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DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2014

Dates

Publication Date
Start Date
2013-11
End Date
2014-06

Citation

Sturdivant, E.J., Zeigler, S.L., Gutierrez, B.T., and Weber, K.M., 2019, Barrier island geomorphology and shorebird habitat metrics–Four sites in New York, New Jersey, and Virginia, 2010–2014: U.S. Geological Survey data release, https://doi.org/10.5066/P944FPA4.

Summary

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 [...]

Contacts

Attached Files

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EF_DisOcean_browse.png
“Example of distance to ocean GeoTIFF for Edwin B. Forsythe NWR, New Jersey.”
thumbnail 120.2 KB
Extension: EF14_DisOcean.zip
EF14_DisOcean.tif 137.82 MB
EF14_DisOcean.tif.ovr 1.82 MB
EF14_DisOcean.tif-ColorRamp.SLD 2.07 KB

Purpose

The dataset described here identifies the Euclidean distance from the center of each 5x5 m GeoTiff cell within the boundaries of the Edwin B. Forsythe NWR, New Jersey study area to the ocean, with the ocean boundary being the mean high water (MHW) ocean shoreline, according to lidar captured in 2014. See Zeigler and others (2019) for additional details. This dataset is part of a series of spatial datasets used to describe characteristics of barrier islands found along the North American Atlantic coast in order to identify habitat for the federally protected piping plover (Charadrius melodus). Information contained in these spatial datasets was used within a Bayesian network to model the probability that a specific set of landscape characteristics would be associated with piping plover habitat.

Additional Information

Raster Extension

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maxY39.76864296308505
maxX-74.09060549074837
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size144515308
dateUploadedThu Jun 20 12:21:10 MDT 2019
nameEF14_DisOcean.tif.ovr
contentTypeimage/tiff
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size1906382
dateUploadedThu Jun 20 12:21:10 MDT 2019
nameEF14_DisOcean.tif-ColorRamp.SLD
contentTypeapplication/sld+xml
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size2122
dateUploadedThu Jun 20 12:21:10 MDT 2019
nameEF14_DisOcean
nativeCrsEPSG:26918
rasterTypeGeoTIFF

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