Reef Cover: a coral reef classification to guide global habitat mapping from remote sensing

Abstract

Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications employed in large-scale reef mapping to date are typically poorly defined, not shared or region-specific. Here we present Reef Cover, a new coral reef geomorphic zone classification, developed to support global-scale coral reef habitat mapping in a transparent and version-based framework. We developed scalable classes by focusing on attributes that can be observed remotely, but whose membership rules also reflect knowledge of reef formation, growth and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. We use the Caroline and Mariana Island chains in the Pacific as a case study to demonstrate use of the classification scheme and its scientific and conservation applications. The primary application of Reef Cover is the Allen Coral Atlas global coral reef mapping project, but the system will support bespoke reef mapping conducted at a variety of spatial scales.

Workflow for the Generation of Expert-Derived Training and Validation Data: A View to Global Scale Habitat Mapping

“…Here we discuss the implementation of the reference sample creation that underpins the mapping framework (Figure 1). For each region, our mapping process consists of a combination of machine learning and object-based analysis (Lyons et al, 2020), and produces two regional products: geomorphic zonation and benthic cover maps, following a well-defined classification scheme developed for the Allen Coral Atlas project (Kennedy et al, 2020). The mapping approach uses multiple input data sources including reference data sets for training and validation (the subject of this paper), and data layers derived from satellite imagery that represent physical attributes (depth, significant wave exposure, slope).…”

“…The benthic composition depicted in the publicly available datasets were cross-walked and relabeled to the general benthic cover classes used in the Allen Coral Atlas (Kennedy et al, 2020): coral/algae, seagrass, microalgal mats, sand, rubble, and rock.…”

“…A maximum of 2 h was set per image quadrat tile, to assign segments with a geomorphic (Figure 3B') or benthic label (Figure 3C'), however, in some cases this could be less dependent upon the extent of the reef surface area and complexity. Geomorphic classes followed the Reef Cover classification scheme (Kennedy et al, 2020) and included reef slope, reef crest, outer reef flat, inner reef flat, shallow lagoon, deep lagoon, back reef slope, sheltered slope, terrestrial reef flat, plateau, and patch reef. Assignment of these classes was based on the description of the individual geomorphic classes and expert visual interpretation of the imagery, water depth, slope, significant wave height, and existing geomorphic maps.…”

“…To avoid introducing misclassified segments into the training set and to reduce the likelihood of error propagation in our mapping workflow, a protocol of quality assurance was developed. This included: (1) weekly review of examples of class assignments to reference segments by experts to fine-tune label assignment across experts; (2) all final reference segment assignment was reviewed by the most experienced expert for that region; (3) classification cues for geomorphic and benthic categories were created; and (4) confirmation of adherence to the classification scheme (Kennedy et al, 2020). Additionally, after reference data segments were created for a mapping region, each expert ranked the mapping categories from 1 to 10 where 1 represents a 51% confidence in labeling a segment with the specific class, and 10 represents 100% confidence.…”

“…Manual interpretation was required for the assignment of geomorphic classes to segments, which was based upon: (1) distinguishing dark versus bright features (a surrogate for hard versus soft substrate, respectively), (2) the use of visual interpretation cues (e.g., color, texture, and brightness) in the satellite imagery, (3) physical attributes (e.g., depth, slope, and wave exposure), (4) neighborhood relationships (e.g., reef crest neighbors reef slope), and (5) detailed classification definitions (Kennedy et al, 2020). A similar process has been used previously for large scale global geomorphic mapping (Andréfouët et al, 2006).…”