study analyzed the beach morphological changes from Kakinada to Konapapapeta on the southeast coast of India before, during, and after the tropical cyclone Phethai using field survey and numerical modeling. Beach profiles were collected using Trimble RTK GPS and shoreline tracking was performed with handheld GPS. Numerical modeling was carried out using MIKE software to estimate the sediment transport rate before, during, and after the cyclone. Although the intensity of the cyclone reduced to a deep depression before landfall, several morphological changes were observed. Erosion was higher in the northern sector, where the beach width was less, and prominent scarps were seen throughout this region after the cyclone. The inundation varied from 40 to 120 m during the cyclone due to a storm surge of 0.5 to 1 m. During the cyclone, significant wave heights reached up to 4 m. The gross sediment transport rate is 3 to 13 times greater during the cyclone period than during the non-cyclone period. The maximum gross sediment transport rate during the Phethai cyclone was 1040 m3/day. Sediment transport was estimated for the same site for the non-cyclone year 2019, and the gross transport rate was 26,174 m3. As it is projected that extreme events are likely to increase due to climate change, output from this type of study is vital to environmental managers to assess erosion and develop long-term mitigation plans.
{"title":"Assessment of beach morphological changes in the east coast of India during cyclone Phethai, through field data and numerical modeling","authors":"V. Noujas, R. S. Kankara, B. Rajan","doi":"10.1080/01490419.2023.2200043","DOIUrl":"https://doi.org/10.1080/01490419.2023.2200043","url":null,"abstract":"Abstract Episodic extreme waves caused by cyclones can have catastrophic consequences for coastal zones, including drastic beach morphology changes. The present study analyzed the beach morphological changes from Kakinada to Konapapapeta on the southeast coast of India before, during, and after the tropical cyclone Phethai using field survey and numerical modeling. Beach profiles were collected using Trimble RTK GPS and shoreline tracking was performed with handheld GPS. Numerical modeling was carried out using MIKE software to estimate the sediment transport rate before, during, and after the cyclone. Although the intensity of the cyclone reduced to a deep depression before landfall, several morphological changes were observed. Erosion was higher in the northern sector, where the beach width was less, and prominent scarps were seen throughout this region after the cyclone. The inundation varied from 40 to 120 m during the cyclone due to a storm surge of 0.5 to 1 m. During the cyclone, significant wave heights reached up to 4 m. The gross sediment transport rate is 3 to 13 times greater during the cyclone period than during the non-cyclone period. The maximum gross sediment transport rate during the Phethai cyclone was 1040 m3/day. Sediment transport was estimated for the same site for the non-cyclone year 2019, and the gross transport rate was 26,174 m3. As it is projected that extreme events are likely to increase due to climate change, output from this type of study is vital to environmental managers to assess erosion and develop long-term mitigation plans.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"305 - 332"},"PeriodicalIF":1.6,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48281847","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 : 2023-04-10DOI: 10.1080/01490419.2023.2200212
Sheikh Mohammed Rabiul Alam, M. S. Hossain
Abstract As is well-documented, cloud cover (CC) obscures the target object and water depth and turbidity impede optical remote sensing (RS). Therefore, Landsat imagery acquired under cloud-free (CFI) and low-tide (LTI) conditions are considered ‘quality’ observations when mapping tidal wetlands such as saltmarsh. Although it is essential to estimate probabilities of acquiring at least one CFI and LTI, previous studies rarely addressed in RS of saltmarsh land cover (SLC). In order to evaluate the probabilities of acquiring at least one CFI and LTI in a year, a month, a season, a pair of seasons, or a certain time interval within a year over south-eastern Bangladesh, all images acquired between 1988 and 2019 were examined. The results indicate that SLC applications are generally affected by CC and tide heights; an average yearly CF probability of 16% can be acquired under LT conditions. Seasonal variations in CFI are apparent and there is a significant shortage of clear observation during the monsoon. The higher number of CFI may not guarantee a higher probability of LTI due to tide dynamics. The availability of both CFI and LTI can be remarkably improved if the present 16-day interval time of Landsat’s acquisition is increased.
