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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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Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinity of Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points;...
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Atlantic coast piping plover (Charadrius melodus) nest sites are typically found on low-lying beach and dune systems, which respond rapidly to coastal processes like sediment overwash, inlet formation, and island migration that are sensitive to climate-related changes in storminess and the rate of sea-level rise. Data were obtained to understand piping plover habitat distribution and use along their Atlantic Coast breeding range. A smartphone application called iPlover was developed to collect standardized data on habitat characteristics at piping plover nest locations. The application capitalized on a network of trained monitors that observe piping plovers throughout their U.S. Atlantic coast breeding range as...
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Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinity of Sodus Bay, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point...


map background search result map search result map Table and accompanying photographs for biogeomorphic classification of shorebird nesting sites on the U.S. Atlantic coast from May to August, 2014 Sodus North camera locations and attitudes for low-altitude aerial images collected during unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Sodus Bay, New York in July 2017 Geotagged low-altitude aerial imagery from unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Chimney Bluffs, New York, USA in July 2017 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cedar Island, VA, 2012 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2010 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2014 Geotagged low-altitude aerial imagery from unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Chimney Bluffs, New York, USA in July 2017 Sodus North camera locations and attitudes for low-altitude aerial images collected during unmanned aerial systems (UAS) flights over of the Lake Ontario shoreline in the vicinity of Sodus Bay, New York in July 2017 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cedar Island, VA, 2012 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2010 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2014 Table and accompanying photographs for biogeomorphic classification of shorebird nesting sites on the U.S. Atlantic coast from May to August, 2014