This dataset contains hotspot point data, derived from satellite-born instruments that detect light in the thermal wavelengths found on the Digital Earth Australia Hotspots application. Typically, satellite data are processed with a specific algorithm that highlights areas with an unusually high temperature. Hotspot sources include the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the National Aeronautics and Space Administration (NASA) Terra and Aqua satellites, the Advanced Very High Resolution Radiometer (AVHRR) night time imagery from the National Oceanic and Atmospheric Administration (NOAA) satellites, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi- NPP satellite. Please note: As these data are stored on a Corporate system, we are only able to supply the web services (see download links).

email earth.observation@ga.gov.au.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

This dataset is true colour cloud-free composite satellite imagery optimised for mapping shallow marine habitats in northern Australia, based on 10-meter resolution Sentinel 2 data collected from 2015 to 2024. It contains composite imagery for 333 Sentinel 2 tiles of northern Australia and the Great Barrier Reef. This dataset offers improved visual clarity of shallow water features as compared to existing satellite imagery, allowing deeper marine features to be observed. These composites were specifically designed to address challenges such as sun glint, clouds and turbidity that typically hinder marine environment analyses. No tides were considered in the selection of the imagery and so this imagery corresponds to an 'All tide' image, approximating mean sea level.

This dataset is an updated version (Version 2), published in July 2024, which succeeds the initial draft version (Version 1, published in March 2024). The current version spans imagery from 2015–2024, an extension of the earlier timeframe that covered 2018–2022. This longer temporal range allowed the imagery to be cleaner with lower image noise allowing deeper marine features to be visible. The deprecated draft version was removed from online download to save on storage space and is now only available on request.

While the final imagery corresponds to true colour based primarily Sentinel 2 bands B2 (blue), B3 (green), and B4 (red), the near infrared (B8) band was used as part of sun glint correction and automated selection of low noise imagery.

Contrast enhancement was applied to the imagery to compress the original 12 bit per channel Sentinel 2 imagery into the final 8-bit per channel GeoTiffs. Black and white point correction was used to enhance the contrast as much as possible without too much clipping of the darkest and lightest marine features. Gamma correction of 2 (red), 2 (green) and 2.3 (blue) was applied allow a wider dynamic range to be represented in the 8-bit data, helping to ensure that little precision was lost in representing darker marine features. As a result, the image brightness is not linearly scaled. Further details of the corrections applied is available from https://github.com/eatlas/AU_NESP-MaC-3-17_AIMS_S2-comp/blob/main/src/processors/s2processor.py.


Methods:

The satellite image composites were created by combining multiple Sentinel 2 images using the Google Earth Engine. The core algorithm was:
1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by
- tile ID
- maximum cloud cover 20%
- date between '2015-06-27' and '2024-05-31'
- asset_size > 100000000 (remove small fragments of tiles)
Note: A maximum cloud cover of 20% was used to improve the processing times. In most cases this filtering does not have an effect on the final composite as images with higher cloud coverage mostly result in higher noise levels and are not used in the final composite.
2. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information).
3. For each SENSING_ORBIT_NUMBER collection filter out all noise-adding images:
3.1 Calculate image noise level for each image in the collection (see "Image noise level calculation for more information") and sort collection by noise level.
3.2 Remove all images with a very high noise index (>15).
3.3 Calculate a baseline noise level using a minimum number of images (min_images_in_collection=30). This minimum number of images is needed to ensure a smoth composite where cloud "holes" in one image are covered by other images.
3.4 Iterate over remaining images (images not used in base noise level calculation) and check if adding image to the composite adds to or reduces the noise. If it reduces the noise add it to the composite. If it increases the noise stop iterating over images.
4. Combine SENSING_ORBIT_NUMBER collections into one image collection.
5. Remove sun-glint (true colour only) and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information).
6. Duplicate image collection to first create a composite image without cloud masking and using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
7. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
8. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022)
9. The final composite was exported as cloud optimized 8 bit GeoTIFF

Note: The following tiles were generated with no "maximum cloud cover" as they did not have enough images to create a composite with the standard settings: 46LGM, 46LGN, 46LHM, 50KKD, 50KPG, 53LMH, 53LMJ, 53LNH, 53LPH, 53LPJ, 54LVP, 57JVH, 59JKJ.

Compositing Process:

The dataset was created using a multi-step compositing process. A percentile-based image compositing technique was employed, with the 15th percentile chosen as the optimal value for most regions. This percentile was identified as the most effective in minimizing noise and enhancing key features such as coral reefs, islands, and other shallow water habitats. The 15th percentile was chosen as a trade off between the desire to select darker pixels that typically correspond to clearer water, and very dark values (often occurring at the 10th percentile) corresponding to cloud shadows.

The cloud masking predictor would often misinterpret very pale areas, such as cays and beaches as clouds. To overcome this limitation a dual-image compositing method was used. A primary composite was generated with cloud masks applied, and a secondary, composite with no cloud masking was layered beneath to fill in potential gaps (or “holes”) caused by the cloud masking mistakes

Image noise level calculation:

The noise level for each image in this dataset is calculated to ensure high-quality composites by minimizing the inclusion of noisy images. This process begins by creating a water mask using the Normalized Difference Water Index (NDWI) derived from the NIR and Green bands. High reflectance areas in the NIR and SWIR bands, indicative of sun-glint, are identified and masked by the water mask to focus on water areas affected by sun-glint. The proportion of high sun-glint pixels within these water areas is calculated and amplified to compute a noise index. If no water pixels are detected, a high noise index value is assigned.

In any set of satellite images, some will be taken under favourable conditions (low wind, low sun-glint, and minimal cloud cover), while others will be affected by high sun-glint or cloud. Combining multiple images into a composite reduces noise by averaging out these fluctuations.

When all images have the same noise level, increasing the number of images in the composite reduces the overall noise. However, in practice, there is a mix of high and low noise images. The optimal composite is created by including as many low-noise images as possible while excluding high-noise ones. The challenge lies in the determining the acceptable noise threshold for a given scene as some areas are more cloudy and sun glint affected than others.

To address this, we rank the available Sentinel 2 images for each scene by their noise index, from lowest to highest. The goal is to determine the ideal number of images (N) to include in the composite to minimize overall noise. For each N, we use the lowest noise images and estimate the final composite noise based on the noise index. This is repeated for all values of N up to a maximum of 200 images, and we select the N that results in the lowest noise.

This approach has some limitations. It estimates noise based on sun glint and residual clouds (after cloud masking) using NIR bands, without accounting for image turbidity. The final composite noise is not directly measured as this would be computationally expensive. It is instead estimated by dividing the average noise of the selected images by the square root of the number of images. We found this method tends to underestimate the ideal image count, so we adjusted the noise estimates, scaling them by the inverse of their ranking, to favor larger sets of images. The algorithm is not fully optimized, and further refinement is needed to improve accuracy.

Full details of the algorithm can be found in https://github.com/eatlas/AU_NESP-MaC-3-17_AIMS_S2-comp/blob/main/src/utilities/noise_predictor.py

Sun glint removal and atmospheric correction:

Sun glint was removed from the images using the infrared B8 band to estimate the reflection off the water from the sun glint. B8 penetrates water less than 0.5 m and so in water areas it only detects reflections off the surface of the water. The sun glint detected by B8 correlates very highly with the sun glint experienced by the visible channels (B2, B3 and B4) and so the sun glint in these channels can be removed by subtracting B8 from these channels.

