Skip to Main Content
Monitoring coral reef benthos with satellites has typically followed a "sensor-down" approach, with the classification algorithm driven by statistics derived from the imagery. I adopt a "reef-up" method, drawing on statistics derived from hyperspectral optical field measurements of substrate reflectance to train image classification. In order to calibrate the satellite data with direct physical measurements of reflectivity, it is necessary to process both the imagery and in situ data to common units of albedo. Building upon a proof-of-concept study conducted by the author in the Red Sea, the link is made by correcting the remote sensing data for the effect of varying bathymetry using in situ measurement of water column optical properties and a digital elevation model constructed from a vessel-based acoustic survey, thereby yielding units of substrate reflectance. Extensive ground verification of the predictive benthic habitat map resulting from image classification showed that eight substrate classes were resolved with an overall accuracy of 69% down to a depth of 6 m, including live and dead coral framework. As compared to conventional from-image classification techniques, the reef-up method offers the potential for higher thematic accuracy while maintaining a greater degree of flexibility for repeat survey using platforms of higher spectral and spatial resolution, expected to come online in the near future. The fact that image acquisition and optical ground-truthing did not occur concurrently, is of particular relevance in confirming that in situ measurements can be made independent of image acquisition and retrospectively linked to appropriate substrate classes. Considering the wealth of hyperspectral data already acquired for shallow reef facies, the work highlights the potential of the reef-up approach for quantifying substrate distribution in coral environments using both air- and spaceborne platforms.