SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2010–2011

Metadata also available as - [Outline] - [Parseable text] - [XML]

Frequently anticipated questions:


What does this data set describe?

Title:
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2010–2011
Abstract:
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.
Supplemental_Information:
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).
  1. How might this data set be cited?
    Zeigler, Sara L., Sturdivant, Emily J., and Gutierrez, Benjamin T., 2019, SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2010–2011: data release DOI:10.5066/P944FPA4, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Sturdivant, Emily J., Zeigler, Sara L., Gutierrez, Benjamin T., and Weber, Kathryn M., 2019, Barrier island geomorphology and shorebird habitat metrics—Four sites in New York, New Jersey, and Virginia, 2010–2014: data release DOI:10.5066/P944FPA4, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested 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.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -72.72692863
    East_Bounding_Coordinate: -73.32061757
    North_Bounding_Coordinate: 40.78858737
    South_Bounding_Coordinate: 40.6099224
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/5d0bc92de4b0941bde4fc60e/?name=FI_SupClas_GeoSet_SubType_VegDen_VegType_browse.png (PNG)
    Landcover classification, geomorphic setting, substrate type, vegetation density, and vegetation type raster layers at an example location in Fire Island, New York.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 25-Oct-2011
    Ending_Date: 25-Oct-2011
    Currentness_Reference:
    Ground condition measured by source orthoimagery.
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: raster digital dataset
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions 3745 x 9953 x 1, type Grid Cell
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 18
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -75.0
      Latitude_of_Projection_Origin: 0.0
      False_Easting: 500000.0
      False_Northing: 0.0
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 5.0
      Ordinates (y-coordinates) are specified to the nearest 5.0
      Planar coordinates are specified in Meter
      The horizontal datum used is D_North_American_1983.
      The ellipsoid used is GRS_1980.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257222101.
  7. How does the data set describe geographic features?
    FI11_SupClas values attribute table
    Values attribute table (FI11_SupClas.tif.vat.dbf), which indicates the landcover classification of every cell in the raster (FI11_SupClas.tif). These metadata list all possible values; some of the values described may not be present in this dataset. (Source: Producer defined)
    VALUE
    Categorical identifier for landcover characteristics (describing vegetation and substrate). (Source: Producer defined)
    ValueDefinition
    1Water: 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).
    2Open Sand: Areas lacking visible vegetation and containing some type of sandy substrate.
    3Herbaceous 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.
    4Shrub 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.
    7Marsh (vegetation or unknown cover): Marsh areas containing mud/peat substrates and either herbaceous vegetation or unknown vegetation cover.
    9Herbaceous 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.
    10Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
    11Marsh (sediment): Marsh areas containing sandy or mud/peat substrates as well as no to sparse vegetation.
    14Marsh (shrub or forest): Marsh areas containing unknown substrate and either shrubby vegetation or forests.
    COUNT
    Number of 5x5 m cells in the given landcover type. (Source: Producer defined)
    Range of values
    Minimum:59776
    Maximum:383082
    Units:number of grid cells
    TYPE
    Definitions of categorical identifiers for landcover characteristics (describing vegetation and substrate) found on Fire Island. (Source: Producer defined)
    ValueDefinition
    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.).
    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.
    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.
    Marsh (sediment)(11) Marsh (sediment): Marsh areas containing sandy or mud/peat substrates as well as no to sparse vegetation.
    Marsh (shrub or forest)(14) Marsh (shrub or forest): Marsh areas containing unknown substrate and either shrubby vegetation or forests.
    Marsh(7) Marsh (vegetation or unknown cover): Marsh areas containing mud/peat substrates and either herbaceous vegetation or unknown vegetation cover.
    Sand(2) Open Sand: Areas lacking visible vegetation and containing some type of sandy substrate.
    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.
    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).
    FI11_GeoSet values attribute table
    Values attribute table (FI11_GeoSet.tif.vat.dbf), which indicates the geomorphic setting classification of every cell in the raster (FI11_GeoSet.tif). These metadata list all possible values; some of the values described may not be present in this dataset. (Source: Producer defined)
    VALUE
    Coded identifier of geomorphic setting type (see Zeigler and others, 2019 for more details). (Source: Producer defined)
    ValueDefinition
    1Beach: 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.
    2Backshore: 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).
    3Dune 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.
    4Washover (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).
    5Barrier 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).
    6Marsh: A relatively flat, low-lying, intermittently water-covered area with generally halophytic grasses existing landward of a barrier island (Neuendorf and others 2011).
    7Ridge/Swale complex: Long subparallel ridges and swales aligned obliquely across the regional trend of the contours (Neuendorf et al. 2011).
    COUNT
    Number of 5x5 m cells pertaining to the applicable geomorphic setting. (Source: Producer defined)
    Range of values
    Minimum:52128
    Maximum:439684
    Units:number of grid cells
    TYPE
    Geomorphic setting type (see Zeigler and others, 2019 for more details). (Source: Producer defined, definition modified from Neuendorf and others (2011).)
    ValueDefinition
    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).
    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).
    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.
    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.
    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).
    Ridge/Swale complex(7) Long subparallel ridges and swales aligned obliquely across the regional trend of the contours (Neuendorf et al. 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).
    FI11_SubType values attribute table
    Values attribute table (FI11_SubType.tif.vat.dbf), which indicates the substrate type classification of every cell in the raster (FI11_SubType.tif). These metadata list all possible values; some of the values described may not be present in this dataset. (Source: Producer defined)
