The TopoBathy 3D layer provides a global seamless topography (land elevation) and bathymetry (water depths) surface to use as a ground in ArcGIS 3D applications.What can you do with this layer?This layer is meant to be used as a ground in ArcGIS Online Web Scenes, ArcGIS Earth, and ArcGIS Pro to help visualize your maps and data in 3D.How do I use this layer?In the ArcGIS Online Web Scene Viewer:Sign-in with ArcGIS Online accountOn the Designer toolbar, click Add LayersClick Browse layersand choose Living Atlas.Search for TopoBathy 3DAdd TopoBathy 3D (Elevation Layer)The TopoBathy 3D will get added under Ground.Change basemap to OceansOptionally, add any other operational layers to visualize in 3D In ArcGIS Pro:Ensure you are logged in with an ArcGIS Online accountOpen a Global SceneOn the Map tab, click Add Data > Elevation Source LayerUnder Portal, click Living Atlas and search for TopoBathy 3DSelect TopoBathy 3D (Elevation Layer) and click OKThe TopoBathy 3D will get added under GroundOptionally, remove other elevation layers from ground and choose the desired basemap Dataset CoverageTo see the coverage and sources of various datasets comprising this elevation layer, view the World Elevation Coverage Map. Additionally, this layer contains data from Vantor’s Precision 3D Digital Terrain Models for parts of the globe.This layer is part of a larger collection of elevation layers. For more information, see the Elevation Layers group on ArcGIS Online.

The Terrain 3D layer provides global elevation surface to use as a ground in ArcGIS 3D applications.What can you do with this layer? Use this layer to visualize your maps and layers in 3D using applications like the Scene Viewer in ArcGIS Online and ArcGIS Pro.Show me how1) Working with Scenes in ArcGIS Pro or ArcGIS Online Scene Viewer2) Select an appropriate basemap or use your own3) Add your unique 2D and 3D data layers to the scene. Your data are simply added on the elevation. If your data have defined elevation (z coordinates) this information will be honored in the scene4) Share your work as a Web Scene with others in your organization or the publicDataset CoverageTo see the coverage and sources of various datasets comprising this elevation layer, view the World Elevation Coverage Map. Additionally, this layer contains data from Vantor’s Precision 3D Digital Terrain Models for parts of the globe.This layer is part of a larger collection of elevation layers. For more information, see the Elevation Layers group on ArcGIS Online.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement.

The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset is a model of top of formations in the Galilee geologic basin that is Moolyember Formation, Clematis Formation, Rewan Formation, Bandanna/Betts Creek Formation, Joe Joe Group, Basement (bottom of Galilee Basin).

Purpose

This dataset was created to provide top of formation including faults for use in groundwater modelling and other purpose for the Galilee geological basin.

Dataset History

A Quality Assurance (QA) and validation process was conducted on the original well and bore data to choose wells/bores that are within 25 kilometres of the BA Galilee Region extent.

The QA/Validation process is as follows:

1.\tWell data

 a.\tObtained excel file "QPED_July_2013_galilee.xlsx" from GA

 b.\tBased on stratigraphic information in "BH_costrat" tab formation names were regularised and simplified based on current naming conventions. 

 c. \tSimplified names added to QPED_July_2013_galileet.xlsx as "Steve_geo" and "Steve_group"

 d.\tProduced new file "GSQ_Geology.xlsx" contained decimal latitude and longitude, KB elevation, top of unit in metres from KB, top of unit in metres AHD, bottom of unit in metres from KB, bottom of unit in metres AHD, original geology, simplified geology, simplified Group geology.

   i.     KB obtained from "BH_wellhist"

   ii.\t   Where no KB information was available ie KB=0, sample the 1S DEM at the well's location to obtain height. KB=DEM+10. Marked well as having lower reliability.

   iii.\t   Calculated Top_m_AHD = KB - Top_m_KB

   iv.\t   Calculated Bottom_m_AHD = KB - Bottom_m_KB

 e.\tBrought GSQ_Geology.xlsx into ArcGIS

 f.\t    Selected wells based on "Steve_geo" field for each model layer to produce a geodatabase for each layer.

   i.\t   GSQ_basement_wells

   ii.\t   GSQ_top_joe_joe_group

   iii.\t   GSQ_top_bandanna_merge

   iv.\t   GSQ_rewan_group

   v.\t   GSQ_clematis

   vi.\t   GSQ_moolyember

 g.\tAdditional wells and reinterpreted tops added to appropriate geodatabase based on well completion reports

 h.\tAdditional wells added to coverages to help model building process

   i.\t   Well_name listed as Fake

   ii.\t   Exception being GSQ_top_basement_fake which was created as a separate geodatabase