Eric Lawrey developed this algorithm by fine tuning the value of the scaling between the B8 channel and each individual visible channel (B2, B3 and B4) so that the maximum level of sun glint would be removed. This work was based on a representative set of images, trying to determine a set of values that represent a good compromise across different water surface

The NSW SPOT 5 imagery product is known as the Planning and Natural Resource Information Intergration Environment Project (PANRIIE) and is a state-wide satellite imagery product provided by the Remote Sensing and Regulatory Mapping team of NSW Government. This project was the largest SPOT project acquisition to be undertaken in Australia at the time. Imagery from 2006 and 2007 are known as "Hotspots", with scenes being delivered for targeted areas across NSW. Capture dates for imagery products for 2005-2007 are:

2005 - October 2004 through to August 2005

2006 - November 2005 through to June 2006

2007 - August 2006 through to October 2007

Imagery data sets have been acquired from SPOT Imaging and processed by GeoImage Pty Ltd. SPOT imagery products offer high resolution over broad areas using the SPOT 5 satellites. A SPOT satellite acquisition covers large areas in a single pass at resolutions up to 2.5m. Such precise coverage is ideal for applications at national and regional scales from 1:250,000 to 1:15,000. Data products supplied for all of NSW are:

State-wide mosaic (only 2005)

Reflectance scenes

Panchromatic scenes

The statewide mosaic is provided as a Red Green Blue (RGB) band combination; contrast enhanced lossless 8-bit JPEG2000 file. The reflectance and panchromatic scenes are available to download from JDAP. The NSW mosaic is available from internal DPE APOLLO Image Webserver for DCCEEW employees. Contact spatial.imagery@environment.nsw.gov.au for further information “Includes material © CNES 2005, 2006, & 2007, Distribution Astrium Services / Spot Image S.A., France, all rights reserved” These image products are only available to other NSW Government agencies upon request.

An International Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends infra-red SST observations from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA and METOP polar-orbiting satellites, microwave SST observations from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) on GCOM-W, and in situ data from ships, and drifting and moored buoys from the Global Telecommunications System (GTS).
All SST observations are filtered for those values suspected to be affected by diurnal warming by excluding cases which have experienced recent surface wind speeds of below 6 m/s during the day and less than 2 m/s during the night, thereby resulting in daily foundation SST estimates that are largely free of diurnal warming effects.

The Bright Earth eAtlas Basemap dataset collection is a satellite-derived global map of the world at a 1:1M scale for most of the world and 1:200k scale for Australia. This map was inspired by Natural Earth II (NEII) and NASA's Blue Marble Next Generation (BMNG) imagery.

Its aim was to provide a basemap similar to NEII but with a higher resolution (~10x).

This basemap is derived from the following datasets: Blue Marble Next Generation 2004-04 (NASA), VMap0 coastline, Coast100k 2004 Australian coastline (GeoScience Australia), SRTM30 Plus v8.0 (UCSD) hillshading, Natural Earth Vector 10m bathymetry and coastline v2.0 (NE), gbr100 hillshading (JCU).

This dataset (World_Bright-Earth-e-Atlas-basemap) contains all the files required to setup the Bright Earth eAtlas basemap in a GeoServer. All the data files are stored in GeoTiffs or shapefiles and so can also be loaded into ArcMap, however no styling has been included for this purpose.

This basemap is small enough (~900 MB) that can be readily used locally or deployed to a GeoServer.

Base map aesthetics (added 28 Jan 2025)

The Bright Earth e-Atlas Basemap is a high-resolution representation of the Earth's surface, designed to depict global geography with clarity, natural aesthetics with bright and soft color tones that enhance data overlays without overwhelming the viewer. The land areas are based on NASA's Blue Marble imagery, with modifications to lighten the tone and apply noise reduction filtering to soften the overall coloring. The original Blue Marble imagery was based on composite satellite imagery resulting in a visually appealing and clean map that highlights natural features while maintaining clarity and readability. Hillshading has been applied across the landmasses to enhance detail and texture, bringing out the relief of mountainous regions, plateaus, and other landforms.

The oceans feature three distinct depth bands to illustrate shallow continental areas, deeper open ocean zones, and the very deep trenches and basins. The colors transition from light blue in shallow areas to darker shades in deeper regions, giving a clear sense of bathymetric variation. Hillshading has also been applied to the oceans to highlight finer structures on the seafloor, such as ridges, trenches, and other geological features, adding depth and dimensionality to the depiction of underwater topography.

At higher zoom levels prominent cities are shown and the large scale roads are shown for Australia.

Rendered Raster Version (added 28 Jan 2025)

A low resolution version of the dataset is available as a raster file (PNG, JPG and GeoTiff) at ~2 km and 4 km resolutions. These rasters are useful for applications where GeoServer is not available to render the data dynamically. While the rasters are large they represent a small fraction of the full detail of the dataset. The rastered version was produced using the layout manager in QGIS to render maps of the whole world, pulling the imagery from the eAtlas GeoServer. This imagery from converted to the various formats using GDAL. More detail is provided in 'Rendered-bright-earth-processing.txt' in the download and browse section.

Change Log 2025-01-28: Added two rendered raster versions of the dataset at 21600x10800 and 10400x5400 pixels in size in PNG, JPG and GeoTiff format. Added

This dataset contains cloud free, low tide composite satellite images for the tropical Australia region based on 10 m resolution Sentinel 2 imagery from 2018 – 2023. This image collection was created as part of the NESP MaC 3.17 project and is intended to allow mapping of the reef features in tropical Australia.

This collection contains composite imagery for 200 Sentinel 2 tiles around the tropical Australian coast. This dataset uses two styles:
1. a true colour contrast and colour enhancement style (TrueColour) using the bands B2 (blue), B3 (green), and B4 (red)
2. a near infrared false colour style (Shallow) using the bands B5 (red edge), B8 (near infrared), and B12 (short wave infrared).
These styles are useful for identifying shallow features along the coastline.

The Shallow false colour styling is optimised for viewing the first 3 m of the water column, providing an indication of water depth. This is because the different far red and near infrared bands used in this styling have limited penetration of the water column. In clear waters the maximum penetrations of each of the bands is 3-5 m for B5, 0.5 - 1 m for B8 and < 0.05 m for B12. As a result, the image changes in colour with the depth of the water with the following colours indicating the following different depths:
- White, brown, bright green, red, light blue: dry land
- Grey brown: damp intertidal sediment
- Turquoise: 0.05 - 0.5 m of water
- Blue: 0.5 - 3 m of water
- Black: Deeper than 3 m
In very turbid areas the visible limit will be slightly reduced.


