This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

The PALEOMAP project produces paleogreographic maps illustrating the Earth's plate tectonic, paleogeographic, climatic, oceanographic and biogeographic development from the Precambrian to the Modern World and beyond.

A series of digital data sets has been produced consisting of plate tectonic data, climatically sensitive lithofacies, and biogeographic data. Software has been devloped to plot maps using the PALEOMAP plate tectonic model and digital geographic data sets: PGIS/Mac, Plate Tracker for Windows 95, Paleocontinental Mapper and Editor (PCME), Earth System History GIS (ESH-GIS), PaleoGIS(uses ArcView), and PALEOMAPPER.

Teaching materials for educators including atlases, slide sets, VHS animations, JPEG images and CD-ROM digital images.

Some PALEOMAP products include: Plate Tectonic Computer Animation (VHS) illustrating motions of the continents during the last 850 million years.

Paleogeographic Atlas consisting of 20 full color paleogeographic maps. (Scotese, 1997).

Paleogeographic Atlas Slide Set (35mm)

Paleogeographic Digital Images (JPEG, PC/Mac diskettes)

Paleogeographic Digital Image Archive (EPS, PC/Mac Zip disk) consists of the complete digital archive of original digital graphic files used to produce plate tectonic and paleographic maps for the Paleographic Atlas.

GIS software such as PaleoGIS and ESH-GIS.

DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

For more information about this tool see Batch Metadata Modifier Tool Toolbar Help.Modifying multiple files simultaneously that don't have identical structures is possible but not advised. Be especially careful modifying repeatable elements in multiple files that do not have and identical structureTool can be run as an ArcGIS Add-In or as a stand-alone Windows executableExecutable runs on PC only. (Not supported on Mac.)The ArcGIS Add-In requires ArcGIS Desktop version 10.2 or 10.3Metadata formats accepted: FGDC CSDGM, ArcGIS 1.0, ArcGIS ISO, and ISO 19115Contact Bruce Godfrey (bgodfrey@uidaho.edu, Ph. 208-292-1407) if you have questions or wish to collaborate on further developing this tool.Modifying and maintaining metadata for large batches of ArcGIS items can be a daunting task. Out-of-the-box graphical user interface metadata tools within ArcCatalog 10.x are designed primarily to allow users to interact with metadata for one item at a time. There are, however, a limited number of tools for performing metadata operations on multiple items. Therefore, the need exists to develop tools to modify metadata for numerous items more effectively and efficiently. The Batch Metadata Modifier Tools toolbar is a step in that direction. The Toolbar, which is available as an ArcGIS Add-In, currently contains two tools. The first tool, which is additionally available as a standalone Windows executable application, allows users to update metadata on multiple items iteratively. The tool enables users to modify existing elements, find and replace element content, delete metadata elements, and import metadata elements from external templates. The second tool of the Toolbar, a batch thumbnail creator, enables the batch-creation of the graphic that appears in an item’s metadata, illustrating the data an item contains. Both of these tools make updating metadata in ArcCatalog more efficient, since the tools are able to operate on numerous items iteratively through an easy-to-use graphic interface.This tool, developed by INSIDE Idaho at the University of Idaho Library, was created to assist researchers with modifying FGDC CSDGM, ArcGIS 1.0 Format and ISO 19115 metadata for numerous data products generated under EPSCoR award EPS-0814387.This tool is primarily designed to be used by those familiar with metadata, metadata standards, and metadata schemas. The tool is for use by metadata librarians and metadata managers and those having experience modifying standardized metadata. The tool is designed to expedite batch metadata maintenance. Users of this tool must fully understand the files they are modifying. No responsibility is assumed by the Idaho Geospatial Data Clearinghouse or the University of Idaho in the use of this tool. A portion of the development of this tool was made possible by an Idaho EPSCoR Office award.

This record provides an overview of the NESP Marine and Coastal Hub scoping study - "National Areas of Interest for Seabed Mapping, Characterisation and Biodiversity Assessment". For specific data outputs from this project, please see child records associated with this metadata. Seabed and marine biodiversity data are time-consuming and costly to collect, so it is imperative that acquisition is focused on areas that align with end user priorities. The value that different stakeholders place on seabed and biodiversity data can be difficult to determine. Therefore, a shared process for identifying survey priorities is required to ensure the maximum shared benefit of future survey investment across research users, funding agencies, infrastructure providers, as well as the wider marine research community. The project aimed to assist with the planning and prioritisation of marine surveys (both physical and biological) by scoping a prioritisation framework for marine surveys undertaking physical and biological seabed data collection in Australia. Focused workshops and targeted engagements with seabed mapping organisations were used to develop a standard set of metadata for agencies to define spatial Areas of Interest (AOI). The standard metadata were used in a prototype prioritisation framework that allows users to transparently and consistently rank and prioritise survey work or data delivery processes. The prioritisation is then based on rankings established by defined sets of criteria. A web-based AOI submission tool and mapping publication service was then developed for these defined areas as part of the AusSeabed Survey Coordination Tool. Adoption of this tool facilitates the development of an interim national areas of interest product to inform future survey planning. This product supports both the needs of Parks Australia's network Science Plans, and consideration of information needs for Indigenous Protected Areas within Sea Country. Outputs • National Areas of Interest polygon & interactive map [dataset] • Code for Survey Coordination Tool [Github Repo] • Final Report with Value Prioritisation Framework [written]

This dataset consists of collections of satellite image composites (Sentinel 2 and Landsat 8) that are created from manually curated image dates for a range of projects. These images are typically prepared for subsequent analysis or testing of analysis algorithms as part of other projects. This dataset acts as a repository of reproducible test sets of images processed from Google Earth Engine using a standardised workflow.

Details of the algorithms used to produce the imagery are described in the GEE code and code repository available on GitHub (https://github.com/eatlas/World_AIMS_Marine-satellite-imagery).


