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.

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.

Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

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]

Controller Mapper Software Market Outlook

The global market size for controller mapper software in 2023 is estimated to be $850 million and is projected to reach approximately $1.65 billion by 2032, growing at a CAGR of 7.8% over the forecast period. This robust growth is driven by increasing demand for customizable and user-friendly interface solutions across multiple platforms and applications.

One of the primary factors fuelling the growth of the controller mapper software market is the burgeoning popularity of gaming. As gaming becomes more mainstream and garners a diverse audience, the need for intuitive controller mapping software that allows users to customize their gameplay experience is on the rise. Moreover, the proliferation of eSports and professional gaming contributes significantly to the demand for advanced controller mapper software, enhancing the gaming experience and offering competitive advantages.

Another crucial growth driver is the increasing emphasis on accessibility. Controller mapper software has proven invaluable for users with disabilities, enabling them to adapt standard input devices to better suit their needs. This aligns with the broader societal trend towards inclusivity and equal access to technology. Governments and organizations are recognizing the importance of accessible technology, further driving the adoption of controller mapper software in various sectors.

The rise in remote work and distance learning, accelerated by the COVID-19 pandemic, has also contributed to the market's growth. As individuals and enterprises increasingly rely on digital tools for productivity and collaboration, the demand for controller mapper software that can streamline workflows and enhance user experience has soared. This trend is expected to continue as remote and hybrid work models become more entrenched in the professional landscape.

Offline Controllers play a pivotal role in the controller mapper software ecosystem, particularly for users who prefer not to rely on constant internet connectivity. These controllers offer enhanced privacy and security, as they operate independently of online networks, reducing the risk of unauthorized access or data breaches. Additionally, offline controllers provide a seamless experience for users in areas with limited or unreliable internet access, ensuring that their gaming or professional activities are not disrupted. As the demand for flexible and secure solutions grows, offline controllers are becoming an essential component of the broader controller mapper software market, catering to diverse user needs and preferences.

Regionally, North America leads the controller mapper software market, driven by technological advancements and high adoption rates of gaming and professional software. Europe also shows significant growth potential, supported by its robust gaming industry and increasing focus on accessibility solutions. Meanwhile, the Asia Pacific region is poised for substantial growth, fueled by the rapid expansion of the gaming market and rising digital literacy.

Platform Analysis

In the controller mapper software market, the platform segment encompasses Windows, macOS, Linux, Android, and iOS. Each platform has its unique set of advantages and user base, contributing to the overall market dynamics. Windows remains the dominant platform, largely due to its widespread use among gamers and professional users. The compatibility and flexibility offered by Windows-based systems make them ideal for running complex controller mapper software efficiently.

macOS, on the other hand, has seen steady growth, particularly among creative professionals and educational institutions. Apple's commitment to accessibility features and high-quality user experience enhances the appeal of controller mapper software on macOS. Moreover, with the increasing use of Mac devices in professional settings, the demand for compatible controller mapping solutions is on the rise.

Linux, although a niche market compared to Windows and macOS, has a dedicated user base that values open-source solutions and customization. Controller mapper software for Linux is often highly configurable and appeals to tech-savvy users who appreciate the flexibility and control offered by this platform. The growing interest in Linux-based gaming systems like SteamOS further boosts the demand for controller mapping tools tailored to this platform.<

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

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.

This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ALPH 98013 cruise. The coverage is the nearshore of the New York and New Jersey Apex. 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.

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 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

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 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 was a clear step change in brightness and the shape and texture of the dark areas matched typical patterns of algae seen in other regions.

Validation:

Since this dataset was manually mapped from noisy and ambiguous imagery, validating this visual mapping approach was essential. The first form of validation was comparing the mapped vegetation boundaries with the benthic reflectance of Flinders reef. This comparison showed a very strong alignment between the manual visual mapping and benthic reflectance (Lawrey, 2024a), with the main deviations occurring around reef edges where the digitised vegetation did not capture all the details. It also deviated in areas where the benthic features were harder to see due to lower water clarity caused by coloured dissolved organic matter increasing the water column light absorption.

No significant adjustments were needed to the visual cue approach following this comparison. However, it highlighted the importance of identifying the local visual cues to compensate for varying depths, satellite sensor brightness shifts and changes in the water clarity.

