This data set uses information from previously reported dye traces and dye traces conducted by the Missouri Geological Survey and included in the report entitled, "Revised Recharge Areas of Selected Springs in the Big Four Region of the Ozarks."

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.

Points depicting the FNSB MACS (Metropolitan Area Commuter System) transit bus stops.

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.

Lines depicting the FNSB MACS (Metropolitan Area Commuter System) transit bus routes.

The Geology of the Northern Jetty Peninsula GIS dataset contains the shapefiles and tables of the basement geology of the Northern Jetty Peninsula in East Antarctica. This dataset is derived from the map product ‘Geology of Northern Jetty Peninsula, Mac.Robertson Land, Antarctica'.

Northern Jetty Peninsula, incorporating Else Platform (~140 km2) and Kamenistaja Platform (~15 km2), represents a mostly ice-free low-lying region located on the western flanks of the Lambert Graben. The region is underlain by granulite-facies Proterozoic gneisses and unmetamorphosed Permian sediments.

GIS-Sebahagian Senarai Inventori Pokok Presint 3 sehingga Mac 2018

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.

Local fire district boundaries across California hosted on ArcGIS Online.Local fire district data obtained from fire departments, cities, counties, and other state entities. The Department of Forestry and Fire Protection (CAL FIRE) makes no guarantees regarding the quality or completeness of the data. It has not been fully reviewed for accuracy and is intended to be used for informational purposes only.Minor changes have been done by Pre-Fire Engineers as well as the Office of the State Fire Marshal (OSFM) to better align edges and remove small overlaps. Boundaries are tied with the corresponding Fire Department Identification (FDID) records kept by OSFM as well as Cal OES MACS 3 Letter IDs used to identify agencies and operational areas dispatching resources. The data currently contains gaps due to a variety of reasons including uncollected information from Federal Lands, reservations, counties, and cities. These gaps however do not necessarily mean there is no coverage or fire protection available. CAL FIRE's goal is to make this data publicly available and easily accessible. To this end, CAL FIRE has reached out to numerous jurisdictions to collect the data currently displayed. It is CAL FIRE's hope that creating this centralized dataset will promote cooperation between neighboring jurisdictions when creating and updating their GIS boundaries, eventually filling in and removing these gaps in the data as well as to better align their borders.This data is updated annually as the Authorities Having Jurisdiction (AHJ) submit updated records and boundaries throughout the year. Not all AHJs submit updates regularly and as such FDID and boundary information may become outdated over time. If you are a representative of one of these AHJs and see outdated information, please submit a Fire Department Information Change Notice.If you would like to submit updated GIS boundaries, please use this application: California Fire District Submission Web AppThis service represents the latest official release as of March 2025.

The approximate extent of seabird colonies on Scullin Monolith, Mac.Robertson Land, Antarctica in 1986/87. The species include Adélie Penguin, Antarctic Petrel, Cape Petrel, Southern Fulmar and …Show full descriptionThe approximate extent of seabird colonies on Scullin Monolith, Mac.Robertson Land, Antarctica in 1986/87. The species include Adélie Penguin, Antarctic Petrel, Cape Petrel, Southern Fulmar and South Polar Skua.

This record provides an overview of the NESP Marine and Coastal Hub Research Plan 2024 project "Unbroken whispers: the ripples connecting sea kin". For specific data outputs from this project, please see child records associated with this metadata. Knowledge, in all its forms, is key to effectively protecting and recovering threatened and migratory whales and dolphins. Indigenous ecological knowledge (IEK) has guided Indigenous peoples through many uncertain climate and ecological fluctuations. IEK has also been used as part of protected area and species management for many thousands of years. More recently, IEK has shown huge potential to contribute to our understanding of threatened and migratory whales and dolphins, but this knowledge has not historically been collated, analysed or properly considered. Consequently, there is an absence of Indigenous perspectives and use of cultural knowledge informing the protection and recovery of EPBC listed threatened and migratory species. This Indigenous-led project will identify and share (where appropriate) cultural knowledge of relationships with whales and dolphins, and connections between land, sea and sky. Indigenous communities will participate in research that explores cultural ideology around kinship and responsibilities to kin, through expressing the knowledge, values and concerns they hold for whales and dolphins. The acquired knowledge and methods will support the cultural governance of sea Country by Indigenous communities and organisations, and policymaking, implementation and review by government agencies in relation to resource use and conservation. Outputs • GIS visualisation package of key geospatial layers related to connecting land and sea in the context of cultural keystone species [dataset] • Final project report [written]

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.

