Table and accompanying photographs for biogeomorphic classification of shorebird nesting sites on the U.S. Atlantic coast from March to September, 2016

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Frequently anticipated questions:


What does this data set describe?

Title:
Table and accompanying photographs for biogeomorphic classification of shorebird nesting sites on the U.S. Atlantic coast from March to September, 2016
Abstract:
Atlantic coast piping plover (Charadrius melodus) nest sites are typically found on low-lying beach and dune systems, which respond rapidly to coastal processes like sediment overwash, inlet formation, and island migration that are sensitive to climate-related changes in storminess and the rate of sea-level rise. Data were obtained to understand piping plover habitat distribution and use along their Atlantic Coast breeding range. A smartphone application called iPlover was developed to collect standardized data on habitat characteristics at piping plover nest locations. The application capitalized on a network of trained monitors that observe piping plovers throughout their U.S. Atlantic coast breeding range as part of the species’ recovery plan. Monitors used iPlover to document nest locations as well as randomly distributed points at beaches and barrier islands on over 930 miles of coast between Maine and North Carolina, USA. This work is one component of a larger research and management program that seeks to understand and sustain ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise. Tabular digital data generated by field data collection with iPlover with accompanying site photographs in JPEG format are presented in this data release.
  1. How might this data set be cited?
    Sturdivant, Emily J., Thieler, E. Robert, Zeigler, Sara L., Winslow, Luke A., Hines, Megan K., Read, Jordan S., and Walker, Jordan I., 2018, Table and accompanying photographs for biogeomorphic classification of shorebird nesting sites on the U.S. Atlantic coast from March to September, 2016: data release DOI:10.5066/P98MI9C5, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Sturdivant, E.J., Thieler, E.R., Zeigler, S.L., Winslow, L.A., Hines, M.K., Read, J.S., and Walker, J.I., 2018, Table and accompanying photographs for biogeomorphic classification of shorebird nesting sites on the U.S. Atlantic coast from March to September, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/P98MI9C5.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -76.540406
    East_Bounding_Coordinate: -69.94125
    North_Bounding_Coordinate: 43.492116
    South_Bounding_Coordinate: 34.640323
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/5aeaf3a1e4b0860c0f727447/?name=iplover_photo_browse.png (PNG)
    Example photo taken by the iPlover app used to collect standardized data on habitat characteristics at piping plover nest locations with a screenshot and icon of the app.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 01-Mar-2016
    Ending_Date: 09-Sep-2016
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: Tabular digital data and JPEG photographs
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a point data set. It contains the following vector data types (SDTS terminology):
      • Point (1044)
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000001. Longitudes are given to the nearest 0.000001. Latitude and longitude values are specified in decimal degrees. The horizontal datum used is World Geodetic System 1984 (WGS 84).
      The ellipsoid used is WGS 1984.
      The semi-major axis of the ellipsoid used is 6378137.
      The flattening of the ellipsoid used is 1/298.257224.
  7. How does the data set describe geographic features?
    iplover_fieldpts_2016.csv
    This file contains attribute information for the classification of biogeomorphic attributes and imagery of coastal habitats associated with shorebird nesting sites for 2016. (Source: U.S. Geological Survey)
    id
    Unique identifying string generated by iPlover application. The id value is used as the prefix of the file name of the photo taken for the point. (Source: iPlover application) character set
    clientversion
    Textual description of the version of the iPlover application used by the user, i.e., installed on their device. (Source: iPlover application)
    ValueDefinition
    2.0-SNAPSHOT2.0-SNAPSHOT is the version of the iPlover application code used for data collection in 2016. It refers to the most updated form (snapshot, as termed in Maven java programming) of iPlover version 2.0.
