Metadata: Identification_Information: Citation: Citation_Information: Originator: Julie C. Bernier Originator: Sydney K. Nick Originator: Breanna N. Williams Publication_Date: 20251203 Title: Coastal Features Extracted from Landsat Satellite Imagery, Sabine Pass to Bay Coquette, Louisiana, 2013-2024 Geospatial_Data_Presentation_Form: vector digital data Larger_Work_Citation: Citation_Information: Originator: Sydney K. Nick Originator: Julie C. Bernier Originator: Breanna N. Williams Originator: Jennifer L. Miselis Publication_Date: 20251203 Title: Coastal Land-Cover and Feature Datasets Derived from Landsat Satellite Imagery, Sabine Pass to Bay Coquette, Louisiana Series_Information: Series_Name: U.S. Geological Survey data release Issue_Identification: doi:10.5066/P1MSCTUB Publication_Information: Publication_Place: St. Petersburg, Florida Publisher: U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center Online_Linkage: https://doi.org/10.5066/P1MSCTUB Description: 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. Purpose: 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. 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. Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 20130420 Ending_Date: 20241230 Currentness_Reference: ground condition Status: Progress: Complete Maintenance_and_Update_Frequency: None planned Spatial_Domain: Bounding_Coordinates: West_Bounding_Coordinate: -89.502445 East_Bounding_Coordinate: -93.852531 North_Bounding_Coordinate: 29.800980 South_Bounding_Coordinate: 29.029450 Keywords: Theme: Theme_Keyword_Thesaurus: USGS Metadata Identifier Theme_Keyword: USGS:018c127f-9402-4070-ac38-c726b8c7c871 Theme: Theme_Keyword_Thesaurus: ISO 19115 Topic Category Theme_Keyword: geoscientificInformation Theme_Keyword: imageryBaseMapsEarthCover Theme: Theme_Keyword_Thesaurus: USGS Thesaurus Theme_Keyword: geomorphology Theme_Keyword: geospatial datasets Theme_Keyword: image collections Theme_Keyword: multispectral imaging Theme_Keyword: coastal ecosystems Theme_Keyword: land use and land cover Theme_Keyword: contouring Theme: Theme_Keyword_Thesaurus: None Theme_Keyword: Landsat Theme_Keyword: barrier island Theme_Keyword: spectral indices Theme_Keyword: Otsu thresholding Theme_Keyword: shoreline Theme_Keyword: sand Place: Place_Keyword_Thesaurus: Geographic Names Information System (GNIS) Place_Keyword: Louisiana Place_Keyword: Gulf of America Place_Keyword: Sabine Pass Place_Keyword: Calcasieu Pass Place_Keyword: Headquarters Canal Place_Keyword: Freshwater Bayou Canal Place_Keyword: Marsh Island Place_Keyword: Point au Fer Island Place_Keyword: Caillou Boca Place_Keyword: Raccoon Point Place_Keyword: Bay Coquette Place: Place_Keyword_Thesaurus: None Place_Keyword: Gulf of Mexico 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 Point_of_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Julie C. Bernier Contact_Position: Geologist Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: jbernier@usgs.gov Data_Set_Credit: 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). Native_Data_Set_Environment: Windows 11 Enterprise version 23H2 (22631.4890); Microsoft Excel for Microsoft 365 MSO (Version 2408 Build 16.0.17928.20538); Esri ArcGIS Pro 3.2.2; ERDAS IMAGINE 2023 Version 16.8.0 Build 2100; MATLAB R2022a Update 8 (9.12.0.2529717) Cross_Reference: Citation_Information: Originator: Julie C. Bernier Originator: Jennifer L. Miselis Originator: Nathaniel G. Plant Publication_Date: 20210921 Title: Satellite-Derived Barrier Response and Recovery Following Natural and Anthropogenic Perturbations, Northern Chandeleur Islands, Louisiana Series_Information: Series_Name: Remote Sensing Issue_Identification: 13(18), 3778; Special Issue "New Insights into Ecosystem Monitoring Using Geospatial Techniques" Online_Linkage: https://doi.org/10.3390/rs13183779 Cross_Reference: Citation_Information: Originator: Hanqiu Xu Publication_Date: 2006 Title: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery Series_Information: Series_Name: International Journal of Remote Sensing Issue_Identification: Volume 27, Issue 14 Other_Citation_Details: Pages 3025-3033 Online_Linkage: https://doi.org/10.1080/01431160600589179 Cross_Reference: Citation_Information: Originator: Hui Li Originator: Cuizhen Wang Originator: Cheng Zhong Originator: Aijun Su Originator: Chengren Xiong Originator: Jinge Wang Originator: Junqi Liu Publication_Date: 20170307 Title: Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index Series_Information: Series_Name: Remote Sensing Issue_Identification: 9(3), 249 Online_Linkage: https://doi.org/10.3390/rs9030249 Cross_Reference: Citation_Information: Originator: Nobuyuki Otsu Publication_Date: 197901 Title: A Threshold Selection Method from Gray-Level Histograms Series_Information: Series_Name: IEEE Transactions on Systems, Man and Cybernetics Issue_Identification: Volume 9, Issue 1 Other_Citation_Details: Pages 62-66 Online_Linkage: https://doi.org/10.1109/TSMC.1979.4310076 Cross_Reference: Citation_Information: Originator: Nicholas M. Enwright Originator: William M. SooHoo Originator: Jason Dugas Originator: Craig P. Conzelmann Originator: Claudia Laurenzano Originator: Darin M. Lee Originator: Kelly Mouton Originator: Spencer J. Stelly Publication_Date: 20201030 Title: 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 Series_Information: Series_Name: U.S. Geological Survey Open-File Report Issue_Identification: 2020-1030, 57p Online_Linkage: https://doi.org/10.3133/ofr20201030 Data_Quality_Information: Attribute_Accuracy: Attribute_Accuracy_Report: 