These data were automated to provide an accurate high-resolution historical shoreline of California suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution sc...

These data were automated to provide an accurate high-resolution historical shoreline of Massachusetts, New Hampshire suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

These data were automated to provide an accurate high-resolution historical shoreline of California Coast suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Historical shoreline surveys were conducted by the National Ocean Service (NOS), dating back to the early 1800s. The maps resulting from these surveys, often called t-sheets, provide a reference of historical shoreline position that can be compared to modern data to identify shoreline change. The t-sheets are stored at the National Archives and many have been scanned by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline Web site (http://www.shoreline.noaa.gov/data/datasheets/t-sheets.html). While some scanned t-sheets were georeferenced and digitized by NOAA, still others remain as non-georeferenced raster files (http://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html). New_Jersey_1839_75_t-sheets.zip features 8 georeferenced raster t-sheets for the New Jersey coastline from 1839 to 1875. The data were scanned by NOAA, but were not georeferenced. The t-sheets included in this data release are: T-121 (1839), T-119 Part 1 (1841), T-1084 (1868), T-1166 (1870), T-1333 (1871), T-1315a (1872), T-1371 (1874), T-1407 (1875). Digital files were georeferenced, corrected to a modern datum, and shorelines digitized to provide a vector polyline depicting the historical shoreline position using ArcGIS 10.3.1. GEoreferenced t-sheets were used to delineate and shorelines for use in long-term shoreline and wetland analyses for Hurricane Sandy wetland physical change assessment.

A Global CoastLine Dataset (GCL_FCS30) with a detailed classification system, including categories for (0) artificial, (1) biogenic, (2) sandy, (3) muddy, (4) rocky, and (5) estuary coastlines for 2010, 2015, and 2020. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index (MNDWI), an adaptive threshold segmentation method based on the Maximum Between-Class Variance Method (OTSU), and the Canny edge detector. The coastline classification was performed using a hybrid transect classifier that integrates a random forest algorithm with globally stable training samples derived from multi-source geophysical data.

These data were automated to provide an accurate high-resolution historical shoreline of Island of Oahu, Hawaii suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Produced by the OSi, this displays the coordinates of the coastal shoreline of Ireland. The vertical datum for the shoreline should be mean sea high water in tidal maritime zone or normal water. None

A global data set of ocean distances from the nearest coastline. NASA's Ocean Biology Processing Group (OBPG) generated this data set using the Generic Mapping Tools (GMT) software package. Distances were computed with GMT using its intermediate-resolution coastline and then gridded globally at a spatial resolution of 0.04 degrees. Bilinear interpolation was then applied to increase the spatial resolution to 0.01 degrees. There is an uncertainty of up to 1 km in the computed distance at any given point.

These data were automated to provide an accurate high-resolution historical shoreline of New Jersey suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution sc...

These data were automated to provide an accurate high-resolution historical shoreline of Mobile Bay, Alabama suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Pan-European coastline-migration map at zoomable scale. The map is collated and harmonized from field-monitoring data and aerial photography provided by partners of EMODnet Geology. Where no such coastline-migration data were available, information from the EUROSION project is provided. For remaining gaps, please consult the coastline-migration map based on satellite data. The main attributes denote degree of landward (by erosion or submergence) or seaward (by accretion or emergence) change. In the visualization provided, three classes are distinguished: landward migration, stable coastline, seaward migration. The criterion for stable coastlines is ≤0.5 meter net change per year over a 10-year period. The current version was finalized in January 2021.

This dataset includes shorelines from 151 years ranging from 1850 to 2001 for the Texas east coastal region from Sabine Pass at the Louisiana border to Aransas Pass at the southern end of San Jose Island. Shorelines were compiled from topographic survey sheets, also known as T-sheets (National Oceanic and Atmospheric Administration (NOAA)), aerial photographs (Bureau of Economic Geology, The University of Texas (UT BEG) at Austin), and lidar data (United States Geological Survey/National Aeronautics & Space Administration and UT BEG). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's (USGS) National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates. . Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner. .