Project test image sets:

As new projects are added to this dataset, their details will be described here:

- NESP MaC 2.3 Benthic reflection estimation (projects/CS_NESP-MaC-2-3_AIMS_Benth-reflect):
This collection consists of six Sentinel 2 image composites in the Coral Sea and GBR for the purpose of testing a method of determining benthic reflectance of deep lagoonal areas of coral atolls. These image composites are in GeoTiff format, using 16-bit encoding and LZW compression. These images do not have internal image pyramids to save on space.
[Status: final and available for download]

- NESP MaC 2.3 Oceanic Vegetation (projects/CS_NESP-MaC-2-3_AIMS_Oceanic-veg):
This project is focused on mapping vegetation on the bottom of coral atolls in the Coral Sea. This collection consists of additional images of Ashmore Reef. The lagoonal area of Ashmore has low visibility due to coloured dissolved organic matter, making it very hard to distinguish areas that are covered in vegetation. These images were manually curated to best show the vegetation. While these are the best images in the Sentinel 2 series up to 2023, they are still not very good. Probably 80 - 90% of the lagoonal benthos is not visible.
[Status: final and available for download]

- NESP MaC 3.17 Australian reef mapping (projects/AU_NESP-MaC-3-17_AIMS_Reef-mapping):
This collection of test images was prepared to determine if creating a composite from manually curated image dates (corresponding to images with the clearest water) would produce a better composite than a fully automated composite based on cloud filtering. The automated composites are described in https://doi.org/10.26274/HD2Z-KM55. This test set also includes composites from low tide imagery. The images in this collection are not yet available for download as the collection of images that will be used in the analysis has not been finalised.
[Status: under development, code is available, but not rendered images]

- Capricorn Regional Map (projects/CapBunk_AIMS_Regional-map): This collection was developed for making a set of maps for the region to facilitate participatory mapping and reef restoration field work planning.
[Status: final and available for download]

- Default (project/default): This collection of manual selected scenes are those that were prepared for the Coral Sea and global areas to test the algorithms used in the developing of the original Google Earth Engine workflow. This can be a good starting point for new test sets. Note that the images described in the default project are not rendered and made available for download to save on storage space.
[Status: for reference, code is available, but not rendered images]


Filename conventions:

The images in this dataset are all named using a naming convention. An example file name is Wld_AIMS_Marine-sat-img_S2_NoSGC_Raw-B1-B4_54LZP.tif. The name is made up of:
- Dataset name (Wld_AIMS_Marine-sat-img), short for World, Australian Institute of Marine Science, Marine Satellite Imagery.
- Satellite source: L8 for Landsat 8 or S2 for Sentinel 2.
- Additional information or purpose: NoSGC - No sun glint correction, R1 best reference imagery set or R2 second reference imagery.
- Colour and contrast enhancement applied (DeepFalse, TrueColour,Shallow,Depth5m,Depth10m,Depth20m,Raw-B1-B4),
- Image tile (example: Sentinel 2 54LZP, Landsat 8 091086)


Limitations:

Only simple atmospheric correction is applied to land areas and as a result the imagery only approximates the bottom of atmosphere reflectance.

For the sentinel 2 imagery the sun glint correction algorithm transitions between different correction levels from deep water (B8) to shallow water (B11) and a fixed atmospheric correction for land (bright B8 areas). Slight errors in the tuning of these transitions can result in unnatural tonal steps in the transitions between these areas, particularly in very shallow areas.

For the Landsat 8 image processing land areas appear as black from the sun glint correction, which doesn't separately mask out the land. The code for the Landsat 8 imagery is less developed than for the Sentinel 2 imagery.

The depth contours are estimated using satellite derived bathymetry that is subject to errors caused by cloud artefacts, substrate darkness, water clarity, calibration issues and uncorrected tides. They were tuned in the clear waters of the Coral Sea. The depth contours in this dataset are RAW and contain many false positives due to clouds. They should not be used without additional dataset cleanup.



Change log:

As changes are made to the dataset, or additional image collections are added to the dataset then those changes will be recorded here.

2nd Edition, 2024-06-22: CapBunk_AIMS_Regional-map
1st Edition, 2024-03-18: Initial publication of the dataset, with CS_NESP-MaC-2-3_AIMS_Benth-reflect, CS_NESP-MaC-2-3_AIMS_Oceanic-veg and code for AU_NESP-MaC-3-17_AIMS_Reef-mapping and Default projects.


Data Format:

GeoTiff images with LZW compression. Most images do not have internal image pyramids to save on storage space. This makes rendering these images very slow in a desktop GIS. Pyramids should be added to improve performance.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Wld-AIMS-Marine-sat-img

Drone-Based Landslide Mapping Market Outlook

According to our latest research, the global Drone-Based Landslide Mapping market size reached USD 1.32 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.8% projected from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of approximately USD 6.4 billion. This remarkable growth is primarily fueled by the rising demand for advanced geospatial solutions in disaster-prone regions, alongside technological advancements in drone hardware and analytics software, which are significantly enhancing landslide risk assessment and mitigation efforts worldwide.

The growth trajectory of the Drone-Based Landslide Mapping market is shaped by a confluence of factors. Increasing frequency and severity of landslides due to climate change have raised the stakes for timely, accurate, and cost-effective mapping solutions. Governments and private sector stakeholders are now prioritizing investments in cutting-edge drone technologies to monitor vulnerable terrains and rapidly assess post-landslide damages. Moreover, the integration of high-resolution imaging sensors, LiDAR, and AI-based analytics in drones has allowed for more precise mapping and early warning systems, reducing the risks associated with manual ground surveys and enabling faster disaster response.

Another critical driver for the market is the expanding application scope of drone-based mapping beyond disaster management. Industries such as construction, mining, agriculture, and forestry are leveraging these technologies for land surveying, slope stability analysis, and environmental monitoring. The ability of drones to access hard-to-reach or hazardous locations, coupled with real-time data transmission and processing capabilities, is transforming traditional workflows. This has significantly reduced operational costs and time, making drone-based solutions a preferred choice for both public and private entities seeking to enhance safety and operational efficiency.

The supportive regulatory environment and growing public-private partnerships are also pivotal in propelling the Drone-Based Landslide Mapping market forward. Governments across Asia Pacific, North America, and Europe have launched initiatives to modernize disaster management infrastructure, often in collaboration with technology providers and research institutes. These efforts are not only fostering innovation in drone hardware and software but also facilitating the standardization of mapping protocols and data integration with existing geospatial information systems. As a result, the market is witnessing increased adoption rates and higher investments in R&D, further accelerating its expansion.

Regionally, the Asia Pacific segment dominates the global market, accounting for over 38% of the total revenue in 2024, driven by the region’s susceptibility to landslides and rapid infrastructure development in countries like China, India, and Japan. North America follows closely, supported by advanced technological infrastructure and significant government funding for disaster management. Europe is also emerging as a key market, with a focus on environmental monitoring and sustainable land use planning. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their adoption of drone-based mapping solutions, primarily in response to growing environmental and infrastructural challenges.

