Individual boundary polylines were created by first making a point shapefile of the line endpoints or a series of points, then converting the points to a polyline. The point/polyline conversion was done using XTools 'Make One Polyline from Points' tool. Point locations were based on latitude/longitude coordinates given in the technical report or geographic landmark (i.e. islands, points, state/international boundary lines, etc.). Points requiring an azimuth bearing were created in a projected view (UTM Zone 17 NAD27) using the Distance and Azimuth Tools v. 1.6 extension developed by Jenness Enterprises.The polyline shapefiles created in step 1 and an existing polyline shapefile of the international boundary were merged together using the ArcView GeoProcessing Wizard.The shapefile generated in step 2 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage.The line coverage topology was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.001) and BUILD commands.The boundary line coverage and an existing Lake Erie shoreline shapefile (derived from ESRI 100k data) were merged together using the ArcView GeoProcessing Wizard.The shapefile generated in step 5 was converted to a line coverage using the ArcToolbox Conversion Tools - Feature Class to Coverage.Topology of the boundary/shoreline coverage was cleaned and updated using the ArcInfo Workstation CLEAN (dangle length and fuzzy tolerance both set to 0.00001) and BUILD commands. BUILD was done for both line and polygon topology.The polygon feature from the coverage generate in step 7 was converted to a shapefile using Theme\Convert to Shapefile in ArcView.

This Python script (Shape2DJI_Pilot_KML.py) will scan a directory, find all the ESRI shapefiles (.shp), reproject to EPSG 4326 (geographic coordinate system WGS84 ellipsoid), create an output directory and make a new Keyhole Markup Language (.kml) file for every line or polygon found in the files. These new *.kml files are compatible with DJI Pilot 2 on the Smart Controller (e.g., for M300 RTK). The *.kml files created directly by ArcGIS or QGIS are not currently compatible with DJI Pilot.

This municipal boundary layer is created by converting the municipal boundary polygon to a polyline layer. This layer is not edited or adjusted, but rather is created from the polygon layer. Last updated Sept 26, 2025.

GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

This dataset contains a GAMS code used to test the model's computational efficiency, and another GAMS code and the data used in the empirical application for Eldorado National Forest in California, USA.

We prepared the empirical application data using ArcGIS. Specifically, we transformed the 1,363 polygons of the Eldorado National Forest into 4,214 polylines using the Polygon-To-Line function in ArcToolbox and identified all pairs of adjacent land parcels and their common edges using the attribute table of the resulting polyline file. We identified 2,858 junction points for those polylines using the Planarize-Lines function in Advanced Editing, and we found all pairs of edges within a distance under 300 meters using the Generate-Near-Table function included in Analysis Tools in ArcToolbox. We created a personal geodatabase in ArcGIS to place these data and used the program mdb2gms.exe included in GAMS to convert them to a GAMS readable format (....

Task4_ImperviousUpdate2022 - this feature class represents the complete set of impervious features created by the GeoTREE Center. The 2020 project used a multi-step geoprocessing workflow to import data from various sources provided by the City of Madison and then through visual interpretation and digitizing of features. The basic original process was to use a variety of semi-automated and manual geoprocessing to pull/create polygon features from a variety of feature classes provided by the City of Madison at the beginning of the project. These feature classes included curb lines (Curb_Line_Rd), bike/ped paths (Bike_Ped_Paths), buildings (Building_Footprint_DaneCo and SWU_ImperviousAreas), and water body areas (HydroPoly_RFPArea). After building these inherited polygons into this feature class the majority of the work in this project was digitizing impervious features in this feature class based on the Subtype schema associated with the source_area field in this feature class. That schema can be seen below:0 Flat Roof1 Sloped Roof2 Parking3 Unpaved Parking4 Sidewalks5 Driveways6 Water Body Areas7 Streets8 Alleys9 Playground10 Other Impervious Areas11 Landscaped Areas12 Undeveloped Areas13 Other Pervious Areas14 Isolated AreasAll original digitizing was done with 2018 imagery from https://gisimg.cityofmadison.com/arcgis/services/ImageServices/2018_CITY_COLOR/ImageServer. Descriptions of fields is below:source_area - this is the main field as required by the original RFP and in which the above subytpe schema was set up. When GeoTREE Center workers were editing they would choose from the subtype choices and create or edit polygons. GeometryFrom - this field will hold a value indicating the feature class that the original polygon was inherited from (e.g.. HydroPoly_RFPArea). It is still possible that a GeoTREE Center worker would have manually edited the polygons with a value in here although most of these geometries likely are directly inherited from the feature class indicated. The polygons with a value of Bike_Ped_Paths buffered' went through a process of buffering the original line features based on a width attribute and then were manually edited by GeoTREE Center workers as needed. The polygons with Curb_Line_RD with GeoTREE Edits in GeometryFrom field wrere the result of a multiple step geoprocessing workflow to translate the lines into polygons with significant manually editiing by GeoTREE Center workers. source_area_int - the integer value stored in source_area field source_area_desc - the source area descriptionIn 2022 the GeoTREE Center used imagery indicated below to visually inspect and digitize newly developed polygon features as well as to find changes in the built environment and to delete/edit/create new features as needed. The GeometryFrom column has a value of 'UNI GeoTREE Created 2022' the feature was created during this 2022 round of digitizing. https://gisimg.cityofmadison.com/arcgis/services/ImageServices/2020_CITY_COLOR_WEBMERC/ImageServerhttps://dcimapapps.countyofdane.com/arcgisimg/services/ColorOrtho6Inch2020WEB/ImageServer

See full Data Guide here.Ground Water Classifications Polygon:

Ground Water Quality Classifications is a polygon feature-based layer compiled at 1:24,000 scale that includes water quality classification information for groundwaters for all areas of the State of Connecticut. Ground Waters means waters flowing through earth materials beneath the ground surface and the Ground Water Quality Classifications is a designation of the use of the ground waters. The Ground Water Quality Classifications is based primarily on the Adopted Water Quality Classifications Map sheets with information collected and compiled from 1986 to 1997 by major drainage basin. The maps were hand-drawn at 1:50,000-scale in ink on Mylar which had been underprinted with a USGS topographic map base. The digital layer includes ground water water quality classifications. It does not include water quality classifications for ground waters below surface waterbodies. Surface Water Quality Classifications are defined separately in a set of data layers comprised of line and polygon features. The Ground Water Quality Classifications and the Surface Water Quality Classifications are usually presented together as a depiction of water quality classifications in Connecticut. The Ground Water Quality Classes are GA, GAA, GAAs, GB and GC. Classes GAA and GA designate areas of existing or potential drinking water. All ground waters not otherwise classified are considered as Class GA. Class GAAs is for ground water that is tributary to a public water supply reservoir. Class GB is used where ground water is not suitable for drinking water. Class GC is used for assimilation of permitted discharges. Modified classes GA-Impaired, GAA-Impaired, GAA-Well-Impaired, GAA-Well and GA-NY are found in the data layer to categorize special cases of GA or GAA that may not be meeting the goal (impaired), surround public water supply wells (Well) or contribute to a public water supply watershed for another state (NY). There are three elements that make up the Water Quality Standards which is an important element in Connecticut's clean water program. The first of these is the Standards themselves. The Standards set an overall policy for management of water quality in accordance with the directive of Section 22a-426 of the Connecticut General Statutes. In simple terms the policies can be summarized by saying that the Department of Energy and Environmental Protection shall: Protect surface and ground waters from degradation, Segregate waters used for drinking from those that play a role in waste assimilation, Restore surface waters that have been used for waste assimilation to conditions suitable for fishing and swimming, Restore degraded ground water to protect existing and designated uses, Provide a framework for establishing priorities for pollution abatement and State funding for clean up, Adopt standards that promote the State's economy in harmony with the environment. The second element is the Criteria, the descriptive and numerical standards that describe the allowable parameters and goals for the various water quality classifications. The final element is the Classification Maps that show the Class assigned to each surface and groundwater resource throughout the State. These maps also show the goals for the water resources, and in that manner provide a blueprint and set of priorities for Connecticut's efforts to restore water quality. Although federal law requires adoption of Water Quality Standards for surface waters, Water Quality Standards for ground waters are not subject to federal review and approval. Connecticut's Standards recognize that surface and ground waters are interrelated and address the issue of competing use of ground waters for drinking and for waste water assimilation. These Standards specifically identify ground water quality goals, designated uses and those measures necessary for protection of public and private drinking water supplies; the principal use of Connecticut ground waters. These three elements comprise the Water Quality Standards and are adopted using the public participation procedures contained in Section 22a-426 of the Connecticut General Statutes. The Standards, Criteria and Maps are reviewed and revised roughly every three years. Any change is considered a revision requiring public participation. The public participation process consists of public meetings held at various locations around the State, notification of all chief elected officials, notice in the Connecticut Law Journal and a public hearing. The Classification Maps are the subject of separate public hearings which are held for the adoption of the map covering each major drainage basin in the State. The Water Quality Standards and Criteria documents are available on the DEEP website, www.ct.gov/deep. The Ground and Surface Water Quality Classifications do not represent conditions at any one particular point in time. During the conversion from a manually maintained to a digitally maintained statewide data layer the Housatonic River and Southwest Coastal Basins information was updated. The publication date of the digital data reflects the official adoption date of the most recent Water Quality Classifications. Within the data layer the adoption dates are: Housatonic and Southwest Basins - March 1999, Connecticut and South Central Basins - February 1993, Thames and Southeast Basins - December 1986. This data is updated.

This dataset is a line and a polygon feature-based layer compiled at 1:24,000 scale that includes water quality classification information for surface waters for all areas of the State of Connecticut. The Surface Water Quality Classifications and the Ground Water Quality Classifications are usually presented together as a depiction of water quality classifications in Connecticut. Water Quality Classifications, based on the adopted Water Quality Standards, establish designated uses for surface and ground waters and identify the criteria necessary to support those uses. This edition of the Surface Water Quality Classifications is based on the Water Quality Standards adopted on February 25, 2011. Surface Water means the waters of Long Island Sound, its harbors, embayments, tidal wetlands and creeks; rivers and streams, brooks, waterways, lakes, ponds, marshes, swamps, bogs, federal jurisdictional wetlands, and other natural or artificial, public or private, vernal or intermittent bodies of water, excluding groundwater. The surface waters includes the coastal waters as defined by Section 22a-93 of the Connecticut General Statutes and means those waters of Long Island Sound and its harbors, embayments, tidal rivers, streams and creeks, which contain a salinity concentration of at least five hundred parts per million under the low flow stream conditions as established by the Commissioner of the Department of Environmental Protection. The Surface Water Quality Classes are AA, A, B, SA and SB. All surface waters not otherwise classified are considered as Class A if they are in Class GA Ground Water Quality Classifications areas. Class AA designated uses are: existing or proposed drinking water, fish and wildlife habitat, recreational use (maybe restricted), agricultural and industrial supply. Class A designated uses are: potential drinking water, fish and wildlife habitat, recreational use, agricultural and industrial supply. Class B designated uses are: fish and wildlife habitat, recreational use, agricultural and industrial supply and other legitimate uses including navigation. Class B* surface water is a subset of Class B waters and is identical in all ways to the designated uses, criteria and standards for Class B waters except for the restriction on direct discharges. Coastal water and marine classifications are SA and SB. Class SA designated uses are: marine fish, shellfish and wildlife habitat, shellfish harvesting for direct human consumption, recreation and other legitimate uses including navigation. Class SB designated uses are: marine fish, shellfish and wildlife habitat, shellfish harvesting for transfer to approved areas for purification prior to human consumption, recreation and other legitimate uses including navigation. There are three elements that make up the Water Quality Standards which is an important element in Connecticut's clean water program. The first of these is the Standards themselves. The Standards set an overall policy for management of water quality in accordance with the directive of Section 22a-426 of the Connecticut General Statutes. The policies can be simply summarized by saying that the Department of Environmental Protection shall: Protect surface and ground waters from degradation, Segregate waters used for drinking from those that play a role in waste assimilation, Restore surface waters that have been used for waste assimilation to conditions suitable for fishing and swimming, Restore degraded ground water to protect existing and designated uses, Provide a framework for establishing priorities for pollution abatement and State funding for clean up, Adopt standards that promote the State's economy in harmony with the environment. The second element is the Criteria, the descriptive and numerical standards that describe the allowable parameters and goals for the various water quality classifications. The final element is the Classification Maps which identify the relationship between designated uses and the applicable Standards and Criteria for each class of surface and ground water. Although federal law requires adoption of Water Quality Standards for surface waters, Water Quality Standards for ground waters are not subject to federal review and approval. Connecticut's Standards recognize that surface and ground waters are interrelated and address the issue of competing use of ground waters for drinking and for waste water assimilation. These Standards specifically identify ground water quality goals, designated uses and those measures necessary for protection of public and private drinking water supplies; the principal use of Connecticut ground waters. These three elements comprise the Water Quality Standards and are adopted using the public participation procedures contained in Section 22a-426 of the Connecticut General Statutes. The Standards, Criteria and Maps are reviewed and revised roughly every three years. Any change is considered a revision requiring public participation. The public participation process consists of public meetings held at various locations around the State, notification of all chief elected officials, notice in the Connecticut Law Journal and a public hearing. The Classification Maps are the subject of separate public hearings which are held for the adoption of the map covering each major drainage basin in the State. The Water Quality Standards and Criteria documents are available on the DEP website, www.ct.gov/dep. The Surface Water Quality Classifications is a line and polygon feature-based layer is based primarily on the Adopted Water Quality Classifications Map Sheets. The map sheets were hand-drawn at 1:50,000-scale in ink on Mylar which had been underprinted with a USGS topographic map base. The information collected and compiled by major drainage basin from 1986 to 1997. Ground Water Quality Classifications are defined separately in a data layer comprised of polygon features. The Ground and Surface Water Quality Classifications do not represent conditions at any one particular point in time. During the conversion from a manually maintained to a digitally maintained statewide data layer the Housatonic River and Southwest Coastal Basins information was updated. A revision to the Water Quality Standards adopted February 25, 2011. These revisions included eliminating surface water quality classes C, D, SC, SD and all the two tiered classifications. The two tiered classifications included a classification for the present condition and a second classification for the designated use. All the tiered classifications were changed to the designated use classification. For example, classes B/A and C/A were changed to class A. The geographic extent of each the classification was not changed. The publication date of the digital data reflects the official adoption date of the most recent Water Quality Classifications. Within the data layer the adoption dates are: Housatonic and Southwest Basins - March 1999, Connecticut and South Central Basins - February 1993, Thames and Southeast Basins - December 1986. Ground water quality classifications may be separately from the surface water quality classifications under specific circumstances. This data is updated.