This map includes shoreline change data for the state of Massachusetts hosted by the Massachusetts Office of Coastal Zone Management.The active data layer in this map is Massachusetts Shoreline Change Transect (1970-2014) with short-term shoreline change rates. To view long-term rates, open map in Map Viewer to turn on layer.The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. In 2018 two new mean high water (MHW) shorelines for Massachusetts were extracted from lidar collected between 2010 and 2014 (described below). 2018 addition shoreline 1The North Shore and South Coast uses 2010 lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise. The South Shore and Outer Cape uses 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Nantucket and Martha’s Vineyard uses 2012 lidar data collected by the USACE (post Sandy)from a 2012 USACE Post Sandy Topographic lidar survey. 2018 addition shoreline 2The North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha’s Vineyard, and the South Coast (around Buzzards Bay to the Rhode Island Border) is from 2013-14 lidar data collected by the (USGS) Coastal and Marine Geology Program. This 2018 update of the rate of shoreline change in Massachusetts includes two types of rates. Some of the rates include a proxy-datum bias correction, this is indicated in the filename with “PDB”. The rates that do not account for this correction have “NB” in their file names. The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates. This release includes both long-term (~150 years) and short term (~30 years) rates. Files associated with the long-term rates have “LT” in their names, files associated with short-term rates have “ST” in their names.

These data provide an accurate high-resolution shoreline compiled from imagery of Baffin Bay, TX . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Sourc...

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

An outline map without names which shows only Canada’s coastline and boundaries.

These data were automated to provide an accurate high-resolution historical shoreline of Fort Morgan, AL suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attributi...

This table contains data on access to parks measured as the percent of population within ½ a mile of a parks, beach, open space or coastline for California, its regions, counties, county subdivisions, cities, towns, and census tracts. More information on the data table and a data dictionary can be found in the Data and Resources section. As communities become increasingly more urban, parks and the protection of green and open spaces within cities increase in importance. Parks and natural areas buffer pollutants and contribute to the quality of life by providing communities with social and psychological benefits such as leisure, play, sports, and contact with nature. Parks are critical to human health by providing spaces for health and wellness activities. The access to parks table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. The format of the access to parks table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

NOTE: Coastline 2007 of New Jersey was extracted from NJDEP's Land Use 2007 data layer. The data was created by extracting water polygons which represented Rivers, Bays and Oceans from the 2007 land use/land cover (LU/LC) layer from NJ DEP's geographical information systems (GIS) database. The source file contains land use and water body polygon information which was transferred from several sources to lines extracted from the 2007 Land Use/Land Cover data set. Photo Interpretation of the 2007 color infrared (CIR) imagery and digitizing of the 2007 Land Use/Land Cover data was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). The data was created by extracting water polygons which represented Rivers, Bays and Oceans from the 2007 land use/land cover (LU/LC) layer from NJ DEP's geographical information systems (GIS) database. The classification system used was a modified Anderson et al., 2007 classification system. The majority of descriptive information contained within this metadata record relates to the original source layer integrated with the USGS National Hydrography Dataset information.

A new version of this dataset exists. To see the last version of the Antarctic Digital Database, have a look here: https://data.bas.ac.uk/collections/e74543c0-4c4e-4b41-aa33-5bb2f67df389/

Coastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with ''land'', ''ice shelf'', ''ice tongue'' or ''rumple'''' attribute. Covering all land and ice shelves south of 60S. Suitable for topographic mapping and analysis. High resolution versions of ADD data are suitable for scales larger than 1:1,000,000. The largest suitable scale is changeable and dependent on the region.

Major changes in v7.5 include updates to ice shelf fronts in the following regions: Seal Nunataks and Scar Inlet region, the Ronne-Filchner Ice Shelf, between the Brunt Ice Shelf and Riiser-Larsen Peninsula, the Shackleton and Conger ice shelves, and Crosson, Thwaites and Pine Island. Small areas of grounding line and ice coastlines were also updated in some of these regions as needed.

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.