Julie C. Bernier Breanna N. Williams 20251219 1.0 raster and tabular digital data Julie C. Bernier Breanna N. Williams Jennifer L. Miselis 20210921 2.0 U.S. Geological Survey data release doi:10.5066/P9HY3HOR St. Petersburg, Florida U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center https://doi.org/10.5066/P9HY3HOR 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). 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. 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. 20171222 20190110 20190416 20200402 20210115 20210405 20211201 20220331 20230105 20230206 20230214 20230222 20230403 20230918 20231028 20231231 20240225 20240405 20241115 ground condition Complete None planned -88.932712 -88.742256 30.097824 29.720211 USGS Metadata Identifier USGS:7fdccb63-4e71-43e4-8f21-3f5d72b2e20a ISO 19115 Topic Category geoscientificInformation imageryBaseMapsEarthCover USGS Thesaurus geomorphology geospatial datasets image collections multispectral imaging coastal ecosystems land use and land cover contouring None Landsat barrier island spectral indices Otsu thresholding shoreline sand vegetated Geographic Names Information System (GNIS) State of Louisiana Chandeleur Islands Gulf of America 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 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). Microsoft Windows 11 Enterprise Version 23H2 (Build 2263.6199); Esri ArcGIS Pro 3.3.2; Python 3 Jupyter Notebooks; ERDAS IMAGINE 2023 Version 16.8.0 Build 2100; MATLAB R2025a Update 1 (25.1.0.2973910); RStudio Version 2025.09.2 Build 418. 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 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. 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. 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). 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, lidar or GPS surveys), SDS positions are not based on a vertical datum (for example, mean sea level or mean high water); instead, SDS positions may include intertidal areas. 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/). 2025 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 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. 2025 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 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). 2025 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 Vector shoreline (representing the boundary between intertidal and open-water areas), sand, and vegetated extents were extracted by contouring the mNDWI and masked NBLI and NDVI images using the calculated Otsu thresholds. The resulting shapefiles (one per image acquisition date) were merged into 3 shapefiles (one each for shoreline [shrln], sand [sandext], and vegetated [vegext] feature extents). Contouring was batch-processed using Python 3 Jupyter Notebooks in ArcGIS Pro. Vector features corresponding to a minimum mapping unit of less than 4 connected pixels were excluded from the final dataset. 2025 nchan_coll2_shrln_20171222_20241115.shp nchan_coll2_sandext_20171222_20241115.shp nchan_coll2_vegext_20171222_20241115.shp 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 Barrier platform, beach, and vegetated widths were calculated from the intersection of the shoreline, sand, and vegetated vectors with transects spaced 300 m (10 pixels) apart alongshore. New Harbor Islands, isolated back-barrier islands that are less than 8 pixels in size, and back-barrier islands that are more than 8 pixels from the contiguous island extent were excluded from the feature metrics dataset. Transects are the same as used in Bernier and others (2021); feature intersections and along-transect widths were calculated in RStudio. Vegetated widths were not calculated along transects 56 and 57, where the oblique nature of the back-barrier marsh island, coupled with a change in baseline orientation, caused inconsistencies in delineating feature extents. Features less than 17 m wide along-transect are considered unresolvable based on the stated 12-m RMSE for Landsat L1TP products (sqrt(12^2 + 12^2) = 16.97) and were also excluded from the final dataset. 2025 nchan_perp_transects_300m.shp nchan_coll2_featmetrics_2017_2024.csv 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 Vector Universal Transverse Mercator 16 0.9996 -87.0 0 500000.0 0.0 row and column 30 30 Meters D WGS 1984 WGS 1984 6378137.0 298.257223563 nchan_coll2_shrln_20171222_20241115.shp, nchan_coll2_sandext_20171222_20241115.shp, nchan_coll2_vegext_20171222_20241115.shp Vector shoreline, sand, and vegetated feature extents in Esri shapefile (.shp) format, contained in nchan_coll2_features.zip. USGS FID Internal feature number Esri Sequential unique whole numbers that are automatically generated. Shape Feature geometry Esri Coordinates defining the features. IMG_DATE 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 2017.975 2024.873 Decimal year 0.001 nchan_perp_transects_300m.shp Cross-shore transect locations in Esri shapefile (.shp) format, contained in nchan_coll2_features.zip. USGS FID Internal feature number Esri Sequential unique whole numbers that are automatically generated. Shape Feature geometry Esri Coordinates defining the features. Transect Transect number USGS 1 132 Dimensionless 1 StartX UTM coordinates of transect starting x position. USGS 319282.942095 325039.250404 Meters 0.000001 StartY UTM coordinates of transect starting y position. USGS 3289009.250058 3326250.763767 Meters 0.000001 Azimuth Azimuth of transect cast USGS 48.408288 117.397400 Meters 0.000001 nchan_coll2_featmetrics_2017_2024.csv Comma-separated values (.csv) file detailing derived feature metrics for each image-acquisition date, contained in nchan_coll2_features.zip. USGS Transect Transect number USGS 1 132 Dimensionless 1 IMG_DATE 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 2017.975 2024.873 Decimal year 0.001 SHRLN_min Distance, in meters, along transect from baseline to first intersection with shoreline vector. USGS 564.520 5072.489 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS SAND_min Distance, in meters, along transect from baseline to first intersection with sand extent vector. USGS 598.141 4957.164 Meters 0.0010 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS SAND_max Distance, in meters, along transect from baseline to last intersection with sand extent vector. USGS 689.084 5012.648 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS VEG_min Distance, in meters, along transect from baseline to first intersection with vegetated extent vector. USGS 695.207 4279.561 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS VEG_max Distance, in meters, along transect from baseline to last intersection with vegetated extent vector. USGS 940.156 4327.063 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS SHRLN_max Distance, in meters, along transect from baseline to last intersection with shoreline vector. USGS 790.196 5336.275 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS ISLP Width, in meters, of barrier-island platform (sea shoreline to back-barrier shoreline). USGS 17.614 1461.495 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS BEACH Width, in meters, of barrier-island beach (maximum sand extent to seaward shoreline). USGS 21.429 431.331 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). USGS VEG Width, in meters, of barrier-island vegetation (vegetated extent). USGS 20.308 1217.769 Meters 0.001 NoData Feature is not present along the transect. USGS Excl Feature was excluded from dataset (see process step description for additional details). 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 nchan_coll2_shrln_20171222_20241115.shp, nchan_coll2_sandext_20171222_20241115.shp, nchan_coll2_vegext_20171222_20241115.shp, nchan_perp_transects_300m.shp, nchan_coll2_featmetrics_2017_2024.csv 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. shapefile, comma-delimited text Download file contains vector sand, vegetation, shoreline, and 300m transect shapefiles (.shp) and comma-separated values (.csv) text files. https://coastal.er.usgs.gov/data-release/doi-P9HY3HOR/data/nchan_coll2_features.zip None Vector datasets were created using Esri ArcGIS Pro version 3.3.2 and can be opened using Esri ArcGIS Pro version 3.1 or higher; these data may also be viewed using free Google Earth Pro or QGIS software. 20251219 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