Change log:

Changes to this dataset and metadata will be noted here:
2024-07-24 - Add tiles for the Great Barrier Reef
2024-05-22 - Initial release for low-tide composites using 30th percentile (Git tag: "low_tide_composites_v1")


Methods:

The satellite image composites were created by combining multiple Sentinel 2 images using the Google Earth Engine. The core algorithm was:
1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by
- tile ID
- maximum cloud cover 0.1%
- date between '2018-01-01' and '2023-12-31'
- asset_size > 100000000 (remove small fragments of tiles)
2. Remove high sun-glint images (see "High sun-glint image detection" for more information).
3. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information).
4. Iterate over all images in the split collections to predict the tide elevation for each image from the image timestamp (see "Tide prediction" for more information).
5. Remove images where tide elevation is above mean sea level to make sure no high tide images are included.
6. Select the 10 images with the lowest tide elevation.
7. Combine SENSING_ORBIT_NUMBER collections into one image collection.
8. Remove sun-glint (true colour only) and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information).
9. Duplicate image collection to first create a composite image without cloud masking and using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
10. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 30th percentile of the images in the collection (i.e. for each pixel the 30th percentile value of all images is used).
11. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022)
12. The final composite was exported as cloud optimized 8 bit GeoTIFF

Note: The following tiles were generated with different settings as they did not have enough images to create a composite with the standard settings:
- 51KWA: no high sun-glint filter
- 54LXP: maximum cloud cover set to 1%
- 54LXP: maximum cloud cover set to 1%
- 54LYK: maximum cloud cover set to 2%
- 54LYM: maximum cloud cover set to 5%
- 54LYN: maximum cloud cover set to 1%
- 54LYQ: maximum cloud cover set to 5%
- 54LYP: maximum cloud cover set to 1%
- 54LZL: maximum cloud cover set to 1%
- 54LZM: maximum cloud cover set to 1%
- 54LZN: maximum cloud cover set to 1%
- 54LZQ: maximum cloud cover set to 5%
- 54LZP: maximum cloud cover set to 1%
- 55LBD: maximum cloud cover set to 2%
- 55LBE: maximum cloud cover set to 1%
- 55LCC: maximum cloud cover set to 5%
- 55LCD: maximum cloud cover set to 1%


High sun-glint image detection:

Images with high sun-glint can lead to lower quality composite images. To determine high sun-glint images, a mask is created for all pixels above a high reflectance threshold for the near-infrared and short-wave infrared bands. Then the proportion of this is calculated and compared against a sun-glint threshold. If the image exceeds this threshold, it is filtered out of the image collection. As we are only interested in the sun-glint on water pixels, a water mask is created using NDWI before creating the sun-glint mask.


Sun-glint removal and atmospheric correction:

Sun-glint was removed from the images using the infrared B8 band to estimate the reflection off the water from the sun-glint. B8 penetrates water less than 0.5 m and so in water areas it only detects reflections off the surface of the water. The sun-glint detected by B8 correlates very highly with the sun-glint experienced by the visible channels (B2, B3 and B4) and so the sun-glint in these channels can be removed by subtracting B8 from these channels.

Eric Lawrey developed this algorithm by fine tuning the value of the scaling between the B8 channel and each individual visible channel (B2, B3 and B4) so that the maximum level of sun-glint would be removed. This work was based on a representative set of images, trying to determine a set of values that represent a good compromise across different water surface conditions.

This algorithm is an adjustment of the algorithm already used in Lawrey et al. 2022


Tide prediction:

To determine the tide elevation in a specific satellite image, we used a tide prediction model to predict the tide elevation for the image timestamp. After investigating and comparing a number of models, it was decided to use the empirical ocean tide model EOT20 (Hart-Davis et al., 2021). The model data can be freely accessed at https://doi.org/10.17882/79489 and works with the Python library pyTMD (https://github.com/tsutterley/pyTMD). In our comparison we found this model was able to predict accurately the tide elevation across multiple points along the study coastline when compared to historic Bureau of Meteorolgy and AusTide data. To determine the tide elevation of the satellite images we manually created a point dataset where we placed a central point on the water for each Sentinel tile in the study area . We used these points as centroids in the ocean models and calculated the tide elevation from the image timestamp.


Using "SENSING_ORBIT_NUMBER" for a more balanced composite:

Some of the Sentinel 2 tiles are made up of different sections depending on the "SENSING_ORBIT_NUMBER". For example, a tile could have a small triangle on the left side and a bigger section on the right side. If we filter an image collection and use a subset to create a composite, we could end up with a high number of images for one section (e.g. the left side triangle) and only few images for the other section(s). This would result in a composite image with a balanced section and other sections with a very low input. To avoid this issue, the initial unfiltered image collection is divided into multiple image collections by using the image property "SENSING_ORBIT_NUMBER". The filtering and limiting (max number of images in collection) is then performed on each "SENSING_ORBIT_NUMBER" image collection and finally, they are combined back into one image collection to generate the final composite.


Cloud Masking:

Each image was processed to mask out clouds and their shadows before creating the composite image.
The cloud masking uses the COPERNICUS/S2_CLOUD_PROBABILITY dataset developed by SentinelHub (Google, n.d.; Zupanc, 2017). The mask includes the cloud areas, plus a mask to remove cloud shadows. The cloud shadows were estimated by projecting the cloud mask in the direction opposite the angle to the sun. The shadow distance was estimated in two parts.

A low cloud mask was created based on the assumption that small clouds have a small shadow distance. These were detected using a 35% cloud probability threshold. These were projected over 400 m, followed by a 150 m buffer to expand the final mask.

A high cloud mask was created to cover longer shadows created by taller, larger clouds. These clouds were detected based on an 80% cloud probability threshold, followed by an erosion and dilation of 300 m to remove small clouds. These were then projected over a 1.5 km distance followed by a 300 m buffer.

The parameters for the cloud masking (probability threshold, projection distance and buffer radius) were determined through trial and error on a small number of scenes. As such there are probably significant potential improvements that could be made to this algorithm.

Erosion, dilation and buffer operations were performed at a lower image resolution than the native satellite image resolution to improve the computational speed. The resolution of these

This dataset consists of a shapefile of the reefs, islands, sand banks, cays and rocks of the whole Great Barrier Reef (GBR) including Torres Strait. This dataset is an extension of the mapping in the GBR Marine Park to include Torres Strait. The Torres Strait region was mapped at a scale of 1:50,000 (Lawrey, E. P., Stewart M., 2016) and these new features are referred to as the "Torres Strait Reef and Island Features" dataset.

The Complete GBR Reef and Island Features dataset integrates the "Torres Strait Reef and Island Features" dataset with the existing "GBR Features" (Great Barrier Reef Marine Park Authority, 2007) to create a single composite dataset of the whole Great Barrier Reef. This dataset includes 9600 features overall with 5685 from the "GBR Features" dataset and 3927 from the "Torres Strait Reef and Island Features" dataset.

These two datasets can be easily separated if necessary based on the "DATASET" attribute.

All new mapped features in Torres Strait were allocated permanent IDs (such as 10-479 for Thursday Island and 09-246 for Mabuiag Reef). These IDs are for easy unambiguous communication of features, especially for unnamed features.

The reference imagery used for the mapping of the reefs is available on request as it is large (~45 GB). These files are saved in the eAtlas enduring repository.