This dataset lists the current Disaster Declarations in Shapefile. This data was compiled and distributed by FEMA Mapping and Analysis Center (MAC). Metadata file can be accessed http://gis.fema.gov/metadata/Declarations_meta.xml Visit gis.fema.gov/data-feeds for more information

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

This dataset corresponds to land area polygons of Australian coastline and surrounding islands. It was generated from 10 m Sentinel 2 imagery from 2022 - 2024 using the Normalized Difference Water Index (NDWI) to distinguish land from water. It was estimated from composite imagery made up from images where the tide is above the mean sea level. The coastline approximately corresponds to the mean high water level. This dataset was created as part of the NESP MaC 3.17 northern Australian Reef mapping project. It was developed to allow the inshore edge of digitised fringing reef features to be neatly clipped to the land areas without requiring manual digitisation of the neighbouring coastline. This required a coastline polygon with an edge positional error of below 50 m so as to not distort the shape of small fringing reefs. We found that existing coastline datasets such as the Geodata Coast 100K 2004 and the Australian Hydrographic Office (AHO) Australian land and coastline dataset did not meet our needs. The scale of the Geodata Coast 100K 2004 was too coarse to represent small islands and the the positional error of the Australian Hydrographic Office (AHO) Australian land and coastline dataset was too high (typically 80 m) for our application as the errors would have introduced significant errors in the shape of small fringing reefs. The Digital Earth Australia Coastline (GA) dataset was sufficiently accurate and detailed however the format of the data was unsuitable for our application as the coast was expressed as disconnected line features between rivers, rather than a closed polygon of the land areas. We did however base our approach on the process developed for the DEA coastline described in Bishop-Taylor et al., 2021 (https://doi.org/10.1016/j.rse.2021.112734). Adapting it to our existing Sentinel 2 Google Earth processing pipeline. The difference between the approach used for the DEA coastline and this dataset was the DEA coastline performed the tidal calculations and filtering at the pixel level, where as in this dataset we only estimated a single tidal level for each whole Sentinel image scene. This was done for computational simplicity and to align with our existing Google Earth Engine image processing code. The images in the stack were sorted by this tidal estimate and those with a tidal high greater than the mean seal level were combined into the composite. The Sentinel 2 satellite follows a sun synchronous orbit and so does not observe the full range of tidal levels. This observed tidal range varies spatially due to the relative timing of peak tides with satellite image timing. We made no accommodation for variation in the tidal levels of the images used to calculate the coastline, other than selecting images that were above the mean tide level. This means tidal height that the dataset coastline corresponds to will vary spatially. While this approach is less precise than that used in the DEA Coastline the resulting errors were sufficiently low to meet the project goals.
This simplified approach was chosen because it integrated well with our existing Sentinel 2 processing pipeline for generating composite imagery. To verify the accuracy of this dataset we manually checked the generated coastline with high resolution imagery (ArcGIS World Imagery). We found that 90% of the coastline polygons in this dataset have a horizontal position error of less than 20 m when compared to high-resolution imagery, except for isolated failure cases. During our manual checks we identified some areas where our algorithm can lead to falsely identifying land or not identifying land. We identified specific scenarios, or 'failure modes,' where our algorithm struggled to distinguish between land and water. These are shown in the image "Potential failure modes": a) The coastline is pushed out due to breaking waves (example: western coast, S2 tile ID 49KPG). b) False land polygons are created because of very turbid water due to suspended sediment. In clear water areas the near infrared channel is almost black, starkly different to the bright land areas. In very highly turbid waters the suspended sediment appears in the near infrared channel, raising its brightness to a level where it starts to overlap with the brightness of the dimmest land features. (example: Joseph Bonaparte Gulf, S2 tile ID 52LEJ). This results in turbid rivers not being correctly mapped. In version 1-1 of the dataset the rivers across northern Australia were manually corrected for these failures. c) Very shallow, gentle sloping areas are not recognised as water and the coastline is pushed out (example: Mornington Island, S2 tile ID 54KUG). Update: A second review of this area indicated that the mapped coastline is likely to be very close to the try coastline. d) The coastline is lower than the mean high water level (example: Great Keppel (Wop-pa) Island, S2 tile ID 55KHQ). Some of these potential failure modes could probably be addressed in the future by using a higher resolution tide calculation and using adjusted NDWI thresholds per region to accommodate for regional differences. Some of these failure modes are likely due to the near infrared channel (B8) being able to penetrate the water approximately 0.5 m leading to errors in very shallow areas. Some additional failures include: - Interpreting jetties as land - Interpreting oil rigs as land - Bridges being interpreted as land, cutting off rivers Methods: The coastline polygons were created in four separate steps: 1. Create above mean sea level (AMSL) composite images. 2. Calculate the Normalized Difference Water Index (NDWI) and visualise as a grey scale image. 3. Generate vector polygons from the grey scale image using a NDWI threshold. 4. Clean up and merge polygons. To create the AMSL composite images, multiple Sentinel 2 images were combined using the Google Earth Engine. The core algorithm was: 1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by - tile ID - maximum cloud cover 20% - date between '2022-01-01' and '2024-06-30' - asset_size > 100000000 (remove small fragments of tiles) 2. Remove high sun-glint images (see "High sun-glint image detection" for more information). 3. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information). 4. Iterate over all images in the split collections to predict the tide elevation for each image from the image timestamp (see "Tide prediction" for more information). 5. Remove images where tide elevation is below mean sea level. 6. Select maximum of 200 images with AMSL tide elevation. 7. Combine SENSING_ORBIT_NUMBER collections into one image collection. 8. Remove sun-glint and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information). 9. Duplicate image collection to first create a composite image without cloud masking and using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used). 10. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used). 11. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022) Next, for each image the NDWI was calculated: 1. Calculate the normalised difference using the B3 (green) and B8 (near infrared). 2. Shift the value range from between -1 and +1 to values between 1 and 255 (0 reserved as no-data value). 3. Export image as 8 bit unsigned Integer grey scale image. During the next step, we generated vector polygons from the grey scale image using a NDWI threshold: 1. Upscale image to 5 m resolution using bilinear interpolation. This was to help smooth the coastline and reduce the error introduced by the jagged pixel edges. 2. Apply a threshold to create a binary image (see "NDWI Threshold" for more information) with the value 1 for land and 2 for water (0: no data). 3. Create polygons for land values (1) in the binary image. 4. Export as shapefile. Finally, we created a single layer from the vectorised images: 1. Merge and dissolve all vector layers in QGIS. 2. Perform smoothing (QGIS toolbox, Iterations 1, Offset 0.25, Maximum node angle to smooth 180). 3. Perform simplification (QGIS toolbox, tolerance 0.00003). 4. Remove polygon vertices on the inner circle to fill out the continental Australia. 5. Perform manual QA/QC. In this step we removed false polygons created due to sun glint and breaking waves. We also removed very small features (1 – 1.5 pixel sized features, e.g. single mangrove trees) by calculating the area of each feature (in m2) and removing features smaller than 200 m2. 15th percentile composite: The composite image was created using the 15th percentile of the pixels values in the image stack. The 15th percentile was chosen, in preference to the median, to select darker pixels in the stack as these tend to correspond to images with clearer water conditions and higher tides. High sun-glint image detection: Images with high sun-glint can lead to lower quality composite images. To determine high sun-glint images, a land mask was first applied to the image to only retain water pixels. This land mask was estimated using NDWI. The proportion of the water pixels in the near-infrared and short-wave infrared bands above a sun-glint threshold was calculated. Images with a high proportion were then filtered out of the image collection.
Sun-glint removal and atmospheric correction: The Top of Atmosphere L1