    collectiongroup
    Indicates the collection group of the data collector so that members of the one group may access points collected by other members of the same group. Collection group membership was assigned to each data collector by the application administrators prior to field collection. Group membership was determined by geographic region with the exception of the Field collection group, which applies to the researchers who collected data at various field sites around the study extent. (Source: U.S. Geological Survey)
    ValueDefinition
    IPLOVER-assateague_islandData point was collected by a user in the Assateague Island National Seashore collection group.
    IPLOVER-cape_lookoutData point was collected by a user in the Cape Lookout, North Carolina collection group.
    IPLOVER-chincoteagueData point was collected by a user in the Chincoteague National Wildlife Refuge collection group.
    IPLOVER-eastern_shore_virginiaData point was collected by a user in the Eastern Shore Virginia collection group.
    IPLOVER-edwin_b_forsytheData point was collected by a user in the Edwin B. Forsythe National Wildlife Refuge collection group.
    IPLOVER-fieldData point was collected by a user in the Field collection group, which includes members of the research team who collected data at locations around the study extent.
    IPLOVER-fire_islandData point was collected by a user in the Fire Island, New York collection group.
    IPLOVER-marthas_vineyardData point was collected by a user in the Martha's Vineyard, Massachusetts collection group.
    IPLOVER-massachusettsData point was collected by a user in the Massachusetts collection group.
    IPLOVER-monomoy_islandData point was collected by a user in the Monomoy Island, Massachusetts collection group.
    IPLOVER-new_jerseyData point was collected by a user in the New Jersey collection group.
    IPLOVER-parker_riverData point was collected by a user in the Parker River, Massachusetts collection group.
    IPLOVER-rachel_carsonData point was collected by a user in the Rachel Carson National Wildlife Refuge, Maine collection group.
    IPLOVER-rhode_islandData point was collected by a user in the Rhode Island collection group.
    IPLOVER-tnc_virginia_coastData point was collected by a user in the The Nature Conservancy - Virginia Coast collection group.
    deviceinfo
    Specifications of the mobile device on which data were collected, generated by the iPlover application. (Source: Smartphone) character set
    datetime
    Date and time on which the data were recorded in the field in local time (usually Eastern Daylight Time, which started March 13, 2016) in YYYY-MM-DD HH:MM format. (Source: U.S. Geological Survey)
    Range of values
    Minimum:2016-03-01 14:34:02
    Maximum:2016-09-09 13:55:14
    latitude
    Latitude coordinate, in decimal-degrees, of data point. (Source: Smartphone GPS)
    Range of values
    Minimum:34.64032974
    Maximum:43.49211595
    Units:signed decimal degrees
    Resolution:1.0E-8
    longitude
    Longitude coordinate, in decimal-degrees, of data point. West longitude is recorded as negative values (Source: Smartphone GPS)
    Range of values
    Minimum:-76.5404059
    Maximum:-69.9412504
    Units:signed decimal degrees
    Resolution:1.0E-8
    accuracy
    The location accuracy reported by the smartphone in meters. These values tend to be imprecise. In iPhones, values are reported in 5m increments. (Source: Smartphone GPS)
    Range of values
    Minimum:3
    Maximum:30
    Units:meters
    Resolution:1.0E-8
    site
    Identifier given to the nest either by iPlover user or by the generator of the random point. (Source: iPlover user defined) character set
    setting
    Geomorphic setting of data point as identified by the user selecting one of seven radio button choices within the iPlover application. (Source: iPlover user)
    ValueDefinition
    backshoreThe upper, usually dry, zone of the shore or beach, lying between the high-water line 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 line and either (i) the dune toe, (ii) the edge of developed areas, or (iii) the edge of dense vegetation (or forest).
    barrier_interiorDescribed 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/swale complexes).
    beachThe 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 between the ocean- or sound-side study area boundary and the mean high water line (approximated for by the upper-most wrack line in the field).
