Earth System Science Data (Nov 2021)

Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys, and historical datasets

  • U. Lebrec,
  • U. Lebrec,
  • V. Paumard,
  • M. J. O'Leary,
  • S. C. Lang

DOI
https://doi.org/10.5194/essd-13-5191-2021
Journal volume & issue
Vol. 13
pp. 5191 – 5212

Abstract

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High-resolution bathymetry forms critical datasets for marine geoscientists. It can be used to characterize the seafloor and its marine habitats, to understand past sedimentary records, and even to support the development of offshore engineering projects. Most methods to acquire bathymetry data are costly and can only be practically deployed in relatively small areas. It is therefore critical to develop cost-effective and advanced techniques to produce regional-scale bathymetry datasets. This paper presents an integrated workflow that builds on satellites images and 3D seismic surveys, integrated with historical depth soundings, to generate regional high-resolution digital elevation models (DEMs). The method was applied to the southern half of Australia's North West Shelf and led to the creation of new high-resolution bathymetry grids, with a resolution of 10 × 10 m in nearshore areas and 30 × 30 m elsewhere. The vertical and spatial accuracy of the datasets have been assessed using open-source Laser Airborne Depth Sounder (LADS) and multibeam echosounder (MBES) surveys as a reference. The comparison of the datasets indicates that the seismic-derived bathymetry has a vertical accuracy better than 1 m + 2 % of the absolute water depth, while the satellite-derived bathymetry has a depth accuracy better than 1 m + 5 % of the absolute water depth. This 30 × 30 m dataset constitutes a significant improvement of the pre-existing regional 250 × 250 m grid and will support the onset of research projects on coastal morphologies, marine habitats, archaeology, and sedimentology. All source datasets are publicly available, and the methods are fully integrated into Python scripts, making them readily applicable elsewhere in Australia and around the world. The regional digital elevation model and the underlying datasets can be accessed at https://doi.org/10.26186/144600 (Lebrec et al., 2021).