Methods:

This project mapped Torres Strait using a combination of existing island datasets as well as a semi-automated and manual digitising of marine features (reefs and sand banks) from the latest aerial and satellite imagery. No features were added to the dataset without confirmed evidence of their existence and position from at least two satellite image sources. The Torres Strait Reef and Island Feature mapping was integrated with the existing "GBR Features" dataset by GBRMPA to ensure that there were no duplicate feature ID allocations and to create a single dataset of the whole GBR. The overall dataset development was as follows: 1. Dataset collation and image preparation: - Collation of existing maps and datasets. - Download and preparation of the Landsat 5, 7, and 8 satellite image archive for Torres Strait. - Spatial position correction of Landsat imagery against a known reference image. 2. Sand Bank features: - Manual digitisation of sand banks from Landsat 5 imagery. - Conversion to a polygon shapefile for integration with the reef features. 3. Reef features: - Semi-automated digitisation of the marine features from Landsat 5 imagery. - Manual trimming, cleaning and checking of marine features against available aerial and satellite imagery. 4. Island features: - Compilation of island features from existing datasets (DNRM 1:25k Queensland Coastline, and Geoscience Australia Geodata Coast 100k 2004) - Correction of the island features from available aerial and Landsat imagery. 5. Merging: of marine and island features into one dataset. 6. Classification: of mapped features, including splitting fringing reefs based on changes in classification. 7. ID allocation: - Clustering to make groups of related features (i.e. an island, plus its fringing reefs and related sand banks; a reef plus its neighbouring patch reefs, etc.).
- Merging with the GBR Features dataset. This was to ensure that there were no duplicate allocations of feature IDs. This involved removing any overlapping features above the Great Barrier Reef Marine Park from the GBR Feature dataset. - Allocation of group IDs (i.e. 10-362) following the scheme used in the GBR Features dataset. Using R scripting. - Allocation of subgroup IDs (10-362b) to each feature in the dataset. Using R scripting. 8. Allocation of names: - Names of features were copied from some existing maps (Nautical Charts, 250k, 100k Topographic maps, CSIRO Torres Strait Atlas). For more information about the methods used in the development of this dataset see the associated technical report (Lawrey, E. P., Stewart M., 2016)

Limitations:

This dataset has mapped features from remote sensing and thus in some parts of Torres Strait where it is very turbid this may result in an underestimate of boundary of features. It also means that some features may be missing from the dataset.

This dataset is NOT SUITABLE FOR NAVIGATION.

The classification of features in this dataset was determined from remote sensing and not in-situ surveys. Each feature has a confidence rating associated with this classification. Features with a 'Low' confidence should be considered only as guidance.

This project only digitised reefs in Torres Strait, no modifications were made to the features from the integrated GBR Features dataset.

Format:

This dataset is available as a shapefile, a set of associated A1 preview maps of the Torres Strait region, ArcMap MXD file with map styling and ArcMap map layer file. The shapefile is also available in KMZ format suitable for viewing in Google Earth. TS_AIMS_NESP_Torres_Strait_Features_V1b_with_GBR_Features.shp (26 MB), TS_AIMS_NESP_Torres_Strait_Features_V1b_with_GBR_Features.kmz: Torres Strait features (3927 polygon features) integrated with the (GBRMPA) GBR Features dataset (5685 polygon features). This dataset covers the entire GBR.

Data Dictionary:

• DATASET: (TS Features, GBR Features) Which dataset this feature belongs to. This attribute is used when the Torres Strait Reef and Island Features dataset is merged with the GBRMPA GBR Features dataset.
• LOC_NAME_S: (e.g. Tobin (Zagarsum) Island (10-147a)) Location Name: Name of the feature and its ID
• GBR_NAME: (e.g. Tobin (Zagarsum) Island) Name of the features with no ID
• CHART_NAME: (e.g. Tobin Island) Name of the feature on the Australian Nautical Charts
• TRAD_NAME: (Zagarsum) Traditional name. From various sources.
• UN_FEATURE: (TRUE, FALSE) Unnamed Feature: If TRUE then the feature is unnamed. Useful for limiting labels in maps to features with names.
• LABEL_ID: (10-147a) ID of the feature
• SORT_GBR_I: (10147) ID of each feature cluster made up from the Latitude ID and Group ID. Used for sorting the features.
• FEAT_NAME: (Island, Rock, Reef, Cay, Mainland, Bank, Terrestrial Reef, Other ) Classification of the feature that is used in the GBR Features dataset. See 3.6 Classification scheme for more information.
• LEVEL_1, LEVEL_2, LEVEL_3: Hierarchical classification of the features. See Appendix 3: Feature Classification Descriptions.
• Checked: (TRUE, FALSE) Flag to record if the feature was reviewed in detail (at a scale of approximately 1:5000) after the initial digitisation. Unchecked features were only reviewed at a coarser scale (1:25000) to spot significant problems.
• IMG_SOURCE: (Aerial, AGRI, Landsat, ESRI) Imagery type used for the final digitisation checking and correction. (AGRI - AGRI PRISM by GA, Landsat is Landsat 8 or Landsat 5, ESRI - ArcMap satellite basemap)
• CLASS_SRC: (Aerial, AGRI, Landsat, Google, Marine Chart) Imagery type used to determine the classification of the feature. Often the classification will be an aggregation of information from multiple image sources. This field will record the highest resolution source used. For some small features the classification was obtained from the Marine Chart, generally for Rocky Reefs.
• CLASS_CONF: (High, Medium, Low) Confidence of the classification applied to the feature. The confidence is dependent on the clarity and range of the imagery available for classification. High - Clear high resolution imagery available (Aerial, Google) with good water visibility. Key characteristics of the classification clear visible. Feature classification fits the context for the neighbouring region. For unconsolidated features (such as sand banks) a High confidence classification would be applied if the shape, colour and context fit and in particular if movement is visible over time-lapse Landsat imagery. Medium - Moderate imagery available (Landsat 8 pan sharpened, some high resolution imagery) that shows key characteristics of the feature and the classification fits the context for the neighbouring region. Low - Only Landsat 5 imagery is available, the feature is small and its origin is unclear from the neighbouring context. This is the default confidence rating for any features that were not individually checked.
• POLY_ORIG: (QLD_DNRM_Coastline_25k, New, GBR_Features, AU_GA_Coast100k_2004) Original source of the polygon prior to any modifications. New features correspond to all the mapped marine features. Most features from the other source would have been modified as part of the checking and trimming of the dataset.
• SUB_NO: (100, 101, ¿) Subgroup number. Numeric count, starting at 100 of each feature in a group. Matches the subgroup ID i.e. 100 -> blank, 101 -> a, 102 -> b, etc.
• CODE: (e.g. 10-147-102-101) Unique code made from the various IDs. This is a GBR Feature attribute.
• UNIQUE_ID: (10147102101) Same as the CODE but without the hyphens, This is a GBR Feature attribute. Note: Version 1b, this attribution is currently out of date.
• FEATURE_C: (100 - 110) Code applied to each of the FEAT_NAMEs.
• QLD_NAME: (Tobin Island) Same as the GBR_NAME
• X_COORD: Longitude in decimal degrees east, in GDA94.
• Y_COORD: Latitude in decimal degrees north, in GDA94.
• SHAPE_AREA: Shape Area in km2
• SHAPE_LEN: Shape perimeter length in km
• CHECKED: (TRUE, FALSE) Whether the features was carefully checked (at a scale of better than ~1:5000) and manually corrected to this level of precision. If FALSE then the feature was only checked to approximately a1:25000 scale.
• PriorityLn: (TRUE, FALSE) Priority Label - If TRUE then this feature's label should be included in a map. Usually correspond to features with names. Use to reduce near duplicate labels of the islands and their surrounding fringing reefs.
• COUNTRY: (Australia, Papua-New Guinea) Sovereignty of the feature. This is based on a spatial join with the Australian Maritime Boundaries 2014a. The Territorial Sea and the Exclusive Economic

The NSW Imagery web service provides access to a repository of the Spatial Services (DCS) maintained standard imagery covering NSW, plus additional sourced imagery. It depicts an imagery map of NSW showing a selection of LANDSAT® satellite imagery, standard 50cm orthorectified imageries, High resolution 10cm Town Imageries. It also contains high resolution imageries within multiple areas of NSW within DFSI, Spatial Services maintained projects and captured by AAM, VEKTA and Jacobs (previously SKM). The image web service is updated periodically when new imageries are available. The imageries are shown progressively from scales larger than 1:150,000 higher resolution imagery overlays lower resolution imagery and most recent imagery overlays older imagery within each resolution. The characteristics of each image such as accuracy, resolution, viewing scale, image format etc varies by sensor, location, capture methodology, source and processing. For specific information about the metadata for the imagery used, please refer to the individual data series within the NSW Data Catalogue. As a consequence of the variety of source data, each map displayed by the user within this map service may have a number of copyright permissions. It is emphasised that the user should check the use constraints for each image data series.