    VALUE
    Coded identifier for discrete substrate type. (Source: Producer defined)
    ValueDefinition
    1000Sand 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 et al. 2011).
    1111Sand: predominantly sandy substrates that contain rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf et al. 2011) with no discernible shells fragments or large rock fragments.
    2222Shell/Gravel/Cobble: substrates containing a mixture of sand, shell or rock fragments, or large rocks.
    3333MudPeat: A sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (Neuendorf and others 2011).
    4444Water: 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.
    6666Development: Any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
    7777Sand 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.
    9999Unknown: Substrate type could not be determined based on aerial imagery
    COUNT
    Number of 5x5 m cells in the raster coverage that contain each substrate type. (Source: Producer defined)
    Range of values
    Minimum:159380
    Maximum:424051
    Units:number of grid cells
    TYPE
    Discrete substrate type. (Source: Producer defined)
    ValueDefinition
    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.).
    Sand(111) Sand: predominantly sandy substrates that contain rock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf et al. 2011) with no discernible shells fragments or large rock fragments.
    MudPeat(3333) MudPeat: A sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (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 et al. 2011).
    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.
    ShellGravelCobble(222) Shell/Gravel/Cobble: substrates containing a mixture of sand, shell or rock fragments, or large rocks.
    Unknown(9999) Unknown: Substrate type could not be determined based on aerial imagery
    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.
    FI11_VegDen values attribute table
    Values attribute table (FI11_VegDen.tif.vat.dbf), which indicates the vegetation density classification of every cell in the raster (FI11_VegDen.tif). These metadata list all possible values; some of the values described may not be present in this dataset. (Source: Producer defined)
    VALUE
    Categorical identifier for discrete vegetation density classes. (Source: Producer defined)
    ValueDefinition
    111None: 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.
    222Sparse: areas where vegetation was apparent and covered less than 20% of the 5x5-m raster cell.
    333Moderate: areas where vegetation appeared to cover 20–90% of the 5x5-m raster cell.
    444Dense: areas where vegetation appeared to cover greater than 90% of the 5x5-m raster cell.
    555Development: In this study, we selected development as the vegetation density for any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
    666None 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.
    777Sparse 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.
    888Moderate 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.
    9999Unknown: Vegetation density could not be determined based on the aerial imagery
    COUNT
    Number of 5x5 m cells in the raster coverage that contain each vegetation density class. (Source: Producer defined)
    Range of values
    Minimum:159380
    Maximum:424051
    Units:number of grid cells
    TYPE
    Discrete vegetation density classes found on Fire Island. (Source: Producer defined)
    ValueDefinition
    Dense(444) Dense: areas where vegetation appeared to cover greater than 90% of the 5x5-m raster cell.
    Development(555) Development: In this study, we selected Development as the vegetation density for any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
    Moderate(333) Moderate: areas where vegetation appeared to cover 20–90% of the 5x5-m raster cell.
    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.
    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.
    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.
    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.
    Sparse(222) Sparse: areas where vegetation was apparent and covered less than 20% of the 5x5-m raster cell.
    Unknown(9999) Unknown: Vegetation density could not be determined based on aerial imagery.
    FI11_VegType values attribute table
    Values attribute table (FI11_VegType.tif.vat.dbf), which indicates the vegetation type classification of every cell in the raster (FI11_VegType.tif). These metadata list all possible values; some of the values described may not be present in this dataset. (Source: Producer defined)
    VALUE
    Categorical identifier for discrete vegetation types. (Source: Producer defined)
    ValueDefinition
    11None: 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.
    22Herbaceous: 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 et al. 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).
    33Shrub: 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.
    44Forest: Areas containing trees and tall (> 5 m) shrubs of the tree growth habit (USDA 2015b). 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.
    55Development: In this study, we selected Development as the vegetation type for any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
    77None 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).
    88Herbaceous 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.
    99Shrub 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.
    9999Unknown: Vegetation type could not be determined based on aerial imagery
    COUNT
    Number of 5x5 m cells in the raster coverage that contain each vegetation type. (Source: Producer defined)
    Range of values
    Minimum:59694
    Maximum:424051
    Units:number of grid cells
    TYPE
    Discrete vegetation types found on Fire Island (Source: Producer defined)
    ValueDefinition
    Development(55) Development: In this study, we selected development as the vegetation type for any location that fell within areas obviously influenced by anthropogenic activities (e.g., housing developments, paved roads or parking lots, recreational sports fields, etc.).
    Forest(44) Forest: Areas containing trees and tall (> 5 m) shrubs of the tree growth habit (USDA 2015b). 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.
    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 et al. 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).
    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.
    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.
    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).
    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.
    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.
    UnknownUnknown: Vegetation type could not be determined based on aerial imagery
    Entity_and_Attribute_Overview:
    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.
    Entity_and_Attribute_Detail_Citation: Methods Open-File Report by Zeigler and others, 2019