Solution Analysis

The solution segment of the Drone-Based Landslide Mapping market is categorized into hardware, software, and services, each playing a distinct role in the ecosystem. Hardware remains the backbone of this segment, comprising drones equipped with advanced sensors, cameras, and LiDAR systems. The evolution of drone hardware has been marked by significant improvements in flight endurance, payload capacity, and sensor accuracy, enabling more comprehensive and precise landslide mapping. Manufacturers are increasingly focusing on ruggedized designs to withstand harsh terrains, while also integrating modular payloads that allow for customized data collection based on specific project requirements.

Software solutions have witnessed exponential growth, as they are essential for processing and analyzing the vast amounts of geospatial data captured by drones. Modern mapping software leverages AI, mac

This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ATSV 99044 cruise. The coverage is the nearshore of the northern South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed.

The research project critically examines the guidelines of the comments sections of the twenty largest online news outlets over the last ten years. Rather than focusing on the familiar negative comments of news consumers and their narratives, we analyze and compare the news outlets’ guidelines and how they have led in what we call ‘a constructive turn’. We propose our own theoretical framework to analyze what is encouraged and what is discouraged in news outlets’ guidelines. Results show an increasing focus on constructiveness in the guidelines of the comment sections and a shift to more positivity, rather than on deleting and filtering negative or toxic comments. Although platforms differ in their views on the role of commenting and the definition of constructiveness, the turn towards the constructive design of the commenting platform is shared among them.

This dataset contains the commentary guidelines in the top 20 English-language online news websites of December 2020 based on research conducted by Similar Web (Source: Similar Web for Gazette). For each news publication, the current commentary guidelines were scrapped from the internet, alongside earlier versions of their guidelines. In total, three moments were used to map the guidelines: 2021, 2015 and 2010. The content was analysed through coding using Nvivo software. We applied a bottom-up approach - by creating simple codes and eventually grouping them together. Each set of guidelines was coded on what behaviour was encouraged and what was discouraged by the news outlet, and what kind of discussion environment the news outlet expects from their commenters in general (e.g. entertaining, healthy, inclusive etc.).

This dataset contains coded content for the project. Following logic was used in uploading the documents:

1 - Nvivo project file - can be opened using Nvivo for Mac - contains all information (files, codes, etc.)

We also upload more user-friendly data (the following documents are uploaded in MS Word format):

2 - Codebook (provides the logical structure of coding applied + number of codes for each category) 3 - Code excerpts for discouraged elements found in the content 4 - Code excerpts for encouraged elements found in the content 5 - Code excerpts for discussion environment elements found in the content

Disclaimer: The user-generated content guidelines of news media companies are their own intellectual property and we do not own any rights to it.

This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ALPH 98013 cruise. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed.

The Federal Housing Enterprises Financial Safety and Soundness Act of 1992 establishes a duty for Fannie Mae and Freddie Mac (the Enterprises) to serve the housing needs of very low-, low-, and moderate-income families in rural areas. FHFA has issued a final rule that provides eligibility for Duty to Serve credit for Enterprise mortgage purchases and other activities in “rural areas,” as defined in the rule. Additionally, the final rule specifies supportfor high-needs rural regions as a Regulatory Activity that the Enterprises may consider when developing their plans for the Duty to Serve program. FHFA’s 2017 Rural Areas File designates census tracts in the Metropolitan Statistical Areas (MSAs) and outside of MSAs of the 50 states, the District of Columbia, and Puerto Rico that are considered rural areas or non-rural areas under the final rule. The File also identifies whether census tracts are located in “high-needs” counties in order to determine whether tracts meet the definition of “high-needs rural regions” in the final rule.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/BusinessEconomy/MD_HousingDesignatedAreas/FeatureServer/5

This dataset contains 63 shapefiles that represent the areas of relevance for each research project under the National Environmental Science Program Marine and Coastal Hub, northern and southern node projects for Rounds 1, 2 & 3.

Methods:
Each project map is developed using the following steps:
1. The project map was drawn based on the information provided in the research project proposals.
2. The map was refined based on feedback during the first data discussions with the project leader.
3. Where projects are finished most maps were updated based on the extents of datasets generated by the project and followup checks with the project leader.

The area mapped includes on-ground activities of the project, but also where the outputs of the project are likely to be relevant. The maps were refined by project leads, by showing them the initial map developed from the proposal, then asking them "How would you change this map to better represent the area where your project is relevant?". In general, this would result in changes such as removing areas where they were no longer intending research to be, or trimming of the extents to better represent the habitats that are relevant.

The project extent maps are intentionally low resolution (low number of polygon vertices), limiting the number of vertices 100s of points. This is to allow their easy integration into project metadata records and for presenting via interactive web maps and spatial searching. The goal of the maps was to define the project extent in a manner that was significantly more accurate than a bounding box, reducing the number of false positives generated from a spatial search. The geometry was intended to be simple enough that projects leaders could describe the locations verbally and the rough nature of the mapping made it clear that the regions of relevance are approximate.

In some cases, boundaries were drawn manually using a low number of vertices, in the process adjusting them to be more relevant to the project. In others, high resolution GIS datasets (such as the EEZ, or the Australian coastline) were used, but simplified at a resolution of 5-10km to ensure an appopriate vertices count for the final polygon extent. Reference datasets were frequently used to make adjustments to the maps, for example maps of wetlands and rivers were used to better represent the inner boundary of projects that were relevant for wetlands.

In general, the areas represented in the maps tend to show an area larger then the actual project activities, for example a project focusing on coastal restoration might include marine areas up to 50 km offshore and 50 km inshore. This buffering allows the coastline to be represented with a low number of verticies without leading to false negatives, where a project doesn't come up in a search because the area being searched is just outside the core area of a project.


Limitations of the data:
The areas represented in this data are intentionally low resolution. The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. This dataset is not a complete representation of the work being done by the NESP MaC projects as it was collected only 3 years into a 7 year program.

Format of the data:
The maps were drawn in QGIS using relevant reference layers and saved as shapefiles. These are then converted to GeoJSON or WKT (Well-known Text) and incorporated into the ISO19115-3 project metadata records in GeoNetwork. Updates to the map are made to the original shapefiles, and the metadata record subsequently updated.

All projects are represented as a single multi-polygon. The multiple polygons was developed by merging of separate areas into a single multi-polygon. This was done to improve compatibility with web platforms, allowing easy conversion to GeoJSON and WKT.

This dataset will be updated periodically as new NESP MaC projects are developed and as project progress and the map layers are improved. These updates will typically be annual.


Data dictionary:
NAME - Title of the layer
PROJ - Project code of the project relating to the layer
NODE - Whether the project is part of the Northern or Southern Nodes
TITLE - Title of the project
P_LEADER - Name of the Project leader and institution managing the project
PROJ_LINK - Link to the project metadata
MAP_DESC - Brief text description of the map area
MAP_TYPE - Describes whether the map extent is a 'general' area of relevance for the project work, or 'specific' where there is on ground survey or sampling activities
MOD_DATE - Last modification date to the individual map layer (prior to merging)


Updates & Processing:
These maps were created by eAtlas and IMAS Data Wranglers as part of the NESP MaC Data Management activities. As new project information is made available, the maps may be updated and republished. The update log will appear below with notes to indicate when individual project maps are updated:
20220626 - Dataset published (All shapefiles have MOD_DATE 20230626)


Location of the data:
This dataset is filed in the eAtlas enduring data repository at: data\custodian
esp-mac-3\AU_AIMS-UTAS_NESP-MaC_Project-extents-maps

Seagrass beds are a dominant marine ecosystem of Tayaritja (the Furneaux Group of Islands) in the north-eastern waters off Tasmania. Historical coarse mapping has indicated extensive beds of Posidonia, Amphibolis, Heterozostera, and Zostera species, potentially comprising some of the largest and deepest seagrass extents found in temperate Australian waters. However, limited data on the distribution and ecological value of these seagrass habitats represents a significant knowledge gap in understanding Australia's wetland natural assets. This project mapped the extent, ecological composition, population structure, and blue carbon value of seagrass beds around Tayaritja, in partnership with the Tasmanian Aboriginal Centre. The study area focused on the coastal waters surrounding Flinders Island in the western Furneaux Group, with mapping extending from the high tide line to the depth limit of reliable optical detection (approximately 30 m), based on analysis of field data and satellite imagery capabilities in the region. The field validation component of this study involved deployment of benthic video platforms to capture imagery of seagrass beds and associated ecosystems. A field campaign deployed a Benthic Observation Survey System (BOSS) and unBaited Remote Underwater stereo-Video system (stereo-uBRUV) at approximately 400 locations to collect photoquadrats and validate remote sensing outputs. Imagery annotation was conducted in the SQUIDLE+ platform. See dataset https://doi.org/10.25959/e4s6-ge74 for habitat maps derived from field validation and remote sensing inputs. The approach developed through this study contributed to the creation of the NESP Standard Operating Procedure (SOP) for Seagrass Mapping using Optical Remote Sensing (https://sustainabledevelopmentreform.github.io/nesp-sop-seagrass-mapping).. See the "Lineage" section of this record for full methodology of field collection techniques.

This DVD-ROM contains digital high resolution seismic reflection data collected during the USGS DIAN 97032 cruise. The coverage is the nearshore of Long Island, NY in the vicinity of Fire Island. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate DVD-ROM driver software installed.

This record provides an overview of the NESP Marine and Coastal Hub scoping study - "Scoping for an Australian Wetland Inventory: identifying knowledge gaps and solutions for mapping Australian marine and coastal wetlands". No data outputs were generated by this project. Marine and coastal wetlands provide extensive ecosystem services—protecting shorelines, improving water quality, supporting healthy fisheries, promoting tourism, storing carbon, and holding cultural significance for Aboriginal and Torres Strait Islander people. Like many wetlands around the world, Australian wetlands continue to be threatened, degraded, and lost due to climate change, development, and other human activities. To support the Australian Government’s development of a national wetland inventory, this project assessed the current state of coastal wetland mapping across five key areas: seagrass, saltmarsh, intertidal macroalgae, shorebird habitat, and blue carbon. It identified major knowledge and inventory gaps through a combination of literature review and consultation with 73 end-users and experts, resulting in 25 targeted recommendations to guide future mapping and data integration. A summary of the status of mapping habitat attributes and ecosystem services such as blue carbon, coastal protection and shorebird habitat is available in the project's Final Technical Report. This report incudes recommendations to guide investment in high-demand areas and support nationally consistent wetland management and reporting to address key knowledge gaps. Outputs • Report reviewing and synthesising knowledge gaps in inventory mapping of marine and coastal wetlands, identifying effective solutions, and guiding subsequent research projects for enhancing wetland mapping [written]

This dataset is a vector shapefile mapping the deep submerged aquatic 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 (Tol, et al., 2023), with an average algae benthic cover of approximately 30 - 40%. This corresponds to only vegetation occurring on the soft sediment of the lagoons.

This dataset was mapped from contrast enhanced Sentinel 2 composite imagery (Lawrey and Hammerton, 2022). Most of the mapped atoll lagoon areas were 30 - 60 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 distinguishing between high and low benthic cover at depth difficult. To compensate for some of these anomalies the benthic mapping was digitised manually based on 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.

Method:

To map the vegetation in the Coral Sea, the primary data sources used were Sentinel-2 image composites optimized for the marine environment (Lawrey and Hammerton, 2022), high-resolution bathymetry data covering part of the region (Beaman, 2017), and drop camera survey results for validation (Tol et al., 2023). An additional set of Sentinel-2 images were collected for Ashmore Reef to help with the mapping of the vegetation in its lagoon (Lawrey and Hammerton, 2024).

Most of the vegetation in the lagoonal floors of the atolls in the Coral Sea occur at a depth of 30 - 60 m. At these depths only the blue channel of the satellite imagery provides any useful visual information. Additionally the contrast between bright sand and dark vegetation is very small in the imagery for area at such depths. Artefacts in the imagery due to clouds, sun glint, waves, and sensor noise can easily obscure these small differences.

To reduce the noise in the imagery a pixelwise statistical median composite was used, created from 4-10 of the clearest Sentinel-2 images of each scene manually selected from 2016-2021. Cloud masking and sun glint correction were applied before image composition (see Lawrey and Hammerton, 2022 for full details). 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.

The atoll lagoonal areas were classified manually and hand digitised as bare, vegetation or reef based on the estimated benthic reflectance. Lighter benthic regions were assumed to be bare sand, while darker regions assumed to be vegetation or reef features. Determining the benthic reflectance at such depth from satellite imagery is potentially ambiguous as areas might appear dark because they are deep, covered in vegetation or reef, affected by coloured dissolved organic matter in the water column absorbing light, or there is a tonal shift from different satellite sensors across the image swath. These factors make image interpretation challenging. To resolve some of these confounding factors the mapping was done using visual cues to identify reference points across the scene to help compensate for tonal and contrast shifts due to depth, water clarity changes and the satellite sensor. These visual cues identify features where there is a high confidence in the benthic cover (sand or vegetation) and these act as local references for classifying the rest of the area between these reference locations. As most areas of the coral atoll lagoons are gently sloping, rapid changes in visual brightness are typically caused by changes in benthic reflectance, rather than changes in depth. We use this to find the edges of vegetation regions.

