Coastal Land-Cover Data Derived from Landsat Satellite Imagery, Northern Chandeleur Islands, Louisiana, 1984-2019

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Frequently anticipated questions:

What does this data set describe?

Title:
Coastal Land-Cover Data Derived from Landsat Satellite Imagery, Northern Chandeleur Islands, Louisiana, 1984-2019
Abstract:
The data release (Bernier, 2021) associated with this metadata record serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery at the northern Chandeleur Islands, Louisiana. To minimize the effects of tidal water-level variations, 75 cloud-free, low-water images acquired between 1984 and 2019 were analyzed. Water, bare earth (sand), vegetated, and intertidal land-cover classes were mapped from Hewes Point to Palos Island using successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare land index (NBLI), and the normalized difference vegetation index (NDVI). Vector shoreline, sand, and vegetated feature extents were extracted for each image by contouring the spectral indices using the calculated threshold values. The geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://doi.org/10.5066/P9HY3HOR.
Supplemental_Information:
Information about the Landsat missions, sensor and band specifications, data products, and data access can be found at https://www.usgs.gov/core-science-systems/nli/landsat. The land-cover and feature-extent datasets published in Bernier (2021) support analyses presented by Bernier and others (2021).
  1. How might this data set be cited?
    Bernier, Julie C., 20210921, Coastal Land-Cover Data Derived from Landsat Satellite Imagery, Northern Chandeleur Islands, Louisiana, 1984-2019:.

    This is part of the following larger work.

    Bernier, Julie C., 20210921, Coastal Land-Cover and Feature Datasets Derived from Landsat Satellite Imagery, Northern Chandeleur Islands, Louisiana: U.S. Geological Survey data release doi:10.5066/P9HY3HOR, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -88.932712
    East_Bounding_Coordinate: -88.742256
    North_Bounding_Coordinate: 30.097824
    South_Bounding_Coordinate: 29.720211
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 25-Mar-1984
    Currentness_Reference:
    Ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: Raster and tabular digital data
  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 591 x 1386 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: 16
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -87.0
      Latitude_of_Projection_Origin: 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 30
      Ordinates (y-coordinates) are specified to the nearest 30
      Planar coordinates are specified in Meters
      The horizontal datum used is D WGS 1984.
      The ellipsoid used is WGS 1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    nchan_landcover.zip
    Zip archive containing thematic land-cover raster datasets in ERDAS IMAGINE (.img) format for each of 75 Landsat image-acquisition dates. (Source: USGS)
    OID
    Internal feature number (Source: Esri) Sequential unique whole numbers that are automatically generated
    Value
    Land-cover class number (Source: USGS)
    ValueDefinition
    0Out - pixels outside of analysis extent
    1Water - pixels classified as water
    3Bare earth (sand) - pixels classified as bare earth (sand) land cover
    4Vegetated - pixels classified as vegetated land cover
    11Intertidal (submerged) - pixels classified as submerged intertidal areas
    12Intertidal (emergent) - pixels classified as emergent intertidal areas
    14Intertidal (reclassed) - pixels classified as intertidal (submerged) after manual cleaning of misclassed seagrass extents
    Values between 0 and 255 that are not defined above but are stored in the 8-bit raster attribute table
    Count
    Number of pixels per land-cover class (Source: Hexagon Geospatial) Number of pixels per land-cover class
    Red
    Red value for RGB color map (Source: Hexagon Geospatial) Red value for RGB colormap
    Green
    Green value for RGB color map (Source: Hexagon Geospatial) Green value for RGB colormap
    Blue
    Blue value for RGB color map (Source: Hexagon Geospatial) Blue value for RGB colormap
    Opacity
    Opacity (on or off) of land-cover class (Source: Hexagon Geospatial) Opacity (on or off) of land-cover class
    Class_Name
    Land-cover type (Source: USGS)
    ValueDefinition
    OutPixels outside of analysis extent
    WaterPixels classified as water
    Bare earth (sand)Pixels classified as bare earth (sand) land cover
    VegetatedPixels classified as vegetated land cover
    Intertidal (submerged)Pixels classified as submerged intertidal areas
    Intertidal (emergent)Pixels classified as emergent intertidal areas
    Intertidal (reclassed)Pixels classified as water after manual cleaning of misclassed seagrass extents
    nchan_landcover.csv
    Comma-separated values file detailing land-cover pixel count and total area for each of 75 Landsat image-acquisition dates (Source: USGS)
    FileName
    File name of classed land-cover raster (Source: USGS) Character string
    ImageDate
    Source image-acquisition date (Source: USGS) Source image acquisition date written as DD-MON-YYYY (2-digit day, 3-letter month, 4-digit year)
    DecYear
    Source image-acquisition date, in decimal years (Source: USGS)
    Range of values
    Minimum:1984.230
    Maximum:2019.027
    Units:Decimal year
    Resolution:0.001
    Water_count
    Number of pixels classed as water (Source: USGS)
    Range of values
    Minimum:777360
    Maximum:811846
    Units:Pixels
    Resolution:1
    BareEarth_count
    Number of pixels classed as bare earth (sand) (Source: USGS)
    Range of values
    Minimum:950
    Maximum:7335
    Units:Pixels
    Resolution:1
    Vegetated_count
    Number of pixels classed as vegetated (Source: USGS)
    Range of values
    Minimum:1924
    Maximum:15804
    Units:Pixels
    Resolution:1
    IntertidalSub_count
    Number of pixels classed as intertidal (submerged) (Source: USGS)
    Range of values
    Minimum:1568
    Maximum:10726
    Units:Pixels
    Resolution:1
    IntertidalEmrg_count
    Number of pixels classed as intertidal (emergent) (Source: USGS)
    Range of values
    Minimum:1547
    Maximum:9493
    Units:Pixels
    Resolution:1
    IntertidalRcl_count
    Number of pixels classed as intertidal (reclassed) (Source: USGS)
    Range of values
    Minimum:0
    Maximum:3610
    Units:Pixels
    Resolution:1
    Water_m2
    Area classed as water, in square meters (Source: USGS)
    Range of values
    Minimum:699624000
    Maximum:730661400
    Units:Square meters
    Resolution:1
    BareEarth_m2
    Area classed as bare earth (sand), in square meters (Source: USGS)
    Range of values
    Minimum:855000
    Maximum:6601500
    Units:Square meters
    Resolution:1
    Vegetated_m2
    Area classed as vegetated, in square meters (Source: USGS)
    Range of values
    Minimum:1731600
    Maximum:14223600
    Units:Square meters
    Resolution:1
    IntertidalSub_m2
    Area classed as intertidal (submerged), in square meters (Source: USGS)
    Range of values
    Minimum:1411200
    Maximum:9653400
    Units:Square meters
    Resolution:1
    IntertidalEmrg_m2
    Area classed as intertidal (emergent), in square meters (Source: USGS)
    Range of values
    Minimum:1392300
    Maximum:8543700
    Units:Square meters
    Resolution:1
    IntertidalRcl_m2
    Area classed as intertidal (reclassed), in square meters (Source: USGS)
    Range of values
    Minimum:0
    Maximum:3249000
    Units:Square meters
    Resolution:1

