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This digital elevation model provides a tool for calibrating tsunami risk to observations of the 1945 Makran tsunami in Karachi Harbour. The DEM bathymetry is derived from soundings made mainly during the first eight years after the tsunami. Although deficient in portraying intertidal backwaters and upland topography, the DEM accurately depicts the sheltered setting of one of the two tide gauges that recorded the 1945 tsunami.
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected...
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Surveys of the bathymetry and backscatter intensity of the sea floor south of Long Island, New York, were carried out in November 1998 using a Simrad EM1000 multibeam echosounder mounted on the Canadian Coast Guard ship Frederick G. Creed. The purpose of the multibeam echosounder surveys was to explore the bathymetry and backscatter intensity of the sea floor in several areas off the southern coast of Long Island along the 20-meter isobath. Survey areas offshore of Fire Island Inlet, Moriches Inlet, Shinnecock Inlet, and southwest of Montauk Point were about 1 kilometer (km) wide and 10 km long. The area was mapped by the U.S. Geological Survey with support from the Canadian Hydrographic Service and the University...
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This dataset contains images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the field work was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing unmanned aerial vehicle (UAV) flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. The UAV was a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. U.S. Geological Survey technicians deployed and mapped 28 targets that appear in some of the images for use as ground control points. All activities were conducted according...
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Investigations of coastal change and coastal resources often require continuous elevation profiles from the seafloor to coastal terrestrial landscapes. Differences in elevation data collection in the terrestrial and marine environments result in separate elevation products that may not share a vertical datum. This data release contains the assimilation of multiple elevation products into a continuous digital elevation model at a resolution of 3-arcseconds (approximately 90 meters) from the terrestrial landscape to the seafloor for the contiguous U.S., focused on the coastal interface. All datasets were converted to a consistent horizontal datum, the North American Datum of 1983, but the native vertical datum for...
Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Alabama, CMGP, California, Canadian Hydrographic Service, Chesapeake Bay, All tags...
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Sandy ocean beaches in the United States are popular tourist and recreational destinations and constitute some of the most valuable real estate in the country. The boundary between land and water along the coastline is often the location of concentrated residential and commercial development and is frequently exposed to a range of natural hazards, which include flooding, storm effects, and coastal erosion. In response, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change hazards. One component of this research effort, the National Assessment of Shoreline Change Project, documents changes in shoreline position as a proxy for coastal change. Shoreline position is an easily understood...
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High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic...
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In spring and summer 2017, the U.S. Geological Survey’s Gas Hydrates Project conducted two cruises aboard the research vessel Hugh R. Sharp to explore the geology, chemistry, ecology, physics, and oceanography of sea-floor methane seeps and water column gas plumes on the northern U.S. Atlantic margin between the Baltimore and Keller Canyons. Split-beam and multibeam echo sounders and a chirp subbottom profiler were deployed during the cruises to map water column backscatter, sea-floor bathymetry and backscatter, and subsurface stratigraphy associated with known and undiscovered sea-floor methane seeps. The first cruise, known as the Interagency Mission for Methane Research on Seafloor Seeps and designated as field...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Accomac Canyon, Atlantic Ocean, CMHRP, Chincoteague Ridge, Coastal and Marine Hazards and Resources Program, All tags...
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The natural resiliency of the New Jersey barrier island system, and the efficacy of management efforts to reduce vulnerability, depends on the ability of the system to recover and maintain equilibrium in response to storms and persistent coastal change. This resiliency is largely dependent on the availability of sand in the beach system. In an effort to better understand the system's sand budget and processes in which this system evolves, high-resolution geophysical mapping of the sea floor in Little Egg Inlet and along the southern end of Long Beach Island near Beach Haven, New Jersey was conducted from May 31 to June 10, 2018, followed by a sea floor sampling survey conducted from October 22 to 23, 2018, as part...
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The natural resiliency of the New Jersey barrier island system, and the efficacy of management efforts to reduce vulnerability, depends on the ability of the system to recover and maintain equilibrium in response to storms and persistent coastal change. This resiliency is largely dependent on the availability of sand in the beach system. In an effort to better understand the system's sand budget and processes in which this system evolves, high-resolution geophysical mapping of the sea floor in Little Egg Inlet and along the southern end of Long Beach Island near Beach Haven, New Jersey was conducted from May 31 to June 10, 2018, followed by a sea floor sampling survey conducted from October 22 to 23, 2018, as part...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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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. This 2018 data release includes rates that incorporate...
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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. This 2018 data release includes rates that incorporate...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...


map background search result map search result map GeoTIFF image of the backscatter intensity of the sea floor offshore of Shinnecock Inlet, New York, in 1998 (3-m resolution, Mercator, WGS 84) Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (JPEG images) Elevation artifacts in digital bathymetric and topographic models for United States east (east_cdem_v1.tif) and west (west_cdem_v.tif) coasts (polygon shapefile, geographic, NAD83) Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Louisiana Multibeam Echosounder, Reson T-20P tracklines, USGS field activity 2017-003-FA, Mississippi River Delta front offshore of southeastern Louisiana (Esri polyline shapefile, GCS WGS 84) Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour Intersects for Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Intersects for the Buzzards Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 Sound velocity profiles - locations, images, and text files for sound velocity profiles calculated from XBT and CTD casts conducted during USGS field activities 2017-001-FA and 2017-002 FA Chirp seismic reflection data from the Edgetech 512i collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS field activity 2018-001-FA (shotpoints point shapefile, survey trackline shapefile, PNG profile images, and SEG-Y trace data). Multibeam Echosounder, Reson T-20P tracklines collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activity 2018-001-FA (Esri polyline shapefile, GCS WGS 84) Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Baseline for the North Carolina coastal region from Cape Hatteras to Cape Lookout (NCcentral) Long and short-term shoreline change rate transects for the northern North Carolina coastal region (NCnorth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (JPEG images) SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 GeoTIFF image of the backscatter intensity of the sea floor offshore of Shinnecock Inlet, New York, in 1998 (3-m resolution, Mercator, WGS 84) Chirp seismic reflection data from the Edgetech 512i collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS field activity 2018-001-FA (shotpoints point shapefile, survey trackline shapefile, PNG profile images, and SEG-Y trace data). Multibeam Echosounder, Reson T-20P tracklines collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activity 2018-001-FA (Esri polyline shapefile, GCS WGS 84) Intersects for Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 Multibeam Echosounder, Reson T-20P tracklines, USGS field activity 2017-003-FA, Mississippi River Delta front offshore of southeastern Louisiana (Esri polyline shapefile, GCS WGS 84) Baseline for the North Carolina coastal region from Cape Hatteras to Cape Lookout (NCcentral) Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Louisiana Sound velocity profiles - locations, images, and text files for sound velocity profiles calculated from XBT and CTD casts conducted during USGS field activities 2017-001-FA and 2017-002 FA Elevation artifacts in digital bathymetric and topographic models for United States east (east_cdem_v1.tif) and west (west_cdem_v.tif) coasts (polygon shapefile, geographic, NAD83)