Coastal Land-Cover Data Derived from Landsat Satellite Imagery, Sabine Pass to Bay Coquette, Louisiana, 2013-2024

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Frequently anticipated questions:

What does this data set describe?

Title:
Coastal Land-Cover Data Derived from Landsat Satellite Imagery, Sabine Pass to Bay Coquette, Louisiana, 2013-2024
Abstract:
This data release serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery from Sabine Pass to Bay Coquette, Louisiana (LA). A total of 179 images acquired between 2013 and 2024 were analyzed. Water, bare earth (sand), and vegetated land-cover classes were mapped using (1) 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) and (2) applying a rule-based classification modified from the workflow described by Bernier and others (2021). Vector shoreline and sand feature extents were extracted for each image by contouring the spectral indices using the calculated threshold values. Funded by the Extending Government Funding and Delivering Emergency Assistance Act (Public Law 117-43) enacted on September 30, 2021, these data support assessments of changes that occurred along the Louisiana coast following the passage of Hurricanes Laura, Delta, and Zeta in 2020 and Hurricane Ida in 2021.
Supplemental_Information:
Information about the Landsat missions, sensor and band specifications, data products, and data access can be found at https://www.usgs.gov/landsat-missions.
  1. How might this data set be cited?
    Nick, Sydney K., and Bernier, Julie C., 20251203, Coastal Land-Cover Data Derived from Landsat Satellite Imagery, Sabine Pass to Bay Coquette, Louisiana, 2013-2024:.

    This is part of the following larger work.