2.\tBore data

 a.\tObtained QLD_DNRM_GroundwaterDatabaseExtract_20131111 from GA

 b.\tUsed files REGISTRATIONS.txt, ELEVATIONS.txt and AQUIFER.txt to build GW_stratigraphy.xlsx

   i.\t   Based on RN

   ii.\t   Latitude from GIS_LAT (REGISTRATIONS.txt)

   iii.\t   Longitude from GIS_LNG (REGISTRATIONS.txt)

   iv.\t   Elevation from (ELEVATIONS.txt)

   v.\t   FORM_DESC from (AQUIFER.txt)

   vi.\t   Top from (AQUIFER.txt)

   vii. \t   Bottom from (AQUIFER.txt)

 c.\t    Brought GW_stratigraphy.xlsx into ArcGIS

 d.\tCreated gw_bores_galilee_dem

   i.\t   Sampled 1S DEM to obtain ground level elevation column RASTERVALU

   ii.\t   Created column top_m_AHD by RASTERVALU - Top

 e.\tSelected bores based on "FORM_DESC" field for each model layer to produce a geodatabase for each layer.

   i.\t   Gw_basement

   ii.\t   GW_bores_joe_joe_group

   iii.\t   GW_bores_bandanna

   iv. \t   Gw_bores_rewan

   v. \t   Gw_bores_clematis

   vi. \t   Gw_bores_moolyember

3.\tGeorectified seismic surfaces

 a.\tExtracted interpreted seismic surfaces for base Permian (interpreted as basement) and top Bandanna (in time) from the following seismic surveys

   i.\t   Y80A, W81A, Carmichael, Pendine, T81A, Quilpie, Ward and Powell Creek seismic survey downloaded https://qdexguest.deedi.qld.gov.au/portal/site/qdex/search?searchType=general 

   ii.\t   Brought TIF images into ArcGIS and georectified

   iii.\t   Digitised shape of contours and faults into geodatabase

       1.\t   Basement_contours and basement_faults

       2.\t   bandanna_contours_new_data and bandanna_faults

   iv.\t   Added field "contour" to geodatabase

   v.\t   Converted contours to depth in "contour" field based on well and bore data (top_m_AHD) and contour progression

   vi.\t   Use the shape and depth derived from OZ SEEBASE to help to add additional contours and faults to basement and bandanna datasets

4.\tAdditional contour and fault surfaces were built derived from underlying surfaces and wells/bore data

 a.\tJoejoe_contours and joejoe)faults

 b.\tRewan_contour_clip (used bandanna_faults as fault coverage)

 c.  \tClematis_contour and clematis_faults

 d.\tMoolyember_contour (used clematis_faults as fault coverage)

5.\tSurface geology

 a.\tExtracted surface geology from QUEENSLAND GEOLOGY_AUGUST_2012 using Galilee BA region boundary with 25 kilometre boundary to form geodatabase QLD_geology_galilee

 b.\tSelected relevant surface geology from QLD_geology_galilee based on field "Name" for each model layer and created new geodatabase layers

   i.\t   Basement_geology: Argentine Metamorphics,Running River Metamorphics,Charters Towers Metamorphics; Bimurra Volcanics, Foyle Volcanics, Mount Wyatt Formation, Saint Anns Formation, Silver Hills Volcanics, Stones Creek Volcanics; Bulliwallah Formation, Ducabrook Formation, Mount Rankin Formation, Natal Formation, Star of Hope Formation; Cape River Metamorphics; Einasleigh Metamorphics; Gem Park Granite; Macrossan Province Cambrian-Ordovician intrusives; Macrossan Province Ordovician-Silurian intrusives; Macrossan Province Ordovician intrusives; Mount Formartine, unnamed plutonic units; Pama Province Silurian-Devonian intrusives; Seventy Mile Range Group; and Kirk River beds, Les Jumelles beds.

   ii.\t   Joe_joe_geology: Joe Joe Group

   iii.\t   Galilee_permian_geology: Back Creek Group, Betts Creek Group, Blackwater Group

   iv.\t   Rewan_geology: Rewan Group

      1.\t   Later also made dunda_beds_geology to be included in Rewan model: Dunda beds

   v.\t   Clematis_geology: Clematis Group

      1.\t   Later also made warang_sandstone_geology to be included in Clematis model: Warang Sandstone

   vi.\t   Moolyember_surface_geology: Moolyember Formation

6.\tDEM for each model layer

 a.\tUsing surface geology geodatabase extent extract grid from dem_s_1s to represent the top of the model layer at the surface

   i.\t   Basement_dem

   ii.\t   Joejoe_dem

   iii.\t   Bandanna_dem

   iv.\t   Rewan_dem and dunda_dem

   v.\t   Clematis_dem and warang_dem

   vi.\t   Moolyember_surface_dem

 b.\tUsed Contour tool in ArcGIS to obtain a 25 metre contour geodatabase from the relevant model DEM

   i.\t   Basement_dem_contours

   ii.\t   Joejoe_dem_contours

   iii.\t   Bandanna_dem_contours

   iv.\t   Rewan_dem_contours and dunda_dem_contours

   v.\t   Clematis_dem_contours and warang_dem_contours

   vi.\t   Moolyember_dem_contours

 c. \tFor the purpose of guiding the model building process additional fields were added to each DEM contour geodatabase was added based on average thickness derived from groundwater bores and petroleum wells.

   i.\t   Basement_dem_contours: Joejoe, bandanna, rewan, clematis, moolyember

   ii.\t   Joejoe_dem_contours: basement, bandanna

   iii.\t   Bandanna_dem_contours: joejoe, rewan

   iv.\t   Rewan_dem_contours and dunda_dem_contours: clematis, rewan

   v.\t   Clematis_dem_contours and warang_dem_contours: moolyember, rewan

   vi.\tMoolyember_dem_contours: clematis

The model building process is as follows:

1.\tUsed the tope to raster tool to create surface based on the following rules

 a.\tEnvironment

      i.\tExtent

         1.\tTop: -19.7012030024424

         2.\tRight: 148.891511819054

         3.\tBottom: -27.5812030024424

         4.\tLeft: 139.141511819054

      ii.\tOutput cell size: 0.01 degrees

      iii.\tDrainage enforcement: No_enforce

 b.\tInput

      i.\tBasement

         1.\tBasement_dem_contour; field - contour; type - contour

         2.\tJoejoe_dem_contour; field - basement; type - contour

         3.\tBasement_contour; field - contour; type - contour

         4.\tGSQ_basement_wells; field - top_m_AHD; type - point elevation

         5.\tGW_basement; field - top_m_AHDl type - point elevation

         6.\tGSQ_top_basement_fake; field - top_m_AHDl type - point elevation

         7.\tBasement_faults; type - cliff

     ii.\tJoe Joe Group

         1.\tJoejoe_dem_contour; field - basement; type - contour

         2.\tBasement_dem_contour; field - joejoe; type - contour

         3.\tpermian_dem_contour; field - joejoe, type - contour

         4.\tjoejoe_contour; field - joejoe; type - contour

         5.\tGSQ_top_joejoe_group; field - top_m_AHD; type - point elevation

         6.\tGW_bores_joe_joe_group; field - top_m_AHDl type - point elevation

         7.\tjoejoe_faults; type - cliff

     iii.\tBandanna Group

         1.\tPermian_dem_contour; field - contour; type -

This layer provides ellipsoidal heights calculated dynamically using a server-side function to add the EGM2008 geoid model to orthometric heights from the Terrain layer. The ellipsoidal heights are in meters and compiles from many authoritative data providers from across the globe.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data with ellipsoidal heights. For visualizations such as hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This can be used for applications that require processing, such as orthorectifying satellite imagery using RPCs that are usually referenced to ellipsoidal heights. If using the ellipsoidal height to orthorectify an image in ArcGIS, verify that the option to apply a geoid height correction is not enabled. As an alternative to directly using the service, consider exporting small sections. There is a limit of 5000 rows x 5000 columns.NOTE: This layer combines data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses requiring the highest accuracy, when using ArcGIS Desktop, you will need to use native coordinates and specify the native resolutions (refer to the table under Data Sources in the Terrain service description) as the cell size geoprocessing environment setting and ensure that request is aligned with the source pixels. For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