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Sara L. Zeigler
    • Emily J. Sturdivant
    • Benjamin T. Gutierrez
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Sara L. Zeigler
    384 Woods Hole Road
    Woods Hole, MA
    US

    508-548-8700 x2290 (voice)
    508-457-2310 (FAX)
    szeigler@usgs.gov

Why was the data set created?

These categorical raster files map 2011 substrate and vegetation characteristics in 5-m cells. The supervised classification raster (FI11_SupClas.tif) depicts landcover attributes (for example, marsh, sand, water, herbaceous vegetation). It was created with a supervised classification of 2011 aerial imagery. Raster files FI11_SubType.tif, FI11_VegDen.tif, FI11_VegType.tif were reclassified from the supervised classification raster with some manual modifications. FI11_SubType.tif maps discrete substrate types; FI11_VegDen.tif maps discrete categories of vegetation density; FI11_VegType.tif maps discrete vegetation types. Raster file FI11_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).

How was the data set created?

  1. From what previous works were the data drawn?
    Orthoimage (source 1 of 2)
    National Geodetic Survey (NGS), and National Oceanic and Atmospheric Administration, 20121115, 2011 NOAA Ortho-rectified Color Mosaic of Fire Island, New York: NOAA's Ocean Service, National Geodetic Survey (NGS), Silver Spring, MD.

    Online Links:

    Type_of_Source_Media: digital data
    Source_Contribution:
    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 2/15/2016. Data were projected to UTM Zone 18N (EPSG:26918) using the ‘Project Raster’ tool in ArcToolbox (version 10.4.1).
    DEM (source 2 of 2)
    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), and JALBTCX (Joint Airborne Lidar Bathymetry Technical Center of eXpertise), 20120501, 2010 US Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX) Lidar: New York (Topo): NOAA's Ocean Service (NOS), Office for Coastal Management (OCM), Charleston, SC.

    Online Links:

    Type_of_Source_Media: digital data
    Source_Contribution:
    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, UTM Zone 18N (EPSG:26918), vertical datum NAVD88. Downloaded on 2/15/2016.
  2. How were the data generated, processed, and modified?
    Date: 2018 (process 1 of 6)
    FI11_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 island’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. ‘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 Cedar Island, 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 (FI11_Development.shp in larger work). 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 island. 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.
    Date: 2018 (process 2 of 6)
    Geomorphic settings raster (FI11_GeoSet.tif)
    We created the geomorphic settings raster dataset (FI11_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 (fiis10_shoreline.shp in the larger work) and custom study area boundary polygons. The custom study area boundary spans the entire coverage of the Fire 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 (fiis10_DCpts.shp and fiis10_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 side of the island. 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.
    Date: 2018 (process 3 of 6)
    Reclassification for the substrate type layer (FI11_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 (FI11_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 (fiis10_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 island shoreline as Water to maintain consistency in landcover classifications relative to tide fluctuations.
    Date: 2018 (process 4 of 6)
    Reclassification for the vegetation density layer (FI11_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 (FI11_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 (fiis10_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.
    Date: 2018 (process 5 of 6)
    Reclassification for the vegetation type layer (FI11_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 (FI11_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 (fiis10_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 island 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 seaward of the full island shoreline as none, or lacking in terrestrial vegetation.
    Date: 10-Aug-2020 (process 6 of 6)
    Added keywords section with USGS persistent identifier as theme keyword. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
    vatnipp@usgs.gov
  3. What similar or related data should the user be aware of?
    Zeigler, Sara L., Sturdivant, Emily J., and Gutierrez, Benjamin T., 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, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Details the methods used to process these data for use in barrier island and piping plover habitat modeling.
    Landis, J. Richard, and Koch, Gary C., 1977, The measurement of observer agreement for categorical data: Biometrics v. 33, p. 159-174.

