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: Assateague Island, MD &amp; 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: assateague-island, assateague-island-national-seashore, atlantic-ocean, barrier-island, bayesian-network, chincoteague-national-wildlife-refuge, 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, maryland, md, mean-high-water, mhw, north-america, 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-5daa3789e4b09fd3b0c9ce8f, va, virginia, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
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

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, biota, charadrius-melodus, cmgp, coastal-and-marine-geology-program, coastal-habitat, delmarva-peninsula, ecology, environment, foraging-ecology, geographic-information-systems, geography, geomorphology, geospatial-analysis, geospatial-datasets, gis, mean-high-water, mhw, north-america, piping-plovers, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa380de4b09fd3b0c9cf24, va, virginia, woods-hole-coastal-and-marine-science-center, wreck-island, wreck-island-natural-area-preserve
Organization: Department of the Interior
DisOcean: Distance to the ocean: 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, environment, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, mean-high-water, mhw, north-america, oceans, parramore-island, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37e2e4b09fd3b0c9cef3, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Wreck 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-hazards, delmarva-peninsula, geographic-information-systems, geomorphology, geoscientificinformation, geospatial-analysis, geospatial-datasets, gis, mean-high-water, mhw, north-america, oceans, st-petersburg-coastal-and-marine-science-center, transect-sampling, u-s-geological-survey, united-states, usa, usgs, usgs-5daa380be4b09fd3b0c9cf21, va, virginia, woods-hole-coastal-and-marine-science-center, wreck-island, wreck-island-natural-area-preserve
Organization: Department of the Interior
shoreline, inletLines: Shoreline polygons and tidal inlet delineations: 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-processes, delmarva-peninsula, geomorphology, geospatial-analysis, geospatial-datasets, north-america, oceans, smith-island, st-petersburg-coastal-and-marine-science-center, study-areas, u-s-geological-survey, united-states, usa, usgs, usgs-5daa3805e4b09fd3b0c9cf1b, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DisOcean: Distance to the ocean: Coast Guard Beach, 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-cod, cape-cod-national-seashore, cmhrp, coast-guard-beach, coastal-and-marine-hazards-and-resources-program, coastal-habitat, environment, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, ma, massachusetts, mean-high-water, mhw, north-america, oceans, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa371ee4b09fd3b0c9ce1b, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Coast Guard Beach, MA, 2013-2014

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, biota, cape-cod, cape-cod-national-seashore, charadrius-melodus, cmgp, coast-guard-beach, coastal-and-marine-geology-program, coastal-habitat, ecology, environment, foraging-ecology, geographic-information-systems, geography, geomorphology, geospatial-analysis, geospatial-datasets, gis, ma, massachusetts, mean-high-water, mhw, north-america, piping-plovers, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa371ce4b09fd3b0c9ce19, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
Development: Development delineation: Coast Guard Beach, MA, 2013-2014

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-cod, cape-cod-national-seashore, cmhrp, coast-guard-beach, coastal-and-marine-hazards-and-resources-program, coastal-armoring, coastal-habitat, coastal-hazards, environment, geographic-information-systems, geospatial-datasets, gis, human-development, land-use-and-land-cover, ma, massachusetts, north-america, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa371be4b09fd3b0c9ce17, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Coast Guard Beach, 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-cod, cape-cod-national-seashore, cmhrp, coast-guard-beach, coastal-and-marine-hazards-and-resources-program, coastal-hazards, geographic-information-systems, geomorphology, geoscientificinformation, geospatial-analysis, geospatial-datasets, gis, ma, massachusetts, mean-high-water, mhw, north-america, oceans, st-petersburg-coastal-and-marine-science-center, transect-sampling, u-s-geological-survey, united-states, usa, usgs, usgs-5daa3719e4b09fd3b0c9ce14, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
National Land Cover Database (NLCD) Land Cover Change Disturbance Science Product

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four
National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011.
These products provide spatially explicit and reliable information on the Nation’s land cover and land cover
change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s
land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016
design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land
cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was
conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing
Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development
and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land
cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for
generating land cover and change products; a continuous fields biophysical parameters modeling method; and
an automated scripted operational system for the NLCD 2016 production. The performance of the developed
strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous
U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was
achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive
and highly automated procedure for NLCD 2016 operational mapping.
Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

Tags: biota, digital-spatial-data, gis, image-processing, imagerybasemapsearthcover, land-cover, land-use-land-cover-theme, n-l-c-d, national-geospatial-data-asset, ngda, nlcd, u-s, u-s-geological-survey-usgs, united-states, us, usgs-5daa080ee4b09fd3b0c9cc2b
Organization: Department of the Interior
National Land Cover Database (NLCD) Forest Theme Disturbance Science Product

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four
National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011.
These products provide spatially explicit and reliable information on the Nation’s land cover and land cover
change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s
land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016
design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land
cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was
conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing
Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development
and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land
cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for
generating land cover and change products; a continuous fields biophysical parameters modeling method; and
an automated scripted operational system for the NLCD 2016 production. The performance of the developed
strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous
U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was
achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive
and highly automated procedure for NLCD 2016 operational mapping.
Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

Tags: biota, digital-spatial-data, gis, image-processing, imagerybasemapsearthcover, land-cover, land-use-land-cover-theme, n-l-c-d, national-geospatial-data-asset, ngda, nlcd, u-s, u-s-geological-survey-usgs, united-states, us, usgs-5daa00e5e4b09fd3b0c9cc0a
Organization: Department of the Interior