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.

The dataset includes two shapefiles consisting of point and polygon locations of selected dams in the Sydney Basin Bioregion. The data were used to show the locations of these dams in maps. Polygon data were used to create points, as described in the History field, that could be shown and labelled in maps for context.

Purpose

For display on report map images

Dataset History

Selected Dam wall (line) features were taken from the 1:250k topographic data Infrastucture theme (see lineage) and converted to a line midpoint (ArcGIS feature conversion polyline to point). Two of the dams needed to be displayed were not found in the 1:250k topographic data. Medway Dam location spatial co-ordinates were sourced from the 2010 Gazetteer (see lineage). Bundanoon dam location was ascertained from Google Earth imagery. Point features for both these dams were manually edited in and appended to the 1:250k topographic data derived dam location data.

Dataset Citation

Bioregional Assessment Programme (2015) SSB Storages Point Locations V01. Bioregional Assessment Derived Dataset. Viewed 14 June 2018, http://data.bioregionalassessments.gov.au/dataset/39b3ca3a-e421-4c68-9499-50c2e9ab2334.

Dataset Ancestors

• Derived From Gazetteer of Australia 2010

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

Authority In the 1963 general session, the Utah State Legislature charged the Division of Water Resources with the responsibility of developing a State Water Plan. This plan is to coordinate and direct the activities of state and federal agencies concerned with Utah’s water resources. As a part of this objective, the Division of Water Resources collects water-related land use data for the entire state. This data includes the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and urban areas. The data produced by the water-related land use program are used for various planning purposes. Some of these include: determining cropland water use, evaluating irrigated land losses and conversion to urban uses, planning for new water development, estimating irrigated acreages for any area, and developing water budgets. Additionally, the data are used by many other state and federal agencies. Previous Methods The land use inventory methods used by the division in conducting water-related land use studies have varied with regard to the procedures used and the precision obtained. During the 1960s and 70s, inventories were prepared using large format vertical-aerial photographs supplemented with field surveys to label boundaries, vegetation types, and other water use information. After identifying crops and labeling photographs, the information was transferred onto a base map and then planimetered or "dot-counted" to determine the acreage. Tables for individual townships and ranges were prepared showing the amount of land in each land use category within each section. Data were then available for use in preparing water budgets. In the early 1980s, the division began updating its methodology for collecting water-related land use data to take advantage of the rapidly growing fields of Remote Sensing and computerized Geographic Information Systems (GIS). For several years during the early 1980’s, the division contracted with the University of Utah Research Institute, Center for Remote Sensing and Cartography (CRSC), to prepare water-related land use inventories. During this period, water-related land use data was obtained by using high altitude color infrared photography and laboratory interpretation, with field checking. In March 1984, several division staff members visited the California Department of Water Resources to observe its methodology for collecting water-related land use data for state water planning purposes. Based on its review of the California methodology and its own experience, the division developed a water-related land use inventory program. This program included the use of 35mm slides, United States Geological Survey (USGS) 7-1/2 minute quadrangle maps, field-mapping using base maps produced from the 35mm photography and a computerized GIS to process, store and retrieve land use data. Areas for survey were first identified from previous land use studies and any other available information. The identified areas were then photographed using an aircraft carrying a high quality 35mm single lens reflex camera mounted to focus along a vertical axis to the earth. Photos were taken between 6,000 and 6,500 feet above the ground using a 24mm lens. This procedure allowed each slide to cover a little more than one square mile with approximately 30 percent overlap on the wide side of the slide and 5 percent on the slide's narrow side. The slides were then indexed according to a flight-line number, slide number, latitude and longitude. All 35mm slides were stored in files at the division offices and cataloged according to township, range and section, and quadrangle map location. Water-related land use areas were then transferred from the slide to USGS 7-1/2 minute quadrangle maps using a standard slide projector with a 100-200mm zoom lens. This step allowed the technician to project the slide onto the back of a quadrangle map. The image showing through the map was adjusted to the map scale with the zoom lens. Field boundaries and other water-use boundaries were then traced on the 7-1/2 minute quadrangle map. Next, a team was sent to use the map in the field to check the boundaries and current year land use field data on the 7-1/2 minute quadrangles. The final step was to digitize and process the field data using ARC/INFO software developed by Environmental Systems Research Institute (ESRI). Starting in 2000 with the land use survey of the Uintah Basin, the division further improved its land use program by using digital data for the purposes of outlining agricultural and other land cover boundaries. The division used satellite data, USGS Digital Orthophoto Quadrangles (DOQs), National Agricultural Imagery Program (NAIP), and other digital images in a heads-up digitizing mode for this process. This allowed the division to use multiple technicians for the digitizing process. Digitizing was done as line and polygon files using ArcView 3.2 with a satellite image, DOQ or NAIP image as a background with other layers added for reference. Boundary files were created in logical groups so that the process of edge-matching along quad lines was eliminated and precision increased. Subsequent inventories were digitized in the ArcMap 9.x software versions. Present Methodology Using the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized. Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed. Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies. In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas. Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county. During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 4.3 million acres of dry and irrigated agricultural land. This represents about 8 percent of the total land area in the state. Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

Hydrographic polygons in and around Fairfax County. The original data in this layer was captured during the 1997 data conversion effort for Fairfax County. After that an update capture was completed in 2014 using stereo models from the 2009 Virginia State imagery. Subsequent to that an update capture was completed in 2022 using stereo models from the 2017 Virginia State imagery. The most recent planimetric update was completed in 2024 using orthoimagery from the 2023 and 2022 Eagleview Orthophotos.This dataset contains lakes, ponds, streams, rivers, etc. within the established constraints of the dataset development. The data set width threshold for double line stream polygons is > 20' and < 20' for all single line features. The determination of a 20-foot-wide stream is based on the average width over the length of the segment. So, if a stream segment on average is 20 feet or wider then that stream becomes a double line stream and will be a polygon as well as a stream centerline down the middle. Typically, from that point on downstream that stream will be a double line stream.Contact: Fairfax County Department of Information Technology GIS DivisionData Accessibility: Publicly AvailableUpdate Frequency: As NeededLast Revision Date: 3/1/2024Creation Date: 1/1/1997Feature Dataset Name: GISMGR.PLANIMETRICLayer Name: GISMGR.HYDRO_AREAS

This dataset resulted from a GPS survey of contaminants at the abandoned Wilkes station, Windmill Islands, Antarctica in January, February 1999. The survey was carried out by Nadia Babicka of Macquarie University in collaboration with Dr Ian Snape of the Australian Antarctic Division. Assistance was provided by David Smith of the Australian Antarctic Data Centre in converting Nadia's GPS data to GIS format and creating an interactive map to display the data.

The zip file available for download from this metadata record includes: 1 A readme file outlining its contents; 2 An ArcMap 9.2 document displaying Nadia's data against a background of topographic data; 3 A folder of photos taken by Nadia. These photos are linked in the ArcMap document to the point, line and polygon features collected by Nadia.

The topographic data displayed in the ArcMap document is from the Australian Antarctic Data Centre's GIS database. The AADC also has an orthophoto of Clark Peninsula which could be used as background in the ArcMap document. Submit a request at https://data.aad.gov.au/aadc/requests/ if you would like a copy of the orthophoto.

A more recent survey was carried out by Kirstie Fryirs of Macquarie University in 2010. Refer to the metadata record with ID 'ASAC_1163_WilkesGIS_2010'.

This dataset depicts the extents of IFR Military Training Routes (IR) from Cycle 1806 (24-MAY-18 to 20-JUN-17). It was created from processing / converting linear DAFIF (Digital Aeronautical Flight Information File) GIS source files gathered from NGA’s NIPRNet to convert the dataset from line to polygon formats with original / source attribution included per segment. It was created and compiled by the Defense Installations Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations and Environment, Business Systems and Information Directorate.While every attempt has been made to provide the best available data quality, this dataset is intended for use at mapping scales 1:50,000 and 1:3,000,000. For this reason, boundaries in this dataset may not perfectly align with DoD site boundaries in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards will remain compliant when this data is incorporated. Boundary data is most suitable for larger map scales.

Input Data

NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)
Southeast Blueprint 2023 subregions: Caribbean

Mapping Steps

Make a copy of the Southeast Caribbean CUSP feature line dataset and reproject it to ESPG 5070.
For the big island of Puerto Rico, special steps were required to deal with CUSP shorelines that did not connect across large rivers.
  Add and calculate a field to use to dissolve the lines.
    Dissolve the lines using the dissolve function, which reveals where there are gaps in the shoreline.
    Use the integrate tool to snap together nearby nodes, using a tolerance of 8 m. This connects the disconnected lines on the big island of Puerto Rico.
    Convert these modified shorelines to a polygon.
    Add and calculate a dissolve field, then dissolve using the dissolve tool. This is necessary because interior waterbodies on the big island of Puerto Rico also have shorelines in the CUSP data. This step produces a layer where inland waterbodies are included as a part of the island where they occur.
    From the resulting layer, select the big island of Puerto Rico and create a separate polygon feature layer from it. This extracts a modified shoreline boundary for the big island of Puerto Rico only. We don’t want to use the modified shorelines created above for other islands that didn’t have an issue of disconnected shoreline segments near large rivers.

