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These digital images were taken at select locations over the Potomac River using 3DR Solo unmanned aircraft systems (UAS) in October 2019. These images were collected for the purpose of evaluating UAS assessment of river habitat data such as water depth, substrate type, and water clarity. Each UAS was equipped with a Ricoh GRII digital camera for natural color photos, used to produce digital elevation models and ortho images, a MicaSense RedEdge multi-spectral camera that captures five specific bands of the visible spectrum (blue, green, red, rededge, and near-infrared), which can be used to classify vegetation, or FLIR Vue Pro R 640 13mm radiometric thermal camera that provides temperature data embedded in every...
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This portion of the data release presents the raw aerial imagery collected during the unmanned aerial system (UAS) survey of the intertidal zone at West Whidbey Island, WA, on 2019-06-04. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. Flights using both a nadir camera orientation and an oblique camera orientation were conducted. For the nadir flights (F04, F05, F06, F07, and F08), the camera was mounted using a fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. The UAS was flown on pre-programmed autonomous flight lines at an approximate altitude of 70 meters above ground...
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This portion of the data release presents a digital surface model (DSM) and hillshade image of the intertidal zone at West Whidbey Island, WA. The DSM has a resolution of 4 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-04. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The raw imagery used to create the DSM was acquired using a UAS fitted with a Ricoh GR II...
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This portion of the data release presents a digital surface model (DSM) and hillshade of the Liberty Island Conservation Bank Wildlands restoration site in the Sacramento-San Joaquin Delta. The DSM has a resolution of 10 centimeters per-pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an Unmanned Aerial System (UAS) on 2018-10-23. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The raw imagery used to create this DSM was...
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This portion of the data release presents the raw aerial imagery collected during the Unmanned Aerial System (UAS) survey of the Liberty Island Conservation Bank Wildlands restoration site in the Sacramento-San Joaquin Delta on 2018-10-23. The imagery was acquired using two Department of Interior owned 3DR Solo quadcopters fitted with Ricoh GR II digital cameras featuring global shutters. The cameras were mounted using a fixed mount on the bottom of the UAS and oriented in a roughly nadir orientation. The UAS were flown on pre-programmed autonomous flight lines at an approximate altitude of 120 meters above-ground-level, resulting in a nominal ground-sample-distance (GSD) of 3.2 centimeters per-pixel. The flight...
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This portion of the data release presents a digital surface model (DSM) and hillshade image of the intertidal zone at Lone Tree Point, Kiket Bay, WA. The DSM has a resolution of 4 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-05. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The raw imagery used to create the DSM was acquired using a UAS fitted with a Ricoh...
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This portion of the data release presents a high-resolution orthomosaic images of the intertidal zone at Post Point, Bellingham Bay, WA. The orthomosaics were derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-06. The orthomosaics are presented with two resolutions: one image, covering the entire survey area, has a resolution of 2 centimeters per pixel; the other image which was derived from a lower-altitude flight, covers an inset area within the main survey area and has a resolution of 1 centimeter per pixel. The raw imagery used to create the orthomosaics was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global...
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This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during the July 2021 unoccupied aerial system (UAS) surveys of the ocean beach at Fort Stevens State Park, OR, and Benson Beach at Cape Disappointment State Park, WA. Eighteen temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using dual-frequency post-processed kinematic (PPK) GPS with corrections...
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Low altitude (300 meters above ground level (AGL)) digital aerial imagery were acquired on May 4 and 5, 2020, from a manned, fixed-wing aircraft using a Sony A7R 36 Megapixel digital camera, along with precise aircraft location Global Navigation Satellite System (GNSS) data. Data were collected in shore-parallel lines, flying at approximately 50 meters per second and capturing true color imagery at 1 Hertz, resulting in image footprints with approximately 75-80% endlap, 60-70% sidelap, and a ground sample distance (GSD) of 5.3 centimeters. The precise time of each image capture (flash event) was recorded, and the corresponding aircraft position was computed in post-processing from the aircraft navigation GNSS data;...
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Low altitude (300 meters above ground level (AGL)) digital aerial imagery were acquired on June 11, 2022, from a manned, fixed-wing aircraft using a Sony A7R 36 Megapixel digital camera, along with precise aircraft location Global Navigation Satellite System (GNSS) data. Data were collected in shore-parallel lines, flying at approximately 50 meters per second and capturing true color imagery at 1 Hertz, resulting in image footprints with approximately 75–80% endlap, 60–70% sidelap, and a ground sample distance (GSD) of 5.3 centimeters. The precise time of each image capture (flash event) was recorded, and the corresponding aircraft position was computed in post-processing from the aircraft navigation GNSS data;...
