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

Abstract

Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.

Cloud Processing for Simultaneous Mapping of Seagrass Meadows in Optically Complex and Varied Water

“…Results presented here demonstrate that georeferenced field data distributed across areas of varying water quality [38,39], enabled establishment of a machine learning classifier that could concomitantly map across differing water clarities. In fact, this method has produced a map product with high user confidence, particularly in seagrass areas (Figure 3b).…”

“…There are currently no automated approaches to remove these areas, and although manual delineation could be used to remove areas of less clear water, this is subjective. We ensured that the field data used represented a range of tidal stages and were in locations where water quality ranged from very clear to lower clarity [38,39]. A random forest classifier as used in this study, is well suited to this scenario.…”

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

“…The principle of using multiple similar satellites is also found with the PlanetScope constellation, composed of 130 Planet Dove satellites. Their total revisit time is less than one day, and they can be found in several recent coral mapping studies [81][82][83][84][85]. Finally, high-resolution sensors are defined as those with a few meters resolution, such as 3 m or less.…”

South Africa’s Red List of Terrestrial Ecosystems (RLEs)

“…Having these data readily available in South Africa in the early 2000s certainly contributed to the innovative work of developing an ecosystem threat status indicator and the associated conservation tools. For RLE roll out globally, the emerging Global Ecosystem Typology [28] and range of ecosystem mapping tools [33,34] represents a good opportunity for nations without spatial data on ecosystem types to build the foundational information required for ecosystem risk assessments. In addition to providing a strong platform for conservation action, the RLE process can unlock national level reporting linked to the Convention on Biological Diversity.…”