This dataset is a vector shapefile mapping the deep vegetation on the bottom of the coral atoll lagoons in the Coral Sea within the Australian EEZ. This mapped vegetation predominantly corresponds to erect macroalgae, erect calcifying algae and filamentous algae, with an average algae benthic cover of approximately 30 - 40%. Marine vegetation on shallow reef areas were excluded due to the difficulty in distinguishing algae from coral. This dataset instead focuses on the vegetation growing on the soft sediments between the reefs in the lagoons. This dataset was mapped from contrast enhanced Sentinel 2 composite imagery (Lawrey and Hammerton, 2022, https://doi.org/10.26274/NH77-ZW79). Most of the mapped atoll lagoon areas were 45 - 70 m deep. Mapping at such depths from satellite imagery is difficult and ambiguous due to there only being a single colour band (Blue B2) that provides useful information about the benthic features at this depth. Additionally satellite sensor noise, cloud artefacts, water clarity changes, uncorrected sun glint, and detector brightness shifts all make it difficult to distinguish between high and low benthic cover at depth. To compensate for some of these anomalies the benthic mapping was digitised manually using visual cues. The most important element was to identify locations where there were clear transitions between sandy areas (with a high benthic reflectance) and vegetation areas (with a low reflectance). These contrast transitions can then act as a local reference for the image contrast between light and dark substrates. These transitions were often clearest around the many patch reefs in the lagoons which have a clear grazing halo of bare sand around their perimeter. These are often then further surrounded by an intensely dark halo, presumably from a high cover of algae. These concentric rings of light and dark substrate provided local references for the image brightness of low and high benthic cover. These cues also indicated where the hard coral substrate were. These were cut out from this dataset. Validation: Since this dataset was mapped manually from noisy and ambiguous imagery it was important to establish the validity of the visual mapping approach. The manual visual mapping was based on the assumption that the higher the benthic cover of algae, the lower the benthic reflectance. The mapped vegetation therefore corresponded to locations where the benthic reflectance is low, noting that we exclude coral reef hard substrate. To verify if we could reliably map low benthic reflectance areas we first mapped North Flinders and Holmes reefs using visual techniques, then compared this with a direct estimate of benthic reflectance determined by combining the satellite imagery with high resolution bathymetry (Lawrey, 2024, https://doi.org/10.26274/s2a8-nw72). This estimate of the benthic reflectance adjusted the satellite image to the brightness and contrast levels expected for the known depths. This showed there was very strong alignment between the manual visual mapping and low benthic reflectance. The main deviations were with the fine details around reefs, and in some parts where the water clarity made it difficult to determine if the area was vegetation, deep, or water with high CDOM. Unfortunately, the high resolution bathymetry needed for the benthic reflectance estimation was not available for the rest of the Coral Sea and so it was mapped from just the manual visual mapping from the satellite imagery (Lawrey and Hammerton, 2022, https://doi.org/10.26274/NH77-ZW79) based on lessons learnt from North Flinders and Holmes reefs. The final mapped areas in Holmes, Tregrosse and Lihou Reefs were validated against a drop camera survey conducted by JCU in 2022 (yet to be published). From this 219 survey locations overlapped the atoll lagoons. Preliminary analysis indicating that areas mapped in this dataset as having high benthic vegetation typically have 15 - 70% (average 42%) algal benthic cover, typically as a mix of erect macroalgae, erect calcareous algae and filamentous algae. Lagoonal areas that were mapped as sand (i.e. areas outside the mapped vegetation, but not on a reef) typically have a much lower algal benthic cover of 0 - 22% with an average of 4%. These areas were also typically turf algae. Method: To allow the deep benthic features to be seen the blue channel of the image composites was greatly contrast enhanced to show the very faint differences in brightness due to changes in the benthos. The amount of contrast enhancement, and thus the maximum depth that could be analysed was limited by the visual anomalies in the imagery and the magnified variations in brightness across the images due to the following: - Remnant patches from masked clouds. - Remnant patches from cloud shadows that were not fully masked. - Sentinel 2 MSI detector brightness offsets. - Uncorrected tonal change across the full Sentinel swath (western side is brighter than the eastern side). - Coloured Dissolved Organic Matter in the water increases the light attenuation, making areas darker than they would appear in clear water. This tends to occur in areas with low water flushing. - Sensor noise in the imagery. - Remnant sun glint correction due to surface waves. At depth (below ~40 m), only sandy areas are visible as they reflect enough light to be visible above the surrounding visual noise. These sandy areas create a negative space around reefs and patches of dark vegetation, making their shapes