    Nick, Sydney K., Bernier, Julie C., Williams, Breanna N., and Miselis, Jennifer L., 20251203, Coastal Land-Cover and Feature Datasets Derived from Landsat Satellite Imagery, Sabine Pass to Bay Coquette, Louisiana: U.S. Geological Survey data release doi:10.5066/P1MSCTUB, 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: -89.502445
    East_Bounding_Coordinate: -93.852531
    North_Bounding_Coordinate: 29.800980
    South_Bounding_Coordinate: 29.029450
  3. What does it look like?
    LocMap_dr22_2240.jpg, LocMap_dr22_2340.jpg, LocMap_dr22_2439.jpg (JPEG)
    A location map in Joint Photographic Experts Group (JPEG) file format is included in each of the landcover zip files. These maps provide additional context for the landcover raster dataset coverage in the study area.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 20-Apr-2013
    Ending_Date: 30-Dec-2024
    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.
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 15
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 1.0
      Longitude_of_Central_Meridian: -93.0
      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 1/298.25722.
  7. How does the data set describe geographic features?
    la_2240_landcover.zip, la_2340_landcover.zip, la_2439_landcover.zip
    Zip archive containing 179 thematic land-cover raster datasets in ERDAS IMAGINE (.img) format for the WRS-2 path 22 row 40 (dr22_2240), WRS-2 path 23 row 40 (dr22_2340), WRS-2 path 24 row 39 (dr22_2439) AOIs. An AOI location map in JPEG format is also included in each zip file. (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 or masked urban areas
    1Water - pixels classified as water
    2Land (unclassed) - pixels classified as land that could not be further grouped into sand, vegetated, or intertidal classes because of missing input ST data
    3Bare earth (sand) - pixels classified as bare earth (sand) land cover
    4Vegetated - pixels classified as vegetated land cover
    11Intertidal - pixels classified as intertidal land cover
    Values of 5 through 10 and 12 through 255 are not used to define a land-cover class but are stored by default in the 8-bit raster attribute table.
    Count (Esri ArcGIS) or Histogram (Erdas Imagine)
    Number of pixels per land-cover class (Source: Hexagon Geospatial) Number of pixels per land-cover class
    Class_Name
    Land-cover type (Source: USGS)
    ValueDefinition
    OutPixels outside of the analysis extent or masked urban pixels. These pixels are set to transparent in the thematic land-cover rasters.
    WaterPixels classified as water (symbolized in dark blue)
    Land (unclassed)Pixels classified as land that could not be further grouped into sand, vegetated, or intertidal classes because of missing input ST data (symbolized in light brown)
    Bare earth (sand)Pixels classified as bare earth (sand) land cover (symbolized in dark brown)
    VegetatedPixels classified as vegetated land cover (symbolized in dark green)
    IntertidalPixels classified intertidal (symbolized in light blue)
    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
    la_imagery.zip
    Zip archive containing Microsoft Excel (.xlsx) and comma-separated values (.csv) files describing source imagery, scene-specific AOI, and tidal water level and tidal stage at selected National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) tide gauges for each of 179 thematic land-cover raster datasets. Gauge locations are shown on the location maps included with each land-cover zip archive. (Source: USGS)
    FILENAME
    File name of classed land-cover raster (Source: USGS) Character string
    SCENE_ID
    Landsat Collection 2, Level 2 source data downloaded from EROS ESPA (Source: USGS) Landsat source imagery data identifier
    IMAGE_DATE
    Source image-acquisition date (Source: USGS) Source image acquisition date written as DD-MON-YYYY (2-digit day, 3-letter month, 4-digit year)
    DEC_YEAR
    Source image-acquisition date, in decimal years (Source: USGS)
    Range of values
    Minimum:2013.301
    Maximum:2024.997
    Units:Decimal year
    Resolution:0.001
    AOI
    Scene-specific area of interest (Source: USGS)
    ValueDefinition
    dr22_2240WRS-2 path 22 row 40 scene-specific AOI
    dr22_2340WRS-2 path 23 row 40 scene-specific AOI
    dr22_2439WRS-2 path 24 row 39 scene-specific AOI
    TexasPointWL_mMSL
    Water level at the Texas Point tide gauge (NOS CO-OPS station #8770822) at the time of image acquisition, in meters relative to mean sea level (MSL). Applies only to the WRS-2 path 24 row 39 (dr22_2439) scene-specific AOI. A dash is used to indicate a no data value. (Source: USGS)
    Range of values
    Minimum:-0.546
    Maximum:0.967
    Units:Meters
    Resolution:0.001
    TexasPoint_TidalStage
    Tidal stage at the Texas Point tide gauge (NOS CO-OPS station #8770822) at the time of image acquisition Applies only to the WRS-2 path 24 row 39 (dr22_2439) scene-specific AOI. A dash is used to indicate a no data value. (Source: USGS)
    ValueDefinition
    risingTidal stage rising between low and high tide
    fallingTidal stage falling between high and low tide
    highHigh tide
    lowLow tide
    falling (p)Predicted tidal stage falling between high and low tide (verified water-level data not available)
    rising (p)Predicted tidal stage rising between high and low tide (verified water-level data not available)
    EugeneIsland_mMSL
    Water level at the Euguene Island tide gauge (NOS CO-OPS station 8764314) at the time of image acquisition, in meters relative to mean sea level (MSL). (Source: USGS)
    Range of values
    Minimum:-0.875
    Maximum:0.511
    Units:Meters
    Resolution:0.001
    EugeneIsland_TidalStage
    Tidal stage at the Euguene Island tide gauge (NOS CO-OPS station 8764314) at the time of image acquisition, in meters relative to mean sea level (MSL). (Source: USGS)
    ValueDefinition
    risingTidal stage rising between low and high tide
    fallingTidal stage falling between high and low tide
    highHigh tide
    lowLow tide
    falling (p)Predicted tidal stage falling between high and low tide (verified water-level data not available)
    rising (p)Predicted tidal stage rising between high and low tide (verified water-level data not available)
    high (p)Predicted tidal stage high (verified water-level data not available)
    low (p)Predicted tidal stage low (verified water-level data not available)
    GrandIsle_mMSL
    Water level at the Grand Isle tide gauge (NOS CO-OPS station #8761724) at the time of image acquisition. Applies only to the WRS-2 path 22 row 40 (dr22_2240) scene-specific AOI. A dash is used to indicate a no data value. (Source: USGS)
    Range of values
    Minimum:-0.474
    Maximum:0.431
    Units:Meters
    Resolution:0.001
    GrandIsle_TidalStage
    Tidal stage at the Grand Isle gauge (NOS CO-OPS station #8761724) at the time of image acquisition. Applies only to the WRS-2 path 22 row 40 (dr22_2240) scene-specific AOI. A dash is used to indicate a no data value. (Source: USGS)
    ValueDefinition
    risingTidal stage rising between low and high tide
    fallingTidal stage falling between high and low tide
    highHigh tide
    lowLow tide
    falling (p)Predicted tidal stage falling between high and low tide (verified water-level data not available)
    rising (p)Predicted tidal stage falling between high and low tide (verified water-level data not available)
    COMMENT
    Comment indicating clip extent for the scenes that were clipped due to interfering cloud cover (Source: USGS)
    ValueDefinition
    analysis extent clipped to west of Calcasieu RiverScene was clipped to west of Calcasieu River
    analysis extent clipped to east of Calcasieu RiverScene was clipped to east of the Calcasieu River
    analysis extent clipped to west of Marsh IslandScene was clipped to west of (including) Marsh Island
    analysis extent clipped to east of Freshwater Bayou CanalScene was clipped to east of Freshwater Bayou Canal
    analysis extent clipped to west of Caillou BocaScene was clipped to north and west of Caillou Boca
    analysis extent clipped to east of Raccoon PointScene was clipped to east of Raccoon Point

