Abstract
An up-to-date knowledge of water depth is essential for a wide range of coastal activities, such as navigation, fishing, study of coastal erosion, or the observation of the rise of water levels due to climate change. This paper presents a coastal bathymetry estimation method that takes a single satellite acquisition as input, aimed at scenarios where in situ data are not available or would be too costly to obtain. The method uses free multispectral images that are easy to obtain for any region of the globe from sources such as the Sentinel-2 or Landsat-8 satellites. In order to address the shortcomings of existing image-only approaches (low resolution, scarce spatial coverage especially in the shallow water zones, dependence on specific physical conditions) we derive a new bathymetry estimation approach that combines a physical wave model with a statistical method based on Gaussian Process Regression learned in an unsupervised way. The resulting system is able to provide a nearly complete coverage of the 2–12-m-depth zone at a resolution of 80 m. Evaluated on three sites around the Hawaiian Islands, our method obtained estimates with a correlation coefficient in the range of 0.7–0.9. Furthermore, the trained models provide equally good results in nearby zones that lack exploitable waves, extending the scope of applicability of the method.
Highlights
Sea coasts are extremely dynamic and fragile regions that are constantly exposed to diverse natural and anthropogenic phenomena, in particular in the context of climate change
The method is divided into three main steps, as shown in Figure 1: 1. physical bathymetry estimation: the physical model is applied to the input satellite image, producing physical bathymetry estimates as output; 2. statistical model optimisation: from a set of candidate statistical Gaussian Process Regression (GPR) models, an optimal one is trained using the input image as well as the physical bathymetry estimates; 3. statistical bathymetry estimation: the optimal statistical model is applied to the multispectral image to obtain high-resolution, high-coverage estimates; the same model can subsequently be reapplied to obtain bathymetry estimates for new images that are spatially or temporally related to the original image
We interpolated the results of the physical model to a regular grid of 80 m in order to be able later to perform meaningful comparisons between the physical and the GPR results
Summary
Sea coasts are extremely dynamic and fragile regions that are constantly exposed to diverse natural and anthropogenic phenomena, in particular in the context of climate change. A precise monitoring of coastal dynamics (such as erosion, waves, currents, moving seabeds, or rising sea levels), and of coastal bathymetry, is crucial for activities such as urban planning, fishing, navigation, or the protection of environment. Coastal bathymetry traditionally relies on in situ campaigns using sonar or airborne LIDAR. Being expensive and time-consuming, these methods are infrequently used and cannot be applied on a regular basis. Remote sensing techniques using satellite images propose a cost-effective alternative: one sensor covers a large geographical area with a repetitive time interval of typically 5–10 days. Two main approaches have been put forth for the purpose of coastal bathymetry:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