The final validation involved comparing vegetation maps of Holmes, Tregrosse, and Lihou Reefs against the results of a drop camera survey conducted by JCU in December 2022 (Tol et al., 2023). The locations of the validation sites are shown in Figure 42. From this survey, 237 locations overlap the atoll lagoons. Figure 42 compares the vegetation density estimated from satellite mapping with the benthic cover assessed through the drop camera survey. This demonstrates a strong relationship between the mapped vegetation density and the benthic cover measured by drop cameras. The data show considerable variability, possibly due to fine-scale vegetation patchiness not captured by the satellite-based mapping. The drop camera results represent very small survey patches (less than 1 m across), while the satellite mapping

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 be considered coral reef or something else? What are all the dark areas in this back reef area, macroalgae, seagrass, coral? - Macroalgae: What does macroalgae look like from satellite imagery. How can we tell it apart from coral or seagrass? - Deep shoal benthic habitat: There are many deep banks and shoals across north

description: 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. For more information on the seismic surveys see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=1998-013-FA These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.; abstract: 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. For more information on the seismic surveys see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=1998-013-FA These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

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.

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) 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 dataset summarises 40 years of seagrass data collection (1983-2022) within Torres Strait and the Gulf of Carpentaria into two GIS shapefiles: (1) a point shapefile that includes survey data for 48,612 geolocated sites, and (2) a polygon geopackage describing seagrass at 641 individual or composite meadows.

Managing seagrass resources in northern Australia requires adequate baseline information on where seagrass is (presence/absence), the mapped extent of meadows, what species are present, and date of collection. This baseline is particularly important as a reference point against which to compare seagrass loss or change through time. The scale of northern Australia and the remoteness of many seagrass meadows from human populations present a challenge for research and management agencies reporting on the state of seagrass ecological indicators. Broad-scale and repeated surveys/studies of areas are logistically and financially impractical. However seagrass data is being collected through various projects which, although designed for specific reasons, are amenable to collating a picture of the extent and state of the seagrass resource.

In this project we compiled seagrass spatial data collected during surveys in Torres Strait and the Gulf of Carpentaria into a standardised form with point-specific and meadow-specific spatial and temporal information. We revisited, evaluated, simplified, standardised, and corrected individual records, including those collected several decades ago by drawing on the knowledge of one of our authors (RG Coles) who led the early seagrass data collection and mapping programs. We also incorporate new data, such as from photo records of an aerial assessment of mangroves in the Gulf of Carpentaria in 2017. This project was funded by the National Environmental Science Programme (NESP) Marine and Coastal Hub and Torres Strait Regional Authority (TSRA) in partnership with the Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University. The project follows on from TropWATER’s previous work compiling 35 years of seagrass spatial point data and 30 years of seagrass meadow extent data for the Great Barrier Reef World Heritage Area (GBRWHA) and adjacent estuaries, funded through successive NESP Tropical Water Quality Hub Projects 3.1 (2015-2016) and 5.4 (2018-2020). These data sets are now publicly available through the eAtlas data portal: https://doi.org/10.25909/y1yk-9w85 . In making this data publicly available for management, the authors and data custodians request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood.

Methods: The data were collected using a variety of survey methods to describe and monitor seagrass sites and meadows. For intertidal sites/meadows, these include walking, observations from helicopters in low hover, and observations from hovercraft when intertidal banks were exposed. For subtidal sites/meadows, methods included free diving, scuba diving, video transects from towed cameras attached to a sled with/without a sled net, video drops with filmed quadrats, trawl and net samples, and van Veen grab samples. These methods were selected and tailored by the data custodians to the location, habitat surveyed, and technology available. Important site and method descriptions and contextual information is contained in the original trip reports and publications for each data set provided in Table 1 of Carter et al. (2022).

Geographic Information System (GIS) Mapping data for historic records (1980s) were transcribed from original logged and mapped data based on coastal topography, dead reckoning fixes and RADAR estimations. More recent data (1990’s onwards) is GPS located. All spatial data were converted to shapefiles with the same coordinate system (GDA 1994 Geoscience Australia Lambert), then compiled into a single point shapefile and a single polygon shapefile (seagrass meadows) using ArcMap (ArcGIS version 10.8 Redlands, CA: Environmental Systems Research Institute, ESRI). Some early spatial data was offset by several hundred metres and where this occurred data was repositioned to match the current coastline projection. The satellite base map used throughout this report is courtesy ESRI 2022.