The General Bathymetric Chart of the Oceans (GEBCO) consists of an international group of experts who work on the development of a range of bathymetric data sets and products. GEBCO operates under the joint auspices of the International Hydrographic Organization (IHO) and the Intergovernmental Oceanographic Commission (IOC) of UNESCO. See our web site for more information about GEBCO at https://www.gebco.net/. This service provides access to GEBCO's latest global bathymetric grid, GEBCO_2014, in the form of shaded relief imagery as layer 'GEBCO_LATEST'. It also provides access to imagery based on the Source Identifier (SID) Grid that accompanies the GEBCO_2014 Grid as GEBCO_LATEST_SID. The GEBCO_2014 Grid is a global grid of elevation data at 30 arc-second intervals. This data set is available as a Web Map Service (WMS) for use as imagery in your applications. The bathymetric portion of the grid was largely generated from a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. However, in areas where they improve on the existing GEBCO Grid, data sets generated by other methods have been included. Further information about the data sets included in the grid can be found in the documentation that accompanies the data set and is available from https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Within the GEBCO grid land elevation data is largely taken from the 30 arc-second version of the Shuttle Radar Topography Mission data set (SRTM30). Further details can be found in the data set's documentation. The imagery for land areas in this WMS, north of 60 degrees south, is largely taken from the NASA Blue Marble: Next Generation data set, produced by Reto Stockli, NASA Earth Observatory, the NASA Goddard Space Flight Center (http://earthobservatory.nasa.gov/Features/BlueMarble). For land areas south of 60 degrees south, coastline and ice shelf information is taken from the Scientific Committee on Antarctic Research (SCAR) Antarctic Digital Database coastline dataset (http://www.add.scar.org). The GEBCO global grid is accompanied by a Source Identifier (SID) grid - this indicates which of the corresponding cells in the GEBCO grid are based on soundings or existing grids and which are interpolated. The SID grid accompanying the GEBCO_2014 Grid is available as a layer in this WMS, GEBCO_LATEST_SID. GEBCO's first gloabl 30 arc-second interval terrain model was the GEBCO_08 Grid, published in January 2009. The WMS layer generated for the 2010 version of the GEBCO_08 Grid is included as a layer, 'GEBCO_08', within this WMS to accommodate users of this existing WMS layer. Information on how to download the GEBCO's grids can be found at https://www.gebco.net/ Please note that GEBCO's grids are mainly deeper water data sets and do not contain detailed bathymetry in shallower water areas. This WMS has been developed by the British Oceanographic Data Centre (BODC) on behalf of the GEBCO community. If imagery from this WMS is included in web sites, reports and digital and printed imagery then we request that the source of the dataset is acknowledged and be of the form 'Imagery reproduced from the GEBCO Grid, version xxxxxx, www.gebco.net'. Where 'version xxxxxx' is the appropriate version number of the GEBCO Grid, given in the layer information below.

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Title:

FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps

Citation:

Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): FABDEM V1-0 adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps. Zenodo, https://doi.org/10.5281/zenodo.7875856.

Supplement to:

Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1425.

Abstract:

This digital elevation model is a version of the FABDEM V1-0 of Hawker et al. (2022; https://doi.org/10.1088/1748-9326/ac4d4f) that was adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps (scale 1:50,000) published in 2014 while source data was compiled between 2000 and 2004. The FABDEM V1-0 (Laurence Hawker, Jeffrey Neal (2021): FABDEM V1-0. https://doi.org/10.5523/bris.25wfy0f9ukoge2gs7a5mqpq2j7; CC BY-NC-SA 4.0) was projected to the Myanmar 2000 datum and clipped to the Ayeyarwady Delta region of interest. The vertical reference of the FABDEM V1-0 was transformed to EGM96 before applying a conversion to continuous mean sea level based on mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; https://doi.org/10.5194/os-17-789-2021) that we transposed to EGM96). Subsequently, inland water bodies were masked using the water body mask of the Copernicus DEM (Airbus Defence and Space, 2020: Copernicus Digital Elevation Model Product Handbook Version 3.0, Airbus, 38 pp.) and cell values with an elevation of more than 7 m below mean sea level were removed.

From the topographic maps, the local spot heights outside of areas masked in the AD-DEM (Seeger et al. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Doi; CC-BY 4.0) were subtracted from elevation values of the FABDEM V1-0 at the same locations (~3630 spot heights). Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate the height residuals and export the raster data at ~30 m grid cell resolution. The mask layer of the AD-DEM was applied to the height residual raster in order to correct for interpolations in areas of data paucity. Subsequently, the interpolated height residuals were subtracted from the pre-processed FABDEM. In delta areas outside the masked regions of the height residual raster, the elevation of the pre-processed FABDEM was maintained (see the figure in the uploaded dataset).

For further information on processing of local and global elevation data for the Ayeyarwady Delta in Myanmar, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.