As part of the Integrated Marine Observing System (IMOS), the Australian Bureau of Meteorology produce high-resolution satellite sea surface temperature (SST) products over the Australian region, designed to suit a range of operational and research applications. All these products follow the latest International Group for High Resolution Sea Surface Temperature (GHRSST: www.ghrsst.org) file formats, assisting international data exchange and collaboration. The highest spatial resolution (1 km x 1 km) data from Advanced Very High Resolution Radiometer (AVHRR) sensors on NOAA polar-orbiting satellites can only be obtained through receiving direct broadcast “HRPT” data from the satellite. In Australia, HRPT data is received by a number of agencies (Bureau of Meteorology, Geoscience Australia, AIMS and CSIRO) and consortia (WASTAC and TERSS) at ground-stations located in Darwin, Townsville, Melbourne, Hobart, Perth and Alice Springs and in Antarctica at Casey and Davis Stations.

The Bureau of Meteorology, in collaboration with CSIRO Marine and Atmospheric Research, is combining raw data from the various ground-stations and producing real-time HRPT AVHRR skin (~ 10 micron depth) SST data files in the GHRSST GDS v2.0 L2P (single swath, geolocated), L3U (single swath, gridded), one and three day daytime/night-time L3C (single sensor, multiple swath, gridded) and one, three and six day daytime/night-time L3S (multiple sensors, multiple swath, gridded) formats. The L2P, L3U, L3C and L3S files for NOAA-15, 17, 18 and 19 satellite data are available through the IMOS FTP server (ftp://aodaac2-cbr.act.csiro.au/imos/GHRSST), IMOS AO-DAAC (http://www.marine.csiro.au/remotesensing/imos/aggregator.html# ) and IMOS Ocean Portal (http://imos.aodn.org.au/webportal/), and will eventually be available through the GHRSST Global Data Assembly Centre (http://ghrsst.jpl.nasa.gov). Archived raw HRPT AVHRR data from Australian and Antarctic ground-stations back to 1992 will be progressively reprocessed into skin SST L2P, L3U, L3C and L3S files and be available to GHRSST and IMOS by June 2013. For the user, there are several advantages to using GHRSST-format SST products. For each SST value the GHRSST files contain a quality level flag (based on proximity to cloud, satellite zenith angle and day/night) and bias and standard deviation error estimates based on 60 day match-ups with drifting buoy SST data. Note that the closer an SST pixel is to cloud, the higher the standard deviation. Therefore, the presence of these quality level flags and error information enable users to tailor the L2P, L3U, L3C or L3S files for their particular research application by trading SST spatial coverage for accuracy and vice versa. Users have the ability to access L3U, L3C and L3S SST products through IMOS OPeNDAP servers, greatly simplifying data access and extraction. Providing real-time HRPT AVHRR SST files in GHRSST-L2P format enables them to be incorporated into global and regional, gap-free, analyses of L2P SST from multiple satellites such as NASA’s G1SST global 1 km daily SST analysis and the Bureau of Meteorology’s daily regional and global SST analyses (RAMSSA and GAMSSA). The new IMOS AVHRR L2P SSTs exhibit approximately 75% the error of the Bureau’s pre-existing HRPT AVHRR level 2 SST data, with standard deviations compared with drifting buoys during night-time of around 0.3°C and during daytime of around 0.4°C for quality level 5 (highest). This significant improvement in accuracy has been achieved by improving cloud clearing and calibration - using regional rather than global drifting buoy SST observations and incorporating a dependence on latitude. For further details on the AVHRR GHRSST products see Paltoglou et al. (2010) (http://imos.org.au/srsdoc.html). Enquiries can be directed to Helen Beggs (h.beggs(at)bom.gov.au).

All the IMOS satellite SST products are supplied in GHRSST netCDF format and are either geolocated swath ("L2P") files or level 3 composite, gridded files that will have gaps where there were no observations during the specified time period. The various L3U (single swath), L3C (single sensor, multiple swath) and L3S (multiple sensors, multiple swaths) are designed to suit different applications. Some current applications of the various IMOS satellite SST products are:

HRPT AVHRR data:

L2P: Ingestion into optimally interpolated SST analysis systems (eg. RAMSSA, GAMSSA, G1SST, ODYSSEA);

L3U: Calculation of surface ocean currents (IMOS OceanCurrents);

L3C: Estimation of diurnal warming of the surface ocean (GHRSST Tropical Warm Pool Diurnal Variation (TWP+) Project);

L3S: Estimation of likelihood of coral bleaching events (ReefTemp II).

L3P: Legacy 14-day Mosaic AVHRR SST which is a weighted mean SST produced daily from multiple NOAA satellites in a cut-down GHRSST netCDF format. This product is still used in a coral bleaching prediction system run at CMAR. The product is produced using the legacy BoM processing system and is less accurate than the new IMOS L3S product.

Geostationary satellite MTSAT-1R data:

L3U: Hourly, 0.05 deg x 0.05 deg SST used for estimation of the diurnal warming of the surface ocean and validation of diurnal warming models (TWP+ Project).

Abstract:The AusBathyTopo 250m (Australia) 2023 Grid is a high-resolution depth model for Australia that replaces the Australian Bathymetry and Topography Grid, June 2009. This publication is the result of a collaborative partnership between Geoscience Australia, the Australian Hydrographic Office, James Cook University, and the University of Sydney. It has been compiled using 1582 unique data sources from multibeam echosounders, single-beam echosounders, LiDAR, 3D seismic first returns, Electronic Navigation Charts and satellite derived bathymetry alongside higher-resolution regional compilations. In particular, the map incorporates new innovations such as the use of earth observation data (satellite based) produced by Digital Earth Australia to improve shallow coastal depth modelling to present a seamless transition between land and sea. All source bathymetry data were extensively edited as 3D point clouds to remove noise, given a consistent WGS84 horizontal datum, and where possible, an approximate MSL vertical datum. This new continental-scale grid represents decades of data collection, analysis, investment and collaboration from Australia’s seabed mapping community and is a significant improvement on the 2009 compilation. The data extends across a vast area from 92°E to 172° E and 8°S to 60° S. This includes areas adjacent to the Australian continent and Tasmania, and surrounding Macquarie Island and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands. Australia's marine jurisdiction offshore from the territory of Heard and McDonald Islands and the Australian Antarctic Territory are not included. We acknowledge the use of the CSIRO Marine National Facility (https://ror.org/01mae9353 ) in undertaking this research. The datasets used were collected by the Marine National Facility on 43 voyages (see Lineage for identification). This dataset is not to be used for navigational purposes.© Commonwealth of Australia (Geoscience Australia) 2023.Downloads and Links:WebservicesAusSeabed Bathymetry (WMS) DownloadsLink to Download Australian Bathymetry and Topography 2023 250m.zipMetadata URL:https://pid.geoscience.gov.au/dataset/ga/148758

The gbr100 dataset is a high-resolution bathymetry and Digital Elevation Model (DEM) covering the Great Barrier Reef, Coral Sea and neighbouring Queensland coastline. This DEM has a grid pixel size of 0.001-arc degrees (~100m) with a horizontal datum of WGS84 and a vertical datum of Mean Sea Level (MSL).

