U.S. Geological Survey 20170616 vector digital data U.S. Geological Survey Data Release doi:10.5066/F7T43RB5 St. Petersburg, Florida U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center https://doi.org/10.5066/F7T43RB5 This shapefile consists of Dauphin Island, AL shorelines extracted from lidar data collected from November 1998 to January 2014. This dataset contains 14 Mean High Water (MHW) shorelines separated into 37 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open ocean, back-barrier, marsh shoreline. Raw lidar point data was converted to a gridded surface, from which a contour of the operational MHW shoreline (0.24 m North American Vertical Datum of 1988 [NAVD 88]; Weber and others, 2005) was identified and extracted. This produced a continuous MHW shoreline for each of the lidar datasets from 1998 – 2014. Shorelines for all 14 dates were compiled into a database for use with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009) to quantify rates of shoreline change over the 1998-2014 time period. The migration of shorelines through time is presented as the linear regression rate (LRR) in the associated transect files (https://coastal.er.usgs.gov/data-release/provisional/ip086178/). To document the position of the Dauphin Island, AL shoreline, from November 1998 to January 2014 as observed from lidar datasets that were acquired by various agencies (USGS, National Aeronautics and Space Administration [NASA], U.S. Army Corps of Engineers [USACE]) using several lidar platforms (for example Airborne Topographic Mapper - ATM, Experimental Advanced Airborne Research Lidar - EAARL). Shorelines derived from these lidar data provide information about the position of the island through time and are used to quantify the rate of change during this time period. These data will aid in developing an understanding of the evolution of the barrier island position, size and shape as well as documenting spatially-variable patterns in erosion and accretion of different sections of the island. Cross-referenced citations are applicable to the dataset as a whole. Additional citations are located within individual process steps that pertain specifically to the method described in that step. 19981102 20140121 ground condition Complete None planned -88.341683558 88.07384796 30.281611631 30.216924258 USGS Metadata Identifier USGS:3a9a5a3a-c586-4a19-b317-0957f691b2a2 ISO 19115 Topic Category Elevation Boundaries GeoscientificInformation Oceans Environment USGS Thesaurus Coastal Processes geomorphology remote sensing marine geology unconsolidated deposits None Vectorization Shoreline data Shoreline Mean High Water Lidar Coastal Shoreline map SPCMSC St. Petersburg Coastal and Marine Science Center None Alabama Dauphin Island Gulf of Mexico USA AL None Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey as the originator of the dataset. U.S. Geological Survey mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov Acknowledgment of the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, as a data source would be appreciated in products developed from these data, and such acknowledgment as is standard for citation and legal practices. Sharing of new data layers developed directly from these data would also be appreciated by the U.S. Geological Survey staff. Users should be aware that comparisons with other datasets for the same area from other time periods may be inaccurate due to inconsistencies resulting from changes in photointerpretation, mapping conventions, and digital processes over time. These data are not legal documents and are not to be used as such. Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.0.4.4000 Cheryl J. Hapke Emily A. Himmelstoss Meredith G. Kratzmann Jeffrey List E. Robert Thieler 20100101 Open-File Report 2010-1118 Woods Hole Coastal and Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Geology Program http://pubs.usgs.gov/of/2010/1118/ K.M. Weber J.H. List K.L.M. Morgan 20050101 Open-File Report 2005-1027 Woods Hole Coastal & Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Geology Program https://pubs.usgs.gov/of/2005/1027/ M. Harris J. Brock A. Nayegandhi M. Duffy 2005 U.S. Geological Survey Open-File report 2005-1427 St. Petersburg, Florida U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center none. https://pubs.usgs.gov/of/2005/1427/ofr-2005-1427.pdf