File name: FABDEM_EGM96_MDT_AD_MMR2000_maskedCop_min7_adjusted_AD.tif

File format: GEOTIFF file

Spatial reference: MMR2000_46N

Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; https://doi.org/10.5194/os-17-789-2021) transposed to EGM96

Cell size: 30 × 30 m

Superseded

https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4-1## A more recent version of this product appears here:

This product will continue to be distributed for archival purposes.

Domain and Product

Changes in the current bathymetry release (version 4) are limited to the region east of the Carquinez Strait (starting around Carquinez Bridge). To facilitate compatibility released by us and our partners, DWR distribute the region west as a separate companion tile and delineate the boundary of active revision in the present product in a place where its source data matches that of other Bay elevation models, e.g., the 2m seamless high-resolution bathymetric and topographic DEM of San Francisco Bay by USGS Earth Resources Observation and Science Center (EROS) (https://topotools.cr.usgs.gov/coned/sanfrancisco.php ), the 2010 San Francisco Bay DEM by National Oceanic and Atmospheric Administration (https://www.ngdc.noaa.gov/metaview/page?xml=NOAA/NESDIS/NGDC/MGG/DEM/iso/xml/741.xml&view=getDataView&header=none ) or the prior (version 3) 10m digital elevation model (https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-v3 ).The 10m DEM for the Bay-Delta is based on the first on the list, i.e. EROS’ 2m DEM for the Bay

New work reported here was done at 2m resolution, although the improvements have been incorporated into the 10m products as much as possible. Relative to the previous DWR release (https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-v3), the 2m DEM product reported here consolidates work at this resolution into a small number of larger surfaces representing approximately one-third of the Delta (link to the Coverage Areas page). Laterally, the 2m models now extend over the levee crest as needed to match well with Delta LiDAR (http://www.atlas.ca.gov/download.html#/casil/imageryBaseMapsLandCover/lidar2009 ), the main terrestrial source of data used in this work. The 10m product (link to the Coverage Areas page) is based on the updated USGS DEM (https://www.sciencebase.gov/catalog/item/58599681e4b01224f329b484 ). In places where updated 2m models overlap the 10 meters, the 10m base elevation model was updated by resampling the new 2m model and adding levee enforcement. At the border between the 2m and 10m models, the two resolutions were locally edge-matched over a small region to maintain smoothness. For more information, please refer to the article: A Revised Continuous Surface Elevation Model for Modeling (link to Chapter 5 in the 2018 Annual Report).

Please note that by agreement with our data providers we distribute only our own integrated maps, not the original source point data.

Data Sources

https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4/resource/981a57b5-e16f-4632-b7e0-4a533534a79c

Coverage Areas

https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4/resource/2e3f2b6e-9f0d-4719-b32a-dff449be18af

This hosted feature layer has been published in RI State Plane Feet NAD 83. Conservation ecologists have coined the term Ecological Land Units (ELU) to describe and map the physical properties of landscapes. Typically, ELUs are defined by the geology, soils, elevation, and landform (hilltop, hillside, valley). A specific ELU has a unique combination of soils, geology, landform, and elevation. ELUs are derived from soil and elevation data using a GIS. It was important that we used readily available data and we kept the derivation of ELUs as simple as possible. After consulting the published literature and conferring with expert soil scientists and plant ecologists, we focused on two aspects of soils, soil drainage class and soil texture. Soil drainage class is very good at distinguishing wet versus dry habitats. Soil texture (sandy, silty, loamy, etc.) is an important habitat component for plants. Using USDA SSURGO (State Soil Survey Geographic Database) data that is readily available from RIGIS, we created a raster dataset (50 feet cell size) of the different soil drainage classes and another raster dataset of the soil texture classes. There are many properties of soils that are available to use for analyses such as this, for example stoniness, depth to bedrock, etc. The two factors we chose are extremely important soil properties in supporting different plant communities. Landform represents where a location is with respect to elevation, slope, and aspect (direction a hillside is facing). Landform distinguishes hilltops, hill sides, valley bottoms, etc. We used the RIGIS digital terrain model as our source of elevation data to measure landform. Landform classes were identified using GIS modeling of slope, aspect, and elevation. The final ELU map is made by adding together the raster datasets for landform, drainage class, and soil texture. Because we were careful with our encoding system, the sum of the three rasters provides us a composite of the individual datasets. For example, a location that is a well-drained (code value 2000) and consists of gravelly sand (code value 100) a sits on a hilltop (code value 21) and would combine to be ELU 2121 (2000+100+21). This process yielded 204 unique ELUs for the state of Rhode Island. Examination of a cumulative distribution function (CDF) of the ELUs showed that most of the ELUs were small and did not occur very often. Conversely, 20 ELUs were quite common and encompassed almost 85% of the land area of RI.Find out more about Mapping ELUs

The DTM is a homogeneous and regular point grid indicating the height of the ground level in order to model its surface. The DTM 1m is achieved by interpolating in Lambert 2008 source data in Lambert 72 and at a 1m-resolution from the Flemish and Brussels regions, and by adding Lambert 2008 data at 1m-resolution from the Walloon Region. The DTM 5m has an additional source, namely drawn structure lines and points adapted during systematic and continuous update by photogrammetric surveys. The DTM 20m is obtained by resampling of the DTM 1m.

