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This part of DS 781 presents data for bathymetry for several seafloor maps of the Offshore of Point Conception Map Area, California. The vector data file is included in "BathymetryHS_OffshorePointConception.zip," which is accessible from https://doi.org/10.5066/F7QN64XQ. Shaded-relief bathymetry of the Offshore of Point Conception map area in southern California was generated largely from acoustic-bathymetry data collected by Fugro Pelagos Inc. Acoustic mapping was completed in 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounders. Bathymetric-lidar data was collected in the nearshore area by the U.S. Army Corps of Engineers (USACE) Joint Lidar Bathymetry...
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This part of DS 781 presents data for the Seafloor character map of the Offshore of Point Conception Map Area, California. The vector data file is included in "SeafloorCharacter_OffshorePointConception.zip," which is accessible from https://doi.org/10.5066/F7QN64XQ. This raster-format seafloor-character map shows four substrate classes in the Offshore of Point Conception map area, California. The substrate classes mapped in this area have been colored to indicate which of the following California Marine Life Protection Act depth zones and slope classes they belong: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Depth Zone 5 (deeper than 200 m), Slope Class 1 (0 degrees...
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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...
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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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This part of DS 781 presents data for the sediment-thickness map of the Pigeon Point to Monterey, California, map region. The raster data file is included in "SedimentThickness_PigeonPointToMontereyBay.zip," which is accessible from https://doi.org/10.5066/F7N29V0Z. As part of the USGS's California State Waters Mapping Project, a 50-m-resolution grid of sediment thickness for the seafloor within the limit of California’s State Waters between Pigeon Point and southern Monterey Bay was generated from seismic-reflection data collected in 2009, 2010, and 2011 (USGS activities (S-15-10-NC, S-N1-09-MB, and S-06-11-MB) supplemented with outcrop and geologic structure from DS 781. Isopach contours at 2.5-meter intervals...
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This part of DS 781 presents data for the bathymetry map of Offshore Scott Creek, California. The raster data file is included in "Bathymetry_OffshoreScottCreek.zip", which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets,...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Acoustic Reflectivity, CMHRP, Coastal and Marine Hazards and Resources Program, Continental/Island Shelf, Marine Nearshore Subtidal, All tags...
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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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This part of DS 781 presents 2-m-resolution data collected by the U.S. Geological Survey in 2007 for the acoustic-backscatter map of the Offshore of Gaviota Map Area, California. The GeoTiff is included in "Backscatter_[USGS07]_OffshoreGaviota.zip," which is accessible from https://doi.org/10.5066/F7TH8JWJ. The acoustic-backscatter map of the Offshore of Gaviota map area in southern California was generated from acoustic-backscatter data collected by the U.S. Geological Survey (USGS) and by Fugro Pelagos Inc. Acoustic mapping was completed between 2007 and 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounders, as well as a 234-kHz SEA SWATHplus bathymetric...
This part of DS 781 presents data for the bathymetry map of the Offshore of Monterey map area, California. Bathymetry data are provided as separate grids depending on resolution. This metadata file refers to the data included in "BathymetryHS_2m_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110,...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Backscatter, Bathymetry, Bathymetry, CMHRP, Coastal and Marine Hazards and Resources Program, All tags...
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These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In March 2022, U.S. Geological Survey and Woods Hole Oceanographic Institute (WHOI) scientists conducted field surveys to re-map the field of view of the CoastCam. Aerial images of the beach for use in structure from motion were taken with a camera (Sony a6000)...
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Marine geophysical mapping of the Queen Charlotte Fault in the eastern Gulf of Alaska was conducted in 2016 as part of a collaborative effort between the U.S. Geological Survey and the Alaska Department of Fish and Game to understand the morphology and subsurface geology of the entire Queen Charlotte system. The Queen Charlotte fault is the offshore portion of the Queen Charlotte-Fairweather Fault: a major structural feature that extends more than 1,200 kilometers from the Fairweather Range of southern Alaska to northern Vancouver Island, Canada. The data published in this data release were collected along the Queen Charlotte Fault between Cross Sound and Noyes Canyon, offshore southeastern Alaska from May 18 to...
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The Bathymetry surface was created by plotting depths of all data points collected relative to North American Vertical Datum of 1988 (NAVD 88), which was converted using the Vertical Datum Transformation tool created by the National Oceanic and Atmospheric Administration's (NOAA) National Geodetic Survey, Office of Coast Survey, and Center for Operation Oceanographic Products and Services. The elevation of the bathymetric raster surface was interpolated between these points in a GIS using a spline interpolator. A total of 432 points were used for interpolation. The points were used as the input to create a polygon feature class. The Spline tool was applied using the points and polygon to interpolate the bathymetric...


map background search result map search result map Bathymetry--Offshore of Scott Creek map area, California Sediment Thickness--Pigeon Point to Monterey, California Bathymetry Hillshade [2m]--Offshore of Monterey, California Backscatter [USGS07]--Offshore of Gaviota Map Area, California Seafloor character--Offshore of Point Conception Map Area, California Bathymetry hillshade--Offshore of Point Conception Map Area, California Bathymetry Raster Surface 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): Cedar Island, VA, 2012–2013 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 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 Multibeam bathymetric data collected in the eastern Gulf of Alaska during USGS Field Activity 2016-625-FA using a Reson 7160 multibeam echosounder (10 meter resolution, 32-bit GeoTIFF, UTM 8 WGS 84, WGS 84 Ellipsoid) ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 DisOcean: Distance to the ocean: Wreck Island, VA, 2014 Simulation and visualization of coastal tsunami impacts from the SAFRR tsunami source - Maximum tsunami elevation model of Half Moon Bay, California Bathymetric data and grid of offshore Head of the Meadow Beach, Truro, on March 18, 2022 Bathymetry Raster Surface Bathymetric data and grid of offshore Head of the Meadow Beach, Truro, on March 18, 2022 Simulation and visualization of coastal tsunami impacts from the SAFRR tsunami source - Maximum tsunami elevation model of Half Moon Bay, California DisOcean: Distance to the ocean: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 Backscatter [USGS07]--Offshore of Gaviota Map Area, California 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 Bathymetry--Offshore of Scott Creek map area, California Bathymetry Hillshade [2m]--Offshore of Monterey, California Bathymetry hillshade--Offshore of Point Conception Map Area, California Seafloor character--Offshore of Point Conception Map Area, California DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2014 Sediment Thickness--Pigeon Point to Monterey, California Multibeam bathymetric data collected in the eastern Gulf of Alaska during USGS Field Activity 2016-625-FA using a Reson 7160 multibeam echosounder (10 meter resolution, 32-bit GeoTIFF, UTM 8 WGS 84, WGS 84 Ellipsoid)