For the latest version of this dataset download the data from http://deepreef.org/bathymetry/65-3dgbr-bathy.html

This dataset was developed as part of the 3DGBR project.

This grid utilises the latest available multibeam, singlebeam, lidar and satellite bathymetry source datasets provided by Federal and State Government agencies, in addition to significant new multibeam data collected during research expeditions in the area.

The large increase in source bathymetry data added much detail to improving the resolution of the current Australian Bathymetry and Topography Grid (Whiteway, 2009). The gbr100 grid provides new insights into the detailed geomorphic shape and spatial relationships between adjacent seabed features.

The accompanying report contains an explanation of the various source datasets used in the development of the new grid, and how the data were treated in order to convert to a similar file format with common horizontal (WGS84) and vertical (mean sea level) datums. Descriptive statistics are presented to show the relative proportion of source data used in the new grid. The report continues with a detailed explanation of the pre-processing and gridding process methodology used to develop the grid. A description is also provided for additional spatial analysis on the new grid in order to derive associated grids and layers. The results section provides a short overview of the improvement of the new grid over the current Australian Bathymetry and Topography Grid (Whiteway, 2009). The report then presents the results of the new grid, called gbr100, and the associated derived map outputs as a series of figures. A table of metadata for the current source data accompanies this report as Appendix 1. The report is available at: http://www.deepreef.org/publications/reports/67-3dgbr-final.html

Data details and format:

gbr100 bathymetry grid: Height/Depth in metres (MSL) Formats: 19000x18000 pixel grid (32 bit float) in ESRI raster grid file, GMT/netCDF grid file, Fledermaus sd file, 100m contour ESRI shapefile, GeoTiff grid file.

Total Vertical Uncertainty: Total Vertical Uncertainty (TVU) in the bathymetry estimated from uncertainty classification of each source dataset. Formats: 19000x18000 pixel grid (32 bit float) in ESRI raster, GeoTiff.

Hillshading: Hillshading for full gbr100 and also ocean areas only. Derived from the gbr100 grid. Format: 19000x18000 pixel grid (8 bit) in GeoTiff.

Funding history:

This dataset was initially developed as part of project 2.5i.1 from the MTSRF program (2010).

Subsequent versions of the dataset were developed from other funding sources.

Version history:

• July 2010 - Version 1 Initial release of the DEM.

• Dec 2014 - Version 3 This version incorporates dozens of new bathymetric surveys including many new navy LADS surveys and some satellite derived bathy to fill in some gaps left by LADS.

• Jan 2016 - Version 4 This version incorporates estimates of bathymetry from satellite imagery in shallow clear waters.

• Nov 2020 - Version 6 This revised 3D depth model (V6 – 10 Nov 2020) is a significant improvement on the previous 2017 version, with all offshore reefs mapped with either airborne lidar bathymetry surveys or satellite derived bathymetry. All the available processed multibeam data are now included. Crowdsourced singlebeam bathymetry adds over 50 thousand line km of source data to the inter-reef seafloor. Work will continue to fill the gaps.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\ongoing\GBR_JCU_Beaman_3DGBR-bathymetry-gbr100 Note: Copies of legacy versions 1, 3 and 4 are stored in the eAtlas and available on request.

eAtlas Processing:

To visualize this dataset on the eAtlas the format of the data was converted from the ESRI ArcInfo grid format into a GeoTiff format. This was done by loading the data in ArcMap then exporting it as a GeoTiff image. Overview images and final compression options were then performed using GDAL tools.

This dataset contains Sentinel 2 and Landsat 8 cloud free composite satellite images of the Coral Sea reef areas and some parts of the Great Barrier Reef. It also contains raw depth contours derived from the satellite imagery. This dataset was developed as the base information for mapping the boundaries of reefs and coral cays in the Coral Sea. It is likely that the satellite imagery is useful for numerous other applications. The full source code is available and can be used to apply these techniques to other locations.

This dataset contains two sets of raw satellite derived bathymetry polygons for 5 m, 10 m and 20 m depths based on both the Landsat 8 and Sentinel 2 imagery. These are intended to be post-processed using clipping and manual clean up to provide an estimate of the top structure of reefs. This dataset also contains select scenes on the Great Barrier Reef and Shark bay in Western Australia that were used to calibrate the depth contours. Areas in the GBR were compared with the GA GBR30 2020 (Beaman, 2017) bathymetry dataset and the imagery in Shark bay was used to tune and verify the Satellite Derived Bathymetry algorithm in the handling of dark substrates such as by seagrass meadows. This dataset also contains a couple of small Sentinel 3 images that were used to check the presence of reefs in the Coral Sea outside the bounds of the Sentinel 2 and Landsat 8 imagery.

The Sentinel 2 and Landsat 8 imagery was prepared using the Google Earth Engine, followed by post processing in Python and GDAL. The processing code is available on GitHub (https://github.com/eatlas/CS_AIMS_Coral-Sea-Features_Img).

This collection contains composite imagery for Sentinel 2 tiles (59 in Coral Sea, 8 in GBR) and Landsat 8 tiles (12 in Coral Sea, 4 in GBR and 1 in WA). For each Sentinel tile there are 3 different colour and contrast enhancement styles intended to highlight different features. These include:
- TrueColour - Bands: B2 (blue), B3 (green), B4 (red): True colour imagery. This is useful to identifying shallow features are and in mapping the vegetation on cays.
- DeepFalse - Bands: B1 (ultraviolet), B2 (blue), B3 (green): False colour image that shows deep marine features to 50 - 60 m depth. This imagery exploits the clear waters of the Coral Sea to allow the ultraviolet band to provide a much deeper view of coral reefs than is typically achievable with true colour imagery. This imagery has a high level of contrast enhancement applied to the imagery and so it appears more noisy (in particular showing artefact from clouds) than the TrueColour styling.
- Shallow - Bands: B5 (red edge), B8 (Near Infrared) , B11 (Short Wave infrared): This false colour imagery focuses on identifying very shallow and dry regions in the imagery. It exploits the property that the longer wavelength bands progressively penetrate the water less. B5 penetrates the water approximately 3 - 5 m, B8 approximately 0.5 m and B11 < 0.1 m. Features less than a couple of metres appear dark blue, dry areas are white. This imagery is intended to help identify coral cay boundaries.

For Landsat 8 imagery only the TrueColour and DeepFalse stylings were rendered.

All Sentinel 2 and Landsat 8 imagery has Satellite Derived Bathymetry (SDB) depth contours.
- Depth5m - This corresponds to an estimate of the area above 5 m depth (Mean Sea Level).
- Depth10m - This corresponds to an estimate of the area above 10 m depth (Mean Sea Level).
- Depth20m - This corresponds to an estimate of the area above 20 m depth (Mean Sea Level).

For most Sentinel and some Landsat tiles there are two versions of the DeepFalse imagery based on different collections (dates). The R1 imagery are composites made up from the best available imagery while the R2 imagery uses the next best set of imagery. This splitting of the imagery is to allow two composites to be created from the pool of available imagery. This allows any mapped features to be checked against two images. Typically the R2 imagery will have more artefacts from clouds. In one Sentinel 2 tile a third image was created to help with mapping the reef platform boundary.

The satellite imagery was processed in tiles (approximately 100 x 100 km for Sentinel 2 and 200 x 200 km for Landsat 8) to keep each final image small enough to manage. These tiles were not merged into a single mosaic as it allowed better individual image contrast enhancement when mapping deep features. The dataset only covers the portion of the Coral Sea where there are shallow coral reefs and where their might have been potential new reef platforms indicated by existing bathymetry datasets and the AHO Marine Charts. The extent of the imagery was limited by those available through the Google Earth Engine.

# Methods:

The Sentinel 2 imagery was created using the Google Earth Engine. The core algorithm was:
1. For each Sentinel 2 tile, images from 2015 – 2021 were reviewed manually after first filtering to remove cloudy scenes. The allowable cloud cover was adjusted so that at least the 50 least cloud free images were reviewed. The typical cloud cover threshold was 1%. Where very few images were available the cloud cover filter threshold was raised to 100% and all images were reviewed. The Google Earth Engine image IDs of the best images were recorded, along with notes to help sort the images based on those with the clearest water, lowest waves, lowest cloud, and lowest sun glint. Images where there were no or few clouds over the known coral reefs were preferred. No consideration of tides was used in the image selection process. The collection of usable images were grouped into two sets that would be combined together into composite images. The best were added to the R1 composite, and the next best images into the R2 composite. Consideration was made as to whether each image would improve the resultant composite or make it worse. Adding clear images to the collection reduces the visual noise in the image allowing deeper features to be observed. Adding images with clouds introduces small artefacts to the images, which are magnified due to the high contrast stretching applied to the imagery. Where there were few images all available imagery was typically used.
2. Sunglint was removed from the imagery using estimates of the sunglint using two of the infrared bands (described in detail in the section on Sun glint removal and atmospheric correction).
3. A composite image was created from the best images by taking the statistical median of the stack of images selected in the previous stage, after masking out clouds and their shadows (described in detail later).
4. The brightness of the composite image was normalised so that all tiles would have a similar average brightness for deep water areas. This correction was applied to allow more consistent contrast enhancement. Note: this brightness adjustment was applied as a single offset across all pixels in the tile and so this does not correct for finer spatial brightness variations.
5. The contrast of the images was enhanced to create a series of products for different uses. The TrueColour colour image retained the full range of tones visible, so that bright sand cays still retain detail. The DeepFalse style was optimised to see features at depth and the Shallow style provides access to far red and infrared bands for assessing shallow features, such as cays and island.
6. The various contrast enhanced composite images were exported from Google Earth Engine and optimised using Python and GDAL. This optimisation added internal tiling and overviews to the imagery. The depth polygons from each tile were merged into shapefiles covering the whole for each depth.

## Cloud Masking

Prior to combining the best images each image was processed to mask out clouds and their shadows.

The cloud masking uses the COPERNICUS/S2_CLOUD_PROBABILITY dataset developed by SentinelHub (Google, n.d.; Zupanc, 2017). The mask includes the cloud areas, plus a mask to remove cloud shadows. The cloud shadows were estimated by projecting the cloud mask in the direction opposite the angle to the sun. The shadow distance was estimated in two parts.

A low cloud mask was created based on the assumption that small clouds have a small shadow distance. These were detected using a 40% cloud probability threshold. These were projected over 400 m, followed by a 150 m buffer to expand the final mask.

A high cloud mask was created to cover longer shadows created by taller, larger clouds. These clouds were detected based on an 80% cloud probability threshold, followed by an erosion and dilation of 300 m to remove small clouds. These were then projected over a 1.5 km distance followed by a 300 m buffer.

The buffering was applied as the cloud masking would often miss significant portions of the edges of clouds and their shadows. The buffering allowed a higher percentage of the cloud to be excluded, whilst retaining as much of the original imagery as possible.

The parameters for the cloud masking (probability threshold, projection distance and buffer radius) were determined through trial and error on a small number of scenes. The algorithm used is significantly better than the default Sentinel 2 cloud masking and slightly better than the COPERNICUS/S2_CLOUD_PROBABILITY cloud mask because it masks out shadows, however there is potentially significant improvements that could be made to the method in the future.

Erosion, dilation and buffer operations were performed at a lower image resolution than the native satellite image resolution to improve the computational speed. The resolution of these operations were adjusted so that they were performed with approximately a 4 pixel resolution during these operations. This made the cloud mask

PLEASE NOTE: This dataset has been superseded by NSW Landuse 2017 v1.5 The 2017 Landuse captures how the landscape in NSW is being used for food production, forestry, nature conservation, infrastructure and urban development. It can be used to monitor changes in the landscape and identify impacts on biodiversity values and individual ecosystems. The NSW 2017 Landuse mapping is dated September 2017. It incorporates tenure based information for National Parks and State Forests in NSW, at the time of mapping. It currently does not include the Greater Sydney Metropolitan Region. Greater Sydney region will be completed in late 2019 and will be incorporated into the NSW 2017 land use product version 1.1. The NSW Landuse 2013, currently contains the best available information for the Greater Sydney region. https://datasets.seed.nsw.gov.au/dataset/nsw-landuse-2013 The 2017 Landuse has complete coverage of all regional centres and towns for NSW. It also includes updates to the fine scale Horticulture mapping for the east coast of NSW - Newcastle to the Queensland boarder. This horticultural mapping includes operations to the commodity level based on field work and high resolution imagery interpretation. The reliability scale is 1:10,000 and include values in the attribute fields of Source, Source Date, Source Scale, Reliability and LU Mapping (Currency) Date. Land use has been mapped on high resolution aerial imagery including ADS (digital imagery) captured by NSW Department of Finance, Service and Innovation, along with using Nearmap, Google Earth and Google Street View. Satellite imagery from LANDSAT (NASA), Sentinel 2 (European Space Agency), SPOT 5, 6 and 7(Airbus) and Planet Imagery, was used in the mapping process to account for Landuse activities that occur as part of a rotational practise. Land use information has been captured in accordance with standards set by the Australian Collaborative Land Use Mapping Program (ACLUMP) and using the Australian Land Use and Management ALUM Classification Version 8. The ALUM classification is based upon the modified Baxter & Russell classification and presented according to the specifications contained in http://www.agriculture.gov.au/abares/aclump/land-use/alum-classification.