Go back to the original Caribbean CUSP lines and convert them to polygons.
Add a dissolve field and dissolve using the dissolve tool. This produces a layer where all inland waterbodies are included as a part of the island where they occur.
From the island boundaries derived from the original CUSP data, remove the polygons that overlap with the big island of Puerto Rico derived from the modified CUSP data. This produces a layer representing all U.S. Caribbean islands except the big island of Puerto Rico.
Merge the modified big island of Puerto Rico layer with the layer for all other islands.
Create and populate a field that has unique IDs for all islands.
Convert the island polygon to a raster using the ArcPy Feature to Raster function. This makes a raster that correctly represents the interior of the islands. However, because the Feature to Raster function for polygons works differently than the Line to Raster function, the shoreline doesn’t perfectly match the result we get when we convert the CUSP lines to a raster. 
Because the Caribbean coastal shoreline condition indicator is created from the CUSP lines, we need the shorelines to match exactly. To reconcile this, go back to the original Caribbean CUSP line data and use the Feature to Raster function again, this time converting the lines to a raster. 
Use the ArcPy Cell Statistics “MAXIMUM” function to combine the two rasters above (one created from the CUSP lines and one created from the CUSP-derived polygons).
Export the raster that represents the extent of Caribbean islands.
Use the Region Group function to give unique values to each island.
Reclassify to make 3 island size classes. The big island of Puerto Rico is the only island in the highest class. The medium island class contains the following islands: Isla Mona, Isla de Vieques, Isla de Culebra, St. Thomas, St. John, and St. Croix. All other islands were put in the smaller class. All other non-island pixels in the Caribbean were given a value of marine.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2023 Data Download or Caribbean-only Southeast Blueprint 2023 Data Download under > 6_Code. Literature Cited National Oceanic and Atmospheric Administration (NOAA), National Ocean Service, National Geodetic Survey. NOAA Continually Updated Shoreline Product (CUSP): Southeast Caribbean. [https://coast.noaa.gov/digitalcoast/data/cusp.html].

Reason for Selection Low-urban historic landscapes indicate significant cultural landscapes whose cultural context has been less impacted by urban development. Cultural landscapes are “properties [that] represent the combined works of nature and of man” (UNESCO 2012). Loss of natural habitat within these cultural landscapes reduces their overall historic and cultural value.Input Data Southeast Blueprint 2023 subregions: CaribbeanSoutheast Blueprint 2023 extent2020 LANDFIRE Existing Vegetation Type (EVT) (v2.2.0) for Puerto Rico and the U.S. Virgin Islands; access the data for U.S. Insular AreasThe following The National Register of Historic Places data for Puerto Rico provided by Eduardo Cancio, Information Systems Specialist with the Puerto Rico State Historic Preservation Office (SHPO) on 2-21-2023 (contact ecancio@prshpo.pr.gov for more information):NRHP_PR_individual_properties.shpNRHP_PR_lineal_districts.shpNRHP_PR_polygonal_districts.shp The National Register of Historic Places reflects what Americans value in their historic built environment. It is the collection of our human imprint on the landscape that records through time our changing relationship with the landscape, bridging between modern life and our history by providing, as closely as possible, experiences that evoke our empathy and understanding of previous eras. OpenStreetMap data “multipolygons” layer, accessed 3-14-2023 A polygon from this dataset is considered a historic site if the “historic” tag is not null. In OpenStreetMap, a historic feature refers to “features that still exist or of which traces are observable, and that are of historic interest, or where the feature class is generally of historical interest”. We only used historic polygons if the name tag is also not null. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.Select USVI historic districts: Polygon boundaries for the Christiansted National Historic District on St. Thomas and Charlotte Amalie Historic and Architectural Historic District on St. Croix, provided by Nikita Beck with the University of the Virgin Islands on 3-6-2023 (contact nikita.beck@uvi.edu for more information)Mapping Steps Identify urban areas using the following classes from 2020 LANDFIRE EVT: Developed-High Intensity, Developed-Low Intensity, Developed-Medium Intensity, Developed-Open Space, Developed-Roads. Classify all urban pixels as 1 and all other pixels as 0.Calculate the percent urban in a 270 m radius circle for each pixel using the Focal Statistics tool in ArcGIS. Since the LANDFIRE data resolution is 30 m, 270 m (9 pixels) approximates a 250 m radius. Retain all pixels that are <50% urban within a 270 m radius. Create a historic places layer by combining the following vector datasets as follows:Buffer National Register point data from the Puerto Rico SHPO by 100 m.Combine National Register polygons from the Puerto Rico SHPO, select USVI historic districts, and OpenStreetMap polygons. Only use OpenStreetMap polygons if both the historic and name columns are null. Buffer the polygons by 30 m.Buffer line data from the Puerto Rico SHPO by 30 m.Merge all buffered point, polygon, and line data into one layer and convert to a 30 m raster representing historic places.Use the historic places raster to remove areas that fall outside of the historic places.Reclassify the above raster into 3 classes, seen in the final indicator values below.Clip to the Caribbean Blueprint 2023 subregion.As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code.Final indicator valuesIndicator values are assigned as follows:2 = Historic place with nearby low-urban buffer1 = Historic place with nearby high-urban buffer0 = Not identified as a historic placeKnown IssuesThere are likely spatial mapping errors for some of the historic areas. Some historic areas with cultural importance are not captured in the National Register of Historic Places.The approach to measuring urban development doesn’t capture degradation to historic places that were historically in larger cities (e.g., courthouses and other downtown buildings). It also doesn’t distinguish between historic places that have always been urban and historic places that used to be low-urban.This layer likely underrepresents some historic areas in the U.S. Virgin Islands compared to Puerto Rico because we were unable to incorporate historic places data from the USVI SHPO during the timeline of this Blueprint update. As a result, some sites on the National Register of Historic Places are not depicted in this indicator.OpenStreetMap is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a historic site) or incorrect tags (e.g., labelling an area as a historic site that does not have historic value). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new historic sites to improve the accuracy and coverage of this indicator in the future.Because open water is considered a non-urban landcover for the purposes of this analysis, this indicator is likely overprioritizing some urbanized historic areas that are close to water, such as marinas and bridges.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedOpenStreetMap. Historic. Data extracted through Geofabrik downloads. Accessed March 14, 2023. [https://wiki.openstreetmap.org/wiki/Key:historic].LANDFIRE, Earth Resources Observation and Science Center (EROS), U.S. Geological Survey. Published August 1, 2022. LANDFIRE 2020 Existing Vegetation Type (EVT) Puerto Rico US Virgin Islands. LF 2020, raster digital data. Sioux Falls, SD. [https://www.landfire.gov].UNESCO (2012) Operational Guidelines for the Implementation of the World Heritage Convention [1]. UNESCO World Heritage Centre. Paris. Page 14. [https://whc.unesco.org/archive/opguide12-en.pdf].

