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Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinity of Sodus Bay, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point...
Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: 3DR Solo quadcopter, CMGP, Charles Point, Coastal and Marine Geology Program, DEM, All tags...
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Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control...
Categories: Data; Tags: 3DR Solo quadcopter, Agisoft Photoscan, Braddock Bay, Braddock Bay Park, Braddock Bay Wildlife Management Area, All tags...
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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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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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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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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...
Types: Citation; Tags: Bald Point State Park, CMGP, CSC, Coastal Services Center, Coastal and Marine Geology Program, 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 (http://coastal.er.usgs.gov/shoreline-change/), documents changes in shoreline position as a proxy for coastal...
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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 (http://coastal.er.usgs.gov/shoreline-change/), documents changes in shoreline position as a proxy for coastal...
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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 (http://coastal.er.usgs.gov/shoreline-change/), documents changes in shoreline position as a proxy for coastal...
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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 (http://coastal.er.usgs.gov/shoreline-change/), documents changes in shoreline position as a proxy for coastal...
Categories: Data; Types: Citation, Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Atlantic Beach, Atlantic Coast, Bald Head Island, Bogue Banks, Browns Inlet, All tags...
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Imagery acquired with unmanned aerial systems (UAS) and coupled with structure-from-motion (SfM) photogrammetry can produce high-resolution topographic and visual reflectance datasets that rival or exceed lidar and orthoimagery. These new techniques are particularly useful for data collection of coastal systems, which requires high temporal and spatial resolution datasets. The U.S. Geological Survey worked in collaboration with members of the Marine Biological Laboratory and Woods Hole Analytics at Black Beach, in Falmouth, Massachusetts to explore scientific research demands on UAS technology for topographic and habitat mapping applications. This project explored the application of consumer-grade UAS platforms...
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Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control...
Tags: 3DR Solo quadcopter, Braddock Bay, Braddock Bay Park, Braddock Bay Wildlife Management Area, Braddock Point, All tags...
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Imagery acquired with unmanned aerial systems (UAS) and coupled with structure-from-motion (SfM) photogrammetry can produce high-resolution topographic and visual reflectance datasets that rival or exceed lidar and orthoimagery. These new techniques are particularly useful for data collection of coastal systems, which requires high temporal and spatial resolution datasets. The U.S. Geological Survey worked in collaboration with members of the Marine Biological Laboratory and Woods Hole Analytics at Black Beach, in Falmouth, Massachusetts to explore scientific research demands on UAS technology for topographic and habitat mapping applications. This project explored the application of consumer-grade UAS platforms...
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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...


map background search result map search result map Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Alabama Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Alabama Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Louisiana Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Florida north (FLnorth) Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for southeastern Florida (FLse) Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southeastern Florida (FLse) Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for central North Carolina (NCcentral) Shorelines of the southern North Carolina (NCsouth) coastal region used in shoreline change analysis CSV file of names, times, and locations of images collected by an unmanned aerial system (UAS) flying over Black Beach, Falmouth, Massachusetts on 18 March 2016 Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Black Beach, Falmouth, Massachusetts on 18 March 2016 (JPEG images) Braddock West camera locations and attitudes for low-altitude aerial images collected during unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Braddock Bay, New York in July 2017 Braddock West orthomosaic from low-altitude aerial imagery from unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Braddock Bay, New York in July 2017 (GeoTIFF image). Seismic Reflection, EdgeTech SB-512i chirp profile images,USGS field activity 2017-003-FA, Mississippi River Delta front offshore of southeastern Louisiana (PNG images) Charles Point digital elevation model (DEM) from low-altitude aerial imagery from unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Sodus Bay, New York in July 2017 (32-bit floating point GeoTIFF image) Long-term and short-term shoreline change rates for the southern coastal region of Cape Cod, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2012–2013 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Edwin B. Forsythe NWR, NJ, 2012 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 CSV file of names, times, and locations of images collected by an unmanned aerial system (UAS) flying over Black Beach, Falmouth, Massachusetts on 18 March 2016 Low-altitude aerial imagery obtained with unmanned aerial systems (UAS) flights over Black Beach, Falmouth, Massachusetts on 18 March 2016 (JPEG images) Braddock West camera locations and attitudes for low-altitude aerial images collected during unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Braddock Bay, New York in July 2017 Braddock West orthomosaic from low-altitude aerial imagery from unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Braddock Bay, New York in July 2017 (GeoTIFF image). Charles Point digital elevation model (DEM) from low-altitude aerial imagery from unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Sodus Bay, New York in July 2017 (32-bit floating point GeoTIFF image) Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Alabama Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Alabama DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Edwin B. Forsythe NWR, NJ, 2012 Long-term and short-term shoreline change rates for the southern coastal region of Cape Cod, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010 Seismic Reflection, EdgeTech SB-512i chirp profile images,USGS field activity 2017-003-FA, Mississippi River Delta front offshore of southeastern Louisiana (PNG images) Shorelines of the southern North Carolina (NCsouth) coastal region used in shoreline change analysis Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southeastern Florida (FLse) Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Louisiana Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Florida north (FLnorth) Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for southeastern Florida (FLse) Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for central North Carolina (NCcentral)