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Articles

Reconstruction of sand wave bathymetry using both satellite imagery and multi-beam bathymetric data: a case study of the Taiwan Banks

, , , &
Pages 3286-3299 | Received 05 Sep 2013, Accepted 08 Feb 2014, Published online: 14 Apr 2014
 

Abstract

Sand waves are a widespread bed-form in the tidal environment. Their formation, migration, and other properties have great significance in the fields of geology and oceanography. Currently, research on sand waves is mainly based on multi-beam bathymetric (MB) data. For large sand wave regions, however, the acquisition ability of MB data becomes limited; instead, interval-line measurement is adopted but with the inherent problem that it cannot achieve the full spatial coverage required. Reconstructed sand wave bathymetry (SWB) using interpolation algorithms cannot reflect the real SWB either. In this article, we propose a new approach in using both satellite imagery and MB data to reconstruct SWB, which takes advantage of characteristic information (ripple and crest) of SWB rendered in satellite imagery and MB data. The new approach is exemplified by a case study of the Taiwan Banks. We use the imagery obtained by the Charge-Coupled Device from the Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting, and the MB data from R2Sonic to reconstruct the digital elevation model. The results show that the root mean square error of the reconstructed water depth is 1.47 m (compared to the MB data not using this approach), suggesting that the new approach is effective in reconstructing SWB. This approach allows a reduction in MB data track density, which may lead to an improvement in efficiency.

Acknowledgements

The authors would like to acknowledge the CRESDA for providing HJ-1B satellite images. The authors would also like to thank Dr Zuojun Yu for her comments on improving the presentation of this article.

Funding

The authors would like to acknowledge the Marine Scientific Public Welfare Research Special Foundation (No: 201105001), the Key Laboratory of Ocean Dynamic Processed and Satellite Oceanography (No: SOED1006) for funding this work.

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