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

This conflated baseline consist of two Digital Shoreline Analysis System (DSAS) Process runs.The original In 2000, the Maryland Geological Survey (MGS) was awarded a Coastal Zone Management grant to complete the acquisition of a recent (ca. 1990) digital shoreline for the coastal regions of Maryland -- the Chesapeake Bay, its tributaries, the coastal bays, and the Atlantic coast. MGS contracted the services of EarthData International, Inc. (EDI), currently of Frederick, Md., to extract shorelines from an existing wetlands delineation, which was based on photo interpretation of 3.75-minute digital orthophoto quarter quads (DOQQs). The 2000 baseline which were not created seaward includes all Maryland shoreline areas except for Anne Arundel, Baltimore, Calvert, Harford and Prince George's counties currently. The newest (2015) updated baselines were created offshore (seaward) of the shorelines utilized in DSAS analysis. The baselines were created by 1) buffering at a distance of 10m around the master shoreline feature class converting the buffer polygon to a line, and erasing the landward portion of the buffer line; and 2)manually digitizing baselines up the centerline of tributaries/rivers and other areas where baselines were needed but the buffer-created baselines did not reach. Funding for this data set was provided by two Projects of Special Merit (CZM # 14-14-1868 CZM 143 and CZM # 14-15-2005 CZM 143), funded by the National Oceanic and Atmospheric Administration (NOAA) and made available to MGS through the Department of Natural Resources (MD DNR) Chesapeake and Coastal Service (CCS). MGS wishes to thank the following project partners: 1) MD DNR CCS, Contact: Mr. Chris Cortina, Role: CCS Project Manager; 2) NOAA, Contact: Mr. Doug Graham, NOAA National Geodetic Survey, Role: Project partner & source of historical and recent shorelines; 3) MD DNR Critical Areas Commission (CAC), Contact: Ms. Lisa Hoerger, Role: Project partner & source of recent shorelines; 4) Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University, Contact: Ryan Mello, Role: Performing the critical area re-mapping for MD DNR CAC and supplying MGS with CAC shorelines; and 5) Ms. Lamere Hennesse, MGS Geologist, retired, Role: Project guidance & technical support.Previous, original Maryland Baselines, credit go to MGS, working collaboratively with Towson University’s Center for Geographic Sciences (CGIS), subsequently used the recent shorelines, along with historical ones, as input into a U.S. Geological Survey (USGS) program, the Digital Shoreline Analysis System (DSAS) (Danforth and Thieler, 1992; Thieler and others, 2001). DSAS determines linear rates of shoreline change (erosion or accretion) along closely spaced, shore-normal transects. This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Map Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Hydrology/MD_ShorelineChanges/MapServer/1

