Coastal Land-Cover Data Derived from Landsat Collection 2 Data, Northern Chandeleur Islands, Louisiana

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Frequently anticipated questions:

What does this data set describe?

Title:
Coastal Land-Cover Data Derived from Landsat Collection 2 Data, Northern Chandeleur Islands, Louisiana
Abstract:
This data release serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery at the northern Chandeleur Islands, Louisiana. To minimize effects of tidal water-level variations, only images that were collected within 2 hours of predicted low tide or that were collected on a rising tide with predicted water levels less than mean sea level at time of image acquisition 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. Barrier platform, beach, and vegetated widths were calculated from the intersection of the shoreline, sand, and vegetated vectors with transects spaced 300 meters (m) (10 pixels) apart alongshore. These data can be used to evaluate decadal-scale barrier landscape changes (Bernier and others, 2021).
Supplemental_Information:
Information about the Landsat missions, sensor and band specifications, data products (including Collection 1 versus Collection 2 processing), and data access can be found at https://www.usgs.gov/landsat-missions.
  1. How might this data set be cited?
    Bernier, Julie C., 20251219, Coastal Land-Cover Data Derived from Landsat Collection 2 Data, Northern Chandeleur Islands, Louisiana:.

    This is part of the following larger work.

    Bernier, Julie C., Williams, Breanna N., and Miselis, Jennifer L., 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: 22-Dec-2017
    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_coll2_landcover.zip
    Zip archive containing thematic land-cover raster datasets in ERDAS IMAGINE (.img) format. Filenames use the convention YYYYMMDD_LC##_nchan_islp_lcr_ce(_cl).img as described above. (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 (cleaned) - pixels re-classified as intertidal (submerged) after manual cleaning of misclassed seagrass extents
    Values of 2, 5 through 10, 13, and 15 through 255 are not used to define a land-cover class but are stored by default 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. Values in the attribute table depend on the software used to view the data but are most commonly 0 (off) and 1 or 255 (on). (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 (cleaned)Pixels re-classified as intertidal after manual cleaning of misclassed seagrass extents
    nchan_coll2_landcover_2017_2024.csv
    Comma-separated values file detailing land-cover pixel count and total area for each image-acquisition date, contained in nchan_coll2_landcover.zip (Source: USGS)
    FileName
    File name of classed land-cover raster (Source: USGS) Character string; YYYYMMDD_LC##_nchan_islp_lcr_ce(_cl).img
    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:2017.975
    Maximum:2024.873
    Units:Decimal year
    Resolution:0.001
    SceneID
    Landsat Collection 2, Level 2 source data identifier downloaded from EROS ESPA (Source: USGS) Landsat source imagery data identifier
    Water_count
    Number of pixels classed as water (Source: USGS)
    Range of values
    Minimum:124105
    Maximum:137091
    Units:Pixels
    Resolution:1
    BareEarth_count
    Number of pixels classed as bare earth (sand) (Source: USGS)
    Range of values
    Minimum:1350
    Maximum:3721
    Units:Pixels
    Resolution:1
    Vegetated_count
    Number of pixels classed as vegetated (Source: USGS)
    Range of values
    Minimum:4220
    Maximum:8181
    Units:Pixels
    Resolution:1
    IntertidalSub_count
    Number of pixels classed as intertidal (submerged) (Source: USGS)
    Range of values
    Minimum:1941
    Maximum:8827
    Units:Pixels
    Resolution:1
    IntertidalEmrg_count
    Number of pixels classed as intertidal (emergent) (Source: USGS)
    Range of values
    Minimum:1363
    Maximum:6324
    Units:Pixels
    Resolution:1
    IntertidalRcl_count
    Number of pixels re-classed as intertidal (cleaned) (Source: USGS)
    Range of values
    Minimum:0
    Maximum:264
    Units:Pixels
    Resolution:1
    Water_m2
    Area classed as water, in square meters (Source: USGS)
    Range of values
    Minimum:111694500
    Maximum:123381900
    Units:Square meters
    Resolution:1
    BareEarth_m2
    Area classed as bare earth (sand), in square meters (Source: USGS)
    Range of values
    Minimum:1215000
    Maximum:3348900
    Units:Square meters
    Resolution:1
    Vegetated_m2
    Area classed as vegetated, in square meters (Source: USGS)
    Range of values
    Minimum:3798000
    Maximum:7362900
    Units:Square meters
    Resolution:1
    IntertidalSub_m2
    Area classed as intertidal (submerged), in square meters (Source: USGS)
    Range of values
    Minimum:1746900
    Maximum:7944300
    Units:Square meters
    Resolution:1
    IntertidalEmrg_m2
    Area classed as intertidal (emergent), in square meters (Source: USGS)
    Range of values
    Minimum:1226700
    Maximum:5691600
    Units:Square meters
    Resolution:1
    IntertidalRcl_m2
    Area classed as intertidal (reclassed), in square meters (Source: USGS)
    Range of values
    Minimum:0
    Maximum:237600
    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 (SPCMSC). Funding and (or) support for this analysis were provided as part of the Extending Government Funding and Delivering Emergency Assistance Act (Public Law 117-43), enacted on September 30, 2021. This document was improved by scientific and metadata reviews by Sydney Nick and Tess Rivenbark-Terrano (USGS SPCMSC).
  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 13 Landsat 6 and Landsat 9 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: 2025 (process 1 of 5)
    For each image acquisition date, Landsat Collection 2 ARD top-of-atmosphere (TOA) reflectance (reflective bands) and TOA brightness temperature (BT; thermal infrared [TIR] bands) were downloaded from EarthExplorer (https://earthexplorer.usgs.gov/) and Level 2 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: 2025 (process 2 of 5)
    All images were batch-processed using Spatial Model Editor in ERDAS IMAGINE. The TOA and BT bands were stacked to create 9-band multispectral images and clipped to the study-area extent. From these composite images, two additional spectral indices, mNDWI (Xu, 2006) and NBLI (Li and others, 2017), 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: 2025 (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. These pixels were mapped as sand using the following rule: if NBLI = sand and NDVI = vegetated, then final = sand (based on visual analysis during workflow development) (Bernier and others, 2021). 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_LC##_nchan_islp_lcr_ce.img, where YYYYMMDD denotes the image-acquisition date (4-digit year, 2-digit month, 2-digit day) and LC## denotes Landsat 8 (LC08) or Landsat 9 (LC09) image source. All steps were batch-processed using the Image Processing toolbox in MATLAB (Otsu thresholding and binary image creation) or Spatial Model Editor in ERDAS IMAGINE (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
    Data sources produced in this process:
    • YYYYMMDD_LC08_nchan_islp_lcr_ce.img
    • YYYYMMDD_LC09_nchan_islp_lcr_ce.img
    Date: 2025 (process 4 of 5)
    For 5 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 (Bernier and others, 2021). The cleaned land-cover rasters use the naming convention YYYYMMDD_LC##_nchan_islp_lcr_ce_cl.img, where YYYYMMDD denotes the image-acquisition date (4-digit year, 2-digit month, 2-digit day) and LC## denotes Landsat 8 (LC08) or Landsat 9 (LC09) image source. 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:
    • YYYYMMDD_LC08_nchan_islp_lcr_ce_cl.img
    • YYYYMMDD_LC09_nchan_islp_lcr_ce_cl.img
    Date: 2025 (process 5 of 5)
    The histogram (pixel count) for each raster was exported to a text file using Spatial Model Editor in ERDAS IMAGINE, merged into a single file in MATLAB, 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_coll2_landcover_2017_2024.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 13(18), 3778; Special Issue "New Insights into Ecosystem Monitoring Using Geospatial Techniques".