We employed the following multi-step process to map and verify the oceanic vegetation: 1. Identifying Visual Cues: We identified a set of potential visual cues to detect likely vegetated areas. These cues relied on distinguishing probable patches of sand to estimate local depth and water conditions and observing transitions between light and dark regions to identify vegetation boundaries. 2. Manually mapping: The vegetation boundaries were manually hand digitised based on visual cues in the satellite imagery for Flinders and Holmes Reefs. 3. Benthic Reflectance Estimation: We developed benthic reflectance estimates for the North Flinders and Holmes Reefs regions using both high-resolution and accuracy bathymetry (Beaman, 2017) and satellite imagery (see Lawrey, 2024a for details). 4. Compared Analysis: The initial vegetation mapping from step 1 was compared against the benthic reflectance from step 3 to identify the most reliable visual cue techniques. This identified which were most robust against changes in depth and tonal shifts. 5. Coral Sea Mapping: Using the insights from the previous steps, we manually mapped the remaining Coral Sea region using only satellite imagery. 6. Validation: The final map was validated against the available drop camera survey data on Lihou and Tregrosse Reefs. We previously, separately mapped reefs (Lawrey, 2024c). This mapping was used ensure that reef areas were not interpreted as vegetation. Reef areas were determined by their granular visual texture, their elevated central region, and by the grazing halos around their base.

Visual Cues for Benthic Cover Identification: The following is a summary of the key visual cues used to classify the areas as either vegetated or unvegetated. 1. Grazing Halos Around Patch Reefs: Grazing halos appear as pale rings of bare sand surrounding a textured dark, rounded feature (patch reef), see Figure 40 for examples. These occur because herbivorous fish forage and clear the surrounding sand of any algae. While grazing halos are well studied in shallow reef systems, (DiFiore et al., 2019) they are not well studied at the depths seen in the Coral Sea atoll lagoons. In this mapping we, however, assume the grazing halos in the Coral Sea are caused by a similar mechanism and thus where we see them, they indicate a central reef structure surrounded by a sandy area that is largely devoid of algae. Based on a review of bathymetry transects of patch reefs in North Flinders reefs, the depths of these grazing halos tend to be very close in depth to the surrounding lagoon. This allows them to act as an excellent reference for the brightness of sand at the depth of the lagoon in the area near the reef. Frequently, dark halos of dense vegetation surround these grazing halos, serving as a brightness reference for high-density vegetation. 2. Atoll Plains: On the atoll plains, particularly on the western side of Tregrosse Reefs platform there are large patches of dark substrate that have pale patches, unrelated to the presence of reefs. In this case, local tonal references were identified at locations where there