    duneA 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.
    marshA relatively flat, low-lying, intermittently water-covered area with generally halophytic grasses existing landward of a barrier island (Neuendorf and others, 2011).
    swaleLong sub-parallel ridges and swales aligned obliquely across the regional trend of the contours. In this study area, these occur typically at the downdrift ends of barrier islands or spits (e.g., a low peninsula or barrier ending in a recurved spit such as the southern end of Assateague Island) (Neuendorf and others, 2011).
    washoverA 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/sound (barrier islands). A washover typically displays a characteristic fan-like shape (Neuendorf and others, 2011).
    vegtype
    Predominant vegetation type within 5x5 m area around point as identified by the user selecting one of four radio button choices within the iPlover application. Vegtype may not always agree with vegdens due to confusion of user. In some cases, users selected vegtype none and vegdens other than none or vegdens none, but categorized a vegtype. (Source: iPlover user)
    ValueDefinition
    herbaceousAreas containing primarily herbaceous vegetation and lacking shrubs, trees, or any other vegetation with woody stems (Forb/herb growth habit; U.S. Department of Agriculture 2015). 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).
    noneAreas lacking vegetation of any type. Such areas were common on beaches, backshores, and washovers that frequently or recently experienced wave-action.
    shrubAreas containing low (less than 5m), multi-stemmed woody plants (subshrub and shrub growth habits; U.S. Department of Agriculture 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.
    vegdens
    Predominant vegetation density within 5x5 m area around point as estimated by the user and selected from one of four radio button choices within the iPlover application. Vegdens may not always agree with vegtype due to confusion of user. In some cases, users selected vegtype none and vegdens other than none or vegdens none, but categorized vegtype. (Source: iPlover user)
    ValueDefinition
    denseVegetation covered more than 90% of the 5x5 m area surrounding an iPlover data point or map cell.
    moderateVegetation covered 20-90% of the 5x5 m area surrounding an iPlover data point or map cell.
    noneNo vegetation observed in the 5x5 m area surrounding an iPlover data point or map cell.
    sparseVegetation was apparent and covered less than 20% of the 5x5 m area surrounding an iPlover data point or map cell.
    substrate
    Predominant substrate type within 5x5 m area around point as identified by the user selecting one of five radio button choices within the iPlover application. (Source: iPlover user)
    ValueDefinition
    mud_peatA sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (Neuendorf and others, 2011).
    sandyRock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011). In this study, a predominantly “sandy” substrate consisted of finer grains with no discernible shells fragments or large rock fragments.
    shell_gravel_cobbleSubstrate containing a mixture of sand, shell or rock fragments, or large rocks.
    unknownSubstrate type unknown or missing.
    waterSubstrate type for any iPlover data point or location that (i) is always submerged (e.g., points several meters into the ocean, bay, or inland water body) or (ii) was submerged at the time of iPlover point collection (i.e., intertidal regions of beaches). Typically, only random points are classified as water.
    setting_2
    Geomorphic setting of the data point recoded to the standard value used in analysis. The setting and setting_2 attributes are both provided to ensure compatibility with previous versions. (Source: Sara L. Zeigler)
    ValueDefinition
    BackshoreThe upper, usually dry, zone of the shore or beach, lying between the high-water line 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 line and either (i) the dune toe, (ii) the edge of developed areas, or (iii) the edge of dense vegetation (or forest).
    BarrierInteriorDescribes 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/swale complexes).
    BeachThe 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 between the ocean- or sound-side study area boundary and the mean high water line (approximated for by the upper-most wrack line in the field).
    DuneA 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.