R. A. Morton T.L. Miller 2005 Open-File Report 2005-1401 Woods Hole Coastal & Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Geology Program none https://pubs.usgs.gov/of/2005/1401/ C.J. Hapke D. Reid B.M. Richmond P. Ruggiero J. List 2006 Open-File Report 2006-1219 Woods Hole Coastal & Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Geology Program none https://pubs.usgs.gov/of/2006/1219/ Thompson D. M. Dalyander, P.S Long, J.W. Plant, N.G. 20170407 Open-File Report 2017-1031 St. Petersburg, Florida U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center https://doi.org/10.3133/ofr20171031 E.R. Thieler E.A. Himmelstoss J.L. Zichichi A. Ergul 2009 Open-File Report 2008-1278 St. Petersburg, Florida U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center Although the current citation is for v. 4.0, at the time of use the version number was 4.3. https://pubs.er.usgs.gov/publication/ofr20081278 The data provided here are a compilation of shorelines from lidar data from 1998 to 2014. The attributes are based on the requirements of the Digital Shoreline Analysis System (DSAS) software and have gone through a series of quality assurance procedures. Adjacent shoreline segments do not overlap and are not necessarily continuous. Shorelines were quality checked for accuracy. This shoreline file is complete and contains all shoreline segments used to calculate shoreline change rates along Dauphin Island, Alabama. These data adequately represented the shoreline position at the time of the survey. Remaining gaps in these data, if applicable, are a consequence of non-existing data or existing data that did not meet quality assurance standards. In order to determine the uncertainties associated with individual shorelines, a methodology following Morton and Miller (2005) and Hapke and others, (2006) was used to estimate a positional uncertainty value for each shoreline. Total shoreline positional uncertainty is a function of the errors inherent in the source data (horizontal and vertical accuracy of the raw lidar data) the conversion of point data to a 3D surface (grid error) and those errors generated in the extraction of the vector shoreline (interpolation uncertainty). Four terms were identified to describe the uncertainty of the resulting lidar shoreline position. The first is the direct horizontal uncertainty from published lidar data. Following the methods described by Hapke and others (2010) the second term is derived from the vertical uncertainty from published lidar data, which is then converted to a horizontal uncertainty based on an averages slope around MHW for each lidar dataset, determined by pulling the slope data from the lidar data at the intersection of MHW and the existing alongshore DSAS transects. The third term is calculated as the “grid error” term. This is a measure of how well the surface (created from 90% of the raw data) captures the actual elevation of the remaining 10% of the data. A comparison of the grid elevation to the raw elevation is then converted to an RMS value describing the grid surface error. The initial calculation of grid surface error for the island-wide dataset was much higher than expected, due to the appearance of houses, various areas of vegetation and water surfaces in the first return data. Thus, the calculation of grid error was constrained to a 20-meter buffer around the feature extracted (MHW) to provide a better estimate of the surface error from which the feature was derived. The fourth and final term used is the interpolation uncertainty, based on the grid cell size. The four terms were summed in quadrature and the resulting shoreline positional uncertainty was applied to each shoreline date in the "UNCERT" field of the attribute table. This value is used to determine the uncertainty of shoreline change rates, when used with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009). 3.1 The horizontal positional accuracy value is presented as an average of the 14 lidar shoreline uncertainty values. Each shoreline was assigned a unique uncertainty based on: 1) Horizontal uncertainty from raw lidar data 2) Additional horizontal uncertainty derived from vertical uncertainty 3) Grid uncertainty term 4) Cell size limitation. The following are the estimated uncertainties for each date. Values are in meters NAVD88. 