The Carnegie Institution for Science, University of Queensland, and Planet, in close collaboration with Paul G. Allen Philanthropies and Hawaii Institute for Marine Biology have identified the following methods to supply unique data to the Allen Coral Atlas, informing deep understanding and accurate analytics for global coral reef conservation.
The analysis of the data relies on specific methodology to establish what is and isn’t a coral reef. Since the bulk of the data will be satellite imagery, additional steps will be taken to correct for the effects of the atmosphere, sun glint on the surface of the water and track the depth of the water. The efficacy of these steps will be verified by field visits to the reefs during the first year of the study.
The following are illustrations and documentation about these respective methods.
Satellite data are made available to the science team in calibrated at-sensor radiance units (W str-1 m-2 s-1) as spatially contiguous orthorectified mosaics. These data require extensive processing using Carnegie algorithms to generate at-surface, sub-surface, and benthic reflectance data from the Planet radiance imagery. Reaching these three levels of processed data requires modeling of the radiometry of each Planet satellite (Dove, SkySat) used in generating coral reef coverage worldwide. Additionally, the following corrections need to be applied to Planet data to support the UQ mapping component (geomorphic zonation and benthic composition) as well as the Carnegie alert-monitoring component:
For validation of the water depth product, reference data from field measured water depths is compared with coincident locations on the map product and to calculate regression values. Field measured depth is sourced from previously collected data from existing programs.
Gao, Bo-Cai. 1996. “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3): 257–66.
Lee, ZhongPing, Kendall L. Carder, and Robert A. Arnone. 2002. “Deriving Inherent Optical Properties from Water Color: A Multiband Quasi-Analytical Algorithm for Optically Deep Waters.” Applied Optics 41 (27): 5755–72.
Lee, Zhongping, Kendall L. Carder, Curtis D. Mobley, Robert G. Steward, and Jennifer S. Patch. 1999. “Hyperspectral Remote Sensing for Shallow Waters. 2. Deriving Bottom Depths and Water Properties by Optimization.” Applied Optics38 (18): 3831–43.
Lee, Zhongping, Alan Weidemann, and Robert Arnone. 2013. “Combined Effect of Reduced Band Number and Increased Bandwidth on Shallow Water Remote Sensing: The Case of Worldview 2.” IEEE Transactions on Geoscience and Remote Sensing51 (5): 2577–86.
Li, Jiwei, Qian Yu, Yong Q. Tian, and Brian L. Becker. 2017. “Remote Sensing Estimation of Colored Dissolved Organic Matter (CDOM) in Optically Shallow Waters.” ISPRS Journal of Photogrammetry and Remote Sensing 128: 98–110.
Wabnitz, Colette C., Serge Andréfouët, Damaris Torres-Pulliza, Frank E. Müller-Karger, and Philip A. Kramer. 2008. “Regional-Scale Seagrass Habitat Mapping in the Wider Caribbean Region Using Landsat Sensors: Applications to Conservation and Ecology.” Remote Sensing of Environment 112 (8): 3455–67.
The classification scheme outlined in Figure 1 and Figure 2 forms the backbone of the Allen Coral Atlas project. The classes and the rules used to define these were revised as the first part of the project and ongoing revision and improvement will take place. The revisions will take into account: Planet Dove data, Planet Dove derived bathymetry, best on offer global wave climatology data, available international community verification and validation data, and most recent regional to global scale coral reef mapping projects, as well as other past global projects and current NOAA UNEP global coral data sets.
Membership rules form the typology of a mapping class defined by different attributes. These attributes not only include the brightness of an object but also the texture, depth, slope, waves or location in relation to other objects. In marine environments, seafloor features are especially challenging to distinguish due to submerged characteristics by variation in water from tides, water column composition from water movement, and surface roughness.
Rule sets used in this methodology are based on past studies, and are being applied to five initial geographically-disparate sites before being applied to reef regions. The reefs within the site vary in reef type, geomorphic zonation and benthic composition but are representative for the region. A region can be a geographic entity (e.g. Raja Ampat, Indonesia) or a group of reefs significant in size (e.g. 237 GBR reefs).
Due to increased bottom reflectance attenuation with increasing water depth, in some cases the only differentiation made is between bright and dark objects. Bright objects are assumed to represent unconsolidated material (e.g. bright = sand), and dark objects consolidated material (e.g. dark = coral, rock or algae).
To provide more confidence in differentiating between coral and algae, historical impacts could be incorporated together with local knowledge. Coral and algae have similar visual characteristics within a high spatial resolution multi-spectral satellite image, making them harder to differentiate from each other. Therefore, historical knowledge of impacts such as cyclones, bleaching, Crown of Thorns, or decline in water quality are helpful in verifying presence of either benthic type. For instance, recent severe bleaching would most likely turn areas (objects) assigned as coral to algae.
Reference data for the benthic mapping will be sourced predominantly through georeferenced photo quadrates that could be collected from boat, on snorkel or scuba or using a Remote Operated Vehicle (ROV) or Autonomous Underwater Vehicle (AUV). Photo quadrates could be acquired at random points or along transects and set intervals, located around and on top of the reef, and its position synchronized with standard hand held GPS. Photos will be analyzed using machine learning to provide a consistent output of benthic mapping categories.