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Julie C. Bernier
  2. Who also contributed to the data set?
    U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center.
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Julie C. Bernier
    Geologist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    jbernier@usgs.gov

Why was the data set created?

Dissemination of thematic raster data representing land-cover classes derived from 52 Landsat 5 Thematic Mapper (TM), 10 Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and 13 Landsat 8 Operational Land Imager (OLI) image datasets from the northern Chandeleur Islands, Louisiana.

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: 2019 (process 1 of 5)
    For each image acquisition date, top-of-atmosphere (TOA) reflectance (reflective bands), TOA brightness temperature (BT; thermal infrared [TIR] bands), and surface reflectance-derived NDVI images were downloaded from the EROS ESPA On Demand Interface (https://espa.cr.usgs.gov/). Person who carried out this activity:
    U.S. Geological Survey
    Attn: Julie C. Bernier
    Geologist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    jbernier@usgs.gov
    Date: 2019 (process 2 of 5)
    All images were batch-processed using Spatial Model Editor in ERDAS IMAGINE 2016. The TOA and BT bands were stacked to create 7- (TM, ETM+) or 9- (OLI) band multispectral images and clipped to the study-area extent. From these composite images, two additional spectral indices, mNDWI and NBLI, were calculated. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Julie C. Bernier
    Geologist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    jbernier@usgs.gov
    Date: 2019 (process 3 of 5)
    Land-cover classification was performed using progressive, automatic thresholding of spectral indices. First, Otsu's method (Otsu, 1979) for automatic histogram thresholding was applied to mNDWI images to create a binary land-water raster for each image acquisition date. Second, water area was masked from NBLI and NDVI images, and Otsu's method was applied to the masked images to create binary bare earth-"unclassed" and vegetated-"unclassed" rasters, respectively. Because thresholding was applied to masked NBLI and NDVI simultaneously, some pixels along the sand-vegetation boundary were classed as both sand and vegetated in this step. We mapped these pixels as sand using the following rule: if NBLI = sand and NDVI = vegetated, then final = sand (based on visual analysis during workflow development). Next, water, bare earth (sand), and vegetated areas were masked from mNDWI images, and Otsu's method was applied to the masked mNDWI image to separate the "submerged" and "emergent" intertidal areas. Finally, the binary land-cover images were converted to thematic rasters, merged, and single-pixel "clumps" were removed using a 3x3 majority filter to create a final land-cover raster dataset. The resulting land-cover rasters use the naming convention YYYYMMDD_lt05_nchan_lcr_ce.img (Landsat 5), YYYYMMDD_le07_nchan_lcr_ce.img (Landsat 7), or YYYYMMDD_lc08_nchan_lcr_ce.img (Landsat 8), where YYYYMMDD denotes the image-acquisition date (4-digit year, 2-digit month, 2-digit day). All steps were batch-processed using the Image Processing toolbox in MATLAB version R2018a (Otsu thresholding and binary image creation) or Spatial Model Editor in ERDAS IMAGINE 2016 (spectral index masking and generation of classed land-cover rasters). Person who carried out this activity:
    U.S. Geological Survey
    Attn: Julie C. Bernier
    Geologist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    jbernier@usgs.gov
    Date: 2020 (process 4 of 5)
    For 15 datasets, thresholding resulted in significant misclassification of back-barrier seagrass beds as vegetated. These were identified based on visual comparison with ancillary datasets and vegetation persistence maps, and misclassed extents were manually cleaned from the final datasets by selecting and re-classing the pixels in ERDAS IMAGINE. The cleaned land-cover rasters use the naming convention YYYYMMDD_lt05_nchan_lcr_ce_cl.img (Landsat 5), YYYYMMDD_le07_nchan_lcr_ce_cl.img (Landsat 7), or YYYYMMDD_lc08_nchan_lcr_ce_cl.img (Landsat 8), where YYYYMMDD denotes the image-acquisition date. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Julie C. Bernier
    Geologist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    jbernier@usgs.gov
    Date: 2020 (process 5 of 5)
    The histogram (pixel count) for each raster was exported to a text file using Spatial Model Editor in ERDAS IMAGINE 2016, merged into a single file, and used to calculate the total area for each land-cover type based on the relationship 1 pixel = 30 meters x 30 meters. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Julie C. Bernier
    Geologist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    jbernier@usgs.gov
    Data sources produced in this process:
    • nchan_landcover.csv
  3. What similar or related data should the user be aware of?
    Bernier, Julie C., Miselis, Jennifer L., and Plant, Nathaniel G., 20210921, Satellite-derived barrier response and recovery following natural and anthropogenic perturbations, northern Chandeleur Islands, Louisiana: Remote Sensing Special Issue "New Insights into Ecosystem Monitoring Using Geospatial Techniques".