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. Logical_Consistency_Report: 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/). Completeness_Report: 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. Positional_Accuracy: Horizontal_Positional_Accuracy: Horizontal_Positional_Accuracy_Report: 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. Lineage: Process_Step: Process_Description: 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). Process_Date: 2024 Process_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Julie C. Bernier Contact_Position: Geologist Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: jbernier@usgs.gov Process_Step: Process_Description: 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/). Process_Date: 2025 Process_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Sydney K. Nick Contact_Position: Geographer Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: snick@usgs.gov Process_Step: Process_Description: 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. Process_Date: 2025 Process_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Sydney K. Nick Contact_Position: Geographer Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: snick@usgs.gov Process_Step: Process_Description: 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). Process_Date: 2025 Source_Produced_Citation_Abbreviation: YYYYMMDD_lc08_AOI_lcr_ce.img Source_Produced_Citation_Abbreviation: YYYYMMDD_lc09_AOI_lcr_ce.img Process_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Julie C. Bernier Contact_Position: Geologist Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: jbernier@usgs.gov Process_Step: Process_Description: 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. Process_Date: 2025 Process_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Sydney K. Nick Contact_Position: Geographer Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: snick@usgs.gov Spatial_Data_Organization_Information: Direct_Spatial_Reference_Method: Vector Spatial_Reference_Information: Horizontal_Coordinate_System_Definition: Planar: Grid_Coordinate_System: 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_Coordinate_Information: Planar_Coordinate_Encoding_Method: row and column Coordinate_Representation: Abscissa_Resolution: 30 Ordinate_Resolution: 30 Planar_Distance_Units: Meters Geodetic_Model: Horizontal_Datum_Name: D WGS 1984 Ellipsoid_Name: WGS 1984 Semi-major_Axis: 6378137.0 Denominator_of_Flattening_Ratio: 298.25722 Entity_and_Attribute_Information: Detailed_Description: Entity_Type: Entity_Type_Label: la_features.zip Entity_Type_Definition: 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. Entity_Type_Definition_Source: USGS Attribute: Attribute_Label: FID Attribute_Definition: Internal feature number Attribute_Definition_Source: Esri Attribute_Domain_Values: Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated Attribute: Attribute_Label: Shape* Attribute_Definition: Feature geometry Attribute_Definition_Source: Esri Attribute_Domain_Values: Unrepresentable_Domain: Coordinates defining the features Attribute: Attribute_Label: IMG_DATE Attribute_Definition: Source image acquisition date Attribute_Definition_Source: USGS Attribute_Domain_Values: Unrepresentable_Domain: Source image acquisition date written as DD-MON-YYYY (2-digit day, month abbreviation, 4-digit year) Attribute: Attribute_Label: DEC_YEAR Attribute_Definition: Source image-acquisition date, in decimal years Attribute_Definition_Source: USGS Attribute_Domain_Values: Range_Domain: Range_Domain_Minimum: 2013.301 Range_Domain_Maximum: 2024.997 Attribute_Units_of_Measure: Decimal year Attribute_Measurement_Resolution: 0.001 Attribute: Attribute_Label: SOURCE Attribute_Definition: Image source (Landsat 8 or Landsat 9) Attribute_Definition_Source: USGS Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: LC08 Enumerated_Domain_Value_Definition: Landsat 8 Enumerated_Domain_Value_Definition_Source: USGS Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: LC09 Enumerated_Domain_Value_Definition: Landsat 9 Enumerated_Domain_Value_Definition_Source: USGS Distribution_Information: Distributor: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center Contact_Person: USGS SPCMSC Data Management Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: gs-g-spcmsc_data_inquiries@usgs.gov Resource_Description: dr22_p22r40_shrln.shp, dr22_p23r40_shrln.shp, dr22_p24r39_shrln.shp, dr22_p22r40_sandext.shp, dr22_p23r40_sandext.shp, dr22_p24r39_sandext.shp Distribution_Liability: 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. Standard_Order_Process: Digital_Form: Digital_Transfer_Information: Format_Name: Shapefile Format_Information_Content: Download files contain Esri vector sand- and shoreline shapefiles (.shp) Digital_Transfer_Option: Online_Option: Computer_Contact_Information: Network_Address: Network_Resource_Name: https://coastal.er.usgs.gov/data-release/doi-P1MSCTUB/data/la_features.zip Fees: None Technical_Prerequisites: 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. Metadata_Reference_Information: Metadata_Date: 20251203 Metadata_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center Contact_Person: USGS SPCMSC Data Management Contact_Address: Address_Type: Mailing and physical Address: 600 4th Street South City: St. Petersburg State_or_Province: FL Postal_Code: 33701 Country: USA Contact_Voice_Telephone: 727-502-8000 Contact_Electronic_Mail_Address: gs-g-spcmsc_data_inquiries@usgs.gov Metadata_Standard_Name: Content Standard for Digital Geospatial Metadata Metadata_Standard_Version: FGDC-STD-001-1998