This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos.

Simulated bidirectional reflectance distribution functions (BRDF) were compared with measurements made just beneath the water’s surface. In Case I water, the set of simulations that varied the particle scattering phase function depending on chlorophyll concentration agreed more closely with the data than other models. In Case II water, however, the simulations using fixed phase functions agreed well with the data and were nearly indistinguishable from each other, on average. The results suggest that BRDF corrections in Case II water are feasible using single, average, particle scattering phase functions, but that the existing approach using variable particle scattering phase functions is still warranted in Case I water.

Empirical methods for estimating shallow-water bathymetry using passive multispectral satellite imagery are robust and globally applicable, in theory, but they require copious local measurements of water depth for algorithm calibration. Such calibration data have historically been unavailable for most locations, but NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), a satellite-based LiDAR, might hold unique promise to fill this critical data gap. Although ICESat-2 was not designed as a marine altimeter, its ATLAS sensor consists of six green (532 nm) lasers that can penetrate a water surface and return photons reflected by the seabed, thereby generating bathymetric profiles. Utilizing TCarta’s NSF SBIR-funded Space-Based Laser Bathymetry Extraction Tool and ICESat-2’s ATL03 geolocated photon data product, we have compared ICESat-2 bathymetric retrievals with a portfolio of soundings acquired in situ using a vessel-mounted single-beam echosounder. This analysis demonstrated very high correlation (R² = 0.96) between the field and space-based bathymetry data. The comparisons were made at multiple Caribbean and Pacific coral reef sites over water depths ranging from 1 to 20 m. Results suggest that ICESat-2 could be an effective approach for calibrating and validating empirical and radiative transfer methods, alike, for estimating shallow-water bathymetry from remote sensing imagery, thereby enabling the immediate potential for shallow-water bathymetric mapping of Earth’s reefs.