From the grant report - EasyGrantsID: 42878 National Fish and Wildlife Foundation – Hurricane Sandy Coastal Resiliency Competitive Grants Program 2013 Title: Assessing Coastal Impoundment resilience and vulnerability in the Northeast. November 2, 2015 Refer correspondence to: Nellie Tsipoura New Jersey Audubon Society 11 Hardscrabble Road Bernardsville, NJ 07924 Cell phone (732) 310-1348 E-mail: nellie.tsipoura@njaudubon.orgCoastal impoundments can be the first line of defense for communities against coastal storms and sea level rise. Many also provide habitats for important animal populations and communities, such as migratory and breeding birds and breeding and rearing areas for fish and crustaceans. Many northeastern impoundments are no longer able to resist storms and several failed during superstorm Sandy, with catastrophic societal and ecological consequences. This project will categorize all of the Northeastern impoundments in terms of their importance in reducing the risks of inundation to adjacent communities, their ecological value, and their vulnerabilities to future storm events and sea level rise. It will also identify restoration options that will enhance the resilience of vulnerable impoundments to sea level rise and future storm events.ObjectivesCoastal impoundments are often the first line of defense for adjacent communities in Mid-Atlantic and Northeast coastal areas against the impacts of storms, tidal surges and, most recently, rising sea levels. Without such defenses, as unfortunately demonstrated during superstorm Sandy, local communities across the region could be at far greater risk from rising sea levels and more severe storm events. Furthermore, in addition to protecting human communities, many impoundments are also of great importance for the ecological resources that they sustain. These include populations of breeding and migratory birds, and crustacean and fish spawning and rearing areas. For societal and ecological reasons, therefore, it is vital that we safeguard coastal impoundments in an era of changing climate.Under the rapidly changing circumstances described above, and in the aftermath of superstorm Sandy, it is vital that managers and planners at the federal, state, and local levels benefit from gaining a better understanding of:1. Which impoundments provide the most protection to coastal communities from storms and sea-level rise2. Which impoundments sustain significant ecological resources3. Which are most vulnerable to future damage and failure and which may inevitably transition to different habitats4. Which should be a priority for restoration and protection, given their risk reduction and ecological values, and themanagement investment needed to maintain themApproachesTo get a better understanding of impoundments and develop the management recommendations, we will develop several products through literature review, mapping, and field measurements.Specific products will include:1) comprehensive GIS Catalog and map of coastal impoundments from Virginia to Massachusetts. This will detail information such as location, size, elevation, proximity to human communities, and hydrological features of each of the coastal impoundments in the study area. The catalog will also include spatial information from a Societal Risk, Ecological Value, and Climate Vulnerability Assessment2) Societal Importance, Ecological Value, and Climate Vulnerability Assessment: This will include an analysis of each northeastern impoundment in terms of(a) the protection that it confers on coastal human communities, from storms and sea-level rise(b) its ecological functions and values, and(c) its vulnerability to future storms and sea-level rise.From these data, priority impoundments will be identified based on their ability and/or potential to reduce risk to communities, and to conserve important ecological communities and habitats.The objective of this document is to identify the quality assurance components that are necessary to implement the project activities under the “Assessing Coastal Impoundment resilience and vulnerability in the Northeast”. This objective will be achieved by mapping impoundments, incorporating National Wetlands Inventory data, and other information pertinent to these impoundments, and measuring embankment width, height, and freeboard from Lidar topography and NOAA data, as outlined below.Creating the coastal impoundment catalog, literature database, and mapWe will compile a complete list of coastal impoundments from Maine to Virginia along with geographic coordinates and attribute information relevant to ecological value, vulnerability, and coastal resiliency. ‘Coastal impoundment’ are defined as an impounded (usually diked) area adjacent to tidal waters, within which water levels are actively managed to benefit wildlife. We will begin our search by systematically browsing through all coastal state wildlife area and National Wildlife Refuge web pages within the studyarea. This list will be supplemented by conducting a formal literature search in Web of Science, Google Scholar, and Google web search. Any scholarly papers, reports, presentations, or web resources that related to managed coastal impoundments within the study area will be retained. References will be entered into an online RefWorks database, which is exportable to multiple formats, searchable, and organized into folders by and local grouping.Following the information provided, photo-interpreters will locate and delineate each impoundment in ESRI’s ArcGIS 10.1. Mapping will be completed at a scale of 1:8000 with a Target Mapping Unit (TMU) of .25 acres using publically available orthophotos provided by each state. The boundaries of the resulting impoundments will be delineated within ArcGIS, using spatial data from the most recent (2011 or later) National Wetlands Inventory (NWI) as a basis for the boundaries. Boundaries will be manually modified as needed to conform with information from refuge maps or other sources. Methods and techniques utilize protocols developed to update wetland maps for the USFWS’s (NWI). This means all mapping meets or exceeds Federal Geographic Data Committee (FGDC) wetlands mapping standards and protocols (https://www.fgdc.gov/standards).The National Wetlands Inventory data for the area within each impoundment will be extracted as a separate GIS file so that information on features such as wetland type, hydrology, salinity, and vegetation cover would be readily available.Using the sources obtained in the literature search, and existing ecological data such as SHARP, ISS, IWMM, we will compile a variety of ecological, physical, and historical data on each impoundment and cluster of impoundments. We will also conduct interviews using the attached questionnaire (Appendix B) to obtain any additional information from site managers about the impoundments, that may not be available in reports. This information will be spatially linked to and retrievable from each impoundment within the GIS database.Descriptors such as impoundment area will be calculated in the GIS while distances to nearby structures will measured manually. All attributes found in the literature review will be also imported into the GIS and the records added to the appropriate impoundment. Senior PIs will review all maps to confirm and document mapping accuracy and to ensure the final impoundment map meets or exceeds all standards outlined above.Lidar-based vulnerability measurementsWe will download Lidar-based elevation data for each cluster of impoundments from respective state websites (attached in the references section) in 2-foot contour format, or convert into this format from raster data when provided. Contours will then beconverted into TIN (triangulated irregular network) format for analysis. The centerline of each berm adjacent to tidal waters will be digitized as a separate polyline shapefile. Perpendicular transects will be generated along this line using an ArcGIS add-in tool (“Transect 2.0”) with a spacing of 200 m between transects. For berms less than 600 m long, we will measure at least three transects per berm by using a spacing of the berm’s length divided by three. At each transect, we will generate a two dimensional cross-section of the berm based on the TIN surface using the ArcGIS extension “Crossview for ArcGIS”. At each berm cross-section we will measure the top width, defined as the breadth of the flattened top portion of the berm (Figure 1). At the three locations with the narrowest top width for each impoundment we will perform a series of more detailed measurements of the cross-sections. These are: 1) elevation above sea level of the exterior and interior toes of the slopes and the top of the berm, and 2) horizontal width of the exterior and interior slopes (see Figure 1). The average percent slope of the exterior and interior banks will be calculated as follows: [(berm elevation) - (toe of slope elevation)] / (slope width). Freeboard, or elevation relative to mean high water (MHW) rather than to mean sea level (MSL), will be calculated by using a stand-alone program published by the National Oceanic and Atmospheric Administration (“vdatum”) to determine the local difference between MHW and MSL. This difference will be subtracted from berm elevation to obtain freeboard.