Additionally, field verification will be used for contextual editing in regards to the habitat information but also in regards to administrative information e.g. such as addition of local reef names. Verification data will be sourced at different levels of detail and accuracy, and at different times throughout the project (Figure 3).
For more information on mapping and monitoring through remote sensing from the University of Queensland, check out this Remote Sensing Toolkit
Beijbom O, Edmunds PJ, Roelfsema C, Smith J, Kline DI, Neal BP, et al. (2015) Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation. PLoS ONE 10(7): e0130312. doi:10.1371/journal.pone.0130312
González-Rivero, M., O. Beijbom, A. Rodriguez-Ramirez, T. Holtrop, Y. González-Marrero, A. Ganase, C. Roelfsema, S. Phinn and O. Hoegh-Guldberg (2016). "Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis." Remote Sensing 8(1): 30.
Lyons, M. B., D. A. Keith, S. R. Phinn, T. J. Mason and J. Elith (2018). "A comparison of resampling methods for remote sensing classification and accuracy assessment." Remote Sensing of Environment 208: 145-153.
Phinn, S.R., Roelfsema, C.M., & Mumby, P.J. (2012). Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs. International Journal of Remote Sensing, 33, 3768-3797
Roelfsema, C, M. Lyons, M. Dunbabin, E. M. Kovacs & S. Phinn (2015) Integrating field survey data with satellite image data to improve shallow water seagrass maps: the role of AUV and snorkeller surveys?, Remote Sensing Letters, 6:2, 135-144, DOI:10.1080/2150704X.2015.1013643
Roelfsema, C. M. and R. S. Phinn (2013). Validation. Coral Reef Remote Sensing: A Guide for Multi-level Sensing Mapping and Assessment. J. Goodman, S. Purkis and S. R. Phinn, Elsiver.
Roelfsema, C. M. and S. R. Phinn (2010). "Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps." Journal of Applied Remote Sensing 4(1): 1-28.
Roelfsema, C.M. and S.R. Phinn (2008). Evaluating Eight Field and Remote Sensing Approaches for Mapping the Benthos of Three Different Coral Reef Environments in Fiji. In: Proceedings of SPIE Asia Pacific Remote Sensing Conference – Remote Sensing of Inlands, Coastal and Oceanic Water, Noumea, New Caledonia, 17-21 November 2008, Volume 7150.
Roelfsema, C.M., Joyce, K. E., Phinn, S.R.,(2006) Evaluation of Benthic Survey Techniques for Validating Remotely Sensed Images of Coral Reefs. Proceedings 10th International Coral Reef Symposium Okinawa.
Roelfsema, C., Kovacs, E., Ortiz, J.C., Wolff, N.H., Callaghan, D., Wettle, M., Ronan, M., Hamylton, S.M., Mumby, P.J., & Phinn, S. (2018a). Coral reef habitat mapping: A combination of object-based image analysis and ecological modelling. Remote Sensing of Environment, 208, 27-41
Roelfsema, C., Kovacs, E., Roos, P., Terzano, D., Lyons, M., & Phinn, S. (2018b). Use of a semi-automated object based analysis to map benthic composition, Heron Reef, Southern Great Barrier Reef. Remote Sensing Letters, 9, 324-333
Roelfsema, C.M., Phinn, S.R., Jupiter, S., Comley, J., & Albert, S. (2013). Mapping Coral Reefs at Reef to Reef-System scales (10-600 km2) using OBIA Driven Ecological and Geomorphic Principles. International Journal of Remote Sensing, 1-22.
A strategy for field verification and engagement with the wider coral reef science, monitoring, and management community is being developed in consultation with the Atlas team, and, before her passing, Dr. Ruth Gates. As the Allen Coral Atlas scales up regionally and then globally, key elements will include building coalitions with key networks institutions (e.g., NGO, research, and government monitoring programs). With innovative data visualization, we will build awareness and understanding of the Atlas as a new standard and tool linking mapping and monitoring among national, regional, and global users, who may also be able to contribute data and provide feedback to help improve the Atlas over time.
The mapping team is collecting data for calibration and validation in selected representative regions to help them test, develop and implement the algorithms. This will be coordinated with a larger program of local engagement and capacity development/training the trainers for additional field verification. Methods will include expanding existing monitoring (e.g., by ensuring a GPS in a float is recording precisely each location where data is collected) and aligning with efforts to use machine learning to analyze georeferenced photo quadrats.
Ultimately, the goals of the habitat mapping and ecological monitoring of the Atlas can help report on progress toward achieving international targets such as the Sustainable Development Goals and Convention on Biological Diversity Aichi targets. We anticipate alignment with existing efforts (e.g., the International Coral Reef Initiative and the Global Coral Reef Monitoring Network) to facilitate planners, managers, and policy makers using the findings and data from the Allen Coral Atlas.
The Coral Reef Watch near real-time 5km global products on the Allen Coral Atlas site are the most recent day's published sea surface temperature (SST), SST Anomaly, Coral Bleaching HotSpot, Degree Heating Week (DHW), a 7-day maximum Bleaching Alert Area, and 7-day SST Trend data from NOAA's Coral Reef Watch program. Please see their website for more information about the program. For more technical details about the 5-km products, see Liu et al. 2017 and 2014, and Heron et al. 2016 and 2015. If these products are used in any way, please follow the citation guidance.