A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms-the standard linear transform and the new ratio transformwere compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10-15 m. However, the linear transform does not distinguish depths Ͼ15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in Ͼ25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4-5 pixels) in water depths Ͼ15-20 m. In general, the ratio transform is more robust than the linear transform. Since the first use of aerial photography over clear shallow water, it has been recognized that water depth can be estimated in some way by remote sensing. The theory developed by Lyzenga (1978 Lyzenga ( , 1981 and expanded by Philpot (1989) and Maritorena et al. (1994) demonstrated the validity of, and problems involved with, using passive remote sensing for determination of water depth. The use of two or more bands allows separation of variations in depth from variations in bottom albedo, but compensation for turbidity, while tractable, can be problematic. Although passive optical systems are limited in depth penetration and constrained by water turbidity, the use of such satellite data might be the only viable way to characterize either extensive or remote coral reef environments. Besides the obvious need for bathymetric information in many remote areas, mapping of coral reefs and characterization of potential for bleaching requires information on water depth. Coral reefs, by their nature, strongly influence the physical structure of their environment, and water depth information is fundamental to discriminating and characterizing coral reef habitat, such as patch reef, spur-and-groove, and seagrass beds. Knowledge of water depth also allows estimation of bottom albedo, which can improve habitat mapping (Mumby et al. 1998 ). Knowledge of the detailed structure of the bottom helps in 1 Corresponding author (richard.stumpf@noaa.gov). This effort was funded by the NOAA, National Ocean Service, Coral Reef Mapping Program. Steve Rohmann provided overall coordination of the Northwestern Hawaiian Islands project. The mention of any company does not constitute an official endorsement by NOAA.
To further develop an empirical solution for the presence of variable bottom types, Stumpf et al. REF improved the linear multiband combination methods with a ratio of reflectances with only two tunable parameters.
Determination of water depth with high-resolution satellite imagery over variable bottom types
Limnology and Oceanography
Biology