U.S. Geological Survey, Wetland and Aquatic Research Center Beck, Holly J. Dugas, Jason Garber, Adrienne Couvillion, Brady Fischer, Michelle 20230828 raster digital data Lafayette, LA U.S. Geological Survey, Wetland and Aquatic Research Center https://doi.org/10.5066/P9109FW1 Coastal Protection and Restoration Authority of Louisiana U.S. Department of Commerce, National Oceanic Atmospheric Administration U.S. Department of Agriculture, Natural Resources Conservation Service U.S. Department of Interior, U.S. Fish and Wildlife Service U.S. Department of the Army, Corp of Engineers U.S. Environmental Protection Agency 2023 document http://www.lacoast.gov Wetland restoration efforts conducted by the Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in Louisiana rely on monitoring efforts to determine the efficacy of these efforts. The Coastwide Reference Monitoring System (CRMS) was developed to assist in a multiple-reference approach that uses aspects of hydrogeomorphic functional assessments and probabilistic sampling for monitoring. The CRMS program includes a suite of approximately 398 sites that encompass the range of hydrological and ecological conditions for each stratum. As part of CRMS, land and water classifications are created from Digital Orthophoto Quarter Quadrangles (DOQQs) approximately every three years at all CRMS sites. A DOQQ is a raster image in which displacement in the image caused by sensor orientation and terrain relief has been removed and combines the image characteristics of a photo with geometric qualities of a map. The DOQQs generated for this project consist of 2021 Color Infrared (CIR) Digital Imagery. These images were classified into land and water categories using a threshold of the near infrared (NIR) band, followed by supervised and unsupervised classification. Initial classification results are then reviewed by multiple image analysts to identify and manually recode errors. The final land-water classifications are intended to serve as both geographic and quantitative assessments of landscape composition on the date of acquisition. Five previous assessments have been conducted (2005, 2008, 2012, 2015/2016, and 2018). Once the program creates enough data points for statistical analyses, these data will be used for land area change rate calculation. The intended use of this data set is to provide information to aid efforts in the conservation, restoration, creation, and enhancement of Louisiana's coastal wetlands. The land-water data is used to measure the occurrence, locations, and rates of land loss/land gain for CRMS Site 3664. Author ORCIDs: Beck, H.J.(0000-0002-0567-9329); Dugas, J.(0000-0001-6094-7560);Garber, A.(0000-0003-1139-8256); Couvillion, B.(0000-0001-5323-1687); Fischer, M.(0000-0002-6783-2819) 20211115 ground condition Complete None planned -89.82803155672016 -89.8179445194676 29.9161685393041 29.90688053780412 ISO 19115 Topic Category imageryBaseMapsEarthCover None CRMS DOQQ land-water wetland mapping restoration marsh protection coastal cartography Geographic Information System Marine Realms Information Bank (MRIB) keywords alteration of wetland habitats wetland wetland restoration Coastal and Marine Ecological Classification Standard Emergent Wetland Forested Wetland Scrub-Shrub Wetland USGS Metadata Identifier USGS:64dce2d3d34e5f6cd552807a None Louisiana Geographic Names Information System State of Louisiana coastal Louisiana It is strongly recommended that these data are directly acquired from the U.S. Geological Survey, Wetland and Aquatic Research Center and not indirectly through other sources which may have changed in some way. The distributor makes no claim as to the data's suitability for other purposes. Acknowledgement of the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of this data. Sharing new data layers developed directly from these data would be appreciated by the USGS WARC staff. Users should be aware that comparison with other datasets for the same area from other time periods may be inaccurate because of inconsistencies resulting from changes in mapping conventions, data collection procedures, and computer processes over time. 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, Wetland and Aquatic Research Center Holly J. Beck Geographer mailing

