Sydney K. Nick Julie C. Bernier 20251203 raster and tabular digital data Sydney K. Nick Julie C. Bernier Breanna N. Williams Jennifer L. Miselis 20251203 U.S. Geological Survey data release doi:10.5066/P1MSCTUB St. Petersburg, Florida U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center https://doi.org/10.5066/P1MSCTUB 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. 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. Information about the Landsat missions, sensor and band specifications, data products, and data access can be found at https://www.usgs.gov/landsat-missions. 20130420 20241230 Ground condition Complete None planned -89.502445 -93.852531 29.800980 29.029450 USGS Metadata Identifier USGS:9c4f3cc1-59cf-4b33-9308-9fde7f2c2cdb ISO 19115 Topic Category geoscientificInformation imageryBaseMapsEarthCover USGS Thesaurus geomorphology geospatial datasets image collections multispectral imaging coastal ecosystems land use and land cover None Landsat spectral indices Otsu thresholding land cover water bare earth sand vegetated Geographic Names Information System (GNIS) Louisiana Gulf of America Sabine Pass Calcasieu Pass Headquarters Canal Freshwater Bayou Canal Marsh Island Point au Fer Island Caillou Boca Raccoon Point Bay Coquette None Gulf of Mexico No access constraints. Please see 'Distribution Information' for details. 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. U.S. Geological Survey Julie C. Bernier Geologist Mailing and physical

600 4th Street South

St. Petersburg FL 33701 USA 727-502-8000 jbernier@usgs.gov LocMap_dr22_2240.jpg, LocMap_dr22_2340.jpg, LocMap_dr22_2439.jpg 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. JPEG 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). 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) Julie C. Bernier Jennifer L. Miselis Nathaniel G. Plant 20210921 Remote Sensing 13(18), 3778; Special Issue "New Insights into Ecosystem Monitoring Using Geospatial Techniques" https://doi.org/10.3390/rs13183779 Hanqiu Xu 2006 International Journal of Remote Sensing Volume 27, Issue 14 Pages 3025-3033 https://doi.org/10.1080/01431160600589179 Hui Li Cuizhen Wang Cheng Zhong Aijun Su Chengren Xiong Jinge Wang Junqi Liu 20170307 Remote Sensing 9(3), 249 https://doi.org/10.3390/rs9030249 Nobuyuki Otsu 197901 IEEE Transactions on Systems, Man and Cybernetics Volume 9, Issue 1 Pages 62-66 https://doi.org/10.1109/TSMC.1979.4310076 Nicholas M. Enwright William M. SooHoo Jason Dugas Craig P. Conzelmann Claudia Laurenzano Darin M. Lee Kelly Mouton Spencer J. Stelly 20201030 U.S. Geological Survey Open-File Report 2020-1030, 57p https://doi.org/10.3133/ofr20201030 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. 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/). 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. 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). 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). 2024 U.S. Geological Survey Julie C. Bernier Geologist Mailing and physical

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St. Petersburg FL 33701 USA 727-502-8000 jbernier@usgs.gov 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/). 2025 U.S. Geological Survey Sydney K. Nick Geographer Mailing and physical

600 4th Street South

St. Petersburg FL 33701 USA 727-502-8000 snick@usgs.gov 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. 2025 U.S. Geological Survey Sydney K. Nick Geographer Mailing and physical

600 4th Street South

St. Petersburg FL 33701 USA 727-502-8000 snick@usgs.gov 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). 2025 YYYYMMDD_lc08_AOI_lcr_ce.img YYYYMMDD_lc09_AOI_lcr_ce.img U.S. Geological Survey Julie C. Bernier Geologist Mailing and physical

600 4th Street South

St. Petersburg FL 33701 USA 727-502-8000 jbernier@usgs.gov 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. 2025 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 U.S. Geological Survey Sydney K. Nick Geographer Mailing and physical