19981102 (4.2) 20011002 (2.1) 20040505 (3.9) 20040919 (4.8) 20050901 (3.4) 20060314 (3.3) 20060921 (2.7) 20070627 (3.3) 20080625 (3.7) 20080908 (4.0) 20100101 (2.8) 20120905 (1.8) 20130712 (1.6) 20140121 (2.2) Average = 3.1 m Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM) United States Geological Survey (USGS) National Aeronautics and Space Administration (NASA) 20000101 tabular digital data Charleston, SC NOAA's Ocean Service, Office for Coastal Management (OCM) https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=22 https://coast.noaa.gov/htdata/lidar1_z/geoid12a/data/22 LAZ (compressed LAS) format file containing LIDAR point cloud data 19981102 ground condition 1998_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U.S. Geological Survey 2009 first tabular digital data U.S. Geological Survey Data Series 418 Saint Petersburg, FL U.S. Geological Survey https://pubs.usgs.gov/ds/418/ LAZ (compressed LAS) format file containing lidar point cloud data 20011003 ground condition 2001_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. Joint Airborne LiDAR Bathymetry Technical Center of Expertise (JALBTCX) 20060523 XYZ point cloud data Charleston, SC NOAA's Ocean Service, Office for Coastal Management (OCM) https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=19 https://coast.noaa.gov/htdata/lidar1_z/geoid12a/data/19 LAZ (compressed LAS) format file containing lidar point cloud data 20040505 ground condition 200405_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U. S. Geological Survey 20081027 first XYZ point cloud data Data Series 384 Saint Petersburg, FL U. S. Geological Survey https://pubs.usgs.gov/ds/384/ LAZ (compressed LAS) format file containing lidar point cloud data 20040919 ground condition 200409_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U.S. Geological Survey 2016 first XYZ point cloud data U.S. Geological Survey Data Release doi:10.5066/F78G8HSG St. Petersburg, FL U.S. Geological Survey Dates of data collection 09/01 - 09/08/2005 http://coastal.er.usgs.gov/data-release/doi-F78G8HSG LAZ (compressed LAS) format file containing lidar point cloud data 20050901 ground condition 2005_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. Joseph W. Long Karen L. M. Morgan Kara Doran 2016 first XYZ point cloud data U.S. Geological Survey Data Release doi:10.5066/F7BZ6443 St. Petersburg, FL U.S. Geological Survey Date of collection 03/14/2006 https://doi.org/10.5066/F7BZ6443 LAZ (compressed LAS) format file containing lidar point cloud data 20060314 ground condition 200603_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. Joseph W. Long Karen L. M. Morgan Kara Doran 20160707 first XYZ point cloud data U.S. Geological Survey Data Release doi:10.5066/F7765CF4 St. Petersburg, FL U.S. Geological Survey Date of collection 09/20 - 09/22/2006 https://doi.org/10.5066/F7765CF4 LAZ (compressed LAS) format file containing LIDAR point cloud data 20060921 ground condition 200609_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U.S. Geological Survey 2008 first XYZ point cloud data Data Series 400 Saint Petersburg, FL U.S. Geological Survey https://pubs.usgs.gov/ds/400/ LAZ (compressed LAS) format file containing lidar point cloud data 20070627 ground condition 2007_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U.S. Geological Survey 2016 first XYZ point cloud data U.S. Geological Survey Data Release doi:10.5066/F7G15XZX St. Petersburg, FL U.S. Geological Survey https://doi.org/10.5066/F7G15XZX LAZ (compressed LAS) format file containing lidar point cloud data 20080625 ground condition 200809_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U.S. Geological Survey 2010 first XYZ point cloud data U.S. Geological Survey Data Series 556 St. Petersburg, FL U.S. Geological Survey https://pubs.usgs.gov/ds/556/ LAZ (compressed LAS) format file containing lidar point cloud data 20080908 ground condition 200806_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM) Joint Airborne Lidar Bathymetry Technical Center of expertise (JALBTCX) 20160523 first XYZ point cloud data Charleston, SC NOAA's Ocean Service, Office for Coastal Management (OCM) https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=1064 https://coast.noaa.gov/htdata/lidar1_z/geoid12a/data/1064 LAZ (compressed LAS) format file containing LIDAR point cloud data 20100101 