GIS-Sebahagian Senarai Inventori Pokok Presint 16 sehingga Mac 2018

Municipal Advisory Councils (MACs) were established by the Board of Supervisors to advise them on matters of concern which relate to the area served. It is a forum where information about land use, transportation and general county information is shared, discussed and where the MAC members may make recommendations on those topics and more. It is a great venue where residents can attend these meetings in their own community and talk about issues that are important to them.This layer is part of a collection of public geospatial datasets produced by the Placer County GIS Division.

Summary: Feature service for FEMA's Integrated Public Alert & Warning System (IPAWS) active alerts.Purpose: Feature service for FEMA's Integrated Public Alert & Warning System (IPAWS) active alerts. IPAWS is FEMA's national system for local alerting that provides authenticated emergency and life-saving information to the public through mobile phones using Wireless Emergency Alerts, to radio and television via the Emergency Alert System, and on the National Oceanic and Atmospheric Administration's Weather Radio.Technical Details: (Depreicated) from FEMA MAC See Source (https://fema.maps.arcgis.com/home/item.html?id=81129275492546098f2873db17301c76)Contact Details: FEMA MAC

This dataset summarises benthic surveys in Yanyuwa Sea Country into 3 GIS shapefiles.
(1) A point (site) shapefile describes seagrass presence/absence at 3248 sites surveyed by small vessel and helicopter.
(2) The meadow shapefile describes attributes of 180 intertidal seagrass meadows.
(3) The interpolation GeoTiff describes variation in seagrass biomass across the seagrass meadows.