    MarshA relatively flat, low-lying, intermittently water-covered area with generally halophytic grasses existing landward of a barrier island (Neuendorf and others, 2011).
    RidgeSwaleThe point occurs in a ridge/swale complex: long sub-parallel ridges and swales aligned obliquely across the regional trend of the contours. Common on the “hooks” (i.e., a low peninsula or barrier ending in a recurved spit and formed at the end of a bay; e.g., the hook of Chincoteague Island) of barrier islands of the Mid-Atlantic, USA (Neuendorf and others, 2011).
    WashoverA 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/sound (barrier islands). A washover typically displays a characteristic fan-like shape (Neuendorf and others, 2011).
    vegtype_2
    Predominant vegetation type at the data point recoded to the standard value used in analysis. The vegtype and vegtype_2 attributes are both provided to ensure compatibility with previous versions. (Source: Sara L. Zeigler)
    ValueDefinition
    HerbaceousAreas containing primarily herbaceous vegetation and lacking shrubs, trees, or any other vegetation with woody stems (Forb/herb growth habit; U.S. Department of Agriculture 2015). 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).
    NoneAreas lacking vegetation of any type. Such areas were common on beaches, backshores, and washovers that frequently or recently experienced wave-action.
    ShrubAreas containing low (less than 5m), multi-stemmed woody plants (subshrub and shrub growth habits; U.S. Department of Agriculture 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.
    vegdens_2
    Predominant vegetation density at the data point recoded to the standard value used in analysis. The vegdens and vegdens_2 attributes are both provided to ensure compatibility with previous versions. Vegetation density may not always agree with vegetation type due to confusion of user. (Source: Sara L. Zeigler)
    ValueDefinition
    DenseVegetation covered more than 90% of the 5x5 m area surrounding an iPlover data point or map cell.
    ModerateVegetation covered 20-90% of the 5x5 m area surrounding an iPlover data point or map cell.
    NoneNo vegetation observed in the 5x5 m area surrounding an iPlover data point or map cell.
    SparseVegetation was apparent and covered less than 20% of the 5x5 m area surrounding an iPlover data point or map cell.
    substrate_2
    Predominant substrate type at the data point recoded to the standard value used in analysis. The substrate and substrate_2 attributes are both provided to ensure compatibility with previous versions. (Source: Sara L. Zeigler)
    ValueDefinition
    MudPeatA sticky, fine-grained, predominantly clay- or silt-sized marine detrital sediment (Neuendorf and others, 2011).
    SandRock or mineral grains with diameters between 0.074 and 4.76 mm (Neuendorf and others, 2011). In this study, a predominantly “sandy” substrate consisted of finer grains with no discernible shells fragments or large rock fragments.
    ShellGravelCobbleIn this study, Shell/Gravel/Cobble described substrate containing a mixture of sand, shell or rock fragments, or large rocks.
    WaterWater is the substrate type for any iPlover data point or location that (i) is always submerged (e.g., points several meters into the ocean, bay, or inland water body) or (ii) was submerged at the time of iPlover point collection (i.e., intertidal regions of beaches). Typically, only random points are classified as water because plovers would not nest there.
    *Substrate type unknown or missing.
    imagefile
    JPEG photo filename if the image is present in the dataset. Value of 'MISSING' indicates that the file was not retrieved by the server. (Source: iPlover application)
    ValueDefinition
    MISSINGImage file is not present in the dataset because it could not be retrieved from the server.
    character set
    Entity_and_Attribute_Overview:
    The first line of the CSV file is a header line with column headings that are equivalent to the attributes defined here.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Emily J. Sturdivant
    • E. Robert Thieler
    • Sara L. Zeigler
    • Luke A. Winslow
    • Megan K. Hines
    • Jordan S. Read
    • Jordan I. Walker
  2. Who also contributed to the data set?
    Please acknowledge the USGS Coastal and Marine Geology Program as a data source in products developed from these data.
  3. To whom should users address questions about the data?
    E. Robert Thieler
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA
    USA