This dataset defines the Mean High Water Springs coastline of New Zealand and offshore islands.

Pilot This is the initial version published while further refinements are being made to the dataset. In addition, a Data Dictionary has been published on the LINZ Data Service, which covers information about the process and indicate data accuracies (https://data.linz.govt.nz/document/25674-nz-coastlines-data-dictionary-pilot/). Expected improvements to the dataset include the creation of a historic Mean High Water Springs (MHWS) coastline layer to show change over time; incorporating the latest elevation data; additional attribution; and defining a primary key. The dataset will be finalised in the next few months.

Please note that this dataset will supersede the currently published mean high water coastline (https://data.linz.govt.nz/layer/105085-nz-coastline-mean-high-water/), which will be removed once this dataset is well established.

Feedback on this dataset is appreciated and can be directed to HydroSurvey@linz.govt.nz.

Purpose The NZ Coastline - Mean High Water Springs (MHWS) dataset represents a significant advancement in providing a national coastline based on elevation data. This dataset aims to enhance the accuracy and reliability of New Zealand's national coastline data. Developed as part of the data improvement plan for key datasets related to resilience and climate change (https://www.linz.govt.nz/products-services/data/types-linz-data/resilience-and-climate-change/key-datasets-resilience-and-climate-change), it offers the most precise representation of the MHWS coastline currently available from LINZ.

Status This dataset was created and is maintained from LINZ Hydrographic and Topographic sources. Originally published in February 2025, this dataset will be updated as new elevation information becomes available. As part of improving the national coastline data for New Zealand a next step will be adding the coast_category as available in the MNZ Coastline - Mean High Water.

Data sources and preparation The base of the coastline is a digital elevation model of 1m resolution derived from LiDAR surveys over a number of years. Using separation values between NZVD2016 and MHWS at distinct locations a contour was rendered on the elevation model. Different separation values were used along the coast, these were derived from LINZ tidal records. Assessment of the correct separation value used was based on visual checks against all available aerial imagery sources at the time of creation. To fill gaps within the elevation coverage the spatial coastline data (1:50,000 scale) is sourced from The Topo50 series where it is described as a line forming the boundary between the land and sea, defined by mean high water.

APIs and web services This dataset is available via our standard APIs. This dataset will be available as an ArcGIS REST service once it is out of pilot.

This web map provides a detailed vector basemap for the world symbolized with a classic Esri topographic map style including a shaded relief layer for added context. The web map is very similar in content and style to the popular World Topographic Map, which is delivered as a tile layer with raster fused map cache. This map includes a vector tile layer that provides unique capabilities for customization and high-resolution display. This comprehensive topographic map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries, designed for use with shaded relief for added context. The layers in this map are built using the same data sources used for the World Topographic Map and other Esri basemaps. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority. Updated Map DesignThis style is an update from our raster Topographic style. The land fill and land use opacity was decreased to better emphasize the relief. Land fill polygon changes from white at a small scale to gray tone at larger scales. Labels of a number of feature classes were improved in color, size, and/or spacing. Open water bathymetric colors were improved to allow a smooth transition to scales without the water depth polygons. Road color, line width and effects were adjusted. Overall, additional feature class specifications were changed in conjunction with the land fill opacity change. Use this Map This map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map. Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. See the Vector Basemap group for other vector web maps. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog.

Contents: gridded sea surface heights above geoid; dynamic topography is the sum of sea level anomaly (SLA) and mean dynamic topography (MDT, Rio05 here)

Use: study of the general circulation (ocean gyres ...)

Global gridded data (1/3°x1/3° on a Mercator grid), available in near-real time and in delayed time in NetCDF format.

In delayed time, two types of products are available: - "Ref" (Reference) series: homogeneous datasets based on two satellites (Topex/Poseidon, Jason-1 + ERS, Envisat) with the same groundtrack. Sampling is stable in time. - "Upd" (Updated) series: up-to-date datasets with up to four satellites at a given time (adding GFO and/or Topex/Poseidon on its new orbit). Sampling and Long Wavelength Errors determination are improved, but quality of the series is not homogeneous.

