Document Type

Article

Publication Date

12-2019

DOI

10.3390/rs11242888

Publication Title

Remote Sensing

Volume

11

Issue

24

Pages

2888 (20 pp.)

Abstract

The intertidal zones are well recognized for their dynamic nature and role in near-shore hydrodynamics. The intertidal topography is poorly mapped worldwide due to the high cost of associated field campaigns. Here we present a combination of remote-sensing and hydrodynamic modeling to overcome the lack of in situ measurements. We derive a digital elevation model (DEM) by linking the corresponding water level to a sample of shorelines at various stages of the tide. Our shoreline detection method is fully automatic and capable of processing high-resolution imagery from state-of-the-art satellite missions, e.g., Sentinel-2. We demonstrate the use of a tidal model to infer the corresponding water level in each shoreline pixel at the sampled timestamp. As a test case, this methodology is applied to the vast coastal region of the Bengal delta and an intertidal DEM at 10 m resolution covering an area of 1134 km2 is developed from Sentinel-2 imagery. We assessed the quality of the DEM with two independent in situ datasets and conclude that the accuracy of our DEM amounts to about 1.5 m, which is commensurate with the typical error bar of the validation datasets. This DEM can be useful for high-resolution hydrodynamic and wave modeling of the near-shore area. Additionally, being automatic and numerically effective, our methodology is compliant with near-real-time monitoring constraints.

Comments

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Original Publication Citation

Khan, M. J. U., Ansary, M. D. N., Durand, F., Testut, L., Ishaque, M., Calmant, S., . . . Papa, F. (2019). High-resolution intertidal topography from sentinel-2 multi-spectral imagery: Synergy between remote sensing and numerical modeling. Remote Sensing, 11(24), 2888. doi:10.3390/rs11242888

Share

Article Location

 
COinS