## Materials and Methods
### Classification Method  
The Caribbean benthic habitat classification was conducted using Trimble eCognition v9.5 software [52], which is composed of a front-end development environment called eCognition Developer and a background processing environment, eCognition Server, which allows for batch and parallel processing of data. The RuleSet for classifying the benthic habitat classes was developed and tested in eCognition Developer using the intrinsic Object-based Image Analysis (OBIA) approach. RuleSets are developed as the means to transfer the way a human interprets and understands an image into a machine-interpretable language called Cognition Network Language (CNL). Objects represent pixels of similar value ranges and are created through image segmentation, which treats objects as entities, providing topological relationships and access to the underlying pixel values [53; 54]. With multiple object scales and different levels of segmentations contained under one hierarchy, the OBIA model can consistently and accurately represent real-world objects [55]. [PERSON] et al. [56] suggested OBIA minimizes within-class spectral variability by assigning all pixels in the object to the same class, makes better use of spatial information such as size, shape, and texture of objects, and facilitates integration of contextual and semantic relationships among geographic objects. The RuleSet was designed in blocks, with each block responsible for a certain step in the analysis. When creating a RuleSet, especially for projects with large amounts of data  
Figure 6: A total of 1653 GPS-referenced field data points representing locations of underwater video transects were used to train the algorithm within (**a**) eastern Dominican Republic (281 points); and (**b**) Saint Croix, USVI (1372 points). and considerable development time, an orderly and clean workflow is important to back-trace any routines. The extraction of each class and its analyses steps were hierarchically placed under descriptive parent processes, which allowed for toggling blocks on and off as well as running single blocks separately, which facilitated the detection of back-tracing errors. The image adaptive RuleSet items were implemented using variables which hold calculated values from local operations. When compared to a global threshold that would not take into account fine-scale local variations, local operations ensure that local variations of the image data as well as from derived layers are considered. The first step, a vector-based segmentation and classification, was implemented to create a land-sea-mask using the digitized geomorphic zones, which separated land, shallow water (<30 m), and deep ocean areas (Figure 6(a),b). This provided the geographic boundaries upon which any further step of the classification ruleset acted. To take advantage of the depth information and spatial relationship of benthic communities, the second step was to create a depth classification, dividing up the bathymetry into 1-m increments for depths between 0-10 m, and 2-m increments for areas >10 m depth (up to 20 m depth) (Figure 6(c),d). The depth classification was done using a raster-value segmentation that aggregates areas of similar depth values into single objects. The third step was to identify the deeper areas in the imagery that were beyond the bathymetry model range but were still included in the extent of the geomorphic zones layer. These areas did not use depth to classify the objects but relied solely on the RGB (red, green, blue) spectral values to assign the benthic habitat class. Figure 8 shows an overview of the classification workflow, which implements two parallel tracks of classifying (a) RGB values and depth (blue path) in areas where underlying depth information was available (<20 m depth); and (b) only RGB values (green path) where depth information was absent (~20-30 m depth).