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This sampling frame is a set of grid-based, finite-area frames spanning the offshore areas surrounding the continental United States (CONUS), and is intended for use with the North American Bat Monitoring Program (NABat). A Generalized Random-Tessellation Stratified (GRTS) Survey Design draw was added to the sample units from the raw sampling grids (https://doi.org/10.5066/P9XBOCVV). The GRTS survey design algorithm assigns a spatially balanced and randomized ordering (GRTS order) to each cell within its respective framework. Grid cells are prioritized numerically; the lower the number, the higher the sampling priority. Cells can then be selected for monitoring following the GRTS order, ensuring both randomization...
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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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In 2021, the U.S. Geological Survey (USGS), in cooperation with the National Geological and Geophysical Data Preservation Program, cataloged and scanned notes and calculations for indirect measurements taken during flood events in Montana. This product provides a publicly available catalog of the field notes, photos, survey information, and calculations for indirect measurements at selected sites. Indirect measurements are surveyed by the USGS after floods by identifying high water marks along rivers indicating the maximum stream stage. These high water marks are used to estimate the peak discharge through standardized methods. Estimates of peak streamflow from the indirect estimates were were added to the National...
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Geophysical and geological survey data were collected off Town Neck Beach in Sandwich, Massachusetts, in May and July 2016. Approximately 130 linear kilometers of subbottom (seismic-reflection) and 234-kilohertz interferometric sonar (bathymetric and backscatter) data were collected along with sediment samples, sea floor photographs, and (or) video at 26 sites within the geophysical survey area. Sediment grab samples were collected at 19 of the 26 sampling sites and video and (or) photographic imagery of the sea floor were taken at all 26 sites. These survey data are used to characterize the sea floor by identifying sediment-texture, seabed morphology, and underlying geologic structure and stratigraphy. Data collected...
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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 USGS Geomagnetism Program operates a network of magnetic observatories that collect vector and scalar magnetometer data for use in Earth main-field modeling, geophysics research, space physics research, and space weather hazard assessment and mitigation. Until mid-2011, only 1-minute time resolution magnetic field measurements were archived with the INTERMAGNET consortium following international magnetic observatory standards. 1-second time resolution magnetic field measurements, which had already been collected by all the USGS observatories for up to almost a decade prior, started being archived with INTERMAGNET on June 13, 2011, or July 27, 2012 in the case of the more recently constructed Deadhorse (DED)...
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The purpose of this field data collection was to test and compare the OceanInsight HDX Mini Spectrometer as an accessible alternative against the more expensive ASD Fieldspec for collecting ground-based hyperspectral reflectance profiles for landcover analysis. The data collection took place in Dog Head Marsh and South Cape Beach within the Waquoit Bay National Estuarine Research Reserve (WBNERR). The hyperspectral profiles were collected side-by-side with both field-spectrometers using comparable sensor collection settings for various ground cover samples. The terrain and vegetation type of these sample were described as well as surveyed using Real Time Kinematic Global Positioning System (RTK-GPS). This data...
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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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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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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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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...
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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, CMHRP, 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...


map background search result map search result map Offshore baseline for the Florida north (FLnorth) coastal region generated to calculate shoreline change rates Shorelines of the Texas west (TXwest) coastal region used in shoreline change analysis Intersects for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Baseline for the southern coast Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Backscatter imagery collected in 2016 by the U.S. Geological Survey off Town Neck Beach Sandwich, Massachusetts during field activity 2016-017-FA (GeoTIFF image) 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: Assawoman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parramore Island, VA, 2014 DisOcean: Distance to the ocean: Smith Island, VA, 2014 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 Cataloging and Digitizing USGS Indirect Measurements for Montana through Water Year 2020 One-second USGS Guam (GUA) magnetic observatory data collected before 2013 Attributed North American Grid-Based Offshore Sampling Frames: Continental United States Topographic and multispectral reflectance products, aerial imagery, ground spectra, vegetation, and associated GPS data collected during uncrewed aircraft system (UAS) operations - Dog Head Marsh at South Cape Beach, Mashpee, MA, October 7-8, 2021 Intersects for the Northern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Northern California Long-term shoreline change rates for the Southern California coastal region using the Digital Shoreline Analysis System version 5.0 Topographic and multispectral reflectance products, aerial imagery, ground spectra, vegetation, and associated GPS data collected during uncrewed aircraft system (UAS) operations - Dog Head Marsh at South Cape Beach, Mashpee, MA, October 7-8, 2021 One-second USGS Guam (GUA) magnetic observatory data collected before 2013 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 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 Backscatter imagery collected in 2016 by the U.S. Geological Survey off Town Neck Beach Sandwich, Massachusetts during field activity 2016-017-FA (GeoTIFF image) ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2012–2013 DisOcean: Distance to the ocean: Smith Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Assawoman Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parramore Island, VA, 2014 Baseline for the southern coast Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Offshore baseline for the Florida north (FLnorth) coastal region generated to calculate shoreline change rates Intersects for the Northern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Northern California Long-term shoreline change rates for the Southern California coastal region using the Digital Shoreline Analysis System version 5.0 Cataloging and Digitizing USGS Indirect Measurements for Montana through Water Year 2020 Attributed North American Grid-Based Offshore Sampling Frames: Continental United States