This project is a partnership between li-Anthawirriyarra rangers, Charles Darwin University, James Cook University, and Mabunji Aboriginal Resource Indigenous Corporation to map the intertidal habitats of the Yanyuwa Indigenous Protected Area (IPA), an area of profound importance to the Marra and Yanyuwa people and to the marine ecosystem of the Gulf of Carpentaria. Benthic habitat maps of Yanyuwa Country were produced, with a focus on seagrass.

Report reference: Groom R, Carter A, Collier C, Firby L, Evans S, Barrett S, Hoffmann L, van de Wetering C, Shepherd L, Evans S, Anderson S. (2023) Mapping Critical Habitat in Yanyuwa Sea Country. Report to the National Environmental Science Program. Charles Darwin University, pp. 40. Available at: https://www.nespmarinecoastal.edu.au/wp-content/uploads/2023/07/NESP-MaC-Hub-Project-1.12_Groom-et-al-FINAL-REPORT.pdf

Methods:
The sampling methods used to study, describe and monitor seagrass meadows were developed by the TropWATER Seagrass Group and tailored to the location and habitat surveyed; these are described in detail in the relevant publications (https://research.jcu.edu.au/tropwater).
Geographic Information System (GIS)

All survey data were entered into a Geographic Information System (GIS) developed for Torres Strait using ArcGIS 10.8. Rectified colour satellite imagery of Yanyuwa Sea Country (Source: Allen Coral Atlas and ESRI), field notes and aerial photographs taken from the helicopter during surveys were used to identify geographical features, such as reef tops, channels and deep-water drop-offs, to assist in determining seagrass meadow boundaries. Three GIS layers were created to describe spatial features of the region: a site layer, seagrass meadow layer, and a seagrass biomass interpolation layer.

Seagrass site layer
This layer contains information on data collected at assessment sites. This layer includes:
1. Temporal survey details – Survey date;
2. Spatial position - Latitude/longitude;
3. Survey location;
4. Seagrass information including presence/absence of seagrass, above-ground biomass (total and for each species), percent cover of seagrass at each site and whether individual species were present/absent at a site;
5. Benthic macro-invertebrate information including the percent cover of hard coral, soft coral, sponges and other benthic macro invertebrates (e.g. ascidian, clam) at a site;
6. Algae information including percent cover of algae at a site and percent contribution of algae functional groups to algae cover at a site;
7. Open substrate – the percent cover of the site that had no flora or habitat forming benthic invertebrates present;
8. Dominant sediment type - Sediment type based on grain size visual assessment or deck descriptions.
9. Survey method and vessel
10. Relevant comments and presence/absence of megafauna and animals of interest (dugong, turtle, dolphin, evidence of dugong feeding trails);
11. Data custodians.

Seagrass meadow layer
Seagrass presence/absence site data, mapping sites, field notes, and satellite imagery were used to construct meadow boundaries in ArcGIS®. The meadow (polygon) layer provides summary information for all sites within each seagrass meadow, including:
1. Temporal survey details – Survey month and year as individual columns and the survey date (the date range the survey took place);
2. Spatial survey details – Survey location, meadow identification number that identifies the reef name and the meadow number. This allows individual meadows to be compared among years;
3. Survey method;
4. Meadow depth for subtidal meadows. Intertidal: meadow was mapped on an exposed bank during low tide;
5. Species presence – a list of the seagrass species in the meadow;
6. Meadow density – Seagrass meadows were classified as light, moderate, dense based on the mean biomass of the dominant species within the meadow. For example, a Thalassia hemprichii dominated meadow would be classed as “light” if the mean meadow biomass was <5 grams dry weight m-2 (g DW m-2), and “dense” if mean meadow biomass was >25 g DW m-2.
7. Meadow community type – Seagrass meadows were classified into community types according to seagrass species composition within each meadow. Species composition was based on the percent each species’ biomass contributed to mean meadow biomass. A standard nomenclature system was used to categorize each meadow.
8. Mean meadow biomass measured in g DW m-2 (+ standard error if available);
9. Meadow area (hectares; ha) (+ mapping precision) of each meadow was calculated in the GDA 2020 Geoscience Australia MGA Zone 53 projection using the ‘calculate geometry’ function in ArcMap. Mapping precision estimates (R; in ha) were based on the mapping method used for that meadow. Mapping precision estimate was used to calculate an error buffer around each meadow; the area of this buffer is expressed as a meadow reliability estimate (R) in hectares;
10. Any relevant comments;
11. Data custodians.

Seagrass biomass interpolation layer
An inverse distance weighted (IDW) interpolation was applied to seagrass site data to describe spatial variation in seagrass biomass within seagrass meadows. The interpolation was conducted in ArcMap 10.8.
Base map
The base map used is courtesy ESRI 2023.

