U.S. Geological Survey
20170616
Lidar_MHW_Shorelines_1998_2014.shp - Mean High Water (MHW) Shorelines Extracted from Lidar Data for Dauphin Island, Alabama from 1998 to 2014.
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
None planned
-88.341683558
88.07384796
30.281611631
30.216924258
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
National Assessment of Shoreline Change: Historical Shoreline Change along the New England and Mid-Atlantic Coasts
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
An Operational Mean High Water Datum for Determination of Shoreline Position from Topographic Lidar Data
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
Extracting Shorelines from NASA Airborne Topographic Lidar-Derived Digital Elevation Models
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
National Assessment of Shoreline Change: Part 2: Historical Shoreline Changes and Associated Coastal Land Loss along the U.S. Southeast Atlantic Coast
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
National Assessment of Shoreline Change: Part 3: Historical Shoreline Changes and Associated Coastal Land Loss along the Sandy Shorelines of the California Coast
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
Correction of elevation offsets in multiple co-located lidar datasets
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
Digital Shoreline Analysis System (DSAS) version 4.0 - An ArcGIS extension for calculating shoreline change
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
1998 Fall Gulf Coast NOAA/USGS/NASA Airborne LiDAR Assessment of Coastal Erosion (ALACE) Project for the US Coastline
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
ATM Coastal Topography-Alabama 2001
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
2004 US Army Corps of Engineers (USACE) Topo/Bathy Lidar: Alabama, Florida, Mississippi and North Carolina
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
EAARL Coastal Topography-Northern Gulf of Mexico
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
EAARL Coastal Topography-Dauphin Island, Alabama, Post-Hurricane Katrina, 2005
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
EAARL Coastal Topography-Louisiana, Mississippi and Alabama, March 2006
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
http://dx.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
EAARL Coastal Topography-Louisiana, Mississippi and Alabama September 2006
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
EAARL Coastