The role of digital bathymetry in mapping shallow marine vegetation from hyperspectral image data

Hyperspectral remote sensing is a proven technology for measurement of coastal ocean colour, including sea-bed mapping in optically shallow waters. Using hyperspectral imagery of shallow (<15m deep) sea bed acquired with the Compact Airborne Spectrographic Imager (CASI-550), we examined how changes in the spatial resolution of bathymetric grids, created from sonar data (echosounding) and input to conventional image classifiers, affected the accuracy of distributional maps of invasive (Codium fragile ssp. tomentosoides) and native (kelp) seaweeds off the coast of Nova Scotia, Canada. The addition of a low-resolution bathymetric grid, interpolated from soundings by the Canadian Hydrographic Service, improved the overall classification accuracies by up to similar to 10%. However, increasing the bathymetric resolution did not increase the accuracy of classification maps produced with the supervised (Maximum Likelihood) classifier as shown by a slightly lower accuracy (2%) when using an intermediate-resolution bathymetric grid interpolated from soundings with a recreational fish finder. Supervised classifications using the first three eigenvectors from a principal-components analysis were consistently more accurate (by at least 27%) than unsupervised (K-means classifier) schemes with similar data compression. With an overall accuracy of 76%, the most reliable scheme was a supervised classification with low-resolution bathymetry. However, the supervised approach was particularly sensitive, and variations in accuracy of 2% resulted in overestimations of up to 53% in the extent of C. fragile and kelp. The use of a passive optical bathymetric algorithm to derive a high-resolution bathymetric grid from the CASI data showed promise, although fundamental differences between this grid and those created with the sonar data limited the conclusions. The bathymetry (at any spatial resolution) appeared to improve the accuracy of the classifications both by reducing the confusion among the spectral classes and by removing noise in the image data. Variations in the accuracy of depth estimates and inescapable positional inaccuracies in the imagery and ground data largely accounted for the observed differences in the classification accuracies. This study provides the first detailed demonstration of the advantages and limitations of integrating digital bathymetry with hyperspectral data for the mapping of benthic assemblages in optically shallow waters.