Couvillion, Brady Lamb, Brian T. Defne, Zafer Ganju, Neil K. 20241003 Raster Digital Data Set https://doi.org/10.5066/P132GNMW Prior research has shown that sediment budgets, and therefore stability, of microtidal marsh complexes scale with areal unvegetated to vegetated marsh ratios (UVVR) suggesting these metrics are broadly applicable indicators of microtidal marsh vulnerability. This effort has developed the UVVR metric using readily available satellite imagery for the coastal areas of the contiguous United States (CONUS). These datasets provide annual averages of the 1) unvegetated fraction, 2) vegetated fraction, 3) water fraction and 4) an unvegetated to vegetated ratio (UVVR) at 30-meter resolution over the coastal areas of the contiguous United States for the year 2020. Additionally, multi-year average values of vegetated ratio, its standard deviation and a UVVR based on the annually-averaged vegetated ratio are provided for the coastal wetlands of the contiguous United States. These datasets are provided as objective and consistent means to help evaluate geomorphic status and vulnerability of coastal wetlands at a national scale. Specifically, the unvegetated to vegetated marsh ratio (UVVR) is useful for establishing vegetative cover status and for tracking changes in the status of salt marshes at the national scale annually. Author ORCIDs: Couvillion, Brady (0000-0001-5323-1687); Lamb, Brian T. (0000-0001-7957-5488); Defne, Zafer (0000-0003-4544-4310); Ganju, Neil K. (0000-0002-1096-0465) 2019 2021 observed Complete As needed -98.8708259584048 -80.2032055337024 31.9464271230289 24.3943803609645 Alexandria Digital Library Feature Type Thesaurus wetlands Marine Realms Information Bank (MRIB) keywords wetlands None wetland change wetland loss wetland gain Vegetation unvegetated fractional cover coastal wetlands Unvegetated to Vegetated Ratio UVVR USGS Metadata Identifier USGS:66e1d001d34ecba8b8677851 Common geographic areas Gulf of Mexico Coast Conterminous United States coastal wetlands Florida Alabama Mississippi Louisiana Texas It is strongly recommended that this data is directly acquired from the distributor described above or from another United States Geological Survey (USGS) Biological Resources Division server and not indirectly through other sources which may have changed the data in some way. The distributor makes no claims as to the data's suitability for other purposes. These data are intended for annual, coarse-scale analyses. It may not be appropriate for analyses at finer spatial or temporal scales. Acknowledgement of the U.S. Geological Survey as a data source would be appreciated in products developed from these data, and such acknowledgment as is standard for citation and legal practices for data source is expected by users of these data. Sharing new data layers developed directly from these data would also be appreciated by USGS staff. Users are advised to read the data set's metadata thoroughly to understand appropriate use and data limitations. Users are advised to contact the author of this research with questions regarding its appropriate use. The distributor shall not be liable for improper or incorrect use of these data, based on the description of appropriate/inappropriate uses described in this metadata document. These data are not legal documents and are not to be used as such. US Geological Survey Brady Couvillion Research Geographer mailing and physical

