Satellite-Derived Bathymetry for Improving Canadian Hydrographic Service Charts

Approximately 1000 Canadian Hydrographic Service (CHS) charts cover Canada’s oceans and navigable waters. Many charts use information collected with techniques that predate the more advanced technologies available to Hydrographic Offices (HOs) today. Furthermore, gaps in survey data, particularly in the Canadian Arctic where only 6% of waters are surveyed to modern standards, are also problematic. Through a Canadian Space Agency (CSA) Government Related Initiatives Program (GRIP) project, CHS is exploring remote sensing techniques to assist with the improvement of Canadian navigational charts. Projects exploring optical/Synthetic Aperture Radar (SAR) shoreline extraction and change detection, as well as optical Satellite-Derived Bathymetry (SDB), are currently underway. This paper focuses on SDB extracted from high-resolution optical imagery, highlighting current results as well as the challenges and opportunities CHS will encounter when implementing SDB within its operational chart production process. SDB is of particular interest to CHS due to its ability to supplement depths derived from traditional hydrographic surveys. This is of great importance in shallow and/or remote Canadian waters where achieving wide-area depth coverage through traditional surveys is costly, time-consuming and a safety risk to survey operators. With an accuracy of around 1 m, SDB could be used by CHS to fill gaps in survey data and to provide valuable information in dynamic areas.

[1]  David R. Lyzenga,et al.  Shallow-water bathymetry using combined lidar and passive multispectral scanner data , 1985 .

[2]  Thierry Toutin Three-Dimensional Geometric Correction of Earth Observation Satellite Data , 2011 .

[3]  D. Rundquist,et al.  Bathymetric Mapping Using IKONOS Multispectral Data , 2004 .

[4]  Tiit Kutser,et al.  The possibility of using the Landsat image archive for monitoring long time trends in coloured dissolved organic matter concentration in lake waters , 2012 .

[5]  Stuart R. Phinn,et al.  Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007 , 2011, Remote. Sens..

[6]  Paul Harris,et al.  Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data , 2015, Remote. Sens..

[7]  Jouni Pulliainen,et al.  Landsat ETM+ Images in the Estimation of Seasonal Lake Water Quality in Boreal River Basins , 2008, Environmental management.

[8]  F. Polcyn,et al.  The Measurement of Water Depth by Remote Sensing Techniques , 1970 .

[9]  Hongxing Liu,et al.  Automated Derivation of Bathymetric Information from Multi-Spectral Satellite Imagery Using a Non-Linear Inversion Model , 2008 .

[10]  D. Lyzenga Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data , 1981 .

[11]  D. Lyzenga Passive remote sensing techniques for mapping water depth and bottom features. , 1978, Applied optics.

[12]  R. Stumpf,et al.  Determination of water depth with high‐resolution satellite imagery over variable bottom types , 2003 .

[13]  Lee Alexander,et al.  Satellite-Derived Bathymetry a Reconnaissance Tool for Hydrography , 2013 .

[14]  John D. Hedley,et al.  Technical note: Simple and robust removal of sun glint for mapping shallow‐water benthos , 2005 .

[15]  S. Maritorena,et al.  Bio-optical properties of oceanic waters: A reappraisal , 2001 .

[16]  Sarah M. Hamylton,et al.  Derivation of High-Resolution Bathymetry from Multispectral Satellite Imagery: A Comparison of Empirical and Optimisation Methods through Geographical Error Analysis , 2015, Remote. Sens..