Couvillion, Brady 2021 Raster Digital Data Set USGS Data Series doi:10.5066/P9ZQI7ZW doi:10.5066/P9ZQI7ZW https://doi.org/10.5066/P9ZQI7ZW These data were used to quantify land area change in a wetlands possible zone of coastal wetlands during a 1985-2020 observation period. The datasets presented in this data release represent annual median estimates of the fractional amount of land, floating aquatic vegetation, submerged aquatic vegetation, and water per Landsat pixel. These data are intended for coarse-scale analysis of wetland change area. The datasets are summarized by 10-digit Hydrologic Unit Code (HUC10), and land area change through time is fit using a penalized regression smooth spline. The trends are therefore generalized in time and are intended to present coarse scale observations of trends in wetland area change. These datasets are provided as objective and consistent means to assess the area of coastal wetlands at landscape scales. Author ORCIDs: Couvillion, Brady(0000-0001-5323-1687) 19900101 19901231 observed Complete As needed -98.8708 -80.2032 31.9464 24.3944 Alexandria Digital Library Feature Type Thesaurus wetlands Marine Realms Information Bank (MRIB) keywords wetlands None wetland change wetland loss wetland gain wetlands wetland area USGS Metadata Identifier USGS:606df81dd34eae125e9c767f 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) 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. U.S. Geological Survey Brady Couvillion Research Geographer mailing and physical

700 Cajundome Blvd.

Lafayette LA 70506 USA 225-578-7484 couvillionb@usgs.gov Environment as of Metadata Creation: Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.5 (Build 6491) Service Pack N/A (Build N/A) These data were compared to Coastwide Reference Monitoring System (CRMS) products from the 2005, 2008, and 2015/2016 land water classifications of CRMS sites (Couvillion et al., 2018). The 2012 aerial imagery-based land/water classifications available from the CRMS program were not used due to the lapse in coverage between Landsat 5 and 8 (May 5, 2012 - Apr 11, 2013). The CRMS aerial imagery based classifications are meticulously corrected for error-prone targets, such as floating aquatic vegetation, via photo-interpretation. These high-resolution assessments have shown an overall accuracy of 96.49% (Couvillion et al 2018). These datasets were therefore designated as “truth,” and Landsat derived percent land estimates were compared to these datasets. Fractional estimates were produced from Landsat imagery for time periods that most closely matched the date of acquisition (DOA) for the 2005, 2008, and 2015/16 CRMS products. To exclude cloud contamination and reduce the effects from other sources of error, median reflectance values of all available cloud-free imagery in a 3-month window surrounding that DOA was used. The resulting datasets were summarized in the CRMS 1km analysis boundary and compared to aerial imagery based percent land estimates from CRMS data. The root mean squared error (RMSE) of these comparisons ranged from 11.88% to 14.04%. The bias ranged from 7.33% to 12.03%. This indicated that at most sites, particularly in those with a land composition of greater than approximately 20% land, the Landsat derived percent land overestimates land compared to the CRMS analyses. While the overestimation of land by Landsat derived products is important to quantify, it is important to note that the pattern of overestimation of land is relatively consistent through time suggesting that change analyses and the trends generated from Landsat are consistently interpretable and informative. These data have 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 logical for the information presented, as described in the abstract. Values fall within the expected ranges. Users are advised to read the rest of the metadata record carefully for additional details. 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 202008 Version 3.0 publication Sioux Falls, South Dakota USGS EROS U.S. Geological Survey. 2019. LANDSAT 4-7 Surface Reflectance (LEDAPS) Product Guide, Version 3.0. EROS. Sioux Falls, South Dakota https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1370_L4-7_C1-SurfaceReflectance-LEDAPS_ProductGuide-v3.pdf Digital and/or Hardcopy 202008 publication date Landsat 5 (LEDAPS) Used for cloud and cloud shadow masking described in process step. 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 202004 publication date NLCD Used in the production of a wetlands possible mask described in the process step. 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 2020 publication date CCAP Used in the production of a wetlands possible mask described in the process step. Datasets were created using Landsat 5 Thematic Mapper (TM) Surface Reflectance imagery. Landsat was chosen as the rich historical record of imagery provides for the ability to create future datasets covering a time period back to 1984. 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 5. Landsat 5 (LEDAPS) 2020 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 “pixel_qa” bands. Values representing clouds or possible clouds as represented in Tables 5-1 through 5-7 of the following document: U.S. Geological Survey. 2019a. LANDSAT 4-7 Surface Reflectance (LEDAPS) 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. Landsat 5 (LEDAPS) 2020 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-SWIR)/(Green+SWIR) NDVI = (NIR-Red)/(NIR+Red) 2020 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 year being analyzed, a yearly median value was calculated for each pixel. Median is used rather than mean as the mean would still be adversely affected by contaminated pixels. 2020 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 5 data which represented pure pixels of land, floating aquatic vegetation, submerged aquatic vegetation, and water conditions. 2020 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 Targets containing aquatic vegetation (AV) have strong vegetation signals, a characteristic usually indicative of vegetated land. Aquatic vegetation is often ephemeral however, and it was the desire of this assessment to quantify land area change. Unless AV is identified correctly and recoded to water, a transient vegetation signal can be misinterpreted as change in wetland area. 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 wetland area change calculations. 2020 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 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 Gulf of Mexico Coast of the United States. 2020 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 wetland area change for coastal wetlands, ancillary datasets including NLCD and CCAP were used to identify wetland areas. Final datasets were masked to include only pixels identified as wetland in one of these two classifications. Additional NLCD-like classifications were created to cover time periods not covered by NLCD or CCAP. Areas classified as wetlands in any of those datasets were included in the wetland possible zone. Finally, this wetland possible mask was buffered to 100-meters to ensure any land area variation along the edges of wetlands would be included for possible wetland change. NLCD CCAP 2020 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 0.00026949458523585577 0.00026949458523585577 Decimal seconds WGS_1984 WGS 84 6378137.0 298.257223563 L5_1985_GOM_Fractional_Land_FAV_SAV_Water.tif 4 band raster geospatial data file. Producer Defined Band_1 Decimal fraction of coverage of land in each raster cell. Producer Defined 0.0 1.0 Decimal fraction of coverage within a 30-meter pixel 0.001 Band_2 Decimal fraction of coverage of FAV in each raster cell. Producer Defined 0.0 1.0 Decimal fraction of coverage within a 30-meter pixel 0.001 Band_3 Decimal fraction of coverage of SAV in each raster cell. Producer Defined 0.0 1.0 Decimal fraction of coverage within a 30-meter pixel 0.001 Band_4 Decimal fraction of coverage of water in each raster cell. Producer Defined 0.0 1.0 Decimal fraction of coverage within a 30-meter pixel 0.001 The entity and attribute information provided here describes the raster data associated with the data set. Please review the detailed descriptions that are provided (the individual attribute descriptions) for information on the values that appear as bands of the data set. The entity and attribute information was generated by the individual and/or agency identified as the originator of the data set. Please review the rest of the metadata record for additional details and information. U.S. Geological Survey GS ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov 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. Digital Data https://doi.org/10.5066/P9ZQI7ZW None. No fees are applicable for obtaining the data set. 20210622 Brady Couvillion U.S. Geological Survey Research Geographer mailing and physical

700 Cajundome Blvd.

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)