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Satellite Derived Bathymetry for Coastal Mapping

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 12316

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Special Issue Editor

Dr. Nenad Leder

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Guest Editor

Nautical Engineering Department, Faculty of Maritime Studies, University of Split, Split, Croatia
Interests: physical oceanology; hydrography; coastal mapping; remote sensing

Special Issue Information

Dear Colleagues,

Originally, the purpose of depth measurement was safe navigation; today, it is used for many applications, such as resource management, offshore activities, environmental protection, military action, science, and so on. The acquisition technique of bathymetric data has evolved from a shipborne platform to airborne and spaceborne acquisition. We can assume that at least 50% of the total global area of the continental shelf is unsurveyed, or surveyed with horizontal and vertical inadequate accuracy defined according to IHO S-44 standards (IHO, Edition 6.0.0, September 2020). As is well known, this is due to the demanding and expensive process of measuring depths from a ship. Therefore, it is necessary to find efficient and preferably cost-effective methods of bathymetry determination in shallow water. One of the most efficient and least expensive methods is satellite-derived bathymetry (SDB). SDB dates back to 1970s, when the significant development of sensors as well as data processing methods was introduced into scientific and operational practice. Bathymetric data production by using high resolution optical satellite imagery is a specific application of remote sensing for depth determination in the coastal area and can be used for determination of the coastline as well. It is founded on empirical, semi-analytical or analytical modelling of light transmission through atmosphere, and the water column in visible and infrared bands. This SDB has recently been considered a new promising technology in the hydrographic surveying process, especially for shallow water area acquisition, and provides a simple reconnaissance tool for hydrographic offices around the world. This Special Issue, “Satellite Derived Bathymetry for Coastal Mapping”, calls for all original research articles intended to cover the latest advances, including, but not limited to, (1) development of the accurate correction of SDB data related to modelling of light transmission through atmosphere and the water column, (2) finding “an ideal image” that depends on meteorological and oceanographic dynamics, especially on clouds, water turbidity, sea bottom characteristics and other water column parameters, (3) description of the most sophisticated SDB survey method that can be applied for the safety of the navigation in shallow water and which satisfy minimum horizontal and vertical accuracies defined according to IHO S-44 standards (IHO, Edition 6.0.0, September 2020) for Special Order and Order 1a categories of hydrographic surveying, and (4) investigate the optimal method for determining the coastline from satellite data, taking into account the definitions of the coastline (IHO S-44 standards, 2020), for the purpose of coastal mapping.

Dr. Nenad Leder
Guest Editor

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Keywords

Satellite imagery
Light transmission
Coastal zone
Hydrographic surveying
Coastal mapping
Safety of the navigation

Published Papers (4 papers)

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Research

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27 pages, 5692 KiB

Open AccessArticle

Satellite-Derived Bathymetry Mapping on Horseshoe Island, Antarctic Peninsula, with Open-Source Satellite Images: Evaluation of Atmospheric Correction Methods and Empirical Models

by Emre Gülher and Ugur Alganci

Remote Sens. 2023, 15(10), 2568; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102568 - 14 May 2023

Cited by 5 | Viewed by 2002

Abstract

Satellite-derived bathymetry (SDB) is the process of estimating water depth in shallow coastal and inland waters using satellite imagery. Recent advances in technology and data processing have led to improvements in the accuracy and availability of SDB. The increased availability of free optical satellite sensors, such as Landsat missions and Sentinel 2 satellites, has increased the quantity and frequency of SDB research and mapping efforts. In addition, machine learning (ML)- and deep learning (DL)-based algorithms, which can learn to identify features that are indicative of water depth, such as color or texture variations, have started to be used for extracting bathymetry information from satellite imagery. This study aims to produce an initial optical image-based SBD map of Horseshoe Island’s shallow coasts and to perform a comprehensive and comparative evaluation with Landsat 8 and Sentinel 2 satellite images. Our research considers the performance of empirical SDB models (classical, ML-based, and DL-based) and the effects of the atmospheric correction methods ACOLITE, iCOR, and ATCOR. For all band combinations and depth intervals, the ML-based random forest and XGBoost models delivered the highest performance and best fitting ability by achieving the lowest error with MAEs smaller than 1 m up to 10 m depth and a maximum correlation of R² around 0.80. These models are followed by the DL-based ANN and CNN models. Nonetheless, the non-linearity of the reflectance–depth connection was significantly reduced by the ML-based models. Furthermore, Landsat 8 showed better performance for 10–20 m depth intervals and in the entire range of (0–20 m), while Sentinel 2 was slightly better up to 10 m depth intervals. Lastly, ACOLITE, iCOR, and ATCOR provided reliable and consistent results for SDB, where ACOLITE provided the highest automation. Full article

(This article belongs to the Special Issue Satellite Derived Bathymetry for Coastal Mapping)

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18 pages, 12232 KiB

Open AccessArticle

Gradient Boosting and Linear Regression for Estimating Coastal Bathymetry Based on Sentinel-2 Images

by Fahim Abdul Gafoor, Maryam R. Al-Shehhi, Chung-Suk Cho and Hosni Ghedira

Remote Sens. 2022, 14(19), 5037; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14195037 - 09 Oct 2022