This webmap features the USGS GAP application of the vegetation cartography design based on NVCS mapping being done at the Alliance level by the California Native Plant Society (CNPS), the California Dept of Fish and Game (CDFG), and the US National Park Service, combined with Ecological Systems Level mapping being done by USGS GAP, Landfire and Natureserve. Although the latter are using 3 different approaches to mapping, this project adopted a common cartography and a common master crossover in order to allow them to be used intercheangably as complements to the detailed NVCS Alliance & Macrogroup Mapping being done in Calif by the California Native Plant Society (CNPS) and Calif Dept of Fish & Wildlife (CDFW). A primary goal of this project was to develop ecological layers to use as overlays on top of high-resolution imagery, in order to help interpret and better understand the natural landscape. You can see the source national GAP rasters by clicking on either of the "USGS GAP Landcover Source RASTER" layers at the bottom of the contents list.Using polygons has several advantages: Polygons are how most conservation plans and land decisions/managment are done so polygon-based outputs are more directly useable in management and planning. Unlike rasters, Polygons permit webmaps with clickable links to provide additional information about that ecological community. At the analysis level, polygons allow vegetation/ecological systems depicted to be enriched with additional ecological attributes for each polygon from multiple overlay sources be they raster or vector. In this map, the "Gap Mac base-mid scale" layers are enriched with links to USGS/USNVC macrogroup summary reports, and the "Gap Eco base scale" layers are enriched with links to the Naturserve Ecological Systems summary reports.Comparsion with finer scale ground ecological mapping is provided by the "Ecol Overlay" layers of Alliance and Macrogroup Mapping from CNPS/CDFW. The CNPS Vegetation Program has worked for over 15 years to provide standards and tools for identifying and representing vegetation, as an important feature of California's natural heritage and biodiversity. Many knowledgeable ecologists and botanists support the program as volunteers and paid staff. Through grants, contracts, and grass-roots efforts, CNPS collects field data and compiles information into reports, manuals, and maps on California's vegetation, ecology and rare plants in order to better protect and manage them. We provide these services to governmental, non-governmental and other organizations, and we collaborate on vegetation resource assessment projects around the state. CNPS is also the publisher of the authoritative Manual of California Vegetation, you can purchase a copy HERE. To support the work of the CNPS, please JOIN NOW and become a member!The CDFG Vegetation Classification and Mapping Program develops and maintains California's expression of the National Vegetation Classification System. We implement its use through assessment and mapping projects in high-priority conservation and management areas, through training programs, and through working continuously on best management practices for field assessment, classification of vegetation data, and fine-scale vegetation mapping.HOW THE OVERLAY LAYERS WERE CREATED:Nserve and GapLC Sources: Early shortcomings in the NVC standard led to Natureserve's development of a mid-scale mapping-friendly "Ecological Systems" standard roughly corresponding to the "Group" level of the NVC, which facilitated NVC-based mapping of entire continents. Current scientific work is leading to the incorporation of Ecological Systems into the NVC as group and macrogroup concepts are revised. Natureserve and Gap Ecological Systems layers differ slightly even though both were created from 30m landsat data and both follow the NVC-related Ecological Systems Classification curated by Natureserve. In either case, the vector overlay was created by first enforcing a .3ha minimum mapping unit, that required deleting any classes consisting of fewer than 4 contiguous landsat cells either side-side or cornerwise. This got around the statistical problem of numerous single-cell classes with types that seemed improbable given their matrix, and would have been inaccurate to use as an n=1 sample compared to the weak but useable n=4 sample. A primary goal in this elimination was to best preserve riparian and road features that might only be one pixel wide, hence the use of cornerwise contiguous groupings. Eliminated cell groups were absorbed into whatever neighboring class they shared the longest boundary with. The remaining raster groups were vectorized with light simplification to smooth out the stairstep patterns of raster data and hopefully improve the fidelity of the boundaries with the landscape. The resultant vectors show a range of fidelity with the landscape, where there is less apparent fidelity it must be remembered that ecosystems are normally classified with a mixture of visible and non-visible characteristics including soil, elevation and slope. Boundaries can be assigned based on the difference between 10% shrub cover and 20% shrub cover. Often large landscape areas would create "godzilla" polygons of more than 50,000 vertices, which can affect performance. These were eliminated using SIMPLIFY POLYGONS to reduce vertex spacing from 30m down to 50-60m where possible. Where not possible DICE was used, which bisects all large polygons with arbitrary internal divisions until no polygon has more than 50,000 vertices. To create midscale layers, ecological systems were dissolved into the macrogroups that they belonged to and resymbolized on macrogroup. This was another frequent source for godzillas as larger landscape units were delineate, so simplify and dice were then run again. Where the base ecol system tiles could only be served up by individual partition tile, macrogroups typically exhibited a 10-1 or 20-1 reduction in feature count allowing them to be assembled into single integrated map services by region, ie NW, SW. CNPS / CDFW / National Park Service Sources: (see also base service definition page) Unlike the Landsat-based raster modelling of the Natureserve and Gap national ecological systems, the CNPS/CDFW/NPS data date back to the origin of the National Vegetation Classification effort to map the US national parks in the mid 1990's.
These mapping efforts are a hybrid of photo-interpretation, satellite and corollary data to create draft ecological land units, which are then sampled by field crews and traditional vegetation plot surveys to quantify and analyze vegetation composition and distribution into the final vector boundaries of the formal NVC classes identified and classified. As such these are much more accurate maps, but the tradeoff is they are only done on one field project area at a time so there is not yet a national or even statewide coverage of these detailed maps.
However, with almost 2/3d's of California already mapped, that time is approaching. The challenge in creating standard map layers for this wide diversity of projects over the 2 decades since NVC began is the extensive evolution in the NVC standard itself as well as evolution in the field techniques and tools. To create a consistent set of map layers, a master crosswalk table was built using every different classification known at the time each map was created and then crosswalking each as best as could be done into a master list of the currently-accepted classifications. This field is called the "NVC_NAME" in each of these layers, and it contains a mixture of scientific names and common names at many levels of the classification from association to division, whatever the ecologists were able to determine at the time. For further precision, this field is split out into scientific name equivalents and common name equivalents.MAP LAYER NAMING: The data sublayers in this webmap are all based on the US National Vegetation Classification, a partnership of the USGS GAP program, US Forest Service, Ecological Society of America and Natureserve, with adoption and support from many federal & state agencies and nonprofit conservation groups. The USNVC grew out of the US National Park Service Vegetation Mapping Program, a mid-1990's effort led by The Nature Conservancy, Esri and the University of California. The classification standard is now an international standard, with associated ecological mapping occurring around the world. NVC is a hierarchical taxonomy of 8 levels, from top down: Class, Subclass, Formation, Division, Macrogroup, Group, Alliance, Association. The layers in this webmap represent 4 distinct programs: 1. The California Native Plant Society/Calif Dept of Fish & Wildlife Vegetation Classification and Mapping Program (Full Description of these layers is at the CNPS MS10 Service Registration Page and Cnps MS10B Service Registration Page . 2. USGS Gap Protected Areas Database, full description at the PADUS registration page . 3. USGS Gap Landcover, full description below 4. Natureserve Ecological Systems, full description belowLAYER NAMING: All Layer names follow this pattern: Source - Program - Level - Scale - RegionSource - Program = who created the data: Nserve = Natureserve, GapLC = USGS Gap Program Landcover Data PADUS = USGS Gap Protected Areas of the USA program Cnps/Cdfw = California Native Plant Society/Calif Dept of Fish & Wildlife, often followed by the project name such as: SFhill = Sierra Foothills, Marin Open Space, MMWD = Marin Municipal Water District etc. National Parks are included and may be named by their standard 4-letter code ie YOSE = Yosemite, PORE = Point Reyes.Level: The level in the NVC Hierarchy which this layer is based on: Base = Alliances and Associations Mac =

This dataset compiles georeferenced media - including videos (480), articles (20), and datasets (6) - specifically curated to facilitate the understanding of reef habitats across northern Australia. It was designed as a research tool for virtual fieldwork with a particular focus on identifying sources of information that allow an understanding of both inshore and offshore reef environments. This dataset provides a record of the literature and media that was reviewed as part of mapping the reef boundaries from remote sensing as part of project NESP MaC 3.17.

This dataset only focuses on media that is useful for understanding shallow reef habitats. It includes videos of snorkelling, diving, spearfishing, and aerial drone imagery. It includes websites, books and journal papers that talk about the structure of reefs and datasets that provide fine scale benthic mapping.

This dataset is likely to not comprehensive. While considerable time was put into collecting relevant media, finding all available information sources is very difficult and time consuming.

A relatively comprehensive search was conducted on:
- AIMS Metadata catalogue for benthic habitat mapping with tow videos and BRUVS
- A review of the eAtlas for benthic habitat mapping
- YouTube searches for video media of fishing, cruises, snorkelling of many named locations.
The dataset is far less comprehensive on existing literature from journals, reports and dataset.

As the NESP MaC 3.17 project progresses we will continue to expand the dataset.


Changelog:

Changes made to the dataset will be noted in the change log and indicated in the dataset via the 'Revision' date.
1st Ed. - 2024-04-10 - Initial release of the dataset


Methods:

Identifying media - YouTube videos
The initial discovery of videos for a given area was achieved by searching for place names in YouTube search using terms such as diving, snorkeling or spearfishing combined with the location name.

Each potential video was reviewed to:

1. Determine if the video had any visual content that would useful for understanding the marine environment.
2. Determine if the footage could be georeferenced to a specific location, the more specific the better.

In cases where the YouTube channel was making travel videos that were of a high quality, then all the relevant videos in that channel were reviewed. A high proportion of the most useful videos were found using this technique.

The most useful videos were those that had named specific locations (typically in their title or description) and contained drone footage and underwater footage. The drone footage would often show enough of the landscape for features to be matched with satellite imagery allowing precise geolocation of the imagery.

To minimise the time required to find relevant videos, the scrubbing feature on YouTube was used to allow the timeline of the video to be quickly reviewed for relevant scenes. The scrubbing feature shows a very quick, but low resolution version of the video as the cursor is moved along the video timeline. This scrubbing was used to quickly look through the videos for any scenes that contained drone footage, for underwater footage. This was particularly useful for travel videos that contained significant footage of overland travel mixed in with boating or shoreline activities. It was also useful for fishing videos where all the fishing activities could be quickly skipped over to focus on any available drone footage or underwater footage from snorkeling or spearfishing.