{"title":"Probabilities of acquiring cloud-free and low-tide Landsat observations for mapping saltmarsh over south-eastern Bangladesh from 1980 to 2019","authors":"Sheikh Mohammed Rabiul Alam, M. S. Hossain","doi":"10.1080/01490419.2023.2200212","DOIUrl":"https://doi.org/10.1080/01490419.2023.2200212","url":null,"abstract":"Abstract As is well-documented, cloud cover (CC) obscures the target object and water depth and turbidity impede optical remote sensing (RS). Therefore, Landsat imagery acquired under cloud-free (CFI) and low-tide (LTI) conditions are considered ‘quality’ observations when mapping tidal wetlands such as saltmarsh. Although it is essential to estimate probabilities of acquiring at least one CFI and LTI, previous studies rarely addressed in RS of saltmarsh land cover (SLC). In order to evaluate the probabilities of acquiring at least one CFI and LTI in a year, a month, a season, a pair of seasons, or a certain time interval within a year over south-eastern Bangladesh, all images acquired between 1988 and 2019 were examined. The results indicate that SLC applications are generally affected by CC and tide heights; an average yearly CF probability of 16% can be acquired under LT conditions. Seasonal variations in CFI are apparent and there is a significant shortage of clear observation during the monsoon. The higher number of CFI may not guarantee a higher probability of LTI due to tide dynamics. The availability of both CFI and LTI can be remarkably improved if the present 16-day interval time of Landsat’s acquisition is increased.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43915105","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}
Abstract The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.
{"title":"Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data","authors":"Yongjin Sun, Wei Zheng, Zhaowei Li, Zhiquan Zhou, Xiaocong Zhou, Zhongkai Wen","doi":"10.1080/01490419.2023.2179140","DOIUrl":"https://doi.org/10.1080/01490419.2023.2179140","url":null,"abstract":"Abstract The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"271 - 302"},"PeriodicalIF":1.6,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46464215","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 : 2023-02-24DOI: 10.1080/01490419.2023.2184436
D. Ganguly, M. Raman
Abstract In this article, the applicability of India’s indigenously developed scatterometer, Scatsat-1 for marine aerosol characterization and wind dependency of aerosol optical depth (AOD) was investigated using 3 years of satellite data. Wind data from Scatsat-1 and AOD with its size fraction from SNPP VIIRS from 2017 to 2019 were used for the investigation. A technique has been demonstrated for identifying remote oceanic regions with mostly marine aerosol using certain criteria like distance from land, higher fraction of coarse AOD, lower variation of AOD with wind direction, and so forth. Daily wind and AOD from 2017 to 2019 were used for studying the relationship between wind speed and AOD at remote oceanic locations away from terrestrial and anthropogenic influence. Five regions of interest (ROIs) of 5° by 5° were identified in different oceanic regions for carrying out the regression analysis. A linear increase of coarse mode AOD and total AOD with an increase in wind speed was observed for all five remote locations while fine mode AOD was unrelated. The slope of the linear relation agreed with Kiliyanpilakkil and Meskhidze (2011) for intermediate wind values. The remote regions also had a lower variation of AOD with change in wind direction implying less transport of aerosol from nearby landmasses.
{"title":"Estimating the Wind Dependency of Aerosol Optical Depth at Remote Oceanic Regions","authors":"D. Ganguly, M. Raman","doi":"10.1080/01490419.2023.2184436","DOIUrl":"https://doi.org/10.1080/01490419.2023.2184436","url":null,"abstract":"Abstract In this article, the applicability of India’s indigenously developed scatterometer, Scatsat-1 for marine aerosol characterization and wind dependency of aerosol optical depth (AOD) was investigated using 3 years of satellite data. Wind data from Scatsat-1 and AOD with its size fraction from SNPP VIIRS from 2017 to 2019 were used for the investigation. A technique has been demonstrated for identifying remote oceanic regions with mostly marine aerosol using certain criteria like distance from land, higher fraction of coarse AOD, lower variation of AOD with wind direction, and so forth. Daily wind and AOD from 2017 to 2019 were used for studying the relationship between wind speed and AOD at remote oceanic locations away from terrestrial and anthropogenic influence. Five regions of interest (ROIs) of 5° by 5° were identified in different oceanic regions for carrying out the regression analysis. A linear increase of coarse mode AOD and total AOD with an increase in wind speed was observed for all five remote locations while fine mode AOD was unrelated. The slope of the linear relation agreed with Kiliyanpilakkil and Meskhidze (2011) for intermediate wind values. The remote regions also had a lower variation of AOD with change in wind direction implying less transport of aerosol from nearby landmasses.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"359 - 375"},"PeriodicalIF":1.6,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47108396","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 : 2023-02-06DOI: 10.1080/01490419.2023.2175084
H. Guarneri, M. Verlaan, D. C. Slobbe, J. Veenstra, F. Zijl, J. Pietrzak, M. Snellen, L. Keyzer, Y. Afrasteh, R. Klees
Abstract Both empirical and assimilative global ocean tidal models are significantly more accurate in the deep ocean than in shelf and coastal waters. In this study, we answered whether this is due to the quality of the models used to reduce tide and surge or the general approach to treat tide and surge as two separate components of the water level obtained from stand-alone models, which ignores the nonlinear tide–surge interaction. In doing so, we used tide gauge observations as partially synthetic altimeter time series, tide–surge water-level time series obtained with the 2D Dutch Continental Shelf Model – Flexible Mesh (DCSM), and tide and surge water-level time series obtained using the DCSM, FES2014 (FES) and the Dynamic Atmospheric Correction (DAC) product. Expressed in the root-sum-square (RSS) of the eight main tidal constituents, we obtained a reduction % when removing the DCSM tide–surge water levels compared to when we removed the sum of the DCSM tide and DCSM surge water levels. The RSS obtained in the latter case was only 3.3% lower than with FES and DAC. We conclude that the lower tidal estimates accuracy in shelf-coastal waters derives from the missing nonlinear tide–surge interactions.
