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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...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Atlantic Ocean, Barrier Island, Bayesian Network, CMGP, Coastal Erosion, All tags...
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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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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline 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 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained...
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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 and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline 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 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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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 and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline 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 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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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 and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline 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 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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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 and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline 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 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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High-resolution topographic surveys were conducted at two pools on the Carmel River between 2014 and 2019 using a survey-grade total station. The Dam Reach pool (DMPOOL) is located within the Dam Reach, approximately 450 meters downstream of the former site of the San Clemente Dam. The Sleepy Hollow pool (SHPOOL) is located within the Sleepy Hollow reach, approximately 2.25 kilometers downstream of the former site of the San Clemente Dam. Both pools were surveyed in 2014, 2015, 2016, 2017, and 2019 using a total station, in conjunction with the channel cross-section surveys also conducted as part of this study (see accompanying file within this data release for topographic survey transect data). For the 2015 survey,...
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This data release supersedes version 1, published in 2017 under https://doi.org/10.5066/F74M93HF. Please see Version_History_P9HG8UDS.txt below for more information. This dataset contains the easting, northing, and elevation values of the river-right and river-left transect endpoint reference benchmarks (RBM and LBM) from survey transects at 10 survey reaches along the Carmel River, central California. Topographic surveys were completed on these transects during eight summer surveys (in 2013, 2014, 2015, 2016, 2017, 2019, 2020 and 2021). See accompanying file within this data release for elevation measurements. All data were collected in NAD83 UTM10N horizontal coordinates and NAVD88 Geoid 12B vertical coordinates,...
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RBRduo pressure and temperature sensors, mounted on aluminum frames, were moored in shallow (< 6 m) water depths in Skagit and Bellingham Bays, Washington, USA, from December 2017 to February 2018, to capture wave heights and periods. Continuous pressure fluctuations are transformed into surface-wave observations of wave heights, periods, and frequency spectra at 30-minute intervals.
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Water depth, turbidity, and current velocity time-series data were collected in Liberty Island Conservation Bank (WVA) in 2017. The turbidity sensors were not calibrated to suspended-sediment concentration at this location. Typically, each zip folder for a deployment period contains two data files from a velocimeter and one data file from a CTD, each of which include data from an optical backscatter sensor. --------- Data were collected from several sites in Little Holland Tract (LHT) and Liberty Island (LI), including the Liberty Island Conservation Bank (LICB), from 2015 to 2017. Table 1 (below) lists the deployment name (DLXXX) and dates for each sampling station location. Station names starting with ‘H’ are...
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Water depth, turbidity, and current velocity time-series data were collected in Liberty Island from 2015 to 2017. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, and direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder...
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This part of the data release provides the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) 2007 bathymetry data collected in Skagit Bay Washington that is provided as a 1-m resolution TIFF image, as well as a 1-m resolution shaded-relief TIFF image. FGDC metadata is also provided. In 2004, 2005, 2007, and 2010 the USGS, PCMSC collected bathymetry and acoustic backscatter data in Skagit Bay, Washington using an interferometric bathymetric sidescan-sonar system mounded to the USGS R/V Parke Snavely and the USGS R/V Karluk. The research was conducted in coordination with the Swinomish Indian Tribal Community, Skagit River System Cooperative, Skagit Watershed Council, Puget Sound Nearshore...


map background search result map search result map High-resolution bathymetry data collected in 2007 in Skagit Bay, Washington Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Liberty Island Conservation Bank (station WVA), Sacramento-San Joaquin Delta, California, 2017 Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Liberty Island (station LVB), Sacramento-San Joaquin Delta, California, 2015-2017 (ver. 2.0, September, 2019) Wave observations from nearshore bottom-mounted pressure sensors in Skagit and Bellingham Bays, Washington, USA from Dec 2017 to Feb 2018 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2014 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 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 Intersects for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term and short-term shoreline change rates for the region of Buzzards Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Baselines for Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Topographic survey transect endpoint coordinates along the Carmel River, central California, 2013 to 2021 (ver. 2.0, March 2022) High resolution topography for two pools on the Carmel River, central California, 2014 to 2019 Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Liberty Island Conservation Bank (station WVA), Sacramento-San Joaquin Delta, California, 2017 High resolution topography for two pools on the Carmel River, central California, 2014 to 2019 Water-level, wind-wave, velocity, and suspended-sediment concentration (SSC) time-series data from Liberty Island (station LVB), Sacramento-San Joaquin Delta, California, 2015-2017 (ver. 2.0, September, 2019) Intersects for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Topographic survey transect endpoint coordinates along the Carmel River, central California, 2013 to 2021 (ver. 2.0, March 2022) High-resolution bathymetry data collected in 2007 in Skagit Bay, Washington Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 Intersects for coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term and short-term shoreline change rates for the region of Buzzards Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Baselines for Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Wave observations from nearshore bottom-mounted pressure sensors in Skagit and Bellingham Bays, Washington, USA from Dec 2017 to Feb 2018