ground condition 2010_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U.S. Geological Survey 20140602 ZYX point cloud data U.S. Geological Survey Data Series 839 St. Petersburg, FL U.S. Geological Survey https://pubs.usgs.gov/ds/0839/ LAZ (compressed LAS) format file containing lidar point cloud data 20120905 ground condition 2012_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. U.S. Geological Survey 20140602 XZY point cloud data U.S. Geological Survey Data Series 838 St. Petersburg, FL U.S. Geological Survey https://dx.doi.org/10.3133/ds838 LAZ (compressed LAS) format file containing lidar point cloud data 20130712 ground condition 2013_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM) 20161215 XYZ point cloud data Charleston, SC NOAA Office for Coastal Management (OCM) https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4966 https://coast.noaa.gov/htdata/lidar1_z/geoid12b/data/4966 LAZ (compressed LAS) format file containing lidar point cloud data 20140121 ground condition 2014_LIDAR Lidar cloud XYZ data points used to generate a surface from which a mean high water (MHW) shoreline was extracted. Published X,Y,Z point data were converted from the originally published geoid to GEOID96 using files downloaded from NOAA's National Geodetic Survey https://www.ngs.noaa.gov/GEOID. After conversion, a survey specific elevation offset was then applied, uniformly, to each dataset according to the values published in Thompson and others (2017). 1998_LIDAR 2001_LIDAR 200405_LIDAR 200409_LIDAR 2005_LIDAR 200603_LIDAR 200609_LIDAR 2007_LIDAR 200806_LIDAR 200809_LIDAR 2010_LIDAR 2012_LIDAR 2013_LIDAR 2014_LIDAR 20160818 dauphin_lidar_1998_11_raw.txt dauphin_lidar_2001_09_raw.txt dauphin_lidar_2004_05_raw.txt dauphin_lidar_2004_09_raw.txt dauphin_lidar_2005_0901_raw.txt dauphin_lidar_2006_0314_raw.txt dauphin_lidar_2006_0921_raw.txt dauphin_lidar_2007_0627_raw.txt dauphin_lidar_2008_0625_raw.txt dauphin_lidar_2008_0908_raw.txt dauphin_lidar_2010_01_raw.txt dauphin_lidar_2012_09_raw.txt dauphin_lidar_2013_07_raw.txt dauphin_lidar_201401_raw.txt Joseph Long USGS mailing and physical address

600 4th Street South

St. Petersburg FL 33701 USA 772 502-8024 727 502-8182 jwlong@usgs.gov M-F, 8:00-4:00 ET Creation of grid surface from raw data (fore example, file used : dauphin_lidar_DATE_raw.txt where DATE corresponds to the yyyy_mmdd of the lidar dataset). XYZ.txt files were imported in to ArcMap (v.10.0), and converted to a point shapefile using the following method: File >> Add data >> Add XY data (XYZ text file chosen). The resulting event layer feature was converted to a shapefile by right clicking the dataset in Table of Contents, and selecting Export Data. Features were then subset into 90/10% training/testing groups, using the following method: ArcToolbox>>GeoSatistical Analyst >> Utilities >> Subset Features(input XYZ point shapefile, output training feature class and test feature class, with size of training feature subset at 90%). A geodatabase (DATE_terrain) was created for each date and a new (empty) feature dataset created within this database. Training point features (90%) were imported into the feature dataset using the following method: ConversionTools >> To Geodatabase >> Feature Class to Feature Class (input training point features (90%) and output to new feature class within geodatabase (DATE_terrain). Right click in geodatabase select >> NEW terrain – use wizard to assign feature class points. Convert terrain to 2-meter grid using the following method: ArcToolbox >> Converstion >> From Terrain >> Terrain to Raster(ex. file created: DIDATEgrid, where DATE is the yyyymmdd of the original lidar data and DI refers to Dauphin Island) Process was repeated for each date. dauphin_lidar_1998_11_raw.txt dauphin_lidar_2001_09_raw.txt dauphin_lidar_2004_05_raw.txt dauphin_lidar_2004_09_raw.txt dauphin_lidar_2005_0901_raw.txt dauphin_lidar_2006_0314_raw.txt dauphin_lidar_2006_0921_raw.txt dauphin_lidar_2007_0627_raw.txt dauphin_lidar_2008_0625_raw.txt dauphin_lidar_2008_0908_raw.txt dauphin_lidar_2010_01_raw.txt dauphin_lidar_2012_09_raw.txt dauphin_lidar_2013_07_raw.txt dauphin_lidar_201401_raw.txt 20160830 DI199811grid DI200109grid DI200405grid DI200409grid DI200509grid DI200603grid DI200609grid DI200706grid DI200806grid DI200809grid DI201001grid DI201209grid