Seagrass Site Layer: This layer contains information on data collected at assessment sites, and includes: 1. Temporal survey details – Survey month and year; 2. Spatial position - Latitude/longitude; 3. Survey name; 4. Depth for each subtidal site is m below MSL Depth and was extracted from the Australian Bathymetry and Topography Grid, June 2009 (Whiteway 2009). This approach was taken due to inconsistencies in depth recordings among data sets, e.g., converted to depth below mean sea level, direct readings from depth sounder with no conversion, or no depth recorded. Depth for intertidal sites was recorded as 0 m MSL, with an intertidal site defined as one surveyed by helicopter, walking, or hovercraft when banks were exposed during low tide;
5. Seagrass information including presence/absence of seagrass, and whether individual species were present/absent at a site; 6. Dominant sediment - Sediment type in the original data sets were based on grain size analysis or deck descriptions. For consistency, in this compilation we include only the most dominant sediment type (mud, sand, shell, rock, rubble), removed descriptors such as “fine”, “very fine”, “coarse”, etc., and replaced redundant terms, e.g. “mud” and “silt” are termed “mud”; 7. Survey methods – In this compilation we have updated and standardised the terms used to describe survey methods from the original reports; and
8. Data custodians.

Seagrass Meadow Layer: Polygons in the meadow layer are drawn from extent data collected during some surveys. Not all surveys collected meadow extent data (e.g., Torres Strait lobster surveys). The seagrass meadow layer is a composite of all the spatial polygon data we could access where meadow boundaries were mapped as part of the survey. All spatial layers were compiled into a single spatial layer using the ArcToolbox ‘merge’ function in ArcMap. Where the same meadow was surveyed multiple times as part of a long-term monitoring program, the overlapping polygons were compiled into a single polygon using the ‘merge’ function in ArcMap. Because meadows surveyed more than once were merged, there were some cases where adjacent polygons overlap each other.

Meadow Data Includes: 1. Temporal survey details – Survey month and year, or a list of survey dates for meadows repeatedly sampled; 2. Survey methods; 3. Meadow persistence – Classified into three categories: a. Unknown – Unknown persistence as the meadow was surveyed less than five times; b. Enduring – Seagrass is present in the meadow ≥90% of the surveys; c. Transitory – Seagrass is present in the meadow <90% of the surveys; 4. Meadow depth – Classified into three categories: a. Intertidal – Meadow was mapped on an exposed bank during low tide, e.g. Karumba monitoring meadow; b. Subtidal – Meadow remains completely submerged during spring low tides, e.g. Dugong Sanctuary meadow; c. Intertidal-Subtidal – Meadow includes sections that expose during low tide and sections that remain completely submerged, e.g. meadows adjacent to the Thursday Island shipping channel; 5. Dominant species of the meadow based on the most recent survey; 6. Presence or absence of individual seagrass species in a meadow; 7. Meadow density categories – Seagrass meadows were classified as light, moderate, dense, variable or unknown based on the consistency of mean above-ground biomass of the dominant species among all surveys, or percent cover of all species combined (see Table 2 in Carter et al. 2022). For example, a Halophila ovalis dominated meadow would be classed as “light” if the mean meadow biomass was always <1 gram dry weight m-2 (g DW m-2) among years, “variable” if mean meadow biomass ranged from <1 - >5 g DW m-2, and “dense” if mean meadow biomass was always >5 g DW m-2 among years. For meadows with density assessments based on both percent cover (generally from older surveys) and biomass, we assessed density categories based on the biomass data as this made the assessment comparable to a greater number of meadows, and comparable to the most recent data. Meadows with only one year of data were assigned a density category based on that year but no assessment of variability could be made and these are classified as “unknown”; 8. The minimum and maximum annual mean above-ground biomass measured in g DW m-2 (+ standard error if available) for each meadow is included for meadows with >1 year of biomass data. For meadows that were only surveyed once the mean meadow biomass (+ standard error if available) is presented as the minimum and maximum biomass of the meadow. “-9999” represents meadows where no above-ground biomass data was collected.; 9. The minimum and maximum annual mean percent cover is included for each meadow with >1 year of percent cover data. For meadows that were only surveyed once the mean meadow percent cover is presented as the minimum and maximum percent cover of the meadow. Older surveys (e.g., 1986 Gulf of Carpentaria surveys) used percent cover rather than biomass. For some surveys percent cover was estimated as discrete categories or ‘data binning’ (e.g., <10% - >50%). “-9999” represents meadows where no percent cover data was collected; 10. Meadow area survey details – The minimum, maximum and total area (hectares; ha) for each meadow: a. Total area - Total area of each meadow was estimated in the GDA 1994 Geoscience Australia Lambert projection using the ‘calculate geometry’ function in ArcMap. For meadows that were mapped multiple times, meadow area represents the merged maximum extent for