SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Fisherman Island, VA, 2014
Dates
Publication Date
2019-12-20
Start Date
2014-01-01
End Date
2014-04-21
Citation
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.
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Fish14_GeoSet.tif
1.5 MB
image/geotiff
Fish14_GeoSet.tif.vat.dbf
627 Bytes
application/unknown
Fish14_SubType.tif
1.5 MB
image/geotiff
Fish14_SubType.tif.vat.dbf
556 Bytes
application/unknown
Fish14_SupClas_GeoSet_SubType_VegDen_VegType_meta.xml Original FGDC Metadata
View
96.44 KB
application/fgdc+xml
SupClas_rock_browse.png “Examples of substrate type, vegetation type, and vegetation density raster la...”
476.07 KB
image/png
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Fish14_SupClas.zip
Fish14_SupClas.tif
1.5 MB
Fish14_SupClas.tif-ColorRamp.SLD
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Fish14_SupClas.tif.vat.dbf
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Fish14_VegDen.zip
Fish14_VegDen.tif
1.5 MB
Fish14_VegDen.tif-ColorRamp.SLD
2.07 KB
Fish14_VegDen.tif.vat.dbf
556 Bytes
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Fish14_VegType.zip
Fish14_VegType.tif
1.5 MB
Fish14_VegType.tif-ColorRamp.SLD
2.07 KB
Fish14_VegType.tif.vat.dbf
556 Bytes
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 (Fish14_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 Fish14_SubType.tif, Fish14_VegDen.tif, Fish14_VegType.tif were reclassified from the supervised classification raster with some manual modifications. Fish14_SubType.tif maps discrete substrate types; Fish14_VegDen.tif maps discrete categories of vegetation density; Fish14_VegType.tif maps discrete vegetation types. Raster file Fish14_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...