DI201307grid DI201401grid Rachel Henderson U.S. Geological Survey mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov Shoreline extracted from DEM. A Mean High Water (MHW) shoreline for Dauphin Island, AL was identified as 0.24 m NAVD88 (Weber and others., 2005) and extracted from each lidar survey within ArcMap, using a method similar to that described in Harris and others (2005). The contour shoreline extracted from ArcMap was produced using the following steps for each lidar date: 1) Contour was extracted from each lidar grid using: ArcToolbox >> 3D Analyst Tools>>Raster Surface >> Contour List, where 0.24 was identified as the only contour, and saved to the geodatabase containing the original terrain and grid data. 2) The contour was smoothed using: ArcToolbox >> Cartography Tools >> Generalization >> Smooth Line using Peak 10 m, accept all other defaults. 3) Manual review/editing of MHW for large errors, or locations with multiple MHW values and ensure the proper location of the MHW shoreline, using a colorized elevation grid for reference. 4)Merge all line segments. Editor>>Merge 6)Add “DATE_” (text string 10 characters) and "UNCERT" (double) to the attribute table. Determine "DATE" from lidar data, "UNCERT" remains blank until completion of shoreline positional uncertainty (to follow). A field titled "NOTES" was also added to include an additional information about the shoreline. DI199811grid DI200109grid DI200405grid DI200409grid DI200509grid DI200603grid DI200609grid DI200706grid DI200806grid DI200809grid DI201001grid DI201209grid DI201307grid DI201401grid 20160915 DI199811_MHW DI200109_MHW DI200405_MHW DI200409_MHW DI200509_MHW DI200603_MHW DI200609_MHW DI200706_MHW DI200806_MHW DI200809_MHW DI201001_MHW DI201209_MHW DI201307_MHW DI201401_MHW Rachel Henderson U.S. Geological Survey mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov All shorelines were appended into one feature class (in a personal geodatabase). ArcToolbox>>Data Management Tools>>General>>Append: select each DIDATE_MHW shoreline. Shorelines for the following dates were appended: 11/02/1998 10/02/2001 05/05/2004 09/19/2004 09/01/2005 03/14/2006 09/21/2006 06/27/2007 06/25/2008 09/08/2008 01/01/2010 09/05/2012 07/12/2013 01/21/2014 DI199811_MHW DI200109_MHW DI200405_MHW DI200409_MHW DI200509_MHW DI200603_MHW DI200609_MHW DI200706_MHW DI200806_MHW DI200809_MHW DI201001_MHW DI201209_MHW DI201307_MHW DI201401_MHW 20161010 DI_lidar_1998_2014 U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center Rachel E. Henderson mailing

600 4th Street South

St. Petersburg FL 33701 US (727)-502-8000 rehenderson@usgs.gov Calculation of the grid surface error. Using the withheld 10% point "testing" dataset for each date(90% point data was used to create each lidar grid surface) the points were overlain on the corresponding raster; vertical RMSE on the interpolated surface was determined from comparisons between actual Z value and interpolated Z value using the following method: 1) Surface information was pulled from the lidar grid and added to 10% point dataset (ArcToolbox>>3D Analyst Tools>>Functional Surface>>Add surface information, add 10% test points LS_date_10.shp as feature class, Input grid surface: date_grid, Output property check Z. 2) Added new attribute field to 10% test point file – "Elev_diff" (float) and calculated the difference in original point height and grid surface "Z" height. 3)Saved table to an Excel file and converted each elevation offset to an absolute value. The average of this offset was calculated, and applied as a term in the total shoreline positional uncertainty calculation. 20161201 Rachel Henderson U.S. Geological Survey mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov Calculation of lidar shoreline positional uncertainty. In order to determine the uncertainties associated with individual shorelines, a methodology following Morton and Miller (2005) and Hapke and others (2006) was used to estimate a positional uncertainty value for each shoreline. Total shoreline positional uncertainty is a function of the errors inherent in the source data (horizontal and vertical accuracy of the raw lidar data) the conversion of point data to a 3D surface (grid error) and those errors generated in the extraction of the vector shoreline (interpolation uncertainty). Four terms were