600 4th Street South

St. Petersburg FL 33701 USA 727-502-8000 snick@usgs.gov Raster Universal Transverse Mercator 15 1.0 -93.0 0 500000.0 0.0 row and column 30 30 Meters D WGS 1984 WGS 1984 6378137.0 298.25722 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. USGS OID Internal feature number Esri Sequential unique whole numbers that are automatically generated Value Land-cover class number USGS 0 Out - pixels outside of analysis extent or masked urban areas USGS 1 Water - pixels classified as water USGS 2 Land (unclassed) - pixels classified as land that could not be further grouped into sand, vegetated, or intertidal classes because of missing input ST data USGS 3 Bare earth (sand) - pixels classified as bare earth (sand) land cover USGS 4 Vegetated - pixels classified as vegetated land cover USGS 11 Intertidal - pixels classified as intertidal land cover USGS 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 Hexagon Geospatial Number of pixels per land-cover class Class_Name Land-cover type USGS Out Pixels outside of the analysis extent or masked urban pixels. These pixels are set to transparent in the thematic land-cover rasters. USGS Water Pixels classified as water (symbolized in dark blue) USGS 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) USGS Bare earth (sand) Pixels classified as bare earth (sand) land cover (symbolized in dark brown) USGS Vegetated Pixels classified as vegetated land cover (symbolized in dark green) USGS Intertidal Pixels classified intertidal (symbolized in light blue) USGS Red Red value for RGB color map Hexagon Geospatial Red value for RGB colormap Green Green value for RGB color map Hexagon Geospatial Green value for RGB colormap Blue Blue value for RGB color map 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). 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. USGS FILENAME File name of classed land-cover raster USGS Character string SCENE_ID Landsat Collection 2, Level 2 source data downloaded from EROS ESPA USGS Landsat source imagery data identifier IMAGE_DATE Source image-acquisition date 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 USGS 2013.301 2024.997 Decimal year 0.001 AOI Scene-specific area of interest USGS dr22_2240 WRS-2 path 22 row 40 scene-specific AOI USGS dr22_2340 WRS-2 path 23 row 40 scene-specific AOI USGS dr22_2439 WRS-2 path 24 row 39 scene-specific AOI USGS 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. USGS -0.546 0.967 Meters 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. USGS rising Tidal stage rising between low and high tide USGS falling Tidal stage falling between high and low tide USGS high High tide USGS low Low tide USGS falling (p) Predicted tidal stage falling between high and low tide (verified water-level data not available) USGS rising (p) Predicted tidal stage rising between high and low tide (verified water-level data not available) USGS 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). USGS -0.875 0.511 Meters 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). USGS rising Tidal stage rising between low and high tide USGS falling Tidal stage falling between high and low tide USGS high High tide USGS low Low tide USGS falling (p) Predicted tidal stage falling between high and low tide (verified water-level data not available) USGS rising (p) Predicted tidal stage rising between high and low tide (verified water-level data not available) USGS high (p) Predicted tidal stage high (verified water-level data not available) USGS low (p) Predicted tidal stage low (verified water-level data not available) USGS 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. USGS -0.474 0.431 Meters 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. USGS rising Tidal stage rising between low and high tide USGS falling Tidal stage falling between high and low tide USGS high High tide USGS low Low tide USGS falling (p) Predicted tidal stage falling between high and low tide (verified water-level data not available) USGS rising (p) Predicted tidal stage falling between high and low tide (verified water-level data not available) USGS COMMENT Comment indicating clip extent for the scenes that were clipped due to interfering cloud cover USGS analysis extent clipped to west of Calcasieu River Scene was clipped to west of Calcasieu River USGS analysis extent clipped to east of Calcasieu River Scene was clipped to east of the Calcasieu River USGS analysis extent clipped to west of Marsh Island Scene was clipped to west of (including) Marsh Island USGS analysis extent clipped to east of Freshwater Bayou Canal Scene was clipped to east of Freshwater Bayou Canal USGS analysis extent clipped to west of Caillou Boca Scene was clipped to north and west of Caillou Boca USGS analysis extent clipped to east of Raccoon Point Scene was clipped to east of Raccoon Point USGS U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center USGS SPCMSC Data Management Mailing and physical

600 4th Street South

St. Petersburg FL 33701 USA 727-502-8000 gs-g-spcmsc_data_inquiries@usgs.gov 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 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. ERDAS IMAGINE, JPEG, comma-delimited text, Microsoft Excel format Download files contain ERDAS IMAGINE (.img) land-cover rasters, JPEG location maps, Microsoft Excel (.xlsx) spreadsheets, and comma-separated values (.csv) text files. https://coastal.er.usgs.gov/data-release/doi-P1MSCTUB/data/la_2439_landcover.zip https://coastal.er.usgs.gov/data-release/doi-P1MSCTUB/data/la_2340_landcover.zip https://coastal.er.usgs.gov/data-release/doi-P1MSCTUB/data/la_2240_landcover.zip https://coastal.er.usgs.gov/data-release/doi-P1MSCTUB/data/la_imagery.zip None 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. 20251203 U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center USGS SPCMSC Data Management Mailing and physical

600 4th Street South

St. Petersburg FL 33701 USA 727-502-8000 gs-g-spcmsc_data_inquiries@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998