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Sydney K. Nick
    • 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. Funding and (or) support for this study 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 Kathryn Weber 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 179 land-cover datasets derived from 139 Landsat 8 and 40 Landsat 9 Operational Land Imager (OLI) images from coastal Louisiana, USA.

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: 2024 (process 1 of 5)
    The regional AOI, which was derived from the State of Louisiana’s Barrier Island Comprehensive Monitoring (BICM) Program habitat mapping extents (Enwright and others, 2020), was subset into three scene-specific AOIs for processing and analysis: WRS-2 path 22 row 40 (dr22_2240), WRS-2 path 23 row 40 (dr22_2340), and WRS-2 path 24 row 39 (dr22_2439). 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)
    For each image acquisition date, Collection-2, Level-2 surface reflectance (SR; reflective bands), surface temperature (ST; thermal infrared [TIR] bands), and SR-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: Sydney K. Nick
    Geographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    snick@usgs.gov
    Date: 2025 (process 3 of 5)
    All images were batch-processed using Spatial Model Editor in ERDAS IMAGINE. The SR bands were stacked to create 8-band multispectral images and clipped to the scene-specific AOI. From these composite images and the corresponding ST 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: Sydney K. Nick
    Geographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    snick@usgs.gov
    Date: 2025 (process 4 of 5)
    Land-cover classification was modified from the workflow described by Bernier and others (2021) using single (mNDWI), multilevel (2 thresholds per image; NBLI), or iterative (NDVI) thresholding of spectral indices and applying a rule-based classification scheme. 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 and urban areas (derived from the BICM “structure” class [Enwright and others, 2020]) were 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. For the WRS-2 path 23 row 40 (dr22_2340) scene-specific AOI only, the downloaded ST images included 2 large areas of missing data east of Freshwater Bayou Canal, which are the result of missing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) input data (see https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature-data-gaps-due-missing-aster-ged for more information) necessary for ST product generation. Without ST data, NBLI could not be calculated for these pixels and any land-cover classification relying on NBLI input is invalid. Therefore, a mask was created from the ST “NoData” pixels and included in the land-cover classification. The following rule-based classification was then applied with each successive step applied to any unclassed pixels from the previous steps, where T represents the single Otsu threshold, T1 and T2 represent the first and second multi-Otsu thresholds, respectively, and Ti represents the second iteration of Otsu thresholding for each spectral index: If mNDWI > T then class (1) = water If ST = NoData then class (2) = land (undifferentiated) If NBLI > T2 then class (3) = bare earth (sand) If NDVI > Ti or (NDVI < Ti and NBLI < T1) then class (4) = vegetated If T1 < NBLI < T2 and NDVI < Ti then class (11) = intertidal Finally, the binary land-cover images were converted to thematic rasters, merged, single-pixel "clumps" were removed using a 3x3 majority filter, and a standard colormap was applied to create a final land-cover raster dataset for each image acquisition date. The resulting land-cover rasters use the naming convention YYYYMMDD_lc##_AOI_lcr_ce.img, where YYYYMMDD denotes the image-acquisition date (4-digit year, 2-digit month, 2-digit day), lc## denotes Landsat 8 (lc08) or Landsat 9 (lc09) image source, AOI denotes the scene-specific AOI, and lcr_ce are process step abbreviations where lcr indicates the "raw" land cover files that were created by thresholding the spectral indices were merged following the rule-based classification and ce indicates that single-pixel "clumps" were removed. 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_AOI_lcr_ce.img
    • YYYYMMDD_lc09_AOI_lcr_ce.img
    Date: 2025 (process 5 of 5)
    For 37 datasets, the images were clipped to either the eastern or western part of the scene-specific AOI to exclude cloud cover. The clipped land-cover rasters use the naming convention YYYYMMDD_lc##_AOI_lcr_ce_east.img or YYYYMMDD_lc##_AOI_lcr_ce_west.img using the same naming convention as described above. All images were batch-processed using Spatial Model Editor in ERDAS IMAGINE. For WRS-2 path 24 row 39 (dr22_2439), images were clipped to east or west of Calcasieu River; for WRS-2 path 23 row 40 (dr22_2340), images were clipped to west of (including) Marsh Island or east of Freshwater Bayou Canal; and for WRS-2 path 22 row 40 (dr22_2240), images were clipped to north and west of Caillou Boca or east of Raccoon Point. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Sydney K. Nick
    Geographer
    600 4th Street South
    St. Petersburg, FL
    USA

