Coastal Features Extracted from Landsat Collection 2 Data, Northern Chandeleur Islands, Louisiana

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/).

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.

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).

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.

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.