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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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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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Water quality in the Barnegat Bay estuary along the New Jersey coast is the focus of a multidisciplinary research project begun in 2011 by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection. This narrow estuary is the drainage for the Barnegat Bay watershed and flushed by just three inlets connecting it to the Atlantic Ocean, is experiencing degraded water quality, algal blooms, loss of seagrass, and increases in oxygen-depletion events. The scale of the estuary and the scope of the problems within it required a regional approach to understand and model water circulation within the bay and adjacent ocean. A continuous elevation surface (terrain model) integrating...
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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 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts...
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The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the beach. In March 2023, U.S. Geological Survey and Woods Hole Oceanographic Institute (WHOI) scientists conducted field surveys to collect topographic and bathymetric data. Images of the beach for use in structure-from-motion...
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The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the beach. In March 2023, U.S. Geological Survey and Woods Hole Oceanographic Institute (WHOI) scientists conducted field surveys to collect topographic and bathymetric data. Images of the beach for use in structure-from-motion...
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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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In 2012, Hurricane Sandy struck the Northeastern US causing devastation among coastal ecosystems. Post-hurricane marsh restoration efforts have included sediment deposition, planting of vegetation, and restoring tidal hydrology. The work presented here is part of a larger project funded by the National Fish and Wildlife Foundation (NFWF) to monitor the post-restoration ecological resilience of coastal ecosystems in the wake of Hurricane Sandy. The U.S. Geological Survey Woods Hole Coastal and Marine Science Center made in-situ observations during 2018-2019 and 2022-2023 at two sites: Thompsons Beach, NJ and Stone Harbor, NJ. Marsh creek hydrodynamics and water quality including currents, waves, water levels, water...
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In coastal areas of the United States, where water and land interface in complex and dynamic ways, it is common to find concentrated residential and commercial development. These coastal areas often contain various landholdings managed by Federal, State, and local municipal authorities for public recreation and conservation. These areas are frequently subjected to a range of natural hazards, which include flooding and coastal erosion. In response, the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data to calculate rates of shoreline change along the conterminous coast of the United States, and select coastlines of Alaska and Hawaii, as part of the Coastal Change Hazards priority...
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Natural cave passages penetrating a coastal aquifer in the Yucatan Peninsula (Mexico) were accessed to test the hypothesis that chemoclines associated with salinity gradients (haloclines) within the flooded cave networks of the karst subterranean estuary are sites of methane oxidation. Two field trips were carried out to the fully-submerged cave system located 6.6 km inland from the coastline in January 2015 and January 2016. Vertical chemical profiles across the water column haloclines were obtained using the OctoPiPi (OPP), a high-resolution water sampler built by the U.S. Geological Survey (USGS). The sampling efforts resulted in cm-scale profiles of major ions (e.g., chloride and sulfate), as well as concentrations...
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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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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 High-resolution geophysical data collected along the Mississippi River Delta front offshore of southeastern Louisiana, U.S. Geological Survey Field Activity 2017-003-FA SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Edwin B. Forsythe NWR, NJ, 2010 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 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 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 Vertical chemical profiles collected across haloclines in the water column of the Ox Bel Ha cave network within the coastal aquifer of the Yucatan Peninsula in January 2015 and January 2016 Polygon boundaries for source data of a continuous terrain model for water circulation studies: Barnegat Bay, New Jersey (Esri polygon shapefile, Geographic, WGS 84) Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Shore of MA 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 Bathymetric data and grid of offshore Marconi Beach, Wellfleet, MA on March 20, 2023 Ground control points Marconi Beach, Wellfleet, MA on March 22, 2023 Discharge measurements at Thompsons Beach, New Jersey, collected October 2018 and September 2022 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 Discharge measurements at Thompsons Beach, New Jersey, collected October 2018 and September 2022 Bathymetric data and grid of offshore Marconi Beach, Wellfleet, MA on March 20, 2023 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 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 Vertical chemical profiles collected across haloclines in the water column of the Ox Bel Ha cave network within the coastal aquifer of the Yucatan Peninsula in January 2015 and January 2016 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 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) DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 Edwin B. Forsythe NWR, NJ, 2010 Polygon boundaries for source data of a continuous terrain model for water circulation studies: Barnegat Bay, New Jersey (Esri polygon shapefile, Geographic, WGS 84) Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Shore of MA High-resolution geophysical data collected along the Mississippi River Delta front offshore of southeastern Louisiana, U.S. Geological Survey Field Activity 2017-003-FA 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 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 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