Abstract:The Bass Strait Digital Elevation Model (DEM) is a compilation of all available bathymetry data for the area of seabed between the coastlines of Victoria and northern Tasmania, extending approximately 460 km from west of King Island to east of Flinders Island. The Bass Strait is bounded by a continental slope incised with numerous canyons, including the prominent Bass Canyon on the eastern side. The region encompasses islands and exposed rocks, drowned paleo-shorelines and dunefields, fringed by a rugged coastline. Bathymetry mapping of the seafloor is vital for the protection of Bass Strait, allowing for safe navigation of shipping, improved environmental management and resource development. Australian Hydrographic Office-supplied ENC tile spot depths were used to develop the general bathymetry variation across the entire region. Shallow- and deep-water multibeam survey data reveal the complexity of the seafloor for the continental shelf and adjacent canyons which incise the western and eastern sides of Bass Strait. Airborne LiDAR bathymetry acquired by the Australian Hydrographic Office cover most of the northern Tasmanian nearshore and coast, with some coverage gaps supplemented by Landsat-8 satellite derived bathymetry data. The Geoscience Australia-developed Intertidal Elevation Model DEM improves the source data over the intertidal zone. Highly accurate photogrammetry coastline data developed for the Tasmania, Victoria and New South Wales coastlines, and Near Surface Feature data representing shoal features observable in aerial imagery, were used to improve the land/water interface of the numerous island and rock features. All source bathymetry data were extensively edited as 3D point clouds to remove noise, given a consistent WGS84 horizontal datum, and where possible, an approximate MSL vertical datum.© Commonwealth of Australia 2022Downloads and Links:Web Serviceshttps://warehouse.ausseabed.gov.au/geoserver/ows?REQUEST=GetCapabilities&SERVICE=WMS&VERSION=1.3.0Downloads available from the expanded catalogue link, belowMetadata URL:https://pid.geoscience.gov.au/dataset/ga/147043

This record was superseded on 5/12/2024 with approval from the Director, National Seabed Mapping as it has been superseded by eCat 150050

The AusBathyTopo 250m (Australia) 2023 Grid is a high-resolution depth model for Australia that replaces the Australian Bathymetry and Topography Grid, June 2009.

This publication is the result of a collaborative partnership between Geoscience Australia, the Australian Hydrographic Office, James Cook University, and the University of Sydney. It has been compiled using 1582 unique data sources from multibeam echosounders, single-beam echosounders, LiDAR, 3D seismic first returns, Electronic Navigation Charts and satellite derived bathymetry alongside higher-resolution regional compilations. In particular, the map incorporates new innovations such as the use of earth observation data (satellite based) produced by Digital Earth Australia to improve shallow coastal depth modelling to present a seamless transition between land and sea. All source bathymetry data were extensively edited as 3D point clouds to remove noise, given a consistent WGS84 horizontal datum, and where possible, an approximate MSL vertical datum. This new continental-scale grid represents decades of data collection, analysis, investment and collaboration from Australia’s seabed mapping community and is a significant improvement on the 2009 compilation.

The data extends across a vast area from 92°E to 172° E and 8°S to 60° S. This includes areas adjacent to the Australian continent and Tasmania, and surrounding Macquarie Island and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands. Australia's marine jurisdiction offshore from the territory of Heard and McDonald Islands and the Australian Antarctic Territory are not included.

We acknowledge the use of the CSIRO Marine National Facility (https://ror.org/01mae9353 ) in undertaking this research. The datasets used were collected by the Marine National Facility on 43 voyages (see Lineage for identification).

This dataset is not to be used for navigational purposes.

Wildfire Mapping from Space Market Outlook

According to our latest research, the global wildfire mapping from space market size reached USD 1.72 billion in 2024, driven by an increasing demand for advanced satellite technologies and data-driven solutions to monitor and manage wildfire incidents worldwide. The market is projected to expand at a robust CAGR of 13.8% from 2025 to 2033, reaching an estimated USD 5.16 billion by 2033. This significant growth is primarily attributed to the rising frequency and severity of wildfires, climate change impacts, and the need for real-time, actionable data for disaster management and environmental monitoring initiatives.

One of the primary growth factors propelling the wildfire mapping from space market is the escalating incidence of wildfires across various continents, particularly in regions such as North America, Australia, and Southern Europe. Climate change has led to hotter and drier conditions, resulting in more frequent and intense wildfires that threaten forests, wildlife, property, and human lives. As a result, governments and private organizations are increasingly investing in advanced satellite imaging and remote sensing technologies to enable early detection, real-time monitoring, and effective response strategies. The integration of these technologies into disaster management frameworks allows for improved situational awareness, resource allocation, and mitigation planning, significantly reducing the adverse impacts of wildfires on both the environment and society.

Another major driver for market expansion is the rapid technological advancements in earth observation platforms, including the proliferation of small satellites, CubeSats, and high-resolution imaging sensors. These innovations have made space-based wildfire mapping more accessible, cost-effective, and accurate than ever before. The ability to deploy large constellations of small satellites enables near-continuous coverage and frequent revisit rates, providing critical data for tracking wildfire progression, assessing burn severity, and supporting post-fire recovery efforts. Additionally, the integration of artificial intelligence and machine learning algorithms with satellite data is enhancing the speed and precision of wildfire detection and prediction, further fueling market growth.

The growing recognition of the importance of environmental monitoring and sustainable land management practices is also contributing to the expansion of the wildfire mapping from space market. Beyond immediate disaster response, space-based mapping solutions are increasingly utilized for long-term forestry management, urban planning, and ecological restoration projects. These applications help stakeholders identify vulnerable areas, monitor vegetation health, and design preventive strategies to reduce wildfire risks. The synergy between public and private sector initiatives, coupled with increasing funding for space-based earth observation programs, is expected to sustain the market’s upward trajectory throughout the forecast period.

From a regional perspective, North America currently dominates the wildfire mapping from space market, accounting for the largest revenue share in 2024. This leadership is underpinned by substantial investments in space-based technologies, the presence of leading satellite operators, and the high incidence of wildfires in the United States and Canada. Europe follows closely, driven by robust environmental policies and collaborative research initiatives. Meanwhile, the Asia Pacific region is anticipated to witness the fastest growth rate over the forecast period, owing to rising awareness, expanding satellite infrastructure, and increasing vulnerability to climate-induced wildfires, particularly in Australia, Southeast Asia, and parts of China.

Technology Analysis

The technology segment in the wildfire mapping from space market encompasses satellite imaging, remote sensing, GIS mapping, and thermal imaging, each playing a pivotal role in enhancing the accur

The CSIRO Marine Research Remote Sensing facility automatically receives and archives data from the USA's National Oceanographic and Atmospheric Administration (NOAA) satellites. Up to 18 passes per day are tracked to receive data. The Advanced Very High Resolution Radiometer (AVHRR) data is received on the High Resolution Picture Transmission (HRPT) signal. Within an hour of reception, these data are automatically processed into full resolution sea surface temperature (SST) images. Raw data originate from the AVHRR sensor on various NOAA polar orbiting satellites, received at various stations around Australia and consolidated ("stitched") by the CSIRO Earth Observation Centre. The stitching removes redundancy and minimises data corruption. Processing from the stitched archive to produce SST is carried out in the CMAR Remote Sensing Facility in Hobart using the split window algorithm of McMillin for NOAA9 and NOAA12 satellites and the NLSST (NOAA non-linear SST) algorithm for the other satellites. Cloud-clearing is performed based on the algorithm of Saunders and Kriebel. Each map is made by combining the estimates over the composite period using a time and spatial neighbourhood median filtering method. Each pixel of the images is the 65 percentile of all cloud-cleared SST estimates during the composite period and within a 4x4 km region. The compositing process also removes most residual cloud contamination. This basedata has been produced by CSIRO for the National Oceans Office, for the purposes of marine mapping, as part of an ongoing commitment to natural resource planning and management through the 'National Marine Bioregionalisation' project. Compositing attempts to overcome the problem of cloud coverage. The compositing technique used here takes the median value of a 4 by 4 neighbourhood of 1km resolution pixels over all the data available for that time period. If there has not been at least one cloud free view of a point on the ground during the composite period, the value recorded may show a false low temperature.