Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra

We investigate the abilities of seven remote sensors to classify coral, algae, and carbonate sand based on 10,632 reflectance spectra measured in situ on reefs around the world. Discriminant and classification analyses demonstrate that full-resolution (1 nm) spectra provide very good spectral separation of the bottom-types. We assess the spectral capabilities of the sensors by applying to the in situ spectra the spectral responses of two airborne hyperspectral sensors (AAHIS and AVIRIS), three satellite broadband multispectral sensors (Ikonos, Landsat-ETM+ and SPOT-HRV), and two hypothetical satellite narrowband multispectral sensors (Proto and CRESPO). Classification analyses of the simulated sensor-specific spectra produce overall classification accuracy rates of 98%, 98%, 93%, 91%, 64%, 58%, and 50% for AAHIS, AVIRIS, Proto, CRESPO, Ikonos, Landsat-ETM+, and SPOT-HRV, respectively. Analyses of linearly mixed sensor-specific spectra reveal that the hyperspectral and narrowband multispectral sensors have the ability to discriminate between coral and algae across many levels of mixing, while the broadband multispectral sensors do not. Applying the results of the general mixing analyses to a specific spatial organization of coral, algae, and sand indicates that the hyperspectral sensors accurately estimate areal cover of the bottom-types regardless of pixel resolution. The narrowband multispectral sensors overestimate coral cover by 11-15%, while the broadband sensors underestimate algae cover by 7-29% and overestimate coral cover by 24-103%. We conclude that currently available satellite sensors are inadequate for assessment of global coral reef status, but that it is both necessary and possible to design a sensor system suited to the task.