    Online Links:

    Xu, Hanqiu, 2006, Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery: International Journal of Remote Sensing Volume 27, Issue 14.

    Online Links:

    Other_Citation_Details: Pages 3025-3033
    Li, Hui, Wang, Cuizhen, Zhong, Cheng, Su, Aijun, Xiong, Chengren, Wang, Jinge, and Liu, Junqi, 20170307, Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index: Remote Sensing 9(3), 249.

    Online Links:

    Otsu, Nobuyuki, 197901, A Threshold Selection Method from Gray-Level Histograms: IEEE Transactions on Systems, Man and Cybernetics Volume 9, Issue 1.

    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?
    Using the workflow described below to map land cover at the northern Chandeleur Islands from Landsat Collection 1 products acquired between 1984 and 2019, Bernier and others (2021) reported classification accuracies (>=70%) that were comparable to accuracies reported for the National Land Cover Database (NLCD).
  2. How accurate are the geographic locations?
    Geodetic accuracy of Landsat data products depend on the accuracy, number, and distribution of the ground control points and the resolution of the digital elevation model (DEM) used. All ARD and Level-2 science products are derived from Landsat Level-1 Precision and Terrain (L1TP) corrected data and 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?
    63 Landsat OLI images (Worldwide Reference System 2 [WRS-2] path 21 row 39) acquired between December 2022 and November 2024 were identified that were cloud free in 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 13 Landsat 8 and 6 Landsat 9 OLI images.
  5. How consistent are the relationships among the observations, including topology?
    For each image-acquisition date, Landsat Collection 2 Analysis Ready Data (ARD) and Level-2 science products were downloaded from the USGS EarthExplorer (https://earthexplorer.usgs.gov/) and USGS Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface (https://espa.cr.usgs.gov/), respectively.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints No access constraints. Please see 'Distribution Information' for details.
Use_Constraints These data are marked with a Creative Commons CC0 1.0 Universal License. These data are in the public domain and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
    Attn: USGS SPCMSC Data Management
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    gs-g-spcmsc_data_inquiries@usgs.gov
  2. What's the catalog number I need to order this data set? YYYYMMDD_LC08_nchan_islp_lcr_ce.img, YYYYMMDD_LC09_nchan_islp_lcr_ce.img, YYYYMMDD_LC08_nchan_islp_lcr_ce_cl.img, YYYYMMDD_LC09_nchan_islp_lcr_ce_cl.img, nchan_coll2_landcover_2017_2024.csv
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (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. 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?
    Raster datasets were created using ERDAS IMAGINE 2023 and can be opened using ERDAS IMAGINE 2020 or Esri ArcGIS Pro version 3.1 or higher; these data may also be viewed using free Google Earth Pro or QGIS software.

Who wrote the metadata?

Dates:
Last modified: 19-Dec-2025
Metadata author:
U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
Attn: USGS SPCMSC Data Management
600 4th Street South
St. Petersburg, FL
USA

727-502-8000 (voice)
gs-g-spcmsc_data_inquiries@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_coll2_landcover-met.faq.html>
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