    Lea, Christopher, and Curtis, Anthony C., 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, U.S. National Park Service, Fort Collins, CO.

    Neuendorf, Klaus K.E., James P. Mehl, Jr., and Jackson, Julia A., 2011, Glossary of geology. Fifth edition: The American Geosciences Institute, Alexandria, VA.

    Sturdivant, Emily J., Thieler, E. Robert, Zeigler, Sara L., Winslow, Luke A., Hines, Megan K., Read, Jordan S., and Walker, Jordan I., 2016, Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast: data release DOI: 10.5066/F70V89X3, U.S. Geological Survey, Reston, VA.

    Online Links:

    Weber, Kathryn M., List, Jeffrey H., and Morgan, Karen L.M., 2005, An operational mean high water datum for determination of shoreline position from topographic lidar data.: Open-File Report 2005-1027, U.S. Geological Survey, Reston, VA.

    Online Links:

    Doran, Kara J., Long, Joseph W., Birchler, Justin J, Brenner, Owen T., Hardy, Matthew W., Karen L. M. Morgan, Stockdon, Hilary F., and Torres, Miguel L., 2017, Lidar-derived Beach Morphology (Dune Crest, Dune Toe, and Shoreline) for U.S. Sandy Coastlines: U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Affiliated datasets created using the same methods. A version of this geomorph points dataset will be available as 10CNT10_morphology.zip. These data were made available to us prior to publication. As a result some of the processing is different from what is described here.
    Godfrey, Paul J., 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, The Conservation Foundation, Washington, D.C..

    U.S. Department of Agriculture (USDA), and USDA Natural Resources Conservation Service (NRCS), 2015, The PLANTS Database (http://plants.usda.gov, 13 January 2014): National Plant Data Team, Greensboro, NC.


How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    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 Fire Island. These maps were derived in the same manner as those distributed here (derived for Fire Island based on 2010–2011 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 (FI11_SubType.tif, FI11_VegType.tif, FI11_VegDen.tif, FI11_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 others 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. This is substantial to almost perfect accuracy based on the Landis and Koch (1977) accuracy thresholds for the kappa coefficient. Based on substrate type, vegetation density, and vegetation type, attribute accuracy for the supervised classification is estimated to be 85-96%. See Zeigler and others (2019) for more details on this validation exercise.
  2. How accurate are the geographic locations?
    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. FI11_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 1.5 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).
    FI11_GeoSet.tif: Documentation indicates that horizontal accuracy of the elevation data is better than +/- 0.75 m at 1 sigma. No datum transformation was performed, because the source DEM was downloaded in NAD83.
    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 island shoreline for the study area (see fiis10_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.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    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.
  5. How consistent are the relationships among the observations, including topology?
    These raster layers (FI11_SubType.tif, FI11_VegType.tif, FI11_VegDen.tif) show discrete landcover classes and were produced based on a supervised classification of aerial imagery (FI11_SupClas.tif). Geomorphic settings contained in FI11_GeoSet.tif were hand-digitized with reference to the orthoimagery and DEM (see Source Information). 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.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints: none
Use_Constraints:
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.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO
    USA

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? These datasets comprise five individual raster files in GeoTiff format (FI11_SupClas.tif, FI11_GeoSet.tif, FI11_SubType.tif, FI11_VegDen.tif, FI11_VegType.tif), which each have an associated values attribute table with discrete attribute values for each 5x5 m cell (FI11_SupClas.tif.vat.dbf, FI11_GeoSet.tif.vat.dbf, FI11_SubType.tif.vat.dbf, FI11_VegDen.tif.vat.dbf, FI11_VegType.tif.vat.dbf). Additionally, the CSDGM FGDC metadata (FI11_SupClas_GeoSet_SubType_VegDen_VegType_meta.xml) and the browse graphic (FI_SupClas_GeoSet_SubType_VegDen_VegType_browse.png) are included. These datasets can be downloaded individually or packaged on-demand in a zip file (see the Digital Transfer Option section).
  3. What legal disclaimers am I supposed to read?
    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.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    To utilize these data, the user must have software capable of reading a 16-bit GeoTIFF with associated values attribute table.

Who wrote the metadata?

Dates:
Last modified: 10-Aug-2020
Metadata author:
Sara L. Zeigler
U.S. Geological Survey
384 Woods Hole Road
Woods Hole, MA
United States

508-548-8700 x2290 (voice)
szeigler@usgs.gov
Metadata standard:
FGDC Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_P944FPA4/FI11_SupClas_GeoSet_SubType_VegDen_VegType_meta.faq.html>
Generated by mp version 2.9.50 on Tue Sep 21 18:18:59 2021