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Lafayette LA 70506 USA 225-578-7484 couvillionb@usgs.gov Environment as of Metadata Creation: Microsoft Windows 11; Esri ArcGIS Pro 3.0.0 To assess the accuracy of the dataset, Landsat derived unvegetated and vegetated percent cover estimates for a given time-period were compared to high resolution estimates of those parameters created from National Agricultural Inventory Program (NAIP) imagery. The samples consisted of 101,640 random points for the Gulf of Mexico (GOM) region which were intersected with the corresponding vegetated, unvegetated substrate (soil and/or sand), or water percent cover derived from NAIP images for a closely corresponding time period. To establish accuracy metrics most pertinent to UVVR calculation, unvegetated substrate and water were combined as a single unvegetated class. For the purposes of this accuracy assessment, the NAIP derived percent cover estimates were used as truth, and compared to the Landsat derived estimates. The coefficient of determination (R^2), root mean squared error (RMSE), and bias of the Landsat derived percent cover estimates were calculated. The R^2 of the percent unvegetated estimates was 0.88, with an RMSE of 15.88% and a bias of -0.07%. This indicates that overall, Landsat-derived percent unvegetated cover is trivially overestimated relative to NAIP analyses, but overall were in close agreement. The R^2 of the percent vegetated estimates was 0.88, with an RMSE of 15.91% and a bias of -0.45%. This indicates that overall, the Landsat derived percent vegetated cover is trivially overestimated compared to the NAIP analyses, but overall were in close agreement. It is important to note that the NAIP imagery is being designated as truth due to its superior spatial resolution; however, it also contains error. The inconsistency in spectral information and low radiometric resolution of NAIP imagery leads to errors in its ability to estimate percent cover, and as such the true accuracy of the Landsat derived percent cover estimates is likely higher than these results indicate. It is also important to note that the Landsat derived estimates are taking into account greater temporal frequency than the NAIP derived estimates. Finally, geo-rectification errors may lead to some differences in the ground area covered by the NAIP and Landsat derived percent cover estimates, which may lead to further discrepancy between the two datasets and explain some level of the inaccuracy. UVVR data has gone through extensive QA/QC to ensure a reasonable level of accuracy. This is not to say, however, that inaccuracy does not remain in the dataset, and users of the dataset should be aware of said potential for inaccuracy. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. Refer to details regarding source imagery horizontal accuracy. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. U.S. Geological Survey 2019 Version 2.0 publication Sioux Falls, South Dakota USGS EROS Digital and/or Hardcopy 2019 observed Landsat 8 (LASRC) input data Collin Homer Jon Dewitz Suming Jin George Xian Catherine Costello Patrick Danielson Leila Gass Michelle Funk James Wickham Stephen Stehman Roger Auch Kurt Riitters 202004 publication ISPRS Journal of Photogrammetry and Remote Sensing vol. 162 n/a Elsevier BV ppg. 184-199 https://doi.org/10.1016/j.isprsjprs.2020.02.019 Digital and/or Hardcopy 2001 2016 observed NLCD input data NOAA 2020 raster digital data Charleston, SC: NOAA Office for Coastal Management National Oceanic and Atmospheric Administration, Office for Coastal Management. 2016. www.coast.noaa.gov/htdata/raster1/landcover/bulkdownload/30m_lc/ Digital and/or Hardcopy 1975 2016 ground condition CCAP input data USGS 2023 raster digital data https://earthexplorer.usgs.gov/ Digital and/or Hardcopy 1985 2023 observed LCMAP Mask Datasets were created using Landsat 5, 7, 8, and 9 Collection 2 Surface Reflectance imagery. Landsat was chosen as the period of record of the imagery provides for the ability to create datasets covering a time period back to 1985. Surface Reflectance data are generated from the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Landsat 8 Surface Reflectance Code (LASRC). Additionally, the LEDAPS methodology provides masks for clouds, cloud shadows, and adjacent clouds, which were used in the next step of the methodology. These images contain atmospherically corrected surface reflectance data from Landsat 8. The images consist of 5 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands. The thermal bands were not used for the purposes of this effort. Landsat 8 (LASRC) 2023 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 United States 225-578-7484 337-266-8616 couvillionb@usgs.gov For a given year, the collection of Landsat imagery was filtered to include only images which intersected the region of interest, have an initial cloud cover estimate of less than 80%, and images which were within a year window of the date of interest. To isolate the growing season, images were further filtered to include months from May through November only. The analysis years of 1985 through 2012 utilized the Landsat 5 and 7 sensors whereas analysis years 2014 through 2023 utilized Landsat 8 and 9. Landsat 8 (LASRC) 2023 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 United States 225-578-7484 337-266-8616 couvillionb@usgs.gov Cloud recognition and exclusion was conducted using the “QA_PIXEL” and “QA_RADSAT” bands. Values representing clouds or possible clouds as represented in Tables 6-2 and 6-3 of the following document: U.S. Geological Survey. 2019. Landsat 8 Surface Reflectance Code (LASRC) Product Guide, Version 2.0. EROS. Sioux Falls, South Dakota Using these values, the collection of all Landsat images was first filtered to include only pixels which were free of clouds or other sources of contamination. 