The Carnegie Institution for Science, University of Queensland, and Planet, in close collaboration with Paul G. Allen Philanthropies and the National Geographic Society in collaboration with Dr. Ruth Gates 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.
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A hierarchical, object-based classification approach is applied to map coral reefs and their geomorphic and benthic zones from Planet Dove image data, physical attributes such as bathymetry, slope, and significant wave-height in combination with machine learning and eco-geomorphological driven classification rules.
Four levels of classification, corresponding to the four scales of coral reef environments (Figure 1), each mapped over a set range of depths, (Figure 2) are used:
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.
The geomorphic and benthic mapping approach currently implemented is combining machine learning with Object Based Analysis (OBA). The satellite image and physical attribute data are first segmented into ‘objects’ following an OBA paradigm. Using a training data set, a machine learning classifier is then used to make a preliminary classification of geomorphic and benthic classes. Based on an established framework for OBA on the Great Barrier Reef (Roelfsema et al. 2018), the preliminary classification is then improved and refined using the relational and contextual principles of OBA.
The OBA paradigm is based on the image and other spatial data to be first segmented into groups of pixels with similar characteristics (e.g. colour or texture, or a physical property such as water depth). This is akin to how we use our eyes to segment images into objects for interpretation. Each image ‘object’ is then given a set of metrics based on its constituent pixels, which could be the mean or standard deviation, or something more complex like gray-level co-occurrence matrix (GLCM), texture metrics.
Training and validation data sets are created for individual reefs or small groups of reefs within a mapping region, based on the segmented image and physical attribute data. These training and validation data sets have two main origins:
The Random Forest classifier is a well-established machine learning algorithm. It is an ‘ensemble learning’ methods, which means it classifies the input data based on a number of constructed ‘decision trees’ that each have some component of random variation in the parameterisation. The output classification is the mode or mean of all the decision trees, and they are particularly useful because they balance predictive performance with overfitting, and are also robust to redundant predictor variables (Breimann et al. XXX). The classifier is trained used the curated training data set, with the input variables included both the pixel and segmented objects from the image and physical environment data.
The output classification from the machine leaning classifier is then processed using a number of automated OBA membership rules. 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.
To create geomorphic and benthic maps for the training set the rule sets are adjusted, developed and tested using the commercial software Trimble eCognition 9.3. This software enables highly efficient and accurate OBA mapping, but the curated training and validation data set is in no way limited to this software – any source of high-resolution and high-accuracy map or labelled segment data can be used as training and validation in the Allen Coral Atlas workflow. The machine learning mapping and the For the mapping regions, the machine learning and OBA refinement stages are implemented in Google Earth Engine, and open source and free cloud-based processing environment that provides capability to access and process the Planet Dove imagery, along with a range of other satellite image archives (e.g. Sentinel 2, Landsat). The image segmentation and OBA refinement workflow was not previously available in Google Earth Engine, so that software capability is a major output of the Allen Coral Atlas project (Lyons et al. in prep).
Wave exposure is the dominant force influencing the ecological makeup and physical structure of coral reefs. Changes in the benthic ecological community as well as some crucial metabolic and biological functions of coral reefs have been linked to variations in wave energy. Long-term geomorphic development of coral reefs is also driven by the relative exposure of coral reefs to wave processes. A thorough understanding of wave exposure is now an important component of benthic ecological surveying in coral reefs. Wave exposure on coral reefs has typically been determined using a suite or computationally onerous models which limits wave modelling to a local or regional basis which. To calculate the wave exposure for every reef in the world a wave model that is flexible, computationally fast and links with global wave models. This model uses principles of wave refraction and diffraction to determine the dissipation of wave energy from deepwater sources to shallow reef environments and through often complex coral reef regions. This provides the local wave height for every reef prior to wave breakpoint and hence the wave exposure index for each coral reef.
Datasets: National Oceanic and Atmospheric Associate (NOAA) Wave Watch III global wave model hindcast reanalysis (1979-present). Planet derived bathymetry.
The OBA protocol incorporates additional attributes: water depth (derived from satellite imagery), slope (calculated from water depth), historical significant wave height and surface reflectance. In this project’s methodology, surface reflectance is considered a proxy for consolidated (dark e.g. reef matrix, coral, algae) or unconsolidated material (bright e.g. sand). Geomorphic mapping requires first the data sets to be divided for shallow reef area and reef type is mapped at final stage.
Membership rulesets to assign a dominant benthic cover type label to a segment are based on the brightness of the segments, band ratios, segment location within each of the geomorphic zones, and with visual assessment and guidance of expert reef knowledge and/or field data. Rules vary between geomorphic zones dependent upon the type of ecological relationship and/or threshold value for a dominant benthic cover type. Dominant benthic cover type labels include: Coral, Algae, Benthic Microalgae Mats (BMA), Seagrass, Rock, Rubble, and Sand. In this segmentation, Algae is dominated by macroalgae (> 2 cm), and Rock includes turf algae (< 2 cm) and crustose coralline algae. Patch reef categories represent small patches that include coral and algae (approximately 10 m - 50 m diameter) that inhabit sandy areas.
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.
is derived from previously collected field data through existing programs, from newly collected field data as part of the project, and through citizen science data. A protocol for data collection is being created and will include a suggested capacity building process.
Field data are required for training and verification of the mapping approach and map products such as: water depth, Geomorphic, Benthic community, benthic change.
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).
Figure 3: Options for different levels of knowledge able to be collected by the communities: (top left) detailed surveys through geolocated benthic photo quadrates:, (top right) basic surveys, through descriptive characterisation in the water; (lower left) local knowledge, where the fisherman or other are asked to provide their input on what is where; and (lower right) remote surveys, where technology is deployed to gather autonomous information about the seafloor.
For more information on mapping and monitoring through remote sensing from the University of Queensland, check out this Remote Sensing Toolkit
The National Geographic Society, in partnership with the Allen Coral Atlas and, prior to her passing, Dr. Ruth Gates, is developing a strategy for field engagement with the wider coral reef science, monitoring and management community. As the Allen Coral Atlas continues to expand, key elements will include building coalitions with networked institutions, including NGOs and research and government monitoring and mapping programs. With innovative data visualization, the team hopes to build awareness and understanding of the Atlas tool so can be leveraged globally by users, who can also contribute data and provide feedback to help improve the Atlas over time.
Field teams are collecting geo-referenced data in selected representative regions to help test, develop and implement the mapping algorithms. The Society is also developing a larger program focused on local engagement and capacity development, and aligning with efforts to use machine learning to analyze georeferenced photo quadrats.
Ultimately, the goals of the habitat mapping and 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 policymakers using the findings and data from the Allen Coral Atlas to achieve conservation impact.
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. 2017and 2014, and Heron et al. 2016 and 2015. If these products are used in any way, please follow the citation guidance.