Absolute geostrophic velocities are also available for gridded merged data.

Regional products with an improved quality are available in local areas ("http://www.aviso.oceanobs.com/html/donnees/produits/hauteurs/regional/")

Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service. The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3) Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022 ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/ This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster. The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4 What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.

AbstractA continuous contour dataset at 100 m intervals for all land south of 60°S, excluding the Balleny Islands. The vertical datum of the contours is EGM2008. Contours are extracted primarily from the PGC Reference Elevation Model of Antarctica (REMA) v1.1 with certain islands filled from Copernicus WorldDEM. Further small areas are interpreted from satellite imagery, and Peter I Øy contours are from the Norwegian Polar Institute. Sources of individual line segments are contained in the attribute table and full compilation information is given in the lineage statement.Note: contours overlap the coastline in small areas, due to resolution of the data used in creation of the lines, and potential errors in coastline and/or contour data. Certain areas are known to contain erroneous data due to faults in the original DEM data.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2022 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related medium resolution dataset at 500 m intervals is also published via Living Atlas.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageAll processing described here was performed in ArcGIS Pro version 2.6.A composite Digital Elevation Model (DEM) was created comprising of three datasets from the Reference Elevation Model of Antarctica v1.1: ‘REMA_100m_peninsula_dem_filled’, ‘REMA_100m_dem’ and ‘REMA_200m_dem_filled’. These DEMs were first converted from ellipsoidal height to height above EGM2008 geoid and then mosaicked together in respective order at 100 m spatial resolution. This 100 m DEM was smoothed by performing ‘Focal Statistics’ using a 3x3 cell size.100 m contours were extracted and all contours with a height <1m were deleted, as well as erroneous offshore contours. All contour ‘dangles’ were identified and then fixed to create a continuous dataset. They were fixed either by interpreting the correct line from satellite imagery or from ‘Copernicus WorldDEM 90m’ contours. Such lines are attributed with ‘interpreted’ in the source field and should be treated with caution. In other locations where the contours significantly overlapped the coastline, contours were redrawn/interpreted to not go offshore. In certain locations, primarily some islands on the Antarctic Peninsula, REMA data was insufficient to produce contours. In these places, contours were produced from the ‘Copernicus WorldDEM 90m’ DEM and smoothed by 300 m using a PAEK smoothing algorithm. Contours for Peter I Øy were incorporated from the Norwegian Polar Institute Data at 100 m intervals. The source of every line is written in the attribute table.All contours were merged together and lines <150 m in length were deleted. Further lines <1500 m were deleted in ‘non-mountainous’ regions, so as to avoid deleting small mountain peak contours but to still simplify the main dataset. These regions were interpreted manually using the hillshade of the DEM used to produce the contours.Original DEM sources and citations:REMA: Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665-674, https://doi.org/10.5194/tc-13-665-2019, 2019.Copernicus WorldDEM: produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.Norwegian Polar Institute (2014). Map data / kartdata Peter I Øy 1:50 000 (P50 Kartdata). Norwegian Polar Institute. https://doi.org/10.21334/npolar.2014.29105abcCitationPlease cite this item as: 'Gerrish, L., Fretwell, P., & Cooper, P. (2020). High resolution vector contours for Antarctica (7.3) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/4bd20a2b-df7d-46a2-acdf-5104c82ff4c7'If using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, accessed [year]'

The FIRM is the basis for floodplain management, mitigation, and insurance activities for the National Flood Insurance Program (NFIP). Insurance applications include enforcement of the mandatory purchase requirement of the Flood Disaster Protection Act, which "... requires the purchase of flood insurance by property owners who are being assisted by Federal programs or by Federally supervised, regulated or insured agencies or institutions in the acquisition or improvement of land facilities located or to be located in identified areas having special flood hazards," Section 2 (b) (4) of the Flood Disaster Protection Act of 1973. In addition to the identification of Special Flood Hazard Areas (SFHAs), the risk zones shown on the FIRMs are the basis for the establishment of premium rates for flood coverage offered through the NFIP.

This database has been created by digitizing data from georeferenced paper FIRM maps and adding information from FIS where available. All FIRMs were georeferenced at a 1:4000 scale or finer. This data should be used as a reference layer, not as an authoritative source.

The hardcopy Flood Insurance Rate Map (FIRM) and the accompanying Flood Insurance Studies (FISs) are the official designation of SFHAs and Base Flood Elevations (BFEs) for the NFIP. For the purposes of the NFIP, changes to the flood risk information published by FEMA may only be performed by FEMA and through the mechanisms established in the NFIP regulations (44 CFR Parts 59-78). These digital data are produced in conjunction with the hardcopy FIRMs and generally match the hardcopy map exactly. However, the hardcopy flood maps and flood profiles are the authoritative documents for the NFIP.

Section 11 of FEMA's "Flood Insurance Rate Map (FIRM) Database Technical Reference: Preparing Flood Insurance Rate Map Databases (Nov. 2022)" document contains a detailed description of each attribute code and a reference to other relevant information.

https://www.fema.gov/flood-maps/guidance-reports/guidelines-standards/technical-references-flood-risk-analysis-and-mapping

https://www.fema.gov/sites/default/files/documents/fema_firm-database-technical-reference_112022.pdf

This shapefile displays the watershed boundaries for NEON's aquatic wadeable and non-wadeable stream and lake sites. The watershed boundary defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The pour point was selected nearest the downstream most sensor set, primarily NEON’s S2 sensor in wadeable streams, S1 or stream gauge in non-wadeable rivers, and the outlet sensor in lakes. For most of the sites NEON's 1 meter Elevation-LiDAR Digital Terrain Model (DTM) was used to derive the watersheds. In cases where NEON data did not provide complete watershed coverage, a 1/3 arc-second (10 meter) resolution Digital Elevation Model (DEM) raster, available from the U.S. Geological Survey (USGS) website, was utilized to provide full coverage of the watershed extent. A mosaic dataset was created to combine individual DTM or DEM tiles, and a local projection defined for the dataset. ArcGIS Pro software with the ArcHydro Tools [for] Pro were used to model and delineate the watershed. Attribute Table Information:DomainNum:NEON ecoclimatic domain number. DomainName: NEON ecoclimatic domain name.SiteName: NEON aquatic site name.SiteID: NEON four character site ID for the aquatic site.SiteType:Type of NEON site (e.g. core aquatic or relocatable aquatic).Science: Identifies the primary science theme as they relate to the NEON Grand Challenges (AD[01]) and if the aquatic site is a wadeable or non-wadeable stream, or lake.StateID: The 2 letter abbreviation for the state where the watershed is located.UTM_Zone: The local projected coordinate system for the aquatic site and model processing.WSAreaKm2: Watershed area in kilometers squared for watersheds derived from NEON’s 1 meter Elevation-LiDAR dataset.Source: States if the watershed was not derived from NEON data, these sites are supplemented with the 10 meter National Elevation Dataset.Area_NED: Watershed area in kilometers squared for sites where the watershed was derived from the 10 meter National Elevation Dataset.AOPLiDAR: Name of the Elevation-LiDAR DTM tile from the NEON data portal, includes site ID, year, and month the data was collected.AOP_Flight: Identifies the NEON AOP Flight Boundaries layer showing the extent and priority of airborne acquisition. AOPCoverag: Identifies percent coverage of the NEON AOP flight box over the aquatic watershed.TIS_Dist: Distance in kilometers from the aquatic site pour point to the corresponding terrestrial tower site.TIS_Bear: Bearing in degrees from the aquatic site pour point to the corresponding terrestrial tower site.TIS_WS: States if the corresponding terrestrial tower is within the aquatic watershed.HUC12Name: Name of the Hydrologic Unit Code with twelve digits based on the prominent water or physical feature(s) within the unit. Naming follows the conventions and rules outlined by the Geographic Names Information System (GNIS) order of priority and if the dominant feature is named in the HU10, the HU12 retains the twelve digit code as the name. HUC12: Hydrologic Unit Code with twelve digits based on the sixth-level (subwatershed) classification designated by the United States Geological Survey. NLCD_(number): Percentage of land cover classifications within the watershed from the National Land Cover Dataset (NLCD) (Table 2). NRCS_(Soil abbreviations): Percentage of soil classifications within the watershed from the Natural Resources Conservation Service (NRCS) (Table 3).

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of America sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf Data Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of America, seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC)South Atlantic Bight Marine Assessment(SABMA); chapter 3 of the final report provides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on the NOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on the Marine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data fromThe Artificial Reefs Interactive Mapping Application(direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida(.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023 NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA)Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic;read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data anddraft final report entitled Assessment of Benthic Habitats for Fisheries Managementprovided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster. Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = "01. mapped hard bottom area" and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value among