2023 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 United States 225-578-7484 337-266-8616 couvillionb@usgs.gov The following indices were calculated for each image in the collection: mNDWI = (Green-SWIR1)/(Green+SWIR1) NDVI = (NIR-Red)/(NIR+Red) NDBI = (SWIR1 – NIR) / (SWIR1 + NIR) SWIR1 is Band 5 in Landsat 5 & 7 and Band 6 in Landsat 8 & 9. 2023 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 United States 225-578-7484 337-266-8616 couvillionb@usgs.gov While cloud recognition and exclusion were implemented in a previous step to exclude sources of contamination, occasionally contaminated pixels are incorrectly identified. To further reduce the impact of these erroneous values, as well as to isolate a pixel value which represented somewhat normal conditions for the growing season being analyzed, a yearly growing season median value was calculated for each pixel. Median is used rather than mean as the mean would still be adversely affected by contaminated pixels. Additionally, values representing the 90th percentile of observations as well as the standard deviation of observations were calculated to be used in the identification of aquatic vegetation. 2023 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 United States 225-578-7484 337-266-8616 couvillionb@usgs.gov High-resolution analyses were used to develop endmembers (to be used in Linear Spectral Unmixing) in Landsat data which represented unvegetated, vegetated, and water conditions. These aerial imagery-based products were aggregated from 1-meter resolution to 30-meter resolution to match the Landsat pixels and the percent unvegetated/vegetated/water in each 30-meter pixel was recorded. Endmember values were calculated from the line which best fit the calibration data. Initially, spectral unmixing consisted of separating land and water categories. The values of these lines at 0% land (100% water) and 100% land (0% water) were 0.273641882 and -0.3821185318, respectively. Following unmixing into land and water categories, a second iteration of Linear Spectral Unmixing was used to quantify the unvegetated and vegetated components of Landsat pixels. For this process, NDVI indices were used. The values of the endmembers for those indices respectively were as follows: 0% vegetated land (100% unvegetated) 0.06304555 and 100% vegetated land (0% unvegetated) 0.684056307. 2023 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 US 225-578-7484 337-266-8616 couvillionb@usgs.gov Targets containing aquatic vegetation (AV) have strong vegetation signals, a characteristic usually indicative of vegetated land. Aquatic vegetation is often times ephemeral however, and it was the desire of this assessment to quantify vegetated and unvegetated land categories, and the change to those parameters in those land locations. Unless FAV is identified correctly and recoded to water, a transient vegetation signal can be misinterpreted as change to the UVVR. Aquatic vegetation has a strong vegetation signal, at least some water signal, and more importantly, those signals are variable. We therefore created an aquatic vegetation mask created by querying pixels that contained a variable NDVI signal as well as a variable mDNWI signal through the period of record. The resulting mask was then used in conjunction with a spectral signature of aquatic vegetation and Linear Spectral Unmixing was used to estimate the portion of each pixel comprised by aquatic vegetation. These values were recoded to water for the purposes of UVVR calculations. 2024 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 US 225-578-7484 337-266-8616 couvillionb@usgs.gov Once fractional estimates of the percent unvegetated, vegetated, and water components of each pixel were calculated, the final UVVR was calculated. As the name suggests, this ratio is calculated as UV/Veg. 2024 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 US 225-578-7484 337-266-8616 couvillionb@usgs.gov The final UVVR dataset was subset to a boundary extending from approximately a 10-meter elevation contour inland, to a seaward boundary of the federal waters boundary, and further subset to include only the coastal areas of the conterminous United States. 2024 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 US 225-578-7484 337-266-8616 couvillionb@usgs.gov As the purpose of this dataset is to provide a UVVR for coastal wetlands, ancillary datasets including LCMAP, NLCD and CCAP were used to identify wetland areas. Final datasets were masked to include only pixels identified as wetland in one of these three classifications. LCMAP NLCD CCAP 2024 Brady Couvillion U.S. Geological Survey, Southeast Region Geographer mailing address

Louisiana State University

Baton Rouge LA 70803 US 225-578-7484 337-266-8616 couvillionb@usgs.gov Raster Grid Cell 28023 69269 4 0.00026949458523585533 0.0002694945852358552 Decimal seconds WGS_1984 WGS 84 6378137.0 298.257223563 LS_2020_UVVW_UVVR_GOM_061124.tif 4 band raster geospatial data file. Producer Defined Band 1 Unvegetated fraction Producer Defined 0.0 1 Fraction Band 2 Vegetated fraction Producer Defined 0.0 1 Fraction Band 3 Water Fraction Producer Defined 0.0 1 Fraction Band 4 UVVR Producer Defined This ratio is calculated as UV/Veg U.S. Geological Survey - ScienceBase mailing and physical

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Denver CO 80225 USA 1-888-275-8747 sciencebase@usgs.gov Distributor assumes no liability for misuse of data. Digital Data https://doi.org/10.5066/P132GNMW None. No fees are applicable for obtaining the data set. 20241003 Brady Couvillion US Geological Survey Research Geographer mailing and physical

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Lafayette LA 70506 USA 225-578-7484 couvillionb@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 Record created using USGS Metadata Wizard tool. (https://github.com/usgs/fort-pymdwizard)