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This dataset is a polygon shapefile delineating the footprint of bathymetric data collected in October, 2021 for an approximately 500 meter (m) reach of the Kalamazoo River upstream of Plainwell, Michigan (MI). Bathymetric data in the river channel were collected with a single beam sonar and Acoustic Current Doppler Profiler operated along 2 longitudinal transects and 48 cross-sectional transects, respectively.
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given the magnitude of the threat posed by sea-level rise, and the urgency to better understand it, there is an increasing need to forecast sea-level rise effects on barrier islands. To address this problem, scientists in the U.S. Geological Survey (USGS) Coastal and Marine Geology program are developing Bayesian networks as a tool to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as the piping plover (Charadrius melodus)...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
This raster dataset describes elevation values, in meters (m), in Sleeping Bear Dunes National Lakeshore in 1955. The 1955 DEM was produced from historical aerial imagery acquired on April 1, 1955 at a flying height of 8,500 ft (1:17,000). Structure from motion (SfM) analysis of this imagery produced a 0.88 m DEM, which was edited and resampled to 1 m.
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As part of a collaborative study with the City of Raleigh, North Carolina, the U.S. Geological Survey developed a suite of high-resolution lidar-derived raster datasets for the Greater Raleigh Area, North Carolina, using repeat lidar data from the years 2013, 2015, and 2022. These datasets include raster representations of digital elevation models (DEMs), DEM of difference, the ten most common geomorphons (i.e. geomorphologic feature), lidar point density, and positive topographic openness. Raster footprints vary by year based on extent of lidar data collection. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded. These metrics have been...
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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...


map background search result map search result map 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 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 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rhode Island National Wildlife Refuge, RI, 2014 Low-altitude aerial imagery from unmanned aerial systems (UAS) at select locations over the Potomac River, October 2019 Digital surface model (DSM) for the Liberty Island Conservation Bank Wildlands restoration site, Sacramento-San Joaquin Delta, California, 2018-10-23 Aerial imagery from UAS survey of the Liberty Island Conservation Bank Wildlands restoration site, Sacramento-San Joaquin Delta, California, 2018-10-23 Digital surface model (DSM) for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05 Orthomosaic imagery for the intertidal zone at Post Point, Bellingham Bay, WA, 2019-06-06 Digital surface model (DSM) for the intertidal zone at West Whidbey Island, WA, 2019-06-04 Aerial imagery from UAS survey of the intertidal zone at West Whidbey Island, WA, 2019-06-04 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2010 Lidar-derived rasters of point density, elevation, and geomorphological features for 2013, 2015, and 2022 for the Greater Raleigh Area, North Carolina Ground control point locations for UAS surveys of the beaches at Fort Stevens State Park, OR, and Cape Disappointment State Park, WA, July 2021 Footprint of bathymetry data collected for a Kalamazoo River Reference Reach upstream of Plainwell, Michigan, in 2021 Delaware Atlantic coast, 2020 – Aircraft Positions Delaware Atlantic coast, 2022 – Aircraft Positions Footprint of bathymetry data collected for a Kalamazoo River Reference Reach upstream of Plainwell, Michigan, in 2021 Digital surface model (DSM) for the intertidal zone at Lone Tree Point, Kiket Bay, WA, 2019-06-05 Orthomosaic imagery for the intertidal zone at Post Point, Bellingham Bay, WA, 2019-06-06 Aerial imagery from UAS survey of the Liberty Island Conservation Bank Wildlands restoration site, Sacramento-San Joaquin Delta, California, 2018-10-23 Aerial imagery from UAS survey of the intertidal zone at West Whidbey Island, WA, 2019-06-04 Digital surface model (DSM) for the intertidal zone at West Whidbey Island, WA, 2019-06-04 Digital surface model (DSM) for the Liberty Island Conservation Bank Wildlands restoration site, Sacramento-San Joaquin Delta, California, 2018-10-23 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2010 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rhode Island National Wildlife Refuge, RI, 2014 Lidar-derived rasters of point density, elevation, and geomorphological features for 2013, 2015, and 2022 for the Greater Raleigh Area, North Carolina Low-altitude aerial imagery from unmanned aerial systems (UAS) at select locations over the Potomac River, October 2019 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014