visible. Most areas of the coral atoll lagoons are gently sloping meaning that sudden changes in visual brightness are likely due to changes in benthic reflectance, rather than changes in depth. We use this fact to find the visual edge of regions of low benthic reflectance (vegetation). The benthic cover (vegetation, coral or sand) was determined by manual inspection of the contrast enhanced imagery, looking for the following visual cues: - Grazing halos around patch reefs: a pale ring corresponding to bare sand surrounding a textured dark, rounded feature (patch reef). The grazing halo is typically at a similar depth to the surrounding area. This was verified by analysing bathymetry transects across patch reefs in North Flinders reef. The grazing halo therefore serves as a local brightness reference for a high benthic reflectance substrate. Often the grazing halo is surrounded by a dark halo of extra dense vegetation. This dark ring provides a brightness reference for high density vegetation. These brightness thresholds are then used to assess the density of the more distance areas around the reef. If the area is close in darkness to the dark ring around the reef then it is considered high density vegetation. If it is closer to the grazing halo bare sand then it is considered to be free of vegetation. - Reefs without grazing halos: In some cases the patch reefs do not have a pale grazing halo around them. In these cases we identify the reef by its pale circular shape, combined with evidence that it is a tall structure, by checking if it is visible in the green channel (B3), indicating that it reaches within 30 m of the surface or the available bathymetry indicates the vertical nature of the reef. These patch reefs also typically have a dark halo around them, often darker than the surrounding flat lagoonal areas. These dark rings are used as an indication of the brightness level corresponding to high density vegetation. - Low relief flat reefs: In the western side of the Tregrosse reefs platform there are quite a few dark round features that according to the bathymetry have only a limited relief of less than 8 m. These often have a small grazing halo around their border. It is unclear what the exact nature is of these reefs are, however we assume they are reefal in nature and so we exclude them from the vegetation mapping. - On the atoll plains, particularly on the western side of Tregrosse Reefs platform there are large patches of dark substrate that have clearly blank sandy patches, unrelated to the presence of reefs. In these cases the pale patches are assumed to be bare sand and serve as a high benthic reflectance guide. Limitations: This dataset was mapped at a scale of 1:400k, with our goal being to limit the maximum boundary error to 400 m. Where the imagery was clear the mapped boundary accuracy is likely to be significantly better than this threshold. The spacing of the digitised polygon vertices was adjusted to reflect the level of uncertainty in the boundary. Where visibility was good the digitisation spacing was 100 - 200 m. In high uncertainty areas the digitised distance was increased to 500 - 1000 m. The likely boundary error is approximately equal to the vertex spacing. Many of the large areas of vegetation were littered with hundreds of small patches of lower or no vegetation. These areas were cut out as holes in the digitisation where the holes were a feature larger than 200 - 300 m in size. The vegetation areas were categorised into three levels of vegetation density (Low, Medium and High) based on how dark the substrate appeared, relative to the nearby reference indicators (dark halos around reefs, and clear patches of bare sand). In practice the accuracy of this categorisation is probably quite low, as areas where only cut into these different categories at a large scale. It was very difficult to determine the extent of the vegetation in the lagoon of Ashmore Reef. The lagoon appears to have a low flushing rate and a high amount of CDOM accumulates in the lagoon, reducing the visibility to the point were most of the benthos of most of the lagoon is not visible. To help map this reef the full series of Sentinel 2 images was carefully reviewed for tonal differences that indicate the areas of sand and vegetation. Only 20% of the boundary of the vegetation could be accurately determined, the rest of the mapped boundary is speculative. Data dictionary: - Density: Estimated density of the benthic cover in three categories, Low, Medium and High. Sandy areas, or areas with very low cover were not digitised. Comparing this data to preliminary drop camera results indicates that Low and Medium correspond to an average of 30% benthic cover and High an average of 40% cover. - EdgeAcc_m: (Integer) Approximate accuracy of the feature boundary. Note that in this edition of the dataset only very few polygons were individually tagged with accuracy values. The spacing of the polygon vertices is a better local scale measure of the edge accuracy. - EdgeSrc (Edge Image Sources): (String, 255 characters) The source of the imagery used to digitise the feature or refine its boundary. - Type: Type of the feature. In this dataset all features were 'Algae'. - TypeConf: This is the confidence that the features mapped correspond to the type specified. Care was taken to exclude reefs substrate in the mapped areas, however due to the relative coarse scale of the dataset, some sandy areas and reef areas would be included in some of the polygons. - Area_km2: Area of the polygons in square kilometres. - NvclEco: Natural Values Common Language Ecosystem classification for this feature type. All features are 'Oceanic vegetated sediments'. - NvclEcoComp: Natural Values Common Language Ecosystem Complex classification. All features are 'Ocean coral reefs eAtlas Processing: No modifications were made to the data as part of publication. Location of the data: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2022-2024-NESP-MaC-2\2.3_Improved-Aus-Marine-Park-knowledge\CS_NESP-MaC-2-3_AIMS_Oceanic-veg

This code repository and dataset details a method for determining benthic reflectance from a combination of satellite imagery and bathymetry. Its key benefit is that it can map benthic reflectance up to 50 - 60 m in depth in clear waters, when using Sentinel 2 B2 channel combined with matching bathymetry data. Benthic reflectance is a measure of how much light the seafloor reflects and is useful for distinguishing areas that are sand (high reflectance) or vegetation such as seagrass, algae and coral (low reflectance). The proposed method allows the reflectance to be estimated much deeper than existing multi-spectral approaches that rely solely on the satellite imagery. These typically only work to depths of 10 - 20 m as they require reflected light across a wide spectral range to disentangle the depth and reflectance. In deeper areas only the blue end of the spectrum remains, making it ambiguous whether an area is dark because it is deep or dark because the substrate is dark. We resolve this ambiguity by using an independent bathymetry digital elevation model. This method only works in regions where all the following conditions are true: 1. The water is clear enough that the benthic features of interest are visible in the imagery. 2. There is a matching bathymetry dataset (with similar resolution and coverage to the satellite imagery and it is not derived from satellite imagery itself). 3. There are enough relatively flat light and dark areas that they can be used to fit the relationship between bathymetry, depth and reflectance. This dataset contains case studies for three reef complexes in the Coral Sea (Flinders Reefs, Holmes Reefs and Lihou Reefs) and a small part of the GBR north east of Townsville including Davies Reefs, Grub Reef, Chicken Reef, and Bowl Reef. It also contains additional tests to determine how sensitive the results are to degraded input data, including using a bathymetry dataset that is 1/10th the resolution (100 m) of the satellite imagery (10 m) and the effect of not performing sun glint correction prior to the benthic reflection estimation. This method was developed to assist in the mapping of oceanic vegetation on seafloor of coral atolls in the Coral Sea. For most of these atolls the available bathymetry is too low resolution and so we need to rely on manual visual mapping. This dataset serves as a visual reference where the vegetation can be mapped with greater confidence due to the estimated benthic reflectance. In this dataset we perform the same processing for each of the first four colour bands of Sentinel 2 imagery (B1 UV - 443 nm, B2 Blue - 490 nm, B3 Green - 560 nm, B4 Red - 665 nm). This is done to assess the relative effectiveness of each band and to what depth they can be used for mapping benthic reflectance. Methods: The benthic reflectance is estimated from satellite imagery and bathymetry. The satellite image brightness is scaled pixel by pixel between two thresholds corresponding to the expected brightness for low and high benthic reflectance. These thresholds are estimated from the bathymetry and a model of the depth verses brightness and benthic reflectance. This model is parameterised in each area using sampling points, chosen across the depth gradient, that have been classified as high or low benthic reflectance. The reflected light is determined by the attenuation coefficient, which is the fraction of light lost in each metre of water, the amount of scattered reflected light from deep water, and the relative brightness of the wet substrate with no water cover. This is based on the model developed by Jupp 1988. To estimate the benthic reflectance we: 1. Start with an cloud free, clear water, low noise Sentinel 2 image composite of a region and matching high resolution Digital Elevation Model (DEM) for the same region. In our case we test the approach in the Coral Sea and the GBR using Sentinel 2 image composites prepared and described in the Marine satellite image test collections (AIMS) (Lawrey and Hammerton, 2024) and the GBR 30 m and 100 m bathymetry (Beaman 2017, Beaman 2020) data sets. The GBR 30 m bathymetry covers the GBR and some of the atolls close to the GBR, including North Flinders and Holmes reefs that we use in this study. The GBR 100 m, provides coverage of the whole Coral Sea, but at a significantly lower resolution, and quality (greater levels of interpolation). 2. We manually select and record locations in the image that visually correspond to high and low benthic reflectance. In deep areas the contrast between areas that are high benthic reflectance (sand) and low benthic reflectance (vegetation or coral) is low. In these areas it is often ambiguous whether the area is dark due to depth or substrate reflectance. We therefore select sampling points where the ambiguity is low. This occurs where there is a transition between dark vegetation and sand in an area that is likely to be flat or gently sloping. 