Coastal Features Extracted 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 Features Extracted 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?
    Bernier, Julie C., Nick, Sydney K., and Williams, Breanna N., 20251203, Coastal Features Extracted 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?
  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: vector digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector 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_features.zip
    Zip archive containing vector shoreline (dr22_p22r40_shrln.shp, dr22_p23r40_shrln.shp, dr22_p24r39_shrln.shp) and sand (dr22_p22r40_sandext.shp, dr22_p23r40_sandext.shp, dr22_p24r39_sandext.shp) feature extents corresponding to each of 179 thematic land-cover raster datasets in Esri shapefile (.shp) format. (Source: USGS)
    FID
    Internal feature number (Source: Esri) Sequential unique whole numbers that are automatically generated
    Shape*
    Feature geometry (Source: Esri) Coordinates defining the features
    IMG_DATE
    Source image acquisition date (Source: USGS) Source image acquisition date written as DD-MON-YYYY (2-digit day, month abbreviation, 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
    SOURCE
    Image source (Landsat 8 or Landsat 9) (Source: USGS)
    ValueDefinition
    LC08Landsat 8
    LC09Landsat 9

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
    • Sydney K. Nick
    • Breanna N. Williams
  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)
    Vector shoreline (representing the boundary between open-water areas and adjacent non-water land cover pixels, including intertidal areas) and the sand extents were extracted by contouring the mNDWI and masked NBLI images using the calculated Otsu thresholds. Shoreline vectors were manually cleaned to remove interior water bodies and contours representing extents of less than 4 connected pixels in the landcover rasters. The resulting shorelines include only the seaward shoreline for mainland land areas or the sea and back-barrier shorelines for barrier islands; however, interior shorelines (for example, along fluvial or tidal inlets or complex wetland shorelines) that are connected to the sea shoreline were not manually clipped and removed. Sand vectors were manually cleaned to remove interior mainland (non-beach) areas and contours representing sand extents of less than 4 connected pixels in the landcover rasters. The resulting shapefiles (one per image acquisition date) were merged into 2 shapefiles (one each for shoreline and sand features) for each scene-specific AOI. For the 37 datasets that were clipped to either the eastern or western part of the scene-specific AOI to exclude cloud cover, the vectors sand and shoreline files were also clipped to the same extent. The resulting sand (sandext) and shoreline (shrln) vector shapefiles use the naming convention AOI_shrln.shp or AOI_sandext.shp, where AOI denotes the scene-specific AOI. Contouring was batch-processed using Python 3 Jupyter Notebooks in ArcGIS Pro. 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
  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 depend 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). The positional accuracy of the satellite-derived features was not systematically evaluated in this study; however, recent analyses of satellite-derived shoreline (SDS) positions extracted using similar methods to those presented here report offsets of 1/3 to 1/2-pixel (10 to 15 m) seaward of measured in-situ shoreline positions. Compared with methods that extract shoreline position from precise elevation measurements (for example, light detection and ranging [lidar] or global positioning system [GPS] surveys), SDS positions are not based on a vertical datum (for example, mean sea level or mean high water); instead, SDS represent instantaneous waterlines at time of image acquisition and may include intertidal areas.
  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 2025 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? dr22_p22r40_shrln.shp, dr22_p23r40_shrln.shp, dr22_p24r39_shrln.shp, dr22_p22r40_sandext.shp, dr22_p23r40_sandext.shp, dr22_p24r39_sandext.shp
  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?
    Vector datasets were created using Esri ArcGIS Pro version 3.2.1 and can be opened using Esri ArcGIS version 10.0 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_features_metadata.faq.html>
Generated by mp version 2.9.51 on Wed Dec 3 11:35:21 2025