## Materials and Methods
### Classification Method
Although depth and RGB values workflow increased the likelihood of correctly classifying the objects, the RGB-only approach was used to distinguish deeper habitats such as sandy bottom and distinguishing between dense and sparse algae hardbottom areas. Spur and groove corals were manually identified and mapped in the imagery, since these areas were easily distinguished with their distinct coral ridges and sand channel patterns. When intersected with the geomorphic zone layer, objects beyond depths greater than 30 m were removed and the resulting outline provided a shelf boundary line. The majority of reef habitat classes were mapped using both RGB and depth, where the depth classification was used in combination with the geomorphic zones to identify and refine the different reef types (i.e., reef crest, fore reef, back reef, and other patch and fringing reefs that were assigned to the \"Coral/Algae\" class) (Figure 8(a),b). An innovative spatial approximation routine in eCognition was developed that takes advantage of the image object hierarchy and data fusion abilities. The routine used the geomorphic zone polygons to provide spectral 'parental guidance' restraints in the classification, focusing the identification of objects within the polygon boundaries based on spectral value thresholds. Using this parental guidance routine within the polygon outline, the predominant spectral response was calculated, and an object was formed around it. This new segment was then allowed to expand beyond the polygon extent, essentially seeding new areas as long as the spectral response remains within the defined narrow range of the predominant values (Figure 8(c)). Additionally, the objects within the polygon that were outside the defined rules of the spectral response range were merged using a best-fitting technique (Figure 8(d)), allowing outside objects to intrude inside the polygon, refining the original object boundary. Hence, the method for coral reef classification took advantage of existing knowledge from the geomorphic zones, using this information for seeding areas and expanding throughout these boundaries. Smaller reef patches were detected using the depth classification, scanning the model with a kernel to identify distinct elevated humps within a certain elevation threshold based on neighborhood values. Narrow fringing reefs were detected using the same spectral parental guidance approach, searching for specified range thresholds of the red-blue ratio within objects that were identified within the geomorphic zones. Figure 7: (**a**) True color surface reflectance image composite of Turks and Caicos Islands. Red rectangle shows the location of Grace Bay in Figure 3d; (**b**) masking of land, shallow (<30 m), and deep ocean (>30 m) areas using the geomorphic zone polygons; (**c**) Shallow bathymetry model (<20 m depth) based on PS surface reflectance; (**d**) Detailed view of the depth classification using 1-m increments for depths between 0–10 m and 2-m increments for areas >10 m depth, highlighting the shallow lagoons, reef crest, and steep slope beyond the fore reef leading to the deep ocean. Within lagoon areas, the classifier separated sand and seagrass beds by investigating the full spectral response of the respective lagoon zone to calculate an object-specific range and subsequent threshold between bright sand and the darker seagrass objects. Objects identified as seagrass were then split into dense and sparse seagrass areas using the same object-specific approach and threshold within selected objects. Seagrass beds are typically not found at depths exceeding 12 m and this was used as a rule to further guide the classification. When considering inland coastal lagoons, these features were identified, since they are cut off from open water. Estuaries and inland bays were classified between sand, seagrass, or muddy bottom based on a spectral threshold of the object. Objects created over deeper areas of the shelf (>12 m depth), often located beyond the fore reef, were the last to be classified.