    727-502-8000 (voice)
    snick@usgs.gov
    Data sources produced in this process:
    • YYYYMMDD_lc08_AOI_lcr_ce_east.img
    • YYYYMMDD_lc08_AOI_lcr_ce_west.img
    • YYYYMMDD_lc09_AOI_lcr_ce_east.img
    • YYYYMMDD_lc09_AOI_lcr_ce_west.img
  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
    Enwright, Nicholas M., SooHoo, William M., Dugas, Jason, Conzelmann, Craig P., Laurenzano, Claudia, Lee, Darin M., Mouton, Kelly, and Stelly, Spencer J., 20201030, Louisiana Barrier Island Comprehensive Monitoring Program: Mapping Habitats in Beach, Dune, and Intertidal Environments Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020-1030, 57p.

    Online Links:

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

  1. How well have the observations been checked?
    Due to the large spatial extent of this analysis and a lack of ground-truth datasets with similar temporal resolution, regional-scale classification accuracy was not quantitatively assessed. Using a similar methodology and workflow, Bernier and others (2021) reported classification accuracies (>=70%) that were comparable to accuracies reported for the National Land Cover Database (NLCD). Visual comparison of the classed land-cover rasters derived in this study with available NLCD data showed good agreement.
  2. How accurate are the geographic locations?
    Geodetic accuracy of Landsat data products depends on the accuracy of the ground control points and the resolution of the digital elevation model (DEM) used. All Level-2 science products are derived from Level-1 Tier-1 Precision and Terrain (L1TP) corrected data and meet pre-defined image-to-image georegistration tolerances of <= 12-meter (m) 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?
    139 Landsat 8 and 40 Landsat 9 OLI images (Worldwide Reference System 2 [WRS-2] path 22 row 40, WRS-2 path 23 row 40, and WRS-2 path 24 row 39) acquired between April 2013 and December 2024 were analyzed. Due to the large geographic extent of each image, and to maximize the number of images analyzed, 37 images were clipped to either the eastern or western part of the scene-specific area of interest (AOI) to exclude cloud cover.
  5. How consistent are the relationships among the observations, including topology?
    For each image-acquisition date, Landsat Collection 2, 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 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_AOI_lcr_ce.img, YYYYMMDD_lc09_AOI_lcr_ce.img, YYYYMMDD_lc08_AOI_lcr_ce_east.img, YYYYMMDD_lc08_AOI_lcr_ce_west.img, YYYYMMDD_lc09_AOI_lcr_ce_east.img, YYYYMMDD_lc09_AOI_lcr_ce_west.img
  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 higher 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: 03-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/la_landcover_metadata.faq.html>
Generated by mp version 2.9.51 on Wed Dec 3 11:35:21 2025