identified to describe the uncertainty of the resulting lidar shoreline position. The first is the direct horizontal uncertainty from published lidar data. Following the methods described by Hapke and others (2010) the second term is derived from the vertical uncertainty from published lidar data, which is then converted to a horizontal uncertainty based on an averages slope around MHW for each lidar dataset, determined by pulling the slope data from the lidar data at the intersection of MHW and the existing alongshore DSAS transects. The third term is calculated as the "grid error" term. This is a measure of how well the surface (created from 90% of the raw data) captures the actual elevation of the remaining 10% of the data. A comparison of the grid elevation to the raw elevation is then converted to an RMS value describing the grid surface error. The initial calculation of grid surface error for the island-wide dataset was much higher than expected, due to the appearance of houses various areas of vegetation and water surfaces in the first return data. Thus, the calculation of grid error was constrained to a 20-meter buffer around the feature extracted (MHW) to provide a better estimate of the surface error from which the feature was derived. The fourth and final term used is the interpolation uncertainty, based on the grid cell size. The four terms were summed in quadrature and the resulting shoreline positional uncertainty was applied to each shoreline date in the "UNCERT" field of the attribute table. This value is used to determine the uncertainty of shoreline change rates when used with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009). 20170301 Rachel Henderson U.S. Geological Survey mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov Features exported from geodatabase to shapefile. 20173010 Rachel Henderson U.S. Geological Survey mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov Added keywords section with USGS persistent identifier as theme keyword. 20201013 U.S. Geological Survey VeeAnn A. Cross Marine Geologist Mailing and Physical

384 Woods Hole Road

Woods Hole MA 02543-1598 508-548-8700 x2251 508-457-2310 vatnipp@usgs.gov Vector String 41 Universal Transverse Mercator 16 0.9996 -87.0 0.0 500000.0 0.0 coordinate pair 0.6096 0.6096 Meter D_North_American_1983 GRS_1980 6378137.0 298.257222101 Lidar_MHW_Shorelines_1998_2014 Vector shorelines U.S. Geological Survey Shape Feature geometry. Esri Coordinates defining the features. OBJECTID Internal feature number. Esri 1 41 DATE_ Date of shoreline position; date of survey as indicated on source material using the MM/DD/YYYY format. USGS 11/02/1998 01/21/2014 19981102 NOTES Notes about each shoreline segment, according to 1) location along the island (Dauphin Island, Little Dauphin Island, Pelican Island) and shoreline type (open-ocean, back-barrier, marsh shoreline). USGS Character string of length 150 UNCERT Estimate of shoreline position uncertainty. Actual shoreline position is within the range of this value (plus or minus, meters). The uncertainty was determined by compiling the sources of uncertainty identified in: 1) raw lidar data 2) conversion of points to grid surface 3) extraction/editing of horizontal line information from grid surface. USGS 1.6 4.8 Shape_Leng Length of feature in meters units (UTM zone 19N, WGS 84) Esri 938.885502 41003.284693 This datasest contains MWH shoreline data extracted from lidar elevation datasets from 1998 to 2014 with associated attributes for use with the Digital Shoreline Analysis System. The attributes required for use with DSAS include auto-generated fields - OBJECTID, Shape, Shape_Leng, and user-created fields - DATE_ and UNCERT. U.S. Geological Survey mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov Lidar_Shorelines_1998_2014.shp Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. WinZip 9.0 Esri polyline shapefile This WinZip file contains a shapefile of lidar derived MHW shorelines from 1998 - 2014 for Dauphin Island, Alabama. Use WinZip or pkUnzip https://coastal.er.usgs.gov/data-release/doi-F7T43RB5/data/Lidar_Shorelines_1998_2014.zip None 20201013 U.S. Geological Survey Rachel Henderson mailing and physical

600 4th Street South

St. Petersburg Florida 33701 US (727)-502-8000 rehenderson@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time