{"title":"The impact of nonlinear tide–surge interaction on satellite radar altimeter-derived tides","authors":"H. Guarneri, M. Verlaan, D. C. Slobbe, J. Veenstra, F. Zijl, J. Pietrzak, M. Snellen, L. Keyzer, Y. Afrasteh, R. Klees","doi":"10.1080/01490419.2023.2175084","DOIUrl":"https://doi.org/10.1080/01490419.2023.2175084","url":null,"abstract":"Abstract Both empirical and assimilative global ocean tidal models are significantly more accurate in the deep ocean than in shelf and coastal waters. In this study, we answered whether this is due to the quality of the models used to reduce tide and surge or the general approach to treat tide and surge as two separate components of the water level obtained from stand-alone models, which ignores the nonlinear tide–surge interaction. In doing so, we used tide gauge observations as partially synthetic altimeter time series, tide–surge water-level time series obtained with the 2D Dutch Continental Shelf Model – Flexible Mesh (DCSM), and tide and surge water-level time series obtained using the DCSM, FES2014 (FES) and the Dynamic Atmospheric Correction (DAC) product. Expressed in the root-sum-square (RSS) of the eight main tidal constituents, we obtained a reduction % when removing the DCSM tide–surge water levels compared to when we removed the sum of the DCSM tide and DCSM surge water levels. The RSS obtained in the latter case was only 3.3% lower than with FES and DAC. We conclude that the lower tidal estimates accuracy in shelf-coastal waters derives from the missing nonlinear tide–surge interactions.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"251 - 270"},"PeriodicalIF":1.6,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41802423","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 : 2023-01-09DOI: 10.1080/01490419.2023.2166173
P. Elmore, B. Calder, F. Petry, G. Masetti, Ron Yager
Abstract When planning for ship navigation or compiling data for a bathymetry map, the navigator or mapper uses many different sources of bathymetry information and navigation hazards. The quality of these sources is inconsistent in general, however, making it challenging to provide a coherent picture for planning. Here, we describe an approach for consistent planning/mapping that uses a combination of soft computing and Bayesian estimation. The case study used to exercise this system involves NOAA Electronic Nautical Charts for an area in the Chesapeake Bay. We first interpolate each set of irregularly spaced soundings to gridded versions of each point-cloud set. Each of these intermediate grids is then aggregated into a fused bathymetric realization using order weighted averaging (OWA) to provide the weights for each source based on their subjective reliabilities. The OWA allows for fusion informed by the user’s subjective risk allowed in the reconstruction of the seafloor surface and provides quantitative methods to generate, use, and record subjective reliability weights. Each sounding point that went into the bathymetry estimate is then categorized as “no-go,” “caution,” or “go” status. Reliability estimates are reused for weighted Bayesian categorization of each output grid cell to compute the navigable surface.
{"title":"Aggregation Methods Using Bathymetry Sources of Differing Subjective Reliabilities for Navigation Mapping","authors":"P. Elmore, B. Calder, F. Petry, G. Masetti, Ron Yager","doi":"10.1080/01490419.2023.2166173","DOIUrl":"https://doi.org/10.1080/01490419.2023.2166173","url":null,"abstract":"Abstract When planning for ship navigation or compiling data for a bathymetry map, the navigator or mapper uses many different sources of bathymetry information and navigation hazards. The quality of these sources is inconsistent in general, however, making it challenging to provide a coherent picture for planning. Here, we describe an approach for consistent planning/mapping that uses a combination of soft computing and Bayesian estimation. The case study used to exercise this system involves NOAA Electronic Nautical Charts for an area in the Chesapeake Bay. We first interpolate each set of irregularly spaced soundings to gridded versions of each point-cloud set. Each of these intermediate grids is then aggregated into a fused bathymetric realization using order weighted averaging (OWA) to provide the weights for each source based on their subjective reliabilities. The OWA allows for fusion informed by the user’s subjective risk allowed in the reconstruction of the seafloor surface and provides quantitative methods to generate, use, and record subjective reliability weights. Each sounding point that went into the bathymetry estimate is then categorized as “no-go,” “caution,” or “go” status. Reliability estimates are reused for weighted Bayesian categorization of each output grid cell to compute the navigable surface.","PeriodicalId":49884,"journal":{"name":"Marine Geodesy","volume":"46 1","pages":"99 - 128"},"PeriodicalIF":1.6,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41429797","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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