7920 NW 71st Street

Gainesville FL 32653 USA 352-264-3496 hbeck@usgs.gov The U.S. Geological Survey (USGS) Wetland and Aquatic Research Center (WARC) would like to acknowledge the assistance of the Coastal Protection and Restoration Authority of Louisiana, U.S. Department of Commerce, U.S. Department of Agriculture, U.S. Department of the Interior, U.S. Department of the Army, and the U.S. Environmental Protection Agency. Environment as of Metadata Creation: Microsoft Windows 10 Enterprise; Esri ArcGIS Pro 3.0.0 Hexagon Geospatial 20180301 Version: 16.5 (v16.5.0.852) Software ESRI inc. 20230703 Version 3.0.0 publication Software Previous examinations have investigated the accuracy of these datasets and the methodology used to produce them in detail. Further information regarding those previous accuracy assessments is available in the metadata of the datasets available at the following location: Couvillion, B.R., Beck, H.J., Dugas, J., Garber, A., and Mouton, K., 2018, Coastwide Reference Monitoring System (CRMS) 2015 land-water classifications: U.S. Geological Survey data release, https://doi.org/10.5066/F7930RDX. Couvillion, B.R., Beck, H.J., Dugas, J., Garber, A., and Mouton, K., 2018, Coastwide Reference Monitoring System (CRMS) 2016 land-water classifications: U.S. Geological Survey data release, https://doi.org/10.5066/P90RE64M. Beck, H.J., Couvillion, B.R., Dugas, J., Garber, A., and Mouton, K., 2021, Coastwide Reference Monitoring System (CRMS) 2018 land-water classification data: U.S. Geological Survey data release, https://doi.org/10.5066/P9AZOHQU. The same methodology used to produce the 2015, 2016, and 2018 datasets mentioned above were used in this analysis and as such, the accuracy assessment investigation for those analyses is informative to this effort as well. Those previous investigations determined a simple overall accuracy of 96.49% for these datasets, however when point selection bias and landscape composition were accounted for, an adjusted accuracy for the overall dataset of 98.88% was attained. This overall accuracy is representative of the methodology used to create the data set as a whole. In other words, it is indicative of average values of all CRMS sites, not necessarily specific CRMS sites. Specific CRMS sites may have accuracies less than or greater than this overall value. In general, the more dis-aggregated a landscape, and the more aquatic vegetation it contains, the higher the probability of error, and consequently the lower the accuracy of land cover classifications will be in that location. All land-water classification data has gone through several iterations of quality assurance/quality control to ensure quality. The initial, largely automated, classification results are reviewed by multiple image analysts to identify and manually recode errors. The QAQC efforts ensure error-prone classes such as floating aquatic vegetation (FAV), shadows, and vegetative overhang are recoded to their appropriate category. This data represents the landscape composition on the date of acquisition (DOA) of the imagery. Land area in this highly dynamic environment is a very fluid parameter, and normal environmental variability can lead to variability in this measure from day to day. The accuracy of the horizontal positions is based on the accuracy of the georeferenced data source (2021 DOQQ). The sources used vary from project to project. All USGS Mapping products adhere to the National Mapping Accuracy Standard. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. Digital Aerial Solutions LLC 20200809 remote-sensing image http://nationalmap.gov Digital and/or Hardcopy 20211030 20211130 Date of source photography 2021 DOQQ Aerial, color-infrared (CIR) imagery collected in the fall and winter of 2021 which served as the primary data source for classification of land-water data. Preprocessing-Imagery Clips: The 2021 aerial images were clipped to the 1 km CRMS site boundaries to remove as much of the surrounding environment as possible. This step was done in an ArcGIS model that used the original raster itself to define pixel alignment, thereby ensuring the resultant products would have no shifts compared to the original imagery. This is done to remove areas which are not part of the classification analysis, and accompanying sources of noise or complexity contained within those areas. This lessens the chance of the classifier incorporating confusing patterns introduced by areas of the image not of interest to this specific analysis. 2022 Holly J Beck U.S. Geological Survey, Southeast Region Geographer mailing address

7920 NW 71St Street

Gainesville FL 32653 US 352-264-3496 352-378-4956 hbeck@usgs.gov Possible Change Identification: Later steps in this methodology will utilize 2018 land/water products as training for the 2021 land/water classifications and as such, areas which appeared to have experienced a change between those time periods need to be identified and excluded from the pool of possible training locations. The methods for the identification of areas of possible change will be discussed in the following two paragraphs: The near-infrared (NIR) wavelengths are particularly useful for discriminating land and water categories. As such, the first step in the identification of areas of possible change was to determine a threshold in the 2021 NIR values, above which pixels were generally land, and below which pixels were generally water. This value was determined to be 22,500 in this particular set of DOQQs. This initial dichotomous split is used to identify areas which may have experienced a change among land and water categories and exclude those areas from the pool of possible training data. Additionally, Sentinel-2 satellite based imagery was used to identify areas of potential change between the Fall of 2018 and Fall of 2021 time periods. Sentinel-2 imagery has a revisit period of 5 days, and a spatial resolution of 10-meters in the visible and near infrared bands. All Sentinel-2 imagery within a 90-day window of the dates of acquisition (DOA) of the DOQQ imagery was compiled, cloud masked, and composited to calculate mean values as well as standard deviations of the values in Band 8, and a Normalized Difference Vegetation Index. Pixels which contained mean values in the Fall of 2021 which exceeded -1 or +1 standard deviation of the observations in the Fall of 2018 were identified as potentially having experienced a change and were therefore also excluded from the pool of possible training examples. 2021 DOQQ 2022 Holly J Beck U.S. Geological Survey, Southeast Region Geographer mailing address