    Online Links:

    Otsu, Nobuyuki, 1979, A Threshold Selection Method from Gray-Level Histograms: IEEE Transactions on Systmes, Man and Cybernetics Volume 9.

    Online Links:

    Other_Citation_Details: Pages 62-66

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

  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
    Geodetic accuracy of Landsat L1TP data products depend on the accuracy of the ground control points and the resolution of the digital elevation model (DEM) used. All L1TP data products and derived Level-2 science products meet pre-defined image-to-image georegistration tolerances of <= 12-meter radial root mean square error (RMSE).
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    193 Landsat TM, ETM+, and OLI images (Worldwide Reference System 2 [WRS-2] path 21 row 39) acquired between March 1984 and January 2019 were identified that were cloud free and, for Landsat 7 ETM+ images, were either pre-scan line corrector failure (SLC-off) or SLC-off gap free over the study area. To minimize the effects of water-level variations on land-cover classification, only images that were collected within 2 hours of predicted low tide or were collected on a rising tide with predicted water levels at time of acquisition less than mean sea level (National Ocean Service [NOS] Center for Operational Oceanographic Products and Services [CO-OPS] station 8760172, Chandeleur Light, LA) were analyzed. The resulting dataset consisted of 52 Landsat 5 TM, 10 Landsat 7 ETM+, and 13 Landsat 8 OLI images.
  5. How consistent are the relationships among the observations, including topology?
    For each image-acquisition date, Landsat Collection 1, Level-1 Precision and Terrain (L1TP) corrected data products and Level-2 science products were downloaded from the USGS Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface (https://espa.cr.usgs.gov/).

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 The U.S. Geological Survey requests that it be acknowledged as the originator of this dataset in any future products or research derived from these data.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey
    Attn: Julie C. Bernier
    Geologist
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    jbernier@usgs.gov
  2. What's the catalog number I need to order this data set? *.img, nchan_landcover.csv
  3. What legal disclaimers am I supposed to read?
    This publication was prepared by an agency of the United States Government. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution imply any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and (or) contained herein. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.
  4. How can I download or order the data?
    • Availability in digital form:
      Data format: This zip archive contains thematic land-cover raster datasets in ERDAS IMAGINE (.img) format, land-cover pixel count and total area in comma-separated values (.csv) format, and accompanying metadata for each of 75 Landsat image-acquisition dates. in format Compressed (zip) archive
      Network links: https://coastal.er.usgs.gov/data-release/doi-P9HY3HOR/data/nchan_landcover.zip
    • Cost to order the data: None, if obtained online

  5. What hardware or software do I need in order to use the data set?
    Raster datasets were created using ERDAS IMAGINE 2016 and can be opened using ERDAS IMAGINE or Esri ArcGIS version 10.0 or higher; these data may also be viewed using the free Google Earth Pro or QGIS viewers.

Who wrote the metadata?

Dates:
Last modified: 21-Sep-2021
Metadata author:
U.S. Geological Survey
Attn: Julie C. Bernier
Geologist
600 4th Street South
St. Petersburg, FL
USA

727-502-8000 (voice)
jbernier@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/nchan_landcover-met.faq.html>
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