AndreaBozzo/ceres-open-data-index huggingface dataset:
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

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, bayesian-network, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-erosion, coastal-habitat, coastal-hazards, coastal-processes, delmarva-peninsula, environment, erosion, geographic-information-systems, geomorphology, geoscientificinformation, geospatial-analysis, geospatial-datasets, gis, hazards, land-use-and-land-cover, mean-high-water, mhw, north-america, parramore-island, probability, sea-level-change, sea-level-rise, shoreline-change, st-petersburg-coastal-and-marine-science-center, transect-sampling, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37e5e4b09fd3b0c9cef7, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
ElevMHW: Elevation adjusted to local mean high water: Ship Shoal Island, VA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, delmarva-peninsula, digital-elevation-models, elevation, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, lidar, mean-high-water, mhw, north-america, oceans, ship-shoal-island, st-petersburg-coastal-and-marine-science-center, topography, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37f2e4b09fd3b0c9cf06, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Ship Shoal Island, VA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, bayesian-network, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-erosion, coastal-habitat, coastal-hazards, coastal-processes, delmarva-peninsula, environment, erosion, geographic-information-systems, geomorphology, geoscientificinformation, geospatial-analysis, geospatial-datasets, gis, hazards, land-use-and-land-cover, mean-high-water, mhw, north-america, probability, sea-level-change, sea-level-rise, ship-shoal-island, shoreline-change, st-petersburg-coastal-and-marine-science-center, transect-sampling, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37f4e4b09fd3b0c9cf08, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Ship Shoal Island, VA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, coastal-processes, delmarva-peninsula, environment, erosion, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, hazards, image-analysis, mean-high-water, mhw, north-america, oceans, scientific-interpretation, sea-level-change, ship-shoal-island, st-petersburg-coastal-and-marine-science-center, study-areas, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37f8e4b09fd3b0c9cf0c, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DisOcean: Distance to the ocean: Smith Island, VA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, delmarva-peninsula, environment, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, mean-high-water, mhw, north-america, oceans, smith-island, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37ffe4b09fd3b0c9cf15, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, delmarva-peninsula, digital-elevation-models, elevation, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, lidar, mean-high-water, mhw, north-america, oceans, parramore-island, st-petersburg-coastal-and-marine-science-center, topography, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37e3e4b09fd3b0c9cef5, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
ElevMHW: Elevation adjusted to local mean high water: Smith Island, VA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, delmarva-peninsula, digital-elevation-models, elevation, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, lidar, mean-high-water, mhw, north-america, oceans, smith-island, st-petersburg-coastal-and-marine-science-center, topography, u-s-geological-survey, united-states, usa, usgs, usgs-5daa3801e4b09fd3b0c9cf17, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Smith Island, VA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, bayesian-network, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-erosion, coastal-habitat, coastal-hazards, coastal-processes, delmarva-peninsula, environment, erosion, geographic-information-systems, geomorphology, geoscientificinformation, geospatial-analysis, geospatial-datasets, gis, hazards, land-use-and-land-cover, mean-high-water, mhw, north-america, probability, sea-level-change, sea-level-rise, shoreline-change, smith-island, st-petersburg-coastal-and-marine-science-center, transect-sampling, u-s-geological-survey, united-states, usa, usgs, usgs-5daa3803e4b09fd3b0c9cf19, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
Brown Treesnake visual survey and radiotelemetry data, Guam 2015

The dataset consists of two spreadsheets that contain brown treesnake data (location, perch taxa, perch height, time of detection, whether visible or not, and size of snake) collected during both visual surveys and radio telemetry within a 55-ha enclosure on Guam.

Tags: biota, radiotelemetry, usgs-5dcf39d0e4b06957976277e0, visual-encounter-surveys
Organization: Department of the Interior
ElevMHW: Elevation adjusted to local mean high water: Parker River, MA, 2014

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 into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cape-ann, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, digital-elevation-models, elevation, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, lidar, ma, massachusetts, mean-high-water, mhw, north-america, oceans, parker-river, parker-river-national-wildlife-refuge, st-petersburg-coastal-and-marine-science-center, topography, u-s-geological-survey, united-states, usa, usgs, usgs-5daa373fe4b09fd3b0c9ce41, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
Multibeam backscatter data collected in the vicinity of Buffalo Reef, Michigan, within Lake Superior during USGS Field Activity 2018-043-FA using a dual-head Reson T20-P multibeam echosounder (8-bit GeoTIFF, UTM Zone 16N, NAD 83, 2-m resolution)

In September 2018, the U.S. Geological Survey, in collaboration with the U.S. Army Corps of Engineers, conducted high-resolution geophysical mapping and sediment sampling to determine the distribution of historical mine tailings on the floor of Lake Superior. Large amounts of waste material from copper mining, locally known as “stamp sands,” were dumped into the lake in the early 20th century, with wide-reaching consequences that have continued into the present. Mapping was focused offshore of the town of Gay on the Keweenaw Peninsula of Michigan, where ongoing erosion and re-deposition of the stamp sands has buried miles of native, white-sand beaches.  Stamp sands are also encroaching onto Buffalo Reef, a large area of cobble/boulder substrate that supports productive fisheries in the lake. 
The objectives of this cooperative mapping project are to develop a framework for scientific research and provide baseline information required for management of resources within the coastal zone of northern Michigan.  High-resolution bathymetry and backscatter data reveal the irregular topography of the shallow, cobble-covered Buffalo Reef and the relatively smooth surface of finer-grained sediment that covers adjacent, deeper parts of the lake floor. Previous research used numerous sediment samples to determine the general distribution of mine tailings on the lake floor in this area, but little information exists on the extent and thickness of the surficial deposits. The main priority of this project is to image the near-surface stratigraphy, specifically the thickness of surficial sand and mud that threaten to cover the reef, with seismic-reflection profiling systems.  In addition to continuous coverage of bathymetric and backscatter data, this report includes a dense grid of closely spaced seismic profiles, which will guide efforts to mitigate the environmental impacts of the shifting stamp sands.

Tags: bathymetry, buffalo-reef, cmhrp, coastal-and-marine-hazards-and-resources-program, department-of-the-interior, doi, elevation, field-activity-number-2018-043-fa, geology, geophysics, geoscientificinformation, geospatial-datasets, geotiff, houghton-county, imagerybasemapsearthcover, inlandwaters, keweenaw-county, keweenaw-peninsula, lacustrine-geology, lake-floor, lake-superior, lakebed-characteristics, lakefloor, michigan, multibeam-backscatter, multibeam-echosounder, multibeam-sonar, r-v-rafael, reson, t20-p, time-series, u-s-geological-survey, united-states-of-america, usgs, usgs-5ddc10fee4b06957976a5739, whcmsc, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior