Sara L. Zeigler
Emily J. Sturdivant
Benjamin T. Gutierrez
20191220
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assateague Island, MD & VA, 2014
1.0
raster digital dataset
data release
DOI:10.5066/P9V7F6UX
Woods Hole Coastal and Marine Science Center, Woods Hole, MA
U.S. Geological Survey, Coastal and Marine Hazards and Resources Program
https://doi.org/10.5066/P9V7F6UX
https://www.sciencebase.gov/catalog/item/5daa378ee4b09fd3b0c9ce93
Emily J. Sturdivant
Sara L. Zeigler
Benjamin T. Gutierrez
Kathryn M. Weber
2019
Barrier island geomorphology and shorebird habitat metrics: Sixteen sites on the U.S. Atlantic Coast, 2013–2014
1.0
data release
DOI:10.5066/P9V7F6UX
Reston, VA
U.S. Geological Survey
Suggested 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.
https://doi.org/10.5066/P9V7F6UX
https://www.sciencebase.gov/catalog/item/5d5ece47e4b01d82ce961e36
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.
These categorical raster files map 2014 substrate and vegetation characteristics in 5-m cells. The supervised classification raster (ASIS14_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 ASIS14_SubType.tif, ASIS14_VegDen.tif, ASIS14_VegType.tif were reclassified from the supervised classification raster with some manual modifications. ASIS14_SubType.tif maps discrete substrate types; ASIS14_VegDen.tif maps discrete categories of vegetation density; ASIS14_VegType.tif maps discrete vegetation types. Raster file ASIS14_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).
This metadata file describes five related datasets. For additional information on processing and use of this geospatial dataset, see the USGS Open-File report by Zeigler and others (2019).
20140101
20140421
Ground condition measured by source orthoimagery.
None planned
-75.40845664
-75.08856148
38.32682781
37.84563817
USGS Metadata Identifier
USGS:5daa378ee4b09fd3b0c9ce93
None
Barrier Island
USGS
CMHRP
Geographic Information Systems
GIS
U.S. Geological Survey
Coastal and Marine Hazards and Resources Program
Woods Hole Coastal and Marine Science Center
St. Petersburg Coastal and Marine Science Center
MHW
Mean High Water
Coastal Habitat
Assateague Island
Assateague Island National Seashore
ISO 19115 Topic Category
oceans
environment
USGS Thesaurus
geospatial datasets
geospatial analysis
image analysis
scientific interpretation
geomorphology
coastal processes
study areas
geographic information systems
erosion
hazards
sea-level change
None
Maryland
MD
VA
Virginia
Chincoteague National Wildlife Refuge
Delmarva Peninsula
Assateague Island
North America
United States
USA
Atlantic Ocean
Assateague Island National Seashore
none
Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey (USGS) as the source of this information.
U.S. Geological Survey
Sara L. Zeigler
mailing address
384 Woods Hole Road
Woods Hole
MA
02543
US
508-548-8700 x2290
508-457-2310
szeigler@usgs.gov
https://www.sciencebase.gov/catalog/file/get/5daa378ee4b09fd3b0c9ce93/?name=SupClas_rock_browse.png
Examples of substrate type, vegetation type, and vegetation density raster layers. These examples are for Rockaway Peninsula, NY and do not represent this dataset.
PNG
Sara L. Zeigler
Emily J. Sturdivant
Benjamin T. Gutierrez
2019
Evaluating barrier island characteristics and piping plover (Charadrius melodus) habitat availability along the U.S. Atlantic coast—Geospatial approaches and methodology
Open-File Report
2019–1071
Reston, VA
U.S. Geological Survey
Details the methods used to process these data for use in barrier island and piping plover habitat modeling.
https://doi.org/10.3133/ofr20191071
Christopher Lea
Anthony C. Curtis
2010
Thematic accuracy assessment procedures: National Park Service vegetation inventory, version 2.0
U.S. National Park Service report
Report NPS/2010/NRR-2010/204
Fort Collins, CO
U.S. National Park Service
Klaus K.E. Neuendorf
James P. Mehl, Jr.
Julia A. Jackson
2011
Glossary of geology. Fifth edition
Alexandria, VA
The American Geosciences Institute
Emily J. Sturdivant
E. Robert Thieler
Sara L. Zeigler
Luke A. Winslow
Megan K. Hines
Jordan S. Read
Jordan I. Walker
2016
Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast
data release
DOI: 10.5066/F70V89X3
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/F70V89X3
Kara J. Doran
Joseph W. Long
Justin J Birchler
Owen T. Brenner
Matthew W. Hardy
Karen L. M. Morgan
Hilary F. Stockdon
Miguel L. Torres
2017
Lidar-derived Beach Morphology (Dune Crest, Dune Toe, and Shoreline) for U.S. Sandy Coastlines
tabular digital data
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/F7GF0S0Z
Paul J. Godfrey
1976
Comparative ecology of East Coast barrier islands—Hydrology, soil, vegetation, in Barrier islands and beaches
Technical proceedings of the 1976 Barrier Islands Workshop, Annapolis, Maryland, May 17–18, 1976
p. 5–31
Washington, D.C.
The Conservation Foundation
U.S. Department of Agriculture (USDA)
USDA Natural Resources Conservation Service (NRCS)
2015
The PLANTS Database (http://plants.usda.gov, 13 January 2014)
Greensboro, NC
National Plant Data Team
The accuracies of the four derived thematic maps for geomorphic setting, substrate type, vegetation type, and vegetation density were assessed for 2014–2015 conditions at 15 study areas, including Assateague Island. These maps were derived in the same manner as those distributed here (derived for Assateague Island based on 2014 conditions). We assume that accuracy of maps of 2014–2015 conditions reflect the accuracy of our methods more generally, and accuracy values are therefore relevant for the files distributed here (ASIS14_SubType.tif, ASIS14_VegType.tif, ASIS14_VegDen.tif, ASIS14_GeoSet.tif).
Accuracy of the 2014–2015 derived raster layers at 15 study sites was assessed by comparing them against 482-501 ground validation points. The number of validation points used depended on the dataset examined. Validation points were subset from the iPlover dataset (Sturdivant and others, 2016). We followed the methodology recommended by the National Park Service (Lea and Curtis, 2010) to produce statistics for overall accuracy, chance agreement, and a kappa coefficient. Classifications were not altered when a difference between a ground validation point and the underlying raster cell was found.
For geomorphic setting (501 validation points), overall accuracy was 46% and chance agreement was 23%, resulting in a kappa coefficient of 0.30. For substrate type (490 validation points), overall accuracy was 96% and chance agreement was 51%, resulting in a kappa coefficient of 0.92. For vegetation type (482 validation points), overall accuracy was 93% and chance agreement was 46%, resulting in a kappa coefficient of 0.88. For vegetation density (490 validation points), overall accuracy was 85% and chance agreement was 39%, resulting in a kappa coefficient of 0.76. See Zeigler and others (2019) for more details on this validation exercise.
These raster layers (ASIS14_SubType.tif, ASIS14_VegType.tif, ASIS14_VegDen.tif) show discrete landcover classes and were produced based on a supervised classification of aerial imagery (ASIS14_SupClas.tif). Geomorphic settings contained in ASIS14_GeoSet.tif were hand-digitized with reference to the orthoimagery and DEM (see Source Information). An area is considered developed (in all applicable raster layers) if it includes human development or is completely surrounded by development (from ASIS14_Development.shp). The data were reviewed using standard USGS review procedures. No checks for topological consistency in addition to those described in the Attribute Accuracy Report were performed on these data.
This dataset is clipped to a custom boundary and may not include the entire spatial extent of the original source dataset. However, the custom boundary spans the entire coverage of the study area relevant to the broader research program (see Zeigler and others, 2019 for more details). The data are therefore considered complete for the information presented as described in the abstract section. Users are advised to read the rest of the metadata record carefully for additional details.
The assumed positional accuracy of these raster coverages is 5 m.
The horizontal accuracy of these layers inherits the accuracy of the source datasets. The orthoimage was used as a source dataset for all five layers. ASIS14_GeoSet.tif was digitized from the source DEM, beach geomorphic feature positions (see larger work), and shoreline polygons (see larger work), in addition to aerial imagery.
The horizontal root-mean-square-error (RMSE) of the orthoimage was 0.53 m. Subsequent datum transformation (see source contribution information) may have added about 0.1 m of uncertainty.
During supervised classification, polygons were hand-digitized, combined and ultimately resampled from 0.35 m to 5 m resolution using a majority moving window. Some positional accuracy loss is expected in the final classified image as a result of changes in resolution size. In particular, we notice the omission of thin, linear features (for example, coastal walls and small roads that would have been classified as development).
ASIS14_GeoSet.tif: Source elevation data were compiled to meet the 1.0 m RMSE horizontal accuracy specification. No projection transformations were required, because the source DEM was downloaded in NAD83 UTM Zone 18N.
The location of shoreline and dune points (Doran and others, 2017) was determined at 10-m intervals parallel to the shore. Accuracy estimates are included in the source dataset. We reprojected source shoreline and dune points using the ‘Project’ tool in ArcToolbox with the datum transformation "WGS_1984_(ITRF00)_To_NAD_1983" (WKID: 108190, accuracy: 0.1 m). Additional steps were taken as part of a semi-automated process to produce a full shoreline for the study area (see asis14_shoreline.shp in the larger work), which was used to delineate the boundaries of some geomorphic settings. In addition to the sources of error inherited from the source datasets, some geomorphic settings were manually digitized as polygons and then converted to raster format with 5x5 m resolution. The rasterization process tends to decrease the precision of the resulting data.
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division
20150501
2014 NOAA Ortho-rectified Mosaic of Hurricane Sandy Coastal Impact Area
remote-sensing image
Silver Spring, MD
NOAA's Ocean Service, National Geodetic Survey (NGS)
https://coast.noaa.gov/dataviewer/#/imagery/search/where:ID=4885
https://coast.noaa.gov/htdata/raster1/imagery/Sandy_2014_4885
https://coast.noaa.gov/dataviewer
https://inport.nmfs.noaa.gov/inport/item/48588
digital data
20140101
20140421
ground condition
Orthoimage
The orthoimage was used to classify the scene into landcover types. Source data were distributed at 0.35 m pixel resolution, in horizontal datum NAD83. Downloaded on 2/21/2017. Data were projected to UTM Zone 18N (EPSG:26918) using the ‘Project Raster’ tool in ArcToolbox (version 10.4.1).
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM)
20151220
2014 NOAA Post-Sandy Topobathymetric LiDAR: Void DEMs South Carolina to New York
map
Silver Spring, MD
NOAA's Ocean Service, National Geodetic Survey (NGS)
https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4967
https://coast.noaa.gov/htdata/raster2/elevation/Post_Sandy_DEM_2014_4967
https://coast.noaa.gov/dataviewer
https://inport.nmfs.noaa.gov/inport/item/48367
digital data
201311
201406
ground condition
DEM
Elevation data used for visual reference while digitizing dunes and geomorphic feature types. Source data were downloaded from https://coast.noaa.gov/dataviewer in horizontal datum NAD 1983 (2011), UTM Zone 18N, vertical datum NAVD88. Downloaded on 4/8/2016.
ASIS14_SupClas.tif
Using the orthoimage, we conducted a supervised classification to delineate substrate and vegetation characteristics. Full methods are provided in the associated Methods OFR (Zeigler and others, 2019). For additional information and example figures, see Zeigler and others (2019). All steps were undertaken in ArcGIS and ArcToolbox version 10.4.1.
Because the signatures of some features closely resembled those of others (e.g., water and the reflection from buildings), we ran the classification in three stages and combined results into a single classification of landcover. We created sub-classifications for (1) bare sand and water, (2) marsh, and (3) vegetation and other features in the barrier’s interior.
To divide the landscape into these three sub-classifications, we hand-digitized masking polygons in ArcGIS. All digitization was performed with the orthoimage for reference. For the marsh classification, we hand-digitized polygons of marsh platforms. For vegetation and inland features, we hand-digitized a polygon that encompassed vegetation and the barrier’s interior by delineating the boundary of open sand and vegetation. Finally, we hand-digitized polygons to encompass areas of human development (ASIS14_Development.shp in larger work). ‘Human development’ could include housing communities, commercial infrastructure, recreational facilities, major roads, and shoreline armoring (e.g., jetties). In sites without any human development, such as in the Virginia Coast Reserve, we omitted the development component of the following steps.
We then conducted the classification for marsh. We used the Classification toolbar to hand-digitize training polygons for the following classes: (1) Water; (2) Marsh, vegetation or unknown cover; (3) Marsh, unvegetated sediment; and (4) Marsh, shrub or forest. We ran the interactive supervised classification routine in the Classification toolbar and clipped the resulting classification image along the boundaries of the marsh mask. In the Image Analysis window, we masked out areas that overlapped with development polygons, if present. The resulting image matched the extent of the marsh polygons and contained cells at the original resolution of the orthoimage (0.35 m) with one of the four values represented by the training polygons.
Next, we created training polygons in the same manner that represented different types of vegetation in the inland portion of the barrier. Training samples represented the following classes: (1) Water; (2) Sand; (3) Herbaceous Vegetation or Shrub, assumed sparse to moderate cover (< 20% cover); (4) Herbaceous Vegetation or Shrub, assumed moderate to dense cover (>20% cover); and (5) Shrub or Forest, assumed moderate to dense cover. We ran the interactive supervised classification routine in the Classification toolbar and clipped the resulting classification image along the boundaries of the vegetation mask. In the Image Analysis window, we masked out areas that overlapped with the marsh or development mask. The resulting image matched the extent of the vegetation mask, minus areas overlapping with marsh and development, and contained cells at the original resolution (0.35 m) of the orthoimage. Cells had one of the five values represented by the training polygons.
We created training polygons in the same manner for the final sub-classification. These samples represented the following classes: (1) Water and (2) Sand. We ran the interactive supervised classification routine in the Classification toolbar. We masked out areas that overlapped with the marsh, development, or vegetation masks in the Image Analysis window. The resulting image covered areas not already covered by the marsh, vegetation, or development and contained cells at the aerial imagery’s original resolution (0.35 m) with a value for either sand or water.
Using the Merge tool in the Image Analysis window in ArcGIS, we combined the rasters for (1) marsh, (2) vegetation, (3) sand/water sub-classifications with the (4) rasterized development layer into a single raster (hereafter, the ‘supervised classification’). We changed the supervised classification’s resolution from the aerial imagery’s original cell size (0.35 m) to a 5 m cell size with the Resample tool in ArcToolbox. We used ‘majority’ as the resampling technique, where the new 5 m cell took on the most common value within the 5x5 m area.
In many instances, cells took on the value of ‘NoData’ around the boundaries of the marsh, vegetation, and development masks when we merged individual sub-classifications. We replaced these NoData values using statistical information from surrounding cells according to the following code in the Raster Calculator tool in ArcToolbox:
CON(isnull([raster]), FOCALMAJORITY([raster], rectangle, 5, 5), [raster]).
This code indicates that, if the value of a cell in the supervised classification is NoData, then replace that value with the value held by the majority of cells in the surrounding 5x5 cell window.
The final supervised classification was a raster layer with a 5 m cell size clipped to the extent of the study area with cells taking on one of nine possible values: (1) water, value = 1; (2) sand, value = 2; (3) herbaceous vegetation or low shrub, sparse to moderate cover, value = 9; (4) herbaceous vegetation or low shrub, moderate to dense cover, value = 3; (5) high shrub or forest, moderate to dense cover, value = 4; (6) marsh, sediment, value = 11; (7) marsh, vegetation or unknown cover, value = 7; (8) marsh, high shrub or forest, value = 14; and (9) development, value = 10. Note, this list contains all possible values in the supervised classification we considered within the larger research program (see Zeigler and others, 2019). Not all values may be represented in this supervised classification specifically.
2018
Geomorphic settings raster (ASIS14_GeoSet.tif)
We created the geomorphic settings raster dataset (ASIS14_GeoSet.tif) by first delineating the boundaries of individual features (e.g., the edges of washover fans), combining these features into a single shapefile, and converting that shapefile to a raster. Each individual geomorphic feature was given a value for identification purposes (‘value’) as well as a priority number (‘priority’) on which to base the merging of all features into a single layer.
For additional information and example figures, see Zeigler and others (2019). All steps were conducted in ArcGIS and ArcToolbox version 10.4.1.
We began by hand-digitizing the boundaries for marsh (value = 6, priority = 6) based on the visual inspection of the orthoimage. The rear-edges of marsh boundaries were drawn such that they extended out past the study area boundary and included areas of open water that were directly across from marsh platforms.
We created a polygon for beach (value = 1, priority = 5) using the shoreline (asis14_shoreline.shp in the larger work) and custom study area boundary polygons. The custom study area boundary spans the entire coverage of the Assateague Island site relevant to the broader research program (see Zeigler and others, 2019 for more details). The boundary polygon’s spatial extent does not affect the quality or completeness of the dataset.
To create the beach polygon, we used the ‘Erase’ tool in ArcToolbox to mask out the shoreline polygons and the hand-digitized marsh polygons from a copy of the full study area polygon. This process essentially removed the features of marsh and anything interior of the shoreline from the study area boundary, leaving only the beach geomorphic setting in the final polygon. Together, the beach and marsh polygons covered the outermost edges of the study area.
We also hand-digitized the boundaries of dunes (value = 3, priority = 7) using the DEM (displayed in 1 m intervals), dune crest and dune toe points (asis14_DCpts.shp and asis14_DTpts.shp in larger work and Doran and others, 2017), and the orthoimage as guides. We digitized the rear of dune complexes such that the boundary fell landward of dune crest points (if present) and along a clear decrease in elevation (according to visual inspection of the source DEM). The front of the dune was also drawn such that the boundary fell along a clear decrease in elevation and passed through dune toe points. We used the ‘Snapping’ feature in the Editing toolbar of ArcGIS to ensure that the front boundary of dune complexes coincided with the dune toe points. In some instances, particularly in washovers, dune crest points were present without obvious changes in elevation (in the DEM) or the presence of dune toe points. In these cases, we hand-digitized the dune boundary such that it tightly encompassed dune crest points.
Boundaries of the remaining geomorphic features were hand-digitized according to visual inspection of the orthoimage and in reference to boundaries already created for beach, marsh, and dunes. The rear, landward boundary of the backshore (value = 2, priority = 4) was digitized such that it overlapped with dune polygons (but did not extend past the landward-most boundary of the dune). When dunes were not present, the landward boundary of the backshore was drawn where non-vegetated sand ended and dense vegetation began. We extended the backshore polygon boundary seaward beyond the shoreline (or the boundary of marsh in the case of the back-barrier).
The characteristic fan-shaped boundaries of washovers (value = 4, priority = 3) were hand-digitized such that they overlapped with the boundaries of marsh along the back-barrier and with the boundaries of dune complexes on the seaward shore. In instances where dunes were not present seaward of a washover, we digitized the washover boundary along wrack if visible in the orthoimage.
We hand-digitized polygons for ridge/swale complexes (value = 7, priority = 2), when present, at the boundary of open sand and vegetation according to the orthoimage. Note: this geomorphic setting was not present at all sites and may not be represented.
For barrier interior (value = 5, priority = 1), the final geomorphic setting, we used the Erase tool in ArcToolbox to remove all other geomorphic settings (i.e., beach, backshore, dune, washover, marsh, and ridge/swale) from a copy of the study area polygon (not published). In this way, the barrier interior setting occurred only in the absence of any other geomorphic features.
Using the Merge tool in ArcToolbox, we combined the individual polygon shapefiles for beach, backshore, dune, washover, barrier interior, marsh, and ridge/swale into a single shapefile. We used the Integrate tool in ArcToolbox (distance = 1 m) to close small gaps between polygons.
We then converted the merged polygon shapefile to a raster with a 5 m cell size, using the priority value for each geomorphic setting type in the attribute table to determine which geomorphic setting type took precedence when two or more settings overlapped (where a raster cell takes the value of the feature with the highest priority value). We selected ‘maximum area cell assignment’ to determine cell values. The extent was set to the study area extent in the geoprocessing Environment Settings window so that the converted raster would be clipped to the extent of the study area.
The final raster provided a categorical map of geomorphic features with every 5x5 m cell categorized as one of the seven possible geomorphic settings.
2018
Reclassification for the substrate type layer (ASIS14_SubType.tif):
All steps were undertaken in ArcGIS and ArcToolbox version 10.4.1. For additional information and example figures, see Zeigler and others (2019).
Each of the classes contained in the supervised classification is associated with a substrate type, vegetation type, and vegetation density for later use in creating raster coverages for these characteristics. For substrate type, we reclassified the original supervised classification value to the following substrate types and associated values:
(1) water -> Water, 4444;
(2) sand -> Sand or ShellGravelCobble, 7777;
(3) herbaceous or shrub, sparse to moderate cover -> Sand or ShellGravelCobble, 7777;
(4) herbaceous or shrub, moderate to dense cover -> Sand or ShellGravelCobble, 7777;
(5) shrub or forest -> Unknown, 9999;
(6) marsh, sediment -> Sand or MudPeat, 1000;
(7) marsh, vegetation or unknown cover -> MudPeat, 3333;
(8) marsh, shrub or forest -> Unknown, 9999;
(9) development -> Development, 6666.
Note, this list contains all possible values for substrate types considered within the larger research program (see Zeigler and others, 2019). Not all values may be represented for this site.
We made one manual change to the reclassification of the supervised classification to create the substrate type layer. For raster cells that were classified as ‘beach’ in the geomorphic settings layer (ASIS14_GeoSet.tif), we manually reclassified the substrate type for these cells to ‘Water’ with 'None' for both vegetation density and vegetation type. The beach geomorphic setting represented all raster cells outside of the shoreline polygons (asis14_shoreline.shp in larger work), and we assumed that these areas would be underwater at some point depending on the tide. We reclassified everything outside of the full shoreline polygons as Water to maintain consistency in landcover classifications relative to tide fluctuations.
2018
Reclassification for the vegetation density layer (ASIS14_VegDen.tif):
All steps were undertaken in ArcGIS and ArcToolbox version 10.4.1. For additional information and example figures, see Zeigler and others (2019).
For vegetation density, we reclassified the original supervised classification value to the following vegetation densities and associated values:
(1) water -> None, 111;
(2) sand -> None or Sparse, 666;
(3) herbaceous or shrub, sparse to moderate cover -> Sparse or Moderate, 777;
(4) herbaceous or shrub, moderate to dense cover -> Moderate or Dense, 888;
(5) shrub or forest -> Moderate or Dense, 888;
(6) marsh, sediment -> None or Sparse, 666;
(7) marsh, vegetation or unknown cover -> Unknown, 9999;
(8) marsh, shrub or forest -> Moderate or Dense, 888;
(9) development -> Development, 555.
Note, this list contains all possible values for vegetation density classes considered within the larger research program (see Zeigler and others, 2019). Not all values may be represented in this dataset.
We made one manual change to the reclassification of the supervised classification to create the vegetation density layer. For raster cells that were classified as ‘beach’ in the geomorphic settings layer (ASIS14_GeoSet.tif), we manually reclassified the substrate type for these cells as ‘Water’ with 'None' for both vegetation density and vegetation type. The beach geomorphic setting represented all raster cells outside of the shoreline polygons (asis14_shoreline.shp in larger work), and we assumed that these areas would be underwater at some point depending on the tide. This also reduced misclassifications where beach wrack (or dead materials washed up on to the beach) was incorrectly identified as vegetation, which was a common issue in the supervised classification process. Thus, we reclassified everything outside of the shoreline polygons as 'None', or lacking terrestrial vegetation.
2018
Reclassification for the vegetation type layer (ASIS14_VegType.tif):
All steps were undertaken in ArcGIS and ArcToolbox version 10.4.1. For additional information and example figures, see Zeigler and others (2019).
For vegetation type, we reclassified the original supervised classification value to the following vegetation types and associated values:
(1) water -> None, 11;
(2) sand -> None or Herbaceous, 77;
(3) herbaceous or shrub, sparse to moderate cover -> Herbaceous or Shrub, 88;
(4) herbaceous or shrub, moderate to dense cover -> Herbaceous or Shrub, 88;
(5) shrub or forest -> Shrub or Forest, 99;
(6) marsh, sediment -> None or Herbaceous, 77;
(7) marsh, vegetation or unknown cover -> Unknown, 9999;
(8) marsh, shrub or forest -> Shrub or Forest, 99;
(9) development -> Development, 55.
Note, this list contains all possible values for vegetation types considered within the larger research program (see Zeigler and others, 2019). Not all values may be represented for this site.
We made one manual change to the reclassification of the supervised classification to create the vegetation type layer. For raster cells that were classified as ‘beach’ in the geomorphic settings layer (ASIS14_GeoSet.tif), we manually reclassified the substrate type for these cells as ‘Water’ with 'None' for both vegetation density and vegetation type. The beach geomorphic setting represented all raster cells outside of the shoreline polygons (asis14_shoreline.shp in larger work), and we assumed that these areas would be underwater at some point depending on the tide. We reclassified everything seaward of the full shoreline as Water to maintain consistency in landcover classifications relative to tide fluctuations. This also reduced misclassifications where beach wrack (or dead materials washed up on to the beach) was incorrectly identified as vegetation, which was a common issue in the supervised classification process. For these reasons, we reclassified everything outside of the shoreline polygons as none, or lacking in terrestrial vegetation.
2018
Added keywords section with USGS persistent identifier as theme keyword.
20200810
U.S. Geological Survey
VeeAnn A. Cross
Marine Geologist
Mailing and Physical
384 Woods Hole Road
Woods Hole
MA
02543-1598
508-548-8700 x2251
508-457-2310
vatnipp@usgs.gov
Raster
Grid Cell
10693
5582
1
Universal Transverse Mercator
18
0.9996
-75
0.0
500000.0
0.0
row and column
5.0
5.0
Meter
D_North_American_1983
GRS_1980
6378137.0
298.257222101
ASIS14_SupClas values attribute table
Values attribute table (ASIS14_SupClas.tif.vat.dbf), which indicates the landcover classification of every cell in the raster (ASIS14_SupClas.tif). These metadata list all possible values; some of the values described may not be present in this dataset.
Producer defined
VALUE
Categorical identifier for landcover characteristics (describing vegetation and substrate).
Producer defined
1
Water: Any location that (i) is always submerged (e.g., locations several meters into the ocean, bay, or inland water body) or (ii) was submerged at the time source aerial imagery was captured (i.e., intertidal regions of beaches).
Producer defined
2
Open Sand: Areas lacking visible vegetation and containing some type of sandy substrate.
Producer defined
3
Herbaceous or Shrub vegetation (moderate to dense): Areas containing herbaceous vegetation or short shrubby vegetation at moderate (20-90% vegetation cover in a 5x5 m area) to dense (greater than 90% vegetation cover) densities.
Producer defined
4
Shrub or Forest (moderate to dense): Areas containing tall shrubs or trees at moderate (20-90% vegetation cover in a 5x5 m area) to dense (greater than 90% vegetation cover) densities.
Producer defined
7
Marsh (vegetation or unknown cover): Marsh areas containing mud/peat substrates and either herbaceous vegetation or unknown vegetation cover.
Producer defined
9
Herbaceous or shrub vegetation (sparse to moderate): Areas containing herbaceous vegetation or short shrubby vegetation at sparse (less than 20% vegetation cover in a 5x5 m area) to moderate (20-90% vegetation cover) densities.
Producer defined
10
Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined
11
Marsh (sediment): Marsh areas containing sandy or mud/peat substrates as well as no to sparse vegetation.
Producer defined
14
Marsh (shrub or forest): Marsh areas containing unknown substrate and either shrubby vegetation or forests.
Producer defined
COUNT
Number of 5x5 m cells in the given landcover type.
Producer defined
59776
383082
number of grid cells
TYPE
Definitions of categorical identifiers for landcover characteristics (describing vegetation and substrate) found on Assateague Island.
Producer defined
Development
(10) Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined
Herbaceous vegetation or Shrub
(3) Herbaceous or shrub vegetation (moderate to dense): Areas containing herbaceous vegetation or short shrubby vegetation at moderate (20-90% vegetation cover in a 5x5 m area) to dense (greater than 90% vegetation cover) densities.
Producer defined
Herbaceous or Shrub (sparse to moderate)
(9) Herbaceous or shrub vegetation (sparse to moderate): Areas containing herbaceous vegetation or short shrubby vegetation at sparse (less than 20% vegetation cover in a 5x5 m area) to moderate (20-90% vegetation cover) densities.
Producer defined
Marsh (sediment)
(11) Marsh (sediment): Marsh areas containing sandy or mud/peat substrates as well as no to sparse vegetation.
Producer defined
Marsh (shrub or forest)
(14) Marsh (shrub or forest): Marsh areas containing unknown substrate and either shrubby vegetation or forests.
Producer defined
Marsh
(7) Marsh (vegetation or unknown cover): Marsh areas containing mud/peat substrates and either herbaceous vegetation or unknown vegetation cover.
Producer defined
Sand
(2) Open Sand: Areas lacking visible vegetation and containing some type of sandy substrate.
Producer defined
Shrub or Forest
(4) Shrub or Forest (moderate to dense): Areas containing tall shrubs or trees at moderate (20-90% vegetation cover in a 5 m area) to dense (greater than 90% vegetation cover) densities.
Producer defined
Water
(1) Water: Any location that (i) is always submerged (e.g., locations several meters into the ocean, bay, or inland water body) or (ii) was submerged at the time source aerial imagery was captured (i.e., intertidal regions of beaches).
Producer defined
ASIS14_GeoSet values attribute table
Values attribute table (ASIS14_GeoSet.tif.vat.dbf), which indicates the geomorphic setting classification of every cell in the raster (ASIS14_GeoSet.tif). These metadata list all possible values; some of the values described may not be present in this dataset.
Producer defined
VALUE
Coded identifier of geomorphic setting type (see Zeigler and others, 2019 for more details).
Producer defined
1
Beach: The relatively thick and temporary accumulation of loose, water-borne material (usually well-sorted sand and pebbles, accompanied by mud, cobbles, boulders, and smoothed rock and shell fragments) that is in active transit along, or deposited on, the shore zone between the limits of low water and high water (Neuendorf and others, 2011). In this study, the beach geomorphic setting occurred from the shoreline seaward to the study area boundary.
Producer defined, definition modified from Neuendorf and others (2011).
2
Backshore: The upper, usually dry, zone of the shore or beach, lying between the mean high water shoreline of mean spring tides and the upper limit of shore-zone processes; it is acted upon by waves or covered by water only during exceptionally severe storms or unusually high tides (Neuendorf and others, 2011). In this study, the backshore geomorphic setting occurred between the mean high water shoreline and either (i) the dune toe, (ii) the edge of developed areas, or (iii) the edge of dense vegetation (or forest).
Producer defined, definition modified from Neuendorf and others (2011).
3
Dune complex: A low mound, ridge, bank, or hill of loose, windblown granular material (generally sand), either bare or covered by vegetation, capable of movement from place to place but retaining its characteristic shape (Neuendorf and others, 2011). In this study, “dune” also describes low-lying areas between dunes (or “interdune” regions) that are part of the larger dune complex.
Producer defined, definition modified from Neuendorf and others (2011).
4
Washover (or overwash): A fan of material deposited from the ocean landward on a mainland beach or barrier island, produced by storm waves breaking over low parts of the mainland beach or barrier and depositing sediment either landward (mainland beaches) or across a barrier island into the bay or sound (barrier islands). A washover typically displays a characteristic fan-like shape (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
5
Barrier Interior: the geomorphic setting described all areas spanning the interior boundary of the dunes (or backshore in the absence of dunes) on the ocean-side to the interior boundary of the marsh, dunes, or backshore on the back-barrier side. This setting was typically used to describe areas that did not fall into any other geomorphic setting (e.g., washovers, ridge or swale complexes).
Producer defined, definition modified from Neuendorf and others (2011).
6
Marsh: A relatively flat, low-lying, intermittently water-covered area with generally halophytic grasses existing landward of a barrier island (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
7
Ridge/Swale complex: Long subparallel ridges and swales aligned obliquely across the regional trend of the contours (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
COUNT
Number of 5x5 m cells pertaining to the applicable geomorphic setting.
Producer defined
52128
439684
number of grid cells
TYPE
Geomorphic setting type (see Zeigler and others, 2019 for more details).
Producer defined, definition modified from Neuendorf and others (2011).
Backshore
(2) The upper, usually dry, zone of the shore or beach, lying between the mean high water shoreline of mean spring tides and the upper limit of shore-zone processes; it is acted upon by waves or covered by water only during exceptionally severe storms or unusually high tides (Neuendorf and others, 2011). In this study, the backshore geomorphic setting occurred between the shoreline and either (i) the dune toe, (ii) the edge of developed areas, or (iii) the edge of dense vegetation (or forest).
Producer defined, definition modified from Neuendorf and others (2011).
Barrier Interior
(5) The barrier interior geomorphic setting described all areas spanning the interior boundary of the dunes (or backshore in the absence of dunes) on the ocean-side to the interior boundary of the marsh, dunes, or backshore on the back-barrier side. This setting was typically used to describe areas that did not fall into any other geomorphic setting (e.g., washovers, ridge or swale complexes).
Producer defined, definition modified from Neuendorf and others (2011).
Beach
(1) The relatively thick and temporary accumulation of loose, water-borne material (usually well-sorted sand and pebbles, accompanied by mud, cobbles, boulders, and smoothed rock and shell fragments) that is in active transit along, or deposited on, the shore zone between the limits of low water and high water (Neuendorf and others, 2011). In this study, the beach geomorphic setting occurred from the shoreline seaward to the study area boundary.
Producer defined, definition modified from Neuendorf and others (2011).
Dune Complex
(3) A low mound, ridge, bank, or hill of loose, windblown granular material (generally sand), either bare or covered by vegetation, capable of movement from place to place but retaining its characteristic shape (Neuendorf and others, 2011). In this study, “dune” also describes low-lying areas between dunes (or “interdune” regions) that are part of the larger dune complex.
Producer defined, definition modified from Neuendorf and others (2011).
Marsh
(6) A relatively flat, low-lying, intermittently water-covered area with generally halophytic grasses existing landward of a barrier island (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
Ridge/Swale complex
(7) Long subparallel ridges and swales aligned obliquely across the regional trend of the contours (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
Washover
(4) A fan of material deposited from the ocean landward on a mainland beach or barrier island, produced by storm waves breaking over low parts of the mainland beach or barrier and depositing sediment either landward (mainland beaches) or across a barrier island into the bay or sound (barrier islands). A washover typically displays a characteristic fan-like shape (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
ASIS14_SubType values attribute table
Values attribute table (ASIS14_SubType.tif.vat.dbf), which indicates the substrate type classification of every cell in the raster (ASIS14_SubType.tif). These metadata list all possible values; some of the values described may not be present in this dataset.
Producer defined
VALUE
Coded identifier for discrete substrate type.
Producer defined
1000
Sand or Mud/Peat: In this study, wet, sandy substrates could not be differentiated from mud/peat in marshy areas. Therefore, we identified substrate as being either of these two types in the Substrate raster layer. Mud/Peat is a sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (Neuendorf and others, 2011). Sand included rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011).
Producer defined
1111
Sand: predominantly sandy substrates that contain rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011) with no discernible shells fragments or large rock fragments.
Producer defined
2222
Shell/Gravel/Cobble: substrates containing a mixture of sand, shell or rock fragments, or large rocks.
Producer defined
3333
MudPeat: A sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
4444
Water: Any location that (i) is always submerged (e.g., locations several meters into the ocean, bay, or inland water body), (ii) was submerged at the time aerial imagery was captured (i.e., intertidal regions of beaches), or (iii) was not submerged at the time aerial imagery was captured but was seaward of the shoreline polygon.
Producer defined, definition modified from Neuendorf and others (2011).
6666
Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined, definition modified from Neuendorf and others (2011).
7777
Sand or ShellGravelCobble: In this study, predominantly sandy substrates could not be differentiated from substrates that were a mix of sand and ShellGravelCobble in the aerial imagery alone. Therefore, we identified substrate as being either of these two types in the substrate type raster layer. Sand included rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011) with no discernible shells fragments or large rock fragments. ShellGravelCobble described substrates containing a mixture of sand, shell or rock fragments, or large rocks.
Producer defined, definition modified from Neuendorf and others (2011).
9999
Unknown: Substrate type could not be determined based on aerial imagery
Producer defined
COUNT
Number of 5x5 m cells in the raster coverage that contain each substrate type.
Producer defined
159380
424051
number of grid cells
TYPE
Discrete substrate type.
Producer defined
Development
(6666) Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined, definition modified from Neuendorf and others (2011).
Sand
(111) Sand: predominantly sandy substrates that contain rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011) with no discernible shells fragments or large rock fragments.
Producer defined
MudPeat
(3333) MudPeat: A sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (Neuendorf and others, 2011).
Producer defined, definition modified from Neuendorf and others (2011).
Sand or MudPeat
(1000) Sand or Mud/Peat: In this study, wet, sandy substrates could not be differentiated from mud/peat in marshy areas. Therefore, we identified substrate as being either of these two types in the Substrate raster layer. Mud/Peat is a sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (Neuendorf and others, 2011). Sand included rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011).
Producer defined
Sand or ShellGravelCobble
(7777) Sand or ShellGravelCobble: In this study, predominantly sandy substrates could not be differentiated from substrates that were a mix of sand and ShellGravelCobble. Therefore, we identified substrate as being either of these two types in the Substrate raster layer. Sand included rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011) with no discernible shells fragments or large rock fragments. ShellGravelCobble described substrates containing a mixture of sand, shell or rock fragments, or large rocks.
Producer defined, definition modified from Neuendorf and others (2011).
ShellGravelCobble
(222) Shell/Gravel/Cobble: substrates containing a mixture of sand, shell or rock fragments, or large rocks.
Producer defined
Unknown
(9999) Unknown: Substrate type could not be determined based on aerial imagery
Producer defined, definition modified from Neuendorf and others (2011).
Water
(4444) Water: Any location that (i) is always submerged (e.g., locations several meters into the ocean, bay, or inland water body), (ii) was submerged at the time aerial imagery was captured (i.e., intertidal regions of beaches), or (iii) was not submerged at the time aerial imagery was captured but was seaward of the shoreline polygon.
Producer defined, definition modified from Neuendorf and others (2011).
ASIS14_VegDen values attribute table
Values attribute table (ASIS14_VegDen.tif.vat.dbf), which indicates the vegetation density classification of every cell in the raster (ASIS14_VegDen.tif). These metadata list all possible values; some of the values described may not be present in this dataset.
Producer defined
VALUE
Categorical identifier for discrete vegetation density classes.
Producer defined
111
None: Areas lacking terrestrial vegetation of any type. Such areas were most frequently associated with the beach geomorphic setting (found seaward of the study area shoreline) assumed to be covered by water.
Producer defined
222
Sparse: areas where vegetation was apparent and covered less than 20% of the 5x5-m raster cell.
Producer defined
333
Moderate: areas where vegetation appeared to cover 20–90% of the 5x5-m raster cell.
Producer defined
444
Dense: areas where vegetation appeared to cover greater than 90% of the 5x5-m raster cell.
Producer defined
555
Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined
666
None or Sparse: Areas lacking vegetation could not consistently be differentiated from areas containing sparse vegetation on open sandy areas (i.e., not covered by water) in the aerial imagery alone. Therefore, we identified vegetation density as being either none or sparse in the vegetation density raster layer. Areas described by these classes either appeared to lack vegetation completely in the aerial imagery or, if vegetation was apparent, covered less than 20% of the 5x5-m raster cell.
Producer defined
777
Sparse or Moderate: In this study, areas with sparse vegetation could not consistently be differentiated from areas containing moderate vegetation in the orthoimagery alone. Therefore, we identified vegetation as being either of these two types in the Vegetation Density raster layer. Areas described as having 'Sparse' vegetation appeared to have vegetation that covered less than 20% of the 5x5-m raster cell. In areas described as 'Moderate', vegetation appeared to cover 20-90% of the 5x5-m raster cell.
Producer defined
888
Moderate or Dense: In this study, areas with moderate vegetation could not consistently be differentiated from areas containing dense vegetation in the aerial alone. Therefore, we identified vegetation as being either of these two classes in the vegetation density raster layer. Areas described by these classes either appeared to have vegetation covering 20-90% or > 90% of the 5x5-m raster cell.
Producer defined
9999
Unknown: Vegetation density could not be determined based on the aerial imagery
Producer defined
COUNT
Number of 5x5 m cells in the raster coverage that contain each vegetation density class.
Producer defined
159380
424051
number of grid cells
TYPE
Discrete vegetation density classes found on Assateague Island.
Producer defined
Dense
(444) Dense: areas where vegetation appeared to cover greater than 90% of the 5x5-m raster cell.
Producer defined
Development
(555) Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined
Moderate
(333) Moderate: areas where vegetation appeared to cover 20–90% of the 5x5-m raster cell.
Producer defined
Moderate or Dense
(888) Moderate or Dense: In this study, areas with moderate vegetation could not consistently be differentiated from areas containing dense vegetation in the aerial alone. Therefore, we identified vegetation as being either of these two classes in the vegetation density raster layer. Areas described by these classes either appeared to have vegetation covering 20-90% or > 90% of the 5x5-m raster cell.
Producer defined
None
(111) None: Areas lacking terrestrial vegetation of any type. Such areas were most frequently associated with the beach geomorphic setting (found seaward of the study area shoreline) assumed to be covered by water.
Producer defined
None or Sparse
(666) None or Sparse: In this study, areas lacking vegetation could not consistently be differentiated from areas containing sparse vegetation on open sandy areas (i.e., not covered by water) in the aerial imagery alone. Therefore, we identified vegetation density as being either none or sparse in the vegetation density raster layer. Areas described by these classes either appeared to lack vegetation completely in the aerial imagery or, if vegetation was apparent, covered less than 20% of the 5x5-m raster cell.
Producer defined
Sparse or Moderate
(777) Sparse or Moderate: In this study, areas with sparse vegetation could not consistently be differentiated from areas containing moderate vegetation in the orthoimagery alone. Therefore, we identified vegetation as being either of these two types in the Vegetation Density raster layer. Areas described as having 'Sparse' vegetation appeared to have vegetation that covered less than 20% of the 5x5-m raster cell. In areas described as 'Moderate', vegetation appeared to cover 20-90% of the 5x5-m raster cell.
Producer defined
Sparse
(222) Sparse: areas where vegetation was apparent and covered less than 20% of the 5x5-m raster cell.
Producer defined
Unknown
(9999) Unknown: Vegetation density could not be determined based on aerial imagery.
Producer defined
ASIS14_VegType values attribute table
Values attribute table (ASIS14_VegType.tif.vat.dbf), which indicates the vegetation type classification of every cell in the raster (ASIS14_VegType.tif). These metadata list all possible values; some of the values described may not be present in this dataset.
Producer defined
VALUE
Categorical identifier for discrete vegetation types.
Producer defined
11
None: Areas lacking terrestrial vegetation of any type. Such areas were associated with the beach geomorphic setting (found seaward of the study area shoreline) assumed to be covered by water.
Producer defined
22
Herbaceous: areas containing primarily herbaceous vegetation of the forb/herb growth habit (USDA, 2015) and lacking shrubs, trees, or any other vegetation with woody stems (Neuendorf and others, 2011). In this study, the Herbaceous vegetation type typically described the vegetation cover found in Godfrey’s (1976) (i) ‘grassland’ ecological zone along the backshore and dunes, dominated by beach grasses (e.g., Ammophila breviligulata) or (ii) ‘intertidal marsh’ ecological zone dominated by cordgrass (e.g., Spartina patens).
Producer defined
33
Shrub: Areas containing low (less than 5 m height), multi-stemmed woody plants of the subshrub or shrub growth habits (USDA, 2015). In this study, the Shrub vegetation type typically described vegetation cover found in Godfrey’s (1976) heath-like ‘shrublands’ ecological zone in stable dune systems.
Producer defined
44
Forest: Areas containing trees and tall (> 5 m) shrubs of the tree growth habit (USDA, 2015). In this study, the Forest vegetation type typically described vegetation cover found in Godfrey’s (1976) ‘woodlands–forests’ ecological zone found in barrier island interiors and dominated by deciduous (e.g., Quercus velutina), pine (e.g., Pinus rigida), and juniper (e.g., Juniperus virginiana) species.
Producer defined
55
Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined
77
None or Herbaceous: In this study, areas landward of the study area shoreline and lacking vegetation could not be differentiated from areas containing sparse vegetation in the aerial imagery alone. Therefore, we identified vegetation as being either of these two types in the vegetation type raster layer. Areas described as 'none' lacked vegetation of any type, while 'herbaceous' indicated areas containing primarily herbaceous vegetation of the forb/herb growth habit (USDA, 2015) and lacking shrubs, trees, or any other vegetation with woody stems. In this study, the herbaceous vegetation type typically described the vegetation cover found in Godfrey’s (1976) (i) ‘grassland’ ecological zone along the backshore and dunes, dominated by beach grasses (e.g., Ammophila breviligulata) or (ii) ‘intertidal marsh’ ecological zone dominated by cordgrass (e.g., Spartina patens).
Producer defined
88
Herbaceous or Shrub: In this study, areas with herbaceous vegetation could not consistently be differentiated from areas containing low shrubs in the aerial imagery alone. Therefore, we identified vegetation as being either of these two types in the vegetation type raster layer. Areas described as 'herbaceous' indicated areas containing primarily herbaceous vegetation of the forb/herb growth habit (USDA, 2015) and lacking shrubs, trees, or any other vegetation with woody stems. In this study, the herbaceous vegetation type typically described the vegetation cover found in Godfrey’s (1976) (i) ‘grassland’ ecological zone along the backshore and dunes, dominated by beach grasses (e.g., Ammophila breviligulata) or (ii) ‘intertidal marsh’ ecological zone dominated by cordgrass (e.g., Spartina patens). Areas described as 'shrub' indicated areas containing low (less than 5 m height), multi-stemmed woody plants of the subshrub or shrub growth habits (USDA, 2015). In this study, the shrub vegetation type typically described vegetation cover found in Godfrey’s (1976) heath-like ‘shrublands’ ecological zone in stable dune systems.
Producer defined
99
Shrub or Forest: In this study, areas with tall shrubby vegetation could not consistently be differentiated from areas containing forests in the aerial alone. Therefore, we identified vegetation as being either of these two types in the vegetation type raster layer. Areas described as 'shrub' indicated areas containing tall (greater than 5 m height) multi-stemmed woody plants of the shrub growth habit (USDA, 2015). In this study, the shrub vegetation type typically described vegetation cover found in Godfrey’s (1976) heath-like ‘shrublands’ ecological zone in stable dune systems. Areas described as 'forest' contain plants of the tree growth habit (USDA, 2015). In this study, the forest vegetation type typically described vegetation cover found in Godfrey’s (1976) ‘woodlands–forests’ ecological zone found in barrier island interiors and dominated by deciduous (e.g., Quercus velutina), pine (e.g., Pinus rigida), and juniper (e.g., Juniperus virginiana) species.
Producer defined
9999
Unknown: Vegetation type could not be determined based on aerial imagery
Producer defined
COUNT
Number of 5x5 m cells in the raster coverage that contain each vegetation type.
Producer defined
59694
424051
number of grid cells
TYPE
Discrete vegetation types found on Assateague Island
Producer defined
Development
(55) Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
Producer defined
Forest
(44) Forest: Areas containing trees and tall (> 5 m) shrubs of the tree growth habit (USDA, 2015). In this study, the Forest vegetation type typically described vegetation cover found in Godfrey’s (1976) ‘woodlands–forests’ ecological zone found in barrier island interiors and dominated by deciduous (e.g., Quercus velutina), pine (e.g., Pinus rigida), and juniper (e.g., Juniperus virginiana) species.
Producer defined
Herbaceous vegetation
(22) Herbaceous: areas containing primarily herbaceous vegetation of the forb/herb growth habit (USDA, 2015) and lacking shrubs, trees, or any other vegetation with woody stems (Neuendorf and others, 2011). In this study, the Herbaceous vegetation type typically described the vegetation cover found in Godfrey’s (1976) (i) ‘grassland’ ecological zone along the backshore and dunes, dominated by beach grasses (e.g., Ammophila breviligulata) or (ii) ‘intertidal marsh’ ecological zone dominated by cordgrass (e.g., Spartina patens).
Producer defined
Herbaceous vegetation or Shrub
(88) Herbaceous or Shrub: In this study, areas with herbaceous vegetation could not consistently be differentiated from areas containing low shrubs in the aerial imagery alone. Therefore, we identified vegetation as being either of these two types in the vegetation type raster layer. Areas described as 'herbaceous' indicated areas containing primarily herbaceous vegetation of the forb/herb growth habit (USDA, 2015) and lacking shrubs, trees, or any other vegetation with woody stems. In this study, the herbaceous vegetation type typically described the vegetation cover found in Godfrey’s (1976) (i) ‘grassland’ ecological zone along the backshore and dunes, dominated by beach grasses (e.g., Ammophila breviligulata) or (ii) ‘intertidal marsh’ ecological zone dominated by cordgrass (e.g., Spartina patens). Areas described as 'shrub' indicated areas containing low (less than 5 m height), multi-stemmed woody plants of the subshrub or shrub growth habits (USDA, 2015). In this study, the shrub vegetation type typically described vegetation cover found in Godfrey’s (1976) heath-like ‘shrublands’ ecological zone in stable dune systems.
Producer defined
None
(11) None: Areas lacking terrestrial vegetation of any type. Such areas were associated with the beach geomorphic setting (found seaward of the study area shoreline) assumed to be covered by water.
Producer defined
None or Herbaceous
(77) None or Herbaceous: In this study, areas landward of the study area shoreline and lacking vegetation could not be differentiated from areas containing sparse vegetation in the aerial imagery alone. Therefore, we identified vegetation as being either of these two types in the vegetation type raster layer. Areas described as 'none' lacked vegetation of any type, while 'herbaceous' indicated areas containing primarily herbaceous vegetation of the forb/herb growth habit (USDA, 2015) and lacking shrubs, trees, or any other vegetation with woody stems. In this study, the herbaceous vegetation type typically described the vegetation cover found in Godfrey’s (1976) (i) ‘grassland’ ecological zone along the backshore and dunes, dominated by beach grasses (e.g., Ammophila breviligulata) or (ii) ‘intertidal marsh’ ecological zone dominated by cordgrass (e.g., Spartina patens).
Producer defined
Shrub
(33) Shrub: Areas containing low (less than 5 m height), multi-stemmed woody plants of the subshrub or shrub growth habits (USDA, 2015). In this study, the Shrub vegetation type typically described vegetation cover found in Godfrey’s (1976) heath-like ‘shrublands’ ecological zone in stable dune systems.
Producer defined
Shrub or Forest
(99) Shrub or Forest: In this study, areas with tall shrubby vegetation could not consistently be differentiated from areas containing forests in the aerial alone. Therefore, we identified vegetation as being either of these two types in the vegetation type raster layer. Areas described as 'shrub' indicated areas containing tall (greater than 5 m height) multi-stemmed woody plants of the shrub growth habit (USDA, 2015). In this study, the shrub vegetation type typically described vegetation cover found in Godfrey’s (1976) heath-like ‘shrublands’ ecological zone in stable dune systems. Areas described as 'forest' contain plants of the tree growth habit (USDA, 2015). In this study, the forest vegetation type typically described vegetation cover found in Godfrey’s (1976) ‘woodlands–forests’ ecological zone found in barrier island interiors and dominated by deciduous (e.g., Quercus velutina), pine (e.g., Pinus rigida), and juniper (e.g., Juniperus virginiana) species.
Producer defined
Unknown
Unknown: Vegetation type could not be determined based on aerial imagery
Producer defined
This section provides a separate detailed entity and attribute information section for each dataset described in these metadata. These datasets comprise five individual raster files in GeoTiff format, which each have an associated values attribute table with discrete attribute values for each 5x5 m cell. The values attribute file is a necessary component of the dataset.
Methods Open-File Report by Zeigler and others, 2019
U.S. Geological Survey - ScienceBase
mailing and physical address
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
USA
1-888-275-8747
sciencebase@usgs.gov
These datasets comprise five individual raster files in GeoTiff format (ASIS14_SupClas.tif, ASIS14_GeoSet.tif, ASIS14_SubType.tif, ASIS14_VegDen.tif, ASIS14_VegType.tif), which each have an associated values attribute table with discrete attribute values for each 5x5 m cell (ASIS14_SupClas.tif.vat.dbf, ASIS14_GeoSet.tif.vat.dbf, ASIS14_SubType.tif.vat.dbf, ASIS14_VegDen.tif.vat.dbf, ASIS14_VegType.tif.vat.dbf). Additionally, the CSDGM FGDC metadata (ASIS14_SupClas_GeoSet_SubType_VegDen_VegType_meta.xml) and the browse graphic (supclas_rock_browse.png) are included. These datasets can be downloaded individually or packaged on-demand in a zip file (see the Digital Transfer Option section).
Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), and have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. The USGS or the U.S. Government shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
GeoTIFF
ArcGIS 10.4
GeoTIFF
This dataset contains 8-bit and 16-bit GeoTIFF files and associated GeoTIFF components of land cover classifications. Also included is the CSDGM metadata and browse graphic.
809.9
https://www.sciencebase.gov/catalog/item/5daa378ee4b09fd3b0c9ce93
https://www.sciencebase.gov/catalog/file/get/5daa378ee4b09fd3b0c9ce93
https://doi.org/10.5066/P9V7F6UX
The first link is to the page containing the data. The second is a direct link to download all data available from the page as a zip file. The final link is to the publication landing page. The data page (first link) may have additional data access options, including web services.
None
To utilize these data, the user must have software capable of reading a 16-bit GeoTIFF with associated values attribute table.
20200810
Sara L. Zeigler
U.S. Geological Survey
Mailing and Physical
384 Woods Hole Road
Woods Hole
MA
02543
United States
508-548-8700 x2290
szeigler@usgs.gov
FGDC Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998