Cited by 9 | Viewed by 2384

Abstract

Thousands of vessels travel around the world every day, making the safety, efficiency, and optimization of marine transportation essential. Therefore, the knowledge of bathymetry is crucial for a variety of maritime applications, such as shipping and navigation. Maritime applications have benefited from recent advancements in satellite navigation technology, which can utilize multi-spectral bands for retrieving information on water depth. As part of these efforts, this study combined deep learning techniques with satellite observations in order to improve the estimation of satellite-based bathymetry. The objective of this study is to develop a new method for estimating coastal bathymetry using Sentinel-2 images. Sentinel-2 was used here due to its high spatial resolution, which is desirable for bathymetry maps, as well as its visible bands, which are useful for estimating bathymetry. The conventional linear model approach using the satellite-derived bathymetry (SDB) ratio (green to blue) was applied, and a new four-band ratio using the four visible bands of Sentienl-2 was proposed. In addition, three atmospheric correction models, Sen2Cor, ALOCITE, and C2RCC, were evaluated, and Sen2Cor was found to be the most effective model. Gradient boosting was also applied in this study to both the conventional band ratio and the proposed FVBR ratio. Compared to the green to blue ratio, the proposed ratio FVBR performed better, with R² exceeding 0.8 when applied to 12 snapshots between January and December. The gradient boosting method was also found to provide better estimates of bathymetry than linear regression. According to findings of this study, the chlorophyll-a (Chl-a) concentration, sediments, and atmospheric dust do not affect the estimated bathymetry. However, tidal oscillations were found to be a significant factor affecting satellite estimates of bathymetry. Full article

(This article belongs to the Special Issue Satellite Derived Bathymetry for Coastal Mapping)

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Graphical abstract

32 pages, 16030 KiB

Open AccessArticle

A New Approach to Satellite-Derived Bathymetry: An Exercise in Seabed 2030 Coastal Surveys

by Pierre Louvart, Harry Cook, Chloe Smithers and Jean Laporte

Remote Sens. 2022, 14(18), 4484; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184484 - 08 Sep 2022

Cited by 2 | Viewed by 2590

Abstract

ARGANS has years of experience in analysing the factors limiting light transmission in coastal environments around the world. This has led its Satellite-Derived Bathymetry (SDB) team to the conclusion that current satellite instruments and their resolution are unable to provide the absolute precision required for safe navigation according to International Hydrographic Bureau (IHO) standards, except in the clearest of waters characterised by sufficiently well-defined environmental parameters. This limitation is caused by the variability itself of the parameters in the radiative transfer equation (RTE), which is too high to provide results within the strict accuracy ranges of IHO S.44, as shown by scatter plots characterised time and again by large biases and standard deviations of several metres. The radiative transfer equation and Hydrolight simulations are not at fault, but their results must be interpreted with extreme caution if the level of accuracy required for safe navigation over large areas is to be guaranteed. Therefore, ARGANS has developed an innovative, alternative method. This is based on the classification of the full range of images pixels allowing for homogeneous sub-areas to be determined and linked together by artificial intelligence and statistical clustering of similar parameters. Thanks to the sponsorship of the European Space Agency and Seabed 2030, ARGANS has been able to test its Water Column Parameter Estimator (WCPE), first in the challenging waters of Madagascar updating over 1000 km of lead line exploratory surveys, then in the South Pacific coral environment and then in the turbid coastal water of Qatar. The parameters determined by WCPE can be extrapolated to unknown regions of similar environment and propagated from one place to the next, using a chain method somewhat inspired by photogrammetric techniques of old. Further progress and automation can be expected from an improved control of sediment plumes that can obscure or distort all optical methods, whether satellite or LIDAR. Full article

(This article belongs to the Special Issue Satellite Derived Bathymetry for Coastal Mapping)

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Review

Jump to: Research

27 pages, 4250 KiB

Open AccessReview

Optical Satellite-Derived Bathymetry: An Overview and WoS and Scopus Bibliometric Analysis

by Tea Duplančić Leder, Martina Baučić, Nenad Leder and Frane Gilić

Remote Sens. 2023, 15(5), 1294; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051294 - 26 Feb 2023

Cited by 11 | Viewed by 3068

Abstract

A technical and scientific overview regarding satellite-derived bathymetry (SDB)—one of the most promising and relatively cheap methods of shallow water depth determination—is presented. The main goal of the article is to present information about the possibilities of the SDB method to meet the demanding standard of bathymetric measurements in coastal mapping areas up to 20 m deep, i.e., up to depth areas where the largest number of ports and access waterways are located, as obtained using the bibliometric analysis. The Web of Science (WoS) and Scopus scientific databases, as well as R studio applications Bibliometrix and Biblioshiny, were used for scientific analysis. The bibliometric analysis presents the quantitative aspects of producing and disseminating scientific and professional articles with SDB as their topic. Therefore, the purpose of this study was to give the academic community an insight into the current knowledge about the SDB method, its achievements and shortcomings. The results of the bibliometric analysis of articles dealing with SDB show that most authors use empirical statistical methods. However, in recent years, articles using automated artificial intelligence methods have prevailed, especially the machine learning method. It is concluded that SDB data can become a very important low-cost source of bathymetric data in shallow coastal areas. Satellite methods have been proven to be very effective in very shallow coastal areas (up to a depth of about 20 m), and their biggest advantage is that the depth data obtained in this way are relatively low cost, while major limitations are associated with the parameters that determine the properties of the atmosphere and water column (clear atmosphere and water column) and bottom material. Procedures for different bathymetric applications are being developed. Regardless of the significant progress of the SDB method, which was manifested in the development of sensors and processing methods, its results still do not meet the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys S-44. Full article

(This article belongs to the Special Issue Satellite Derived Bathymetry for Coastal Mapping)

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