Sturdivant, E.J., Zeigler, S.L., Gutierrez, B.T., and Weber, K.M., 2019, Barrier island geomorphology and shorebird habitat metrics–Sixteen sites on the U.S. Atlantic Coast, 2013–2014: U.S. Geological Survey data release, https://doi.org/10.5066/P9V7F6UX.
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 [...]
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 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.
Click on title to download individual files attached to this item.
CaLo14_SupClas_GeoSet_SubType_VegDen_VegType_meta.xml Original FGDC Metadata
View
96.78 KB
application/fgdc+xml
CaLo14_GeoSet.tif
244.68 MB
image/geotiff
CaLo14_GeoSet.tif.vat.dbf
627 Bytes
application/unknown
CaLo14_SubType.tif
244.68 MB
image/geotiff
CaLo14_SubType.tif.vat.dbf
556 Bytes
application/unknown
SupClas_rock_browse.png “Examples of substrate type, vegetation type, and vegetation density raster la...”
476.07 KB
image/png
Extension:
CaLo14_SupClas.zip
CaLo14_SupClas.tif
244.68 MB
CaLo14_SupClas.tif.vat.dbf
610 Bytes
CaLo14_SupClas.tif-ColorRamp.SLD
2.07 KB
Extension:
CaLo14_VegDen.zip
CaLo14_VegDen.tif
244.68 MB
CaLo14_VegDen.tif.vat.dbf
556 Bytes
CaLo14_VegDen.tif-ColorRamp.SLD
2.07 KB
Extension:
CaLo14_VegType.zip
CaLo14_VegType.tif
244.68 MB
CaLo14_VegType.tif.vat.dbf
556 Bytes
CaLo14_VegType.tif-ColorRamp.SLD
2.07 KB
Related External Resources
Type: Related Primary Pubication
Zeigler, S.L., Sturdivant, E.J., and Gutierrez, B.T., 2019, Evaluating barrier island characteristics and piping plover (Charadrius melodus) habitat availability along the U.S. Atlantic coast—Geospatial approaches and methodology: U.S. Geological Survey Open-File Report 2019–1071, https://doi.org/10.3133/ofr20191071.
Zeigler, S.L., Gutierrez, B.T., Sturdivant, E.J., Catlin, D.H., Fraser, J.D., Hecht, A., Karpanty, S.M., Plant, N.G., and Thieler, E.R., 2019, Using a Bayesian network to understand the importance of coastal storms and undeveloped landscapes for the creation and maintenance of early successional habitat: PLoS ONE, v. 14, no. 7, e0209986, https://doi.org/10.1371/journal.pone.0209986.
These categorical raster files map 2014 substrate and vegetation characteristics in 5-m cells. The supervised classification raster (CaLo14_SupClas.tif) depicts landcover attributes (for example, marsh, sand, water, herbaceous vegetation). It was created with a supervised classification of 2014 aerial imagery. Raster files CaLo14_SubType.tif, CaLo14_VegDen.tif, CaLo14_VegType.tif were reclassified from the supervised classification raster with some manual modifications. CaLo14_SubType.tif maps discrete substrate types; CaLo14_VegDen.tif maps discrete categories of vegetation density; CaLo14_VegType.tif maps discrete vegetation types. Raster file CaLo14_GeoSet.tif maps discrete geomorphic settings (e.g. beach, dunes, washovers) and was digitized manually with reference to source datasets. 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 (Zeigler and others, 2019).
Preview Image
Examples of substrate type, vegetation type, and vegetation density raster la...