Special Issue "Satellite Derived Bathymetry for Coastal Mapping"

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

Deadline for manuscript submissions: 15 June 2022 | Viewed by 664

Special Issue Editor

Dr. Nenad Leder
E-Mail Website
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

Manuscript Submission Information

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Keywords

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

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Determining confidence intervals in physics based satellite derived bathymetry.

Abstract: Increasingly, satellite derived bathymetry (SDB) data are used to fill expanded data gaps in optical shallow waters, assimilated into charts or used to reduce risks and costs for survey planning. Confidence intervals of SDB data are therefore of major importance for many hydrographical applications. Confidence intervals are subjecft to high spatial and temporal variability.

However, methods to generate SDB rarely estimate confidence intervals for the individual SDB measurements. Typically, confidence interval measures are provided by comparing the SDB to overlapping external data (e.g. echo-sounding or lidar) – as far as this data exist.

This work presents a mathematical framework to calculate the confidence intervals in water depth retrieval starting from the sensor's signal-to-noise ratio (SNR). This framework requires a fully physics based retrieval algorithm. We describe the operational WATCOR-X software for completeness. We evaluate the performance of WATCOR-X and current limitations of the described approach, for several areas and satellite scenes.

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