Shorelines of the central North Carolina (NCcentral) coastal region used in shoreline change analysis

An operational Mean High Water (MHW) shoreline was extracted from the lidar surveys within MATLAB v7.6 using a method similar to the one developed by Stockdon et al. (2002). Shorelines were extracted from cross-shore profiles which consist of bands of lidar data 2 m wide in the alongshore direction and spaced every 20 m along the coast. For each profile, the seaward sloping foreshore points were identified and a linear regression was fit through them. The regression was evaluated at the operational MHW elevation to yield the cross-shore position of the MHW shoreline. If the MHW elevation was obscured by water points, or if a data gap was present at MHW, the linear regression was simply extrapolated to the operational MHW elevation. A lidar positional uncertainty associated with this point was also computed. The horizontal offset between the datum-based lidar MHW shoreline and the proxy-based historical shorelines nearly always acts in one direction and the "bias" value was computed at each profile (Ruggiero and List, 2009). In addition an uncertainty associated with the bias was also computed, which can also be thought of as the uncertainty of the HWL shorelines due to water level fluctuations. Repeating this procedure at successive profiles generated a series of X,Y points that contain a lidar positional uncertainty, a bias, and a bias uncertainty value. Ruggiero, P. and List, J.H., 2009. Improving Accuracy and Statistical Reliability of Shoreline Position and Change Rate Estimates. Journal of Coastal Research: v.25, n.5, pp.1069-1081. Stockdon, H.F., Sallenger, A.H., List, J.H., and Holman, R.A., 2002. Estimation of Shoreline Position and Change using Airborne Topographic Lidar Data: Journal of Coastal Research, v.18, n.3, pp.502-513. The 1997 lidar data were processed in 2008 (20081113) and re-processed in 2016 to include a previously missing portion of data. The 2009-2010 lidar data were processed in 2014 (20140301).

The lidar data were collected in projected coordinates (WGS 84 UTM zone 18N). The series of operational MHW points extracted from the cross-shore lidar profiles were converted into a calibrated route shapefile for use in ArcGIS using a Python script. The script generates a point shapefile, converts it to a polyline-M file, saves the uncertainty information in an accessory dBase (.dbf) file and finally generates a calibrated route for the newly-created polyline-M file. Calibration is based on the unique and sequential profile ID value provided with the point data and stored as the M-value. This value is also stored as an attribute in the uncertainty .dbf file and is used as the common attribute field linking the shoreline route file (shoreline.shp) to the uncertainty table (shorelines_uncertainty.dbf) storing the lidar positional uncertainty, the bias correction value, and the uncertainty of the bias correction for each point of the original lidar data. During the rate calculation process DSAS uses linear referencing to retrieve the uncertainty and bias values stored in the associated table. Visually identified HWL-type proxy shorelines are virtually never coincident with datum-based MHW-type shorelines. In fact, HWL shorelines are almost universally estimated to be higher (landward) on the beach profile than MHW shorelines. Not accounting for this offset will cause shoreline change rates to be biased toward slower shoreline retreat, progradation rather than retreat, or faster progradation than in reality (for the typical case where datum-based shorelines are more recent data than the proxy-based shoreline dates), depending on actual changes at a given site. Ruggiero, Peter, and List, J.H., 2009, Improving accuracy and statistical reliability of shoreline position and change rate estimates: Journal of Coastal Research, v. 25, no. 5, p. 1069–1081. Ruggiero, Peter, Kaminsky, G.M., and Gelfenbaum, Guy, 2003, Linking proxy-based and datum-based shorelines on high-energy coastlines—Implications for shoreline change analyses: Journal of Coastal Research, special issue 38, p. 57–82. For a detailed explanation of the method used to convert the lidar shoreline to a route, please refer to "Appendix 2- A case study of complex shoreline data" in the DSAS user guide: Himmelstoss, E.A. 2009. "DSAS 4.0 Installation Instructions and User Guide" in: Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan. 2009. Digital Shoreline Analysis System (DSAS) version 4.0 - An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. https://woodshole.er.usgs.gov/project-pages/DSAS/version4/images/pdf/DSASv4_3.pdf The 1997 lidar data were processed in 2010 (20100323) and re-processed in 2016 to include a previously missing portion of data. The 2009-2010 lidar data were processed in 2014 (20140317).

Data from the previously-published National Assessment of Shoreline Change study for the Southeast Atlantic (USGS Open-File Report 2005-1401 and USGS Open-File Report 2005-1326) were used as the starting point for the project's continued efforts to compile as many quality shorelines as possible for the region. Digital shorelines, if available from another agency, were acquired. This process step and all subsequent process steps were performed by the same person - M.G. Kratzmann.

Digitized shorelines were downloaded in vector format from the NOAA Historical Shoreline Survey Viewer, currently available as a Google Earth KMZ (https://nosimagery.noaa.gov/images/shoreline_surveys/noaa_shoreline_surveys.kmz). The extracted shorelines are digitized vectors of the mean high water (MHW) position derived from the scanned and georeferenced T-sheet raster imagery that NOAA also manages. Although NOAA labels historical shorelines as MHW, they are proxy-based and therefore considered HWL for the purposes of shoreline change rate calculation with the proxy-datum bias correction. The modern lidar shoreline used in the analysis is datum-based, not proxy-based, and designated as operational MHW which reflects this difference. NOAA topographic survey sheets (T-sheets) and shorelines were quality checked and edited where necessary. Edgematching was corrected when discovered. If no digital shoreline vectors were available, the original T-sheet scan and world file were downloaded from the NOAA Google Earth viewer. Vector shorelines were digitized from the georeferenced T-sheets using standard editing tools in ArcMap v10. Quality assessments were performed and shorelines were edited to remove any overlap between adjacent t-sheets from the same time period. No edge-matching between neighboring shorelines was attempted. The DSAS-required attribute fields were added to the shapefiles and populated.

Historical shorelines were merged in Esri's ArcToolbox (v10), Data Management Tools > General > Merge. Then the merged file was projected in ArcToolbox (v10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = geographic (NAD 83); output projection = UTM zone 18N (WGS 84); transformation = NAD_1983_To_WGS_1984_1.

Historical shorelines were appended to the lidar route data in ArcToolbox (v10) > Data Management Tools > General > Append to produce a single shoreline file for the region. The final shoreline dataset was coded with attribute fields (Date, Uncertainty (Uncy), Source, Source_b, Year_, Default_D, Location). These fields are required for the Digital Shoreline Analysis System (DSAS), which was used to calculate shoreline change rates.

The appended shoreline file and the shoreline uncertainty table (.dbf) were imported into a personal geodatabase in ArcCatalog v10.2 by right-clicking on the geodatabase > Import (feature class for shoreline file and table for uncertainty table) for use with the Digital Shoreline Analysis System (DSAS) v4.3 software to perform rate calculations.

The shoreline feature class was exported from the personal geodatabase back to a shapefile in ArcCatalog v10.2 by right-clicking on the shoreline file > Export > To Shapefile (single) for publication purposes.

The data were projected in ArcToolbox v10.2 > Data Management Tools > Projections and Transformations > Project. Parameters: input projection = UTM zone 18N (WGS84); output projection = geographic coordinates (WGS84); transformation = none.

Keywords section of metadata optimized for discovery in USGS Coastal and Marine Geology Data Catalog.

Added keywords section with USGS persistent identifier as theme keyword.