Format of the data:
This dataset consists of 1 point layer package, 1 polygon layer package and 1 raster file:
1. Yanyuwa Sea Country sites 2021-2022.lpk
- Symbology representing seagrass presence/absence at each survey site
2. Yanyuwa Sea Country seagrass meadows 2021-2022.lpk
- Symbology representing dominant species (in terms of biomass) for each intertidal meadow.
3. Yanyuwa Sea Country seagrass biomass interpolation 2021-2022.lpk
- Symbology representing the spatial variation in seagrass biomass within each seagrass meadow.

Data dictionary:
Yanyuwa Sea Country sites 2021-2022 (point data)
SITE (text) - Unique identifier representing a single sample site
MEADOW (text) - Unique identifier representing what meadow the sample site is located in. Blank if sample site is not located within a meadow
SURVEY_DATE (numeric) – survey date (day/month/year)
MONTH (text) – survey month
YEAR (numeric) – survey year
SURVEY_NAME (text) – Name of survey location
LOCATION (text) – Name of survey location
LATITUDE (numeric) – Site location in decimal degrees south
LONGITUDE (numeric) – Site location in decimal degrees east
TIME (numeric) – sample time (24 hours; GMT +9:30) (NT time - subtidal sites only)
DEPTH (numeric) – depth recorded from vessel depth sounder (metres) for subtidal sites. Intertidal sites depth recorded as 0.
DBMSL (numeric) – depth below mean sea level (metres) for subtidal sites. Intertidal sites depth recorded as 0.
TIDAL (text) – identifying if the site was in an intertidal or subtidal location
SUBSTRATE (text) – tags identifying the types of substrates at the sample site. Possible tags are Mud, Sand, Coarse Sand, Silt, Shell, Rock, Reef, Rubble and various combinations. Listed in order from most dominant substrate to least dominant.
SEAGRASS_P (numeric) – Absence (0) or Presence (1) of seagrass
SEAGRASS_C (numeric) - Estimated % of seagrass cover at sample site
SEAGRASS_B (numeric) - Estimated total biomass per square metre for sample site calculated from the mean of three replicate quadrats. Unit is gdw m-2.
SEAGRASS_SE (numeric) – standard error of biomass at sample site calculated from the three replicate quadrats used to estimate biomass at a sample site. Unit is gdw m-2.
EXCLUDE_B (numeric) – Include (0) or Exclude (1). Any site identified that needs to be excluded from contributing to the calculation of mean meadow biomass, e.g. where a visual estimate of biomass could not be optioned (i.e. no visibility at the site, only a van Veen sediment grab was used at the site)
C. rotundata (numeric) – Estimated biomass of Cymodocea rotundata at the sample site. Unit is gdw m-2.
C. serrulata (numeric) – Estimated biomass of Cymodocea serrulata at the sample site. Unit is gdw m-2.
E. acoroides (numeric) – Estimated biomass of Enhalus acoroides at the sample site. Unit is gdw m-2.
H. uninervis (narrow) (numeric) – Estimated biomass of Halodule uninervis (narrow leaf morphology) at the sample site. Unit is gdw m-2.
H. uninervis (wide) (numeric) – Estimated biomass of Halodule uninervis (wide leaf morphology) at the sample site. Unit is gdw m-2.
H. decipiens (numeric) – Estimated biomass of Halophila decipiens at the sample site. Unit is gdw m-2.
H. ovalis (numeric) – Estimated biomass of Halophila ovalis at the sample site. Unit is gdw m-2.
H. spinulosa (numeric) – Estimated biomass of Halophila spinulosa at the sample site. Unit is gdw m-2.
H. tricostata (numeric) – Estimated biomass of Halophila tricostata at the sample site. Unit is gdw m-2.
S. isoetifolium (numeric) – Estimated biomass of Syringodium isoetifolium at the sample site. Unit is gdw m-2.
T. ciliatum (numeric) – Estimated biomass of Thalassodendron ciliatum at the sample site. Unit is gdw m-2.
T. hemprichii (numeric) – Estimated biomass of Thalassia

MACs - Municipal Advisory Councils, and CCs - Community Councils, are established by the Board of Supervisors. They are bodies appointed by the Board to provide an extra avenue for communication from the affected communities back to the Board member who represents them, about issues of concern to them. Their boundaries come from the Supervisors who established them. Data was spatially adjusted in 2020. NAME: CAC/MAC nameTYPE: "MAC" = Municipal Advisory Councils, "CC" = Community CouncilMaintained by Mickey Zoleizo, 9/2015