Lidar_MHW_Shorelines_1998_2014.shp - Mean High Water (MHW) Shorelines Extracted from Lidar Data for Dauphin Island, Alabama from 1998 to 2014.

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).

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.

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.

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

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.

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).

Added keywords section with USGS persistent identifier as theme keyword.