Classifying benthic habitats and deriving bathymetry at the Caribbean Netherlands using multispectral Imagery
Benthic habitats (habitats occurring at the bottom of a water body) and coral reef ecosystems provide many functions. Currently, however, worldwide coral reefs are threatened by a number of factors and are degrading rapidly. Benthic maps are important for management, research and planning, but the benthic communities around St. Eustatius have not yet been accurately mapped or described.
Remote sensing imagery has been found to be a useful tool in providing timely and up-to-date information for benthic mapping and offers an approach that may complement the limitations of field sampling. Remote sensing in water, however, presents challenges mainly due to the complex physical interactions of absorption and scattering between water and light. Shorter wavelengths (-450 nm) penetrate deepest into the water column and longer wavelengths (-500-750 nm) are more rapidly absorbed and scattered. Therefore, the potential extent of use of remote sense imagery in the oceans relies more on shorter wavelengths (blue band), which have inherently noisier signals due to atmospheric effects.
This research explores the utility of multispectral imagery to identify and classify marine benthic habitats in the Dutch Caribbean island of St Eustatius. These include the comparison of two sensors with different spatial and spectral resolution, QuickBird (2.4m, 4 bands) and WorldView-2 (2.0m, 8 bands) for mapping benthic habitats. The study first investigates the existing methodologies for benthic habitat classification. The benefits of atmospheric correction, corrections for sun-glint effect and water column attenuation on the accuracy of classification maps are also assessed. Then, an object and pixel-based supervised classifications for the characterization of sea grass, sand and coral are performed. This research also evaluates the possibility to extract water depth from multispectral satellite imagery by the use of a ratio transform method. Bathymetric data is important for water column correction, to improve the classification accuracy and for the study of the ecology of the habitats.