    (508) 548-8700 x2350 (voice)
    rthieler@usgs.gov

Why was the data set created?

Data (tabular digital data of field data and JPEG images) were obtained to understand the biogeomorphic characteristics of piping plover (Charadrius melodus) habitat along their U.S. Atlantic coast breeding range.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 01-Mar-2016 (process 1 of 4)
    Data were collected in the field with the iPlover smartphone application. iPlover data were primarily collected during the breeding season, which is roughly April to July.
    Prior to field collection, all data collectors participated in a training seminar and were given written protocols (https://github.com/usgs/iplover/blob/master/iPloverUserGuide.pdf). Users recorded (i) information on the habitat characteristics at a given location, (ii) images of the habitat, and (iii) spatial coordinates of the location of either a piping plover nest or a random point.
    Users logged two types of sites: nest sites and random points. The nest sites were identified by users in the course of monitoring efforts. Random point locations were randomly generated and sent to data collectors by Sara L. Zeigler. Random point generation was constrained by the boundaries of pre-determined sub-sites. These boundaries usually were defined by the terrestrial extent of a barrier island. Random points were collected to represent unsuitable biogeomorphic characteristics.
    Once at the site, users logged a data point by selecting ‘Log a Nest Site’ within iPlover, version 2.0 (Figure 5 in Thieler and others, 2016). This opened a field survey page where they 1) logged the geolocation, date, and time, 2) took a photograph, and 3) categorized the biogeomorphology of the site. To log the geolocation, date, and time, they held the mobile device directly over the site and selected lock location, at which point the app recorded the geolocation coordinates, date, and time detected by the device’s internal sensors. They next stepped 5 m away from the site to take a photograph using the device’s built-in camera. Next, the user manually completed a simple habitat assessment according to a fixed set of variables and their potential categorical values listed in iPlover. Users were instructed to base their habitat assessment on the area within a 5x5 m area containing the nest site (or random point) at its center. Users assigned a value for each of the four habitat variables - Geomorphic Setting, Substrate Type, Vegetation Type, and Vegetation Density – by selecting a radio button (Figure 5 in Thieler and others, 2016). Users typed a site identification and notes in free text fields. iPlover records were locally stored on smartphones while users were in the field. Once an Internet connection was available, users uploaded locally-stored records to a central PostgreSQL (version 9.4) database.
    The data first passed through a Java parser on a server at the USGS EROS Data Center that converted the JSON data produced by the iPlover application into insert statements that are used against the PostgreSQL (version 9.4) table. The photos were uploaded to a separate server with greater storage capacity (Amazon Web Services S3 bucket). Most photographs uploaded through the application did not retain EXIF data because of the privacy practices of the individual users or the mobile operating systems.
    The process date indicates the beginning of the data collection.
    Date: 2017 (process 2 of 4)
    At the end of the field collection season, the PostgreSQL (version 9.4) 'entries' table was exported into a comma-separated values (CSV) file. The photos were downloaded from the cloud server. The table was manipulated as a DataFrame object in the Pandas Python package (version 3.3) and saved as a CSV as follows:
    1) Attributes that were of no further use were removed, i.e. 'OBJECTID', ‘altitude’, ‘altaccuracy’, ‘notes’, and ‘deleted’. Altitude and the associated altitude accuracy had no relationship to the data because altitude measurements of mobile devices were highly imprecise.
    2) The attribute 'imagefile' was created and populated by concatenating the value for the ‘id’ attribute with the ‘.jpg’ extension. The image filenames from the 'imagefile' attribute were checked against the existing image files; when an image listed in 'imagefile' did not exist, the value was replaced with ‘MISSING’. Eleven photos were missing.
    3) Any rows that did not record a unique piping plover nest site or random point were removed. These included records used for various tests, records of American oystercatcher nest sites, and records of duplicate sites. These points were identified using values in the collectiongroup, location and notes attributes and by viewing the photos. For example, all records for which the notes attribute value indicated that the record was logged as a test were eliminated.
    If faces of individuals were present in photos, they were blurred using a 20 pixel Field Blur filter to an area manually delineated with the Lasso tool in Adobe PhotoShop CS6 Version 13.0.6 x64. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Emily J. Sturdivant
    GIS Specialist
    384 Woods Hole Rd.
    Woods Hole, MA

    (508) 548-8700 x2230 (voice)
    508-457-2310 (FAX)
    esturdivant@usgs.gov
    Date: 2017 (process 3 of 4)
    When iPlover was updated between 2014 and 2015, choices used for the various biogeomorphic state classifications were reorganized, modified, and added to better reflect characteristics observed in the field. To enable analysis of the combined datasets, values logged in 2014 were reclassified to the choices used in 2015 and 2016. The coded values from 2015 and 2016 were also reclassified to a standard domain. The reclassified values are included in the columns of the table indicated by attribute names with the suffix “_2.” The data contain both identifiers to ensure compatibility with data released for previous years.
    The reclassification was performed using the following reclassification table developed by Sara L. Zeigler. Column 1 has values recorded by iPlover based on user choices and columns 2–5 indicate the reclassification to standard values for each biogeomorphic variable.
    
