Farasan Islands Habitat Mapping in Saudi Arabia Using CASI and QuickBird Imagery
Map products derived from remote sensing technology increase our understanding and ability to manage tropical marine environments. The enhanced mapping capabilities of hyperspectral sensors are well understood; yet technology uptake, particularly for large scale tasks, has been slow. The study presented represents one of the largest hyperspectral projects to date, and paves the way towards increased use of this technology. Hyperspectral CASI-550 imagery and multispectral QuickBird imagery, was acquired over 3,168 km2 of the Farasan Islands. In addition to the typical image processing steps, inopportune water condensation in the CASI sensors lens necessitated further processing to remove an across-track artifact. We present a simple protocol for correcting this abnormality, utilizing an abundance of optically deep water to model and correct the error. Investment in optical, bathymetric, and other supporting field data, along with the acquisition of the QuickBird imagery was vital. Data pre-processing facilitated thematic mapping with accuracy comparable to other studies, while allowing the use of spectral unmixing to discriminate coral from within algae dominated patches in shallow water (0-5 m) environments. The unmixing model proved robust, was readily adaptable to the CASI sensor and provides additional habitat information beyond the level of thematic mapping alone.
INTRODUCTION
The use of optical remote sensing technology to characterize reefscapes has increased in recent years. Drawing on a global dataset of reflectance spectra, Hochberg et al. (2003) showed that most reef components can be spectrally grouped into 12 fundamental categories; brown, green and red fleshy algae; calcareous and turf algae; brown,
blue and bleached coral; gorgonian/softcoral; seagrass; terrigenous mud; and sand. As such, spectral discrimination is sufficient to classify
basic reef components such as coral, algae, and sand, but insufficient at the species level. The vast majority of work to date has concentrated on
multispectral, predominantly satellite based sensors (e.g. Landsat TM, IKONOS, QuickBird), which offer reliable data relatively cheaply. However, multispectral sensors collect data within only a few discrete bands and this spectral paucity may preclude discrimination of some
habitat components. Hyperspectral sensors (e.g. AVIRIS, AISA, CASI, PHILLS), by contrast, provide higher levels of spectral detail. This may
enable classification of image pixels into a greater number of descriptive classes, or facilitate deriving the relative fractional contribution of different spectral-endmembers (Goodman and Ustin 2007).