Where a video lacked direct clues to the location (such as in the title), but the footage contained particularly relevant and useful footage, additional effort was made listen to the conversations and other footage in the videos for additional clues. This includes people in the video talking about the names of locations, or any marine charts in the footage, or previous and proceeding scenes, where the location could be determined, adding constraints to the location of the relevant scene. Where the footage could not be precisely determine, but the footage was still useful then it was added to a video playlist for the region.

In many remote locations there were so few videos that the bar for including the videos was quite low as these videos would at least provide some general indication of the landscape.

When on PC, Google Maps was used to look up locations and act as reference satellite imagery for locating places, QGIS was used to record the polygons of locations and YouTube in a browser was used for video review.

YouTube Playlists:
The initial collection of videos were compiled into YouTube playlists corresponding to relatively large regions. Using playlists was the most convenient way to record useful videos when viewing YouTube from an iPad. This compilation was done prior to the setup of this dataset.

Localising Playlists:
For YouTube playlists the region digitised was based on the region represented by the playlist name and the collection of videos. Google maps was used to help determine the locations of each region. Where a particularly useful video is found in one of the playlists and its location can be determined accurately then this video was entered into this database as an individual video with its own finer scale mapping. However this process of migrating the videos from the playlists to more highly georeferenced individual videos in the dataset is incomplete.

The playlists are really a catch-all for potentially useful videos.

Localising individual videos:
Candidate videos were quickly assessed for likely usefulness by reviewing the title and quickly scrubbing through the video looking for any marine footage, in water or as drone footage. If a video had a useful section then the focus was to determine the location of that part of the footage as accurately as possible. This was done by searching for locations listed in the title, chapter markers, video description, or mentions in video. These were then looked up in Google Maps. In general we would start with any drone footage that shows a large area with distinct features that could be matched with satellite imagery. The region around named locations were scanned for matching coastline and marine features. Once a match was found then the footage would be reviewed to track the likely area that the video covers in multiple scenes.

The video region was then digitised approximately in QGIS into the AU_AIMS_NESP-3-17_Reef-map-geo-media.shp shapefile. Notes were then added about the important features seen in the footage. A link to the video, including the time code so that it would start at the relevant portion of the video. Long videos showing multiple locations were added as multiple entries, each with a separate polygon location and a different URL link with a different start time.

Articles and Datasets
While this dataset primarily focuses on videos, we started adding relevant datasets, websites, articles and reports. These categories of media are not complete in this version of the dataset.


Data dictionary:

RegionName: (String, 255 characters): Name of the location, Examples: 'Oyster Stacks Snorkelling Area', 'Kurrajong Campground', 'South Lefroy Bay'
State: (String, 30 characters): Abbreviation of the state that the region corresponds to. For example: 'WA', 'QLD', 'NT'. For locations far offshore link the location to the closest state or to an existing well known region name. For example: Herald Cay -> Coral Sea, Rowley shoals -> WA.
MediaType: (String, 20 characters): One of the following:
- Video
- Video Playlist
- Website
- Report
- EIS
- Book
- Journal Paper

HabitatRef: (Int): An indication that this resource shows high accuracy spatial habitat information can be used for improving the UQ habitat reference datasets. This attribute should indicate which resources should be reviewed and converted to habitat reference patches. It should be reserved for where a habitat can be located on satellite imagery with sufficient precision that it has high confidence. Media that corresponds to information that is deeper than 15 m is excluded (assigned a HabitatRef of 0) as this is too deep to be used by the UQ habitat mapping.
- 1 - Use for habitat reference data.
- 0 - Only provides general information about the patch. Imagery can be spatially located accurately or detail is insufficient.

Highlight: (String, 255 characters): This records the classification of reef mapping, or research question that this video is most useful for. Not all videos need this classification. In general this attribute should be reserved for those videos that have the highest level of useful information. Think of it as a shortlist of videos that someone trying to understand a particular aspect of categorising reefs from satellite imagery should review. The following are some of the questions associated with each category that the videos provide some answers.
- High tidal range fringing reef: Here we want to understand the structure of fringing reefs in the Kimberleys and Northern Territory where the tides are large and the water is turbid. Is there coral on the tops of the reef flats? Won't the coral dry out if it grows on the reef flat? How will it get enough light if it grows on the reef slope?
- Ancient coastline: Along many parts of WA there are shallow rocky reefs off the coast that appear to be acient coastline. What is the nature of these reefs? Does coral or macroalgae grow on them?
- Seagrass: What does seagrass look like from satellite imagery
- Ningaloo backreef coral: Ningaloo is a very large reef system with a large sandy back. Should the whole back reef

This dataset contains a shapefile of the boundaries of more than 20,000 tropical coral reefs, rocky reefs and sand banks of tropical Australia, covering Indian Ocean, Timor Sea, Gulf of Carpentaria, Torres Strait, Great Barrier Reef and the Coral Sea. This dataset provides the first comprehensive mapping of the reefs of tropical Australia, covering intertidal rocky reefs through to offshore oceanic coral reefs. It includes all reefs shallower than approximately 30 m on the continental shelf and up to 60 m depth in oceanic waters. It is intended to support national environmental accounting, national habitat mapping, creating reef maps, marine science planning, marine park planning, mapping of sea country for traditional owner groups and identifying important marine habitat that should be considered in environmental impact assessments. This dataset is made from the integration of coral reef mapping datasets developed for different regions (GBR, Torres Strait, Coral Sea and Northern and Western Australia) into a single national-scale dataset. It contains 21,832 mapped features. The features are classified according to the Reef Boundary Type classification, and cross walked to the Natural Values Common Language (NVCL) and Seamap Australia Classification scheme. Some features outside the Australian Exclusive Economic Zone (EEZ) are included. Sovereignty of each reef is assigned to allow easy filtering to exclude reefs outside of Australia. The bathymetry statistics (10th, 50th and 90th percentile) of each reef are calculated from the Multi-resolution bathymetry composite surface for Australian waters (EEZ) (Flukes, 2024) and AusBathyTopo (Australia) 250m 2024 (Geoscience Australia, 2024) datasets. The current version v0-1 should be considered as a draft as additional QAQC work is needed to ensure consistency of the feature classifications, along with a validation of the dataset. A rough estimate of the feature classification accuracy for v0-1 is 80-95%. Features in the Coral Sea, Torres Strait and North and West Australia are mapped at a spatial scale of 1:250,000, with a 90th percentile boundary error of approximately 100 m. Features in the GBR Marine Park are less accurate because they are based on older mapping, with typical 90th percentile boundary errors of approximately 300 m, though in some cases boundary errors exceed 1 km. In this dataset we standardise attributes across datasets, and for the GBR we apply corrections to the input source mapping. The GBR marine park mapping is originally based on the GBRMPA GBR Features indicative reef boundaries datasets. We apply corrections to the feature classifications, remove false positive reefs and add reefs missing from the original dataset. Analysis considerations: This dataset provides the outer boundary of coral reef habitats and thus includes multiple habitats within coral reefs such as reef slope, reef crest and reef flats. For the Northern and Western Australia coral reefs are represented by both the 'active' portion of the reef (RB_Type_L3 = 'Coral Reef') and the dormant geological reef flat that is largely covered in sand (RB_Type_L3 = 'Coral Reef Flat'). In some high tidal range conditions in Western Australia and Northern Territory, reefs have grown above the mean tidal level. These are recorded as 'High Intertidal Coral Reef' and are often surrounded by, or adjacent to, deeper, more conventional reefs. If the analysis requires the full geological extent of reefs, these sub-reef types should be dissolved together. This can be achieved by using Level 2 of the Reef Boundary Type classification, in which all of these types are considered coral reefs. The depth category (DepthCat) of each feature provides an indication of the depth of the shallowest portion (10 percent) of the feature. In many cases in the Coral Sea and in Northern and Western Australia, the depth category was determined from satellite imagery. This approach was used to ensure consistent classification across the large study area where the resolution of available bathymetry datasets was insufficient to resolve the heights of small reef features. Because the depth category is based on the top portion of the feature, there may be a significant proportion that is substantially deeper than the assigned classification. This reflects the limitation of assigning a single depth class to large reef features. Where depth categories were not assigned in the source datasets (Torres Strait, GBR and approximately 30 percent of the Northern and Western Australia features), the depth category of features was estimated using two datasets. For reefs inside the Australian Exclusive Economic Zone (EEZ), the 'Multi-resolution bathymetry composite surface for Australian waters (EEZ)' (Flukes, 2024) was used. For reefs outside the EEZ, the AusBathyTopo (Australia) 250 m 2024 (Geoscience Australia, 2024) bathymetry was used. The validity of bathymetry estimates from digital elevation models is highly variable and depends on the detail of the source bathymetry. Estimating the depth of the tops of small reef features is very sensitive to the resolution of the source DEM, with errors of 5 - 20 m or more being common. In the Coral Sea, we incorporated both the Atoll Platforms dataset and the Reefs and Cays dataset so that these large oceanic structures were represented. These 'Atoll Platform' features were mapped as 'Oceanic Platform' in the Reef Boundary Type classification and 'Oceanic unvegetated sediments' in the NVCL. This can be partly misleading because it implies that these areas are unvegetated; however, significant proportions of lagoonal areas are covered in vegetation. If this distinction is important for your analysis, the vegetated areas have been mapped separately in the Coral Sea Oceanic Vegetation dataset (Lawrey, 2024). Methods: The complete dataset can be regenerated from scratch by cloning the companion Git repository and executing scripts 01 to 06 sequentially. These scripts automate the downloading of third-party inputs, application of manual overrides, normalisation of attributes, merging of regional layers, estimation of sovereignty, derivation of depth classes, and cross-walking to the Natural Values Common Language. Because every manual correction is stored as an external shapefile and no interactive editing is required, the workflow is completely reproducible. Step 1 - acquire inputs (01-download-input-data.py) The script fetches the four regional reef boundary layers, the 2024 EEZ land union shapefile and the two bathymetry mosaics. Step 2 – patch, standardise and merge regional datasets Three patch scripts prepare the source layers to a common schema for merging. The most significant modification was for the Torres Strait and the GBR where manually drawn edits were applied to the source dataset. A point shapefile was used to indicate edit commands (move, reshape, merge, remove, and classification adjustments) and a polygon shapefile used to supply the corrected outlines or to add previously unmapped reefs. The North and West Australian Reef Features was already aligned with the dataset schema and the only modification needed was to trim unused attributes. For the Coral Sea, Atoll Platforms are represented as a separate dataset to the reef boundaries. The Atoll Platforms features were incorporated into the dataset by cookie cutting the Coral Sea reef boundaries to create features that represent the lagoonal floor of the atolls in the Coral Sea. After these operations all three sources share the same schema and were then merged into a single shapefile for subsequent processing. Step 3 – assign sovereignty The country associated with feature was assigned using the Union of the ESRI Country shapefile and the Exclusive Economic Zones layer. This was to allow convenient filtering of features to the Australian EEZ. The attribution is supplied for research convenience and is not an official statement of maritime boundaries. Step 4 – derive bathymetry statistics and depth classes This step involves ensuring all features are assigned a depth category, as this is needed for assigning shallow or mesophotic classification in the Natural Values Common Language. Where the source dataset does not have a DepthCat assigned one is derived from the Multi-resolution bathymetry composite surface for Australian waters (EEZ) (Flukes, 2024) and the AusBathyTopo 250m 2024 (Geoscience Australia, 2024) datasets. As part of this processing the 10th, 50th and 90th percentile depths (DEM10p, DEM50p, DEM90p) is estimated for each feature. Step 5 – cross-walk to external classification schemes A crosswalk lookup table is used to translates each feature into Natural Values Common Language and Seamap Australia classifications. Features that fail to match this table are flagged for review. This final step also recalculates the feature areas (Area_km2) and compacts the size of the text attributes to make the shapefile as small as possible. Dataset versions: This dataset will be progressively improved over time as improvements to the source datasets become available. Version v0-1 - Initial release (5 Aug 2025): This initial version is intended for use in the updated Natural Values Ecosystems (2022) dataset https://seamapaustralia.org/map/#066e7a74-7378-45aa-b10a-564304aaa6f7 for NESP MaC 4.20. All features have been visually reviewed against satellite imagery, but no additional in-depth validation has been applied. This version uses v0-4 of the North and West Australian Features dataset. That version was based solely on remote sensing for mapping and classification. Comparison with bathymetry and marine chart datasets was intentionally withheld so they could be used for validation. Version v0-4 focused on digitising coral reefs first, then rocky reefs, and finally sediment features. As a result, some sediment-based feature types are significantly under-represented. There are probably hundreds of sand banks missing, and