Reason for SelectionThe Southeast United States is a global biodiversity hotspot that supports many rare and endemic reptile and amphibian species (Barrett et al. 2014, EPA 2014). These species are experiencing dramatic population declines driven by habitat loss, pollution, invasive species, and disease (Sutherland and deMaynadier 2012, EPA 2014, CI et al. 2004). Amphibians provide an early signal of environmental change because they rely on both terrestrial and aquatic habitats, are sensitive to pollutants, and are often narrowly adapted to specific geographic areas and climatic conditions. As a result, they serve as effective indicators of ecosystem health (CI et al. 2004, EPA 2014). Their association with particular microhabitats and microclimates makes amphibians vulnerable to climate change, and Southeast amphibians are predicted to lose significant amounts of climatically suitable habitat in the future (Barrett et al. 2014). PARCAs also represent the condition and arrangement of embedded isolated wetlands. Many amphibians breed in temporary (i.e., ephemeral) wetlands surrounded by upland habitat, which are not well-captured by existing indicators in the Blueprint (Erwin et al. 2016).Input DataSoutheast Blueprint 2024 extent2023 U.S. Census TIGER/Line state boundaries, accessed 4-5-2024: download the data Southeast Priority Amphibian and Reptile Conservation Areas (PARCAs) PARCAs for all Southeast states except for Mississippi, Virginia, and Kentucky, shared by José Garrido with the Amphibian and Reptile Conservancy (ARC) on 3-5-2024PARCAs for Mississippi, shared by Luis Tirado with ARC on 4-26-2024 (these PARCAs were identified more recently and were not yet captured in ARC’s Southeast PARCAs dataset)South Atlantic PARCAs: Neuse Tar River PARCA (this PARCA was identified through a project funded by the South Atlantic Landscape Conservation Cooperative and is not yet captured in ARC’s Southeast PARCAs dataset; we added this PARCA after consultation with ARC staff) To view a map depicting some of the PARCAs provided, scroll to the bottom of the work page of the ARC website under the heading “PARCAs Nationwide”; to access the data, email info@ARCProtects.org. PARCA is a nonregulatory designation established to raise public awareness and spark voluntary action by landowners and conservation partners to benefit amphibians and/or reptiles. Areas are nominated using scientific criteria and expert review, drawing on the concepts of species rarity, richness, regional responsibility, and landscape integrity. Modeled in part after the Important Bird Areas program developed by BirdLife International, PARCAs are intended to be nationally coordinated but locally implemented at state or regional scales. Importantly, PARCAs are not designed to compete with existing landscape biodiversity initiatives, but to complement them, providing an additional spatially explicit layer for conservation consideration.
PARCAs are intended to be established in areas: capable of supporting viable amphibian and reptile populations, occupied by rare, imperiled, or at-risk species, and rich in species diversity or endemism. For example, species used in identifying the PARCAs in the Southeast include: alligator snapping turtle, Barbour’s map turtle, one-toed amphiuma, Savannah slimy salamander, Mabee’s salamander, dwarf waterdog, Neuse river waterdog, chicken turtle, spotted turtle, tiger salamander, rainbow snake, lesser siren, gopher frog, Eastern diamondback rattlesnake, Southern hognose snake, pine snake, flatwoods salamander, gopher tortoise, striped newt, pine barrens tree frog, indigo snake, and others. There are four major implementation steps: Regional PARC task teams or state experts can use the criteria and modify them when appropriate to designate potential PARCAs in their area of interest. Following the identification of all potential PARCAs, the group then reduces these to a final set of exceptional sites that best represent the area of interest. Experts and stakeholders in the area of interest collaborate to produce a map that identifies these peer-reviewed PARCAs. Final PARCAs are shared with the community to encourage the implementation of voluntary habitat management and conservation efforts. PARCA boundaries can be updated as needed. Mapping Steps Merge the three PARCA polygon datasets and convert from vector to a 30 m pixel raster using the ArcPy Feature to Raster function. Give all PARCAs a value of 1.Add zero values to represent the extent of the source data and to make it perform better in online tools. Convert to raster the TIGER/Line state boundaries for all SEAFWA states except for Virginia and Kentucky and assign them a value of 0. We excluded Virginia and Kentucky because PARCAs have not yet been identified for these states. Use the Cell Statistics “MAX” function to combine the two above rasters.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code.Final indicator valuesIndicator values are assigned as follows:1 = Priority Amphibian and Reptile Conservation Area (PARCA) 0 = Not a PARCA (excluding Kentucky and Virginia)Known IssuesThe mapping of this indicator is relatively coarse and doesn’t always capture differences in pixel-level quality in the outer edge of PARCAs. For example, some PARCAs include developed areas.This indicator is binary and doesn’t capture the full continuum of value across the Southeast.The methods of combining expert knowledge and data in this indicator may have caused some poorly known and/or under-surveyed areas to be scored too low.This indicator underprioritizes important reptile and amphibian habitat in Kentucky and Virginia because PARCAs have not yet been identified for these areas. ARC is working to expand PARCAs to more states in the future.Because of the state-by-state PARCA development and review process, sometimes PARCA boundaries stop at the state line, though suitable habitat for reptiles and amphibians does not always follow jurisdictional boundaries.This indicator excludes “protected” PARCAs maintained by ARC that are too small and spatially explicit to share publicly due to concerns about poaching. As a result, it underprioritizes some important reptile and amphibian habitat. However, these areas are, with a few exceptions in northwest Arkansas and Tennessee, generally well-represented in the Blueprint due to their value for other indicators.This indicator contains small gaps 1-2 pixels wide between some adjoining PARCAs that likely should be continuous, often on either side of a state line. These are represented in the source data as separate polygons with tiny gaps between them, and these translate into gaps in the resulting indicator raster. This results from the PARCA digitizing process and does not reflect meaningful differences in priority.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedAmphibian and Reptile Conservancy. Priority Amphibian and Reptile Conservation Areas (PARCAs). Revised February 7, 2024. Apodaca, Joseph. 2013. Determining Priority Amphibian and Reptile Conservation Areas (PARCAs) in the South Atlantic landscape, and assessing their efficacy for cross-taxa conservation: Geographic Dataset. [https://www.sciencebase.gov/catalog/item/59e105a1e4b05fe04cd000df]. Barrett, Kyle, Nathan P. Nibbelink, John C. Maerz; Identifying Priority Species and Conservation Opportunities Under Future Climate Scenarios: Amphibians in a Biodiversity Hotspot. Journal of Fish and Wildlife Management 1 December 2014; 5 (2): 282–297. [https://doi.org/10.3996/022014-JFWM-015]. Conservation International, International Union for the Conservation of Nature, NatureServe. 2004. Global Amphibian Assessment Factsheet. [https://www.natureserve.org/sites/default/files/amphibian_fact_sheet.pdf]. Environmental Protection Agency. 2014. Mean Amphibian Species Richness: Southeast. EnviroAtlas Factsheet. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/MeanAmphibianSpeciesRichness.pdf]. Erwin, K. J., Chandler, H. C., Palis, J. G., Gorman, T. A., & Haas, C. A. (2016). Herpetofaunal Communities in Ephemeral Wetlands Embedded within Longleaf Pine Flatwoods of the Gulf Coastal Plain. Southeastern Naturalist, 15(3), 431–447. [https://www.jstor.org/stable/26454722]. Sutherland and deMaynadier. 2012. Model Criteria and Implementation Guidance for a Priority Amphibian and Reptile Conservation Area (PARCA) System in the USA. Partners in Amphibian and Reptile Conservation, Technical Publication PARCA-1. 28 pp. [https://parcplace.org/wp-content/uploads/2017/08/PARCA_System_Criteria_and_Implementation_Guidance_FINAL.pdf]. U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch. TIGER/Line Shapefile, 2023, U.S. Current State and Equivalent National. 2023. [https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html].

This web map provides a detailed vector basemap for the world symbolized with a classic Esri topographic map style including contours and the World Hillshade 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 map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, administrative boundaries, and 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 vector basemaps. This map also includes contour lines. The tile layer contains a multisource map style. Even though there are two source paths in the layer's json, these are referenced from a single vector tile layer in this web map. The root.json style file calls two vector Hosted Tile Layers to display all the data in the map. One source (esri) contains all the basemap tiles for this layer. The other source (contours) contains all the contour lines. Use the new Map Viewer Beta to view all the features in this layer as intended.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 MapThis 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 items referenced in this map. In the new Map Viewer Beta, this web map can be added through the Open existing map button.Customize this MapBecause this map includes a vector tile layer, you can customize it to change its content and cartographic design. You are able to turn on and off layers, change symbols for features, switch to alternate local languages, 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.