7920 NW 71St Street

Gainesville FL 32653 US 352-264-3496 352-378-4956 hbeck@usgs.gov Training Sample: From the pixels remaining in the 2018 land/water product, a random, stratified sample of 10,000 pixels was taken for each individual CRMS site. This location specific training sample was used in supervised classification, described in the next step, to classify land/water in the 2021 imagery. 2022 Holly J Beck U.S. Geological Survey, Southeast Region Geographer mailing address

7920 NW 71St Street

Gainesville FL 32653 US 352-264-3496 352-378-4956 hbeck@usgs.gov Supervised Classification: the training data in the previous processing step was used to train a random trees classifier in ArcGIS. The maximum number of trees was set to 100, maximum tree depth was set to 50. 2022 Holly J Beck U.S. Geological Survey, Southeast Region Geographer mailing address

7920 NW 71St Street

Gainesville FL 32653 US 352-264-3496 352-378-4956 hbeck@usgs.gov Draft land-water dataset compilation: The draft land-water classification was compiled by recoding changes from the previous two steps and creating one compiled land-water classification. A clump and sieve, using 8 connected neighbors was run to eliminate features less than the Minimum Mapping Unit of 4 square meters. Features of less than 4 square meters were recoded to the opposite land cover category and are therefore not represented in this dataset. 2022 Holly J Beck U.S. Geological Survey, Southeast Region Geographer mailing address

7920 NW 71St Street

Gainesville FL 32653 US 352-264-3496 352-378-4956 hbeck@usgs.gov Data Improvements: The draft land-water classification was further edited to ensure accuracy. Experienced image analysts meticulously analyzed the initial classification created in the previous steps and edited the classification as needed. When errors were identified the analyst would recode the dataset in ArcPro using the Image Analyst extension Pixel Editor. 2022 Holly J Beck U.S. Geological Survey, Southeast Region Geographer mailing address

7920 NW 71St Street

Gainesville FL 32653 US 352-264-3496 352-378-4956 hbeck@usgs.gov This dataset was then QAQC’d further by additional image interpreters not involved in the classification process. Any errors identified by these reviewers were recoded following the same methodology outlined in the previous two steps. 2023 Holly J Beck U.S. Geological Survey, Southeast Region Geographer mailing address

7920 NW 71St Street

Gainesville FL 32653 US 352-264-3496 352-378-4956 hbeck@usgs.gov Raster Grid Cell 1003 1003 1 Universal Transverse Mercator 15 0.9996 -93.0 0.0 500000.0 0.0 row and column 1.0 1.0 meters North_American_Datum_1983 GRS 1980 6378137.0 298.2572221010042 CRMS_2021_Land_Water_Classification_CRMS3664.tif Raster geospatial data file. Producer Defined OID Internal object identifier. Producer Defined Sequential unique whole numbers that are automatically generated. Value Data identifier created by spatial software Producer Defined 0 2 Count Number of pixels in that class Producer Defined 113.0 868002.0 Class Class name Producer Defined Class name either Out, Land, or Water All areas characterized as emergent vegetation, wetland forest, scrub-shrub, or uplands are classified as land, while open water aquatics, and mud flats are classified as water. Items within the attribute table in addition to ArcInfo items (e.g., area. perimeter) include: 1) Class- classified as either land or water, 2) Acres- acreage figures Attributes used included OID, Value, Count, and Class_Names. All other attributes in the raster files such as Red, Green, Blue, Opacity were not used. Producer defined U.S. Geological Survey - ScienceBase mailing and physical

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 USA 1-888-275-8747 sciencebase@usgs.gov Downloadable data Although these data have been processed successfully on a computer system at the U.S. Geological Survey, Wetland and Aquatic Research Center, no warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data. It is strongly recommended that these data are directly acquired from a U.S. Geological Survey server, and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. Digital Data https://doi.org/10.5066/P9109FW1 None None 20230828 U.S. Geological Survey, Wetland and Aquatic Research Center Holly Beck Geographer mailing and physical address

7920 NW 71st Street

Gainesville FL 32653 USA 352-264-3496 hbeck@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time None Acknowledgement of the U.S. Geological Survey, Wetland and Aquatic Research Center as the metadata source would be appreciated. Please cite the original metadata when using portions of the record when creating a similar record for slightly altered data for reprojection or subsetting. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. http://www.esri.com/metadata/esriprof80.html ESRI Metadata Profile