    Geomorphic Setting ('setting' --> 'setting_2')
    backshore --> Backshore
    barrier_interior --> BarrierInterior
    beach --> Beach
    dune --> Dune
    marsh --> Marsh
    swale --> RidgeSwale
    washover --> Washover
    
    Substrate Type ('substrate' --> 'substrate_2')
    mud_peat --> MudPeat
    sandy --> Sand
    shell_gravel_cobble --> ShellGravelCobble
    unknown --> *
    water --> Water
    
    Vegetation Density ('vegdens' -- 'vegdens_2')
    dense --> Dense
    moderate --> Moderate
    none --> None
    sparse --> Sparse
    
    Vegetation Type ('vegtype' --> 'vegtype_2')
    forest --> Forest
    herbaceous --> Herbaceous
    none --> None
    shrub --> Shrub
    
    Person who carried out this activity:
    U.S. Geological Survey
    Attn: Sara L. Zeigler
    Project Scientist
    384 Woods Hole Rd.
    Woods Hole, MA

    (508) 548-8700 x2350 (voice)
    508-457-2310 (FAX)
    szeigler@usgs.gov
    Date: 06-Oct-2017 (process 4 of 4)
    EXIF data for the photographs were populated using the ASCII table of field collection data. This was necessary because most photographs uploaded through the application did not include EXIF data. The lack of EXIF data was caused by privacy practices of the users or the mobile operating systems.
    Attribute formatting was modified in the Pandas Python package (version 3.3) to match the requirements for EXIF data: The ‘datetime’ attribute was split into two attributes: ‘date’ and ‘time’ with all formatting removed. The attributes and final formatting was as follows: id = unique-id-string.jpg; lat = DD.dddddd; lon = -DD.dddddd; time = hhmmss; date = YYYYMMDD. A Python (version 2.7) script was then run that incorporated this information, along with additional information, into the appropriate locations in the EXIF header of each full-resolution JPEG file. If latitude or longitude was negative, the appropriate GPS tag was set to South or West. The script used ExifTool (version 9.4.6.0) to write the information to the image headers of the full-resolution images.
    The following tags were populated in the JPEG image headers. Information is duplicated in some tags. This was done because different software packages access different tags.
    GPS tags: The values populated are unique for each image and based on the information exported from the associated iPlover points CSV file. These positions and time actually represent when the data were recorded in the iPlover app, which should be 5 m and a few seconds offset from the location and time at which the photograph was actually taken according to standard iPlover data collection.
    GPSLatitudeRef
    GPSLatitude
    GPSLongitudeRef
    GPSLongitude
    GPSTimeStamp
    GPSDateStamp
    
    JPEG tags: The tag is listed along with the information used to populate it - which is the same for every image taken.
    Comment: Photograph collected in iPlover, 2016
    
    EXIF tags: The tag is listed along with the information used to populate it - which is the same for every image.
    ImageDescription: Photograph collected in iPlover, 2016
    Artist: Anonymous iPlover user
    Copyright: Public Domain. Please credit U.S. Geological Survey
    
    IPTC tags: The tag is listed along with the information used to populate it - which is the same for every image.
    Credit: U.S. Geological Survey
    Contact: WHSC_data_contact@usgs.gov
    Keywords: Shorebird habitat, US Atlantic coast, USGS
    CopyrightNotice: Public Domain. Please credit U.S. Geological Survey
    Caption-Abstract: Photograph collected in iPlover, 2016
    
    XMP tags: The tag is listed along with the information used to populate it - which is the same for every image.
    Caption: Photograph collected in iPlover, 2016
    
    To extract the information from the image headers using ExifTool, the following command can be used (tested with ExifTool version 9.4.6.0): exiftool.exe -csv -f -filename -GPSDateStamp -GPSTimeStamp -GPSLongitude -GPSLatitude -n -Artist -Credit -comment –Contact -keywords -Caption -Copyright -CopyrightNotice -Caption-Abstract -ImageDescription *.jpg > out.csv
    The -csv flag writes the information out in a comma-delimited format. The -n option formats the latitude and longitude as signed decimal degrees. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Emily Sturdivant
    Geographer
    384 Woods Hole Rd.
    Woods Hole, MA

    (508) 548-8700 x2230 (voice)
    508-457-2310 (FAX)
    esturdivant@usgs.gov
  3. What similar or related data should the user be aware of?
    Neuendorf, Klaus K.E., Mhel, James P. Jr., and Jackson, Julia A., 2011, Glossary of Geology: The American Geosciences Institute, Alexandria, VA.

    U.S. Department of Agriculture, and Natural Resources Conservation Service, 2015, The PLANTS Database: National Plant Data Team, Greensboro, NC.

    Online Links:

    Godfrey, Paul J., 1976, Comparative ecology of East Coast barrier islands: hydrology, soil, vegetation.: The Conservation Foundation, Annapolis, MD.

    Other_Citation_Details: pages 5-31
    Thieler, E. Robert, Zeigler, Sara L., Winslow, Luke A., Hines, Megan K., Read, Jordan S., and Walker, Jordan I., 2016, Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability: PLoS ONE, San Francisco, CA.

    Online Links:

    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:


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

  1. How well have the observations been checked?
    iPlover users selected attributes for Geomorphic Setting, Substrate Type, Vegetation Type, and Vegetation Density that best characterized each nest or random point location. As such, there may be some variation in the assessment of a category between individual users. The reliability of the user-chosen classification was estimated as described below. The formal accuracy assessment described below was performed for data collected with the same protocols in two prior years (Sturdivant and others, 2016).
    The accuracy of the classifications of Geomorphic Setting, Substrate Type, Vegetation Type, and Vegetation Density was evaluated using the classification of four subject-matter experts as validation (Thieler and others, 2016). The expert validation was performed on a 10 percent random sample of points collected using iPlover in 2014 and 2015 (k = 181, n = 1799). Attribute values were updated between 2014 and 2015 to provide greater specificity and conformance with biogeomorphic nomenclature. Thus, values in the 2014 dataset were first reclassified to 2015 values.
    Subject-matter experts used the photograph and geolocation of each sample point to assign the biogeomorphic classification (across all four variables). Experts were selected based on their experience and history of relevant publications on the geology and geomorphology of coastal environments in the study domain, as well as demonstrated expertise in the interpretation of remotely sensed data. Truth values for each sample point were selected according to expert consensus.
    The following percentages indicate the agreement between expert consensus and field user classification for all test points for which expert consensus could be determined: 58% for geomorphic setting (90 agreeing points out of 156 truth), 80% for substrate type (121 out of 151), 84% for vegetation type (141 out of 167), and 77% for vegetation density (118 out of 153) (see Table 2 and Supplemental Table 2 in Thieler and others, 2016). Of the 181 locations, an expert consensus could be reach in 151, 167, and 153 cases for substrate type, vegetation type, and vegetation density respectively. Systematic errors were more common for the substrate and vegetation variables compared to geomorphic setting (Table 3 in Thieler and others, 2016). What experts classified as Sand was frequently classified as Shell/Gravel/Cobble by iPlover field users and vice versa, accounting for 86% of the differences in this variable. Vegetation Type was frequently classified as Herbaceous by experts and as None by iPlover users and vice versa, accounting for 81% of the differences for this habitat variable. Finally, the most frequent differences in Vegetation Density were between None and Sparse (51% of differences) and between Sparse and Moderate (23%; Table 3 in Thieler and others, 2016). The most common differences between the classifications of iPlover field users and experts occurred at the "boundaries" of categorical habitat characteristics.
    Experts and iPlover field users also disagreed frequently over whether a point fell in no vegetation or sparse vegetation or whether a point fell within sparse or moderate vegetation cover. Experts and iPlover field users were instructed to visually estimate vegetation density and did not use field-based techniques such as quadrat sampling to quantify density. Therefore, differences in classifications between experts and iPlover field users would be expected in places where vegetation density was close to 20%, separating Sparse from Moderate vegetation, or close to 90%, separating Moderate from Dense vegetation.
  2. How accurate are the geographic locations?
    Positional accuracy was estimated by comparing the smartphone geolocation data to that collected by a SpectraPrecision SP80 GNSS receiver at 44 piping plover nest points in Virginia and Massachusetts in 2015. We determined the horizontal difference between the geolocation data obtained from iPlover (i.e., the smartphone’s built-in geolocation functionality; Table 1 in Thieler and others, 2016) with high-resolution nest location data collected with the GNSS receiver. Smartphones from 2008 to 2016 (beginning with the 3G iPhone) use a hybrid positioning system that combines A-GPS, Wi-Fi, and cellular positioning methods that are generally accurate within 3-8 m (Zandbergen 2009; Boccardo and others 2015). GNSS data consist of a combination of real-time kinematic and post-processed positions that are accurate within 3±2 cm in the horizontal. GNSS data are thus two orders of magnitude more accurate than average smartphone GPS data, so we were able to evaluate smartphone accuracy easily. The differences in x (0.3 +/- 4.4 m) and y (0.08 +/- 5.7 m) UTM coordinates of the smartphone and GNSS locations are nearly distributed around zero. The average difference between the GNSS points and the smartphones was 5.8 +/- 4.2 m. We did not find spatial variability in accuracy from site to site. For one site where we had repeated measurements at different times on the same day, the difference in geolocation was well within the error reported here. The ‘accuracy’ attribute indicates the location accuracy reported by geolocation application program interface. These values tend to be imprecise.

    References:
    Boccardo, P., Chiabrando, F., Dutto, F., Tonolo, F.G., Lingua, A.: UAV Deployment Exercise for Mapping Purposes– Evaluation of Emergency Response Applications: Sensors, 2015;15(7):15717, doi:10.3390/s150715717.

    Zandbergen, P.A., 2009, Accuracy of iPhone Locations– A comparison of assisted GPS, WiFi and cellular positioning: Transactions in GIS, 2009;13:5-25, doi:10.1111/j.1467-9671.2009.01152.x.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    This dataset contains all of the points logged in the iPlover application in 2016 except 115 records logged for testing purposes. Some data records are missing photographs, which are noted with the value 'MISSING' in the imagefile column. Faces of individuals were blurred in all photos in which they appeared for privacy concerns.
  5. How consistent are the relationships among the observations, including topology?
    No additional checks for topological consistency were performed on this 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. The U.S. Geological Survey requests to be acknowledged as originator of these data in future products or derivative research.
  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? The USGS data release contains the biogeomorphic classification and accompanying photographs of points recorded in the citizen science application iPlover. The data release includes a comma-separated values file and a zip file with corresponding site photographs (JPEG) collected by the users on their mobile devices. Additionally, the CSDGM FGDC metadata in XML format is included.
  3. What legal disclaimers am I supposed to read?
    Neither the U.S. Government, the Department of the Interior, nor the U.S. Geological Survey, 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 U.S. Geological Survey in the use of these data or related materials. Any use of trade, product, or firm 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?
    The user must have software capable of uncompressing archived zip files, such as WinZip or Pkware. These data are available as a generic ASCII formatted data file. The user must have a software program capable of reading the comma-delimited data file. An image viewer can be used to see the JPEG images. JPEG header information can be viewed with an image metadata viewer, such as with ExifTool as described above.

Who wrote the metadata?

Dates:
Last modified: 17-May-2018
Metadata author:
Emily Sturdivant
U.S. Geological Survey
GIS Specialist
384 Woods Hole Road
Woods Hole, MA
USA

(508) 548-8700 x2230 (voice)
(508) 457-2310 (FAX)
esturdivant@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/data_release/DR_P98MI9C5/iplover_fieldpts_2016_metadata.faq.html>
Generated by mp version 2.9.49 on Mon Sep 10 17:44:59 2018