3. Each Sentinel 2 swath is imaged by 12 detectors that are staggered and overlap (MSI Overview, n.d.). In clear water areas there is a noticeable brightness difference between successive detectors in the image. Some combination of the slight differences in the sensors and parallax angle between odd and even detectors result in light and dark banding in the Sentinel 2 imagery. With the large amount of contrast stretching that is needed to estimate the benthic reflectance in deep waters, these small brightness differences can result in very large errors in the final benthic reflectance. To compensate for this we divide the Sentinel 2 imagery into sections corresponding to each of the staggered detectors in the Sentinel 2 MSI instrument, rather than whole Sentinel 2 image scenes. The depth verses image brightness modelling is performed independently on each detector. 4. We extract from the satellite imagery and the DEM triplets of bathymetry, brightness and reflectance classification. 5. We then visualise and review the depth verses brightness curves to look for outliers, then review the underlying imagery and bathymetry data for the potential reason for large deviation. To correct the outliers we would typically move the point a locations less affected the cause of the error. In most cases the errors were due to the limited resolution of the bathymetry and its errors in very shallow areas (< 1-2 m deep). We also reviewed the number of sample points in each band of depths, adding new points to improve the coverage of all depths. 6. We then fit a model for each reflectance level, and detector segment, mapping the relationship between depth and image brightness. We use a simple model, based on Jupp 1988, that assumes that the brightness of the reflected signal is the addition of scattered light plus the reflected light that exponentially decays with depth. We parameterise this model for each detector swath area based on the data established in step 4 and least squares fitting, using scipy.optimize.curve_fit. This model estimates the depth averaged attenuation coefficient for the downwelling and upwelling light for each Sentinel 2 band and the background scattered light, which largely matches the brightness of open ocean water. This model assumes that the attenuation coefficient and background scattered light is constant over the detector segment. While this model doesn't fully parameterise all the inherent optical properties of the water it does provide a very good fit under most conditions. 7. We then synthesise an estimate for the upper (corresponding to the high reflectance model) and lower (corresponding to the low reflectance model) brightness expected for each location based on the bathymetry. 8. The brightness of the original satellite imagery is then scaled between these limits, estimating the reflectance for each pixel. In deep areas the contrast is greatly enhanced scaling the high and low reflectance areas to match the contrast of shallow areas. Small errors in the model (offsets in estimates of the high and low reflectance) get magnified by the amount of contrast enhancement. To limit these errors we constrain the maximum contrast enhancement to that needed to normalise to a depth of approximately 50 m. This threshold was determined experimentally. 9. To reduce the amount of noise in the image we apply a small gaussian filter, with a filter sigma radius of 15 m (1.5 pixels in the final image). Limitations of the data: This approach only works in areas where the water is clear enough to see the benthic features and there is an independent source of high quality bathymetry. The bathymetry needs to be close in resolution to the imagery, otherwise it introduces significant errors in the conversion of satellite imagery to reflectance. In the tests performed in this study we achieve very good results using bathymetry that was three times lower resolution (30 m) than the satellite imagery (10 m). Tests using bathymetry at 1/10th the satellite imagery showed significant problems. Each area being mapped requires manually selecting points of high and low reflectance to perform the conversion. In some regions there may be insufficient high and low reflectance areas at each depth level to create the curves needed to fit the models. If the water constituents are consistent across the imagery then the scattering from suspended sediment, raising the brightness, or increases in the light absorption from coloured dissolved organic matter (CDOM), lowering the brightness, will be compensated for by the empirical sampling and modelling of the depth verses brightness for the image. If however, there is a variation in the water across the scene, such as a turbidity gradient or plumes of high CDOM coming off reefs and marine vegetation, then these brighter or darker regions will be misinterpreted by the algorithm as changes in the benthic reflectance rather than changes in water conditions. This means that any areas with a higher concentration of CDOM will be darker and falsely interpreted as having a lower benthic reflectance. Any areas that are affected by suspended sediment will be brighter and falsely interpreted by the algorithm as a higher benthic reflectance. The conversion from the satellite imagery to benthic reflectance is calibrated by manual sampling of locations across the image. These locations are classified as high or low benthic reflectance based on visual inspection of the imagery. In practice no parts of the images correspond to pure white (benthic reflectance of 1) or pure black (benthic reflectance of 0). The points labelled as high benthic reflectance typically correspond to sandy areas and low benthic reflectance areas correspond to algae or reef areas. These areas correspond to intermediate benthic reflectance values. In this study we assume that the high reflectance sampling locations correspond to a benthic reflectance of 0.8 and the low reflectance locations corresponding to a benthic reflectance of 0.4. These values are only approximate and were chosen to ensure the resulting benthic reflectance data had sufficient contrast to assist in vegetation mapping. The resulting maps are thus closer to a relative estimate of benthic reflectance than a calibrated estimate of benthic reflectance from 0 to 1. This dataset only contains a limited study area in the Coral Sea (Flinders Reefs, Holmes Reefs, Lihou Reef) and a small part of the GBR north east of Townsville including Davies Reefs, Grub Reef, Chicken Reef, and Bowl Reef. This method is appropriate for use in select locations where there is both good bathymetry and clear water. It is most useful for studying coral atolls. In this method we cut up the Sentinel 2 imagery into narrow swaths corresponding to each of the staggered detectors of the MSI instrument. We independently model the depth verses image brightness and reflectance for each of these detector swaths to compensate for the slight brightness differences in each detector swath. This however requires that there are enough useful benthic sampling sites (high and low benthic reflection) in each modelled area. Output file descriptions: This data repository contains the files used as part of the analysis including: - new-data/Depth-Reflectance-Sampling-Points.shp: This file contains 1678 manually positioned point locations across the study areas, classified as high or low reflectance. These points are used to characterize the relationship between depth, image brightness, and benthic reflectance. The points were initially placed in areas with high confidence in benthic reflectance, such as edges between sandy and vegetated areas or around grazing halos. Outliers in the brightness versus depth curves were identified and adjusted based on classification mistakes or bathymetry issues, with points moved to flatter locations when necessary. - new-data/Swath-analysis-areas-Poly.shp: This file contains areas where the brightness versus depth relationship is modeled independently. The Sentinel 2 imagery is divided into areas matching the width of each detector to account for their slightly different brightness offset characteristics, which are magnified by the benthic estimation processing. The depth versus brightness and benthic reflectance modeling is performed separately for each detector. - new-data/metadata/Benth-Reflect_dataset-bounds.shp: This contains the boundary of the dataset and is used for the creation of the dataset metadata record. It represents the extent of the study area. The following correspond to analysis results for 7 test case studies. - output/55KFA-8: North Flinders reef composite of 8 images and GBR 2020 30 m bathymetry - Reference case with clearest imagery and good bathymetry. - output/55KFA-1: North Flinders reef with single best image - To show much much difference using an image composite makes to the result. - output/55KFA-8-gbr100: North Flinders reef with lower resolution 100 m bathymetry - To show the effect of lower resolution bathymetry. - output/55KFA-8-NoSGC: North Flinders reef with an 8 image composite, but without sun glint correction - To show the benefit of sun glint correction. - output/55KEB: Holmes Reefs GBR 2020 30 m bathymetry - For comparison with JCU drop camera benthic surveys. - output/56KLF-7-gbr100: Lihou Reefs - To see the effectiveness when the bathymetry is limited. - output/55KEV: GBR north east of Townsville including Davies Reefs, Grub Reef, Chicken Reef, and Bowl Reef - To assess the performance in waters with lower water clarity and high suspended sediment than the Coral Sea. Data dictionary: The following files are available for each of the test case studies. output/*/02B-Depth_Reflect-class_S2-Bright.csv - Latitude: Location of point sample - Longitude: Location of point sample - ID: Sequential counter of point sample - Reflect: Substrate brightness, either 'High' or 'Low' - SWATH_SEG: Integer 1 or 2, corresponding to two separate areas to repeat the modelling over. - Depth_m: Bathymetry of the point sample in metres - S2_R1_B1: Sentinel 2 image brightness band 1 (UV) - S2_R1_B2: Sentinel 2 image brightness band 2 (Blue) - S2_R1_B3: Sentinel 2 image brightness band 3 (Green) - S2_R1_B4: Sentinel 2 image brightness band 4 (Red) output/03C-benthic-reflect_SEG_{swath}.tif - Estimated benthic reflectance scaled from 1 - 255. 0 is reserved for no data. References: Jupp, D. L. B., 1988. Background and extensions to Depth of Penetration (DOP) mapping in shallow coastal waters. Symposium on Remote Sensing of the Coastal Zone. Gold Coast, Queensland, Session 4, Paper 2 MultiSpectral Instrument (MSI) Overview. (n.d.). Sentinel Online. Retrieved March 8, 2024, from https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument eAtlas Processing: No modifications were made to the data as part of publication. Location of the data: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2022-2024-NESP-MaC-2\2.3_Improved-Aus-Marine-Park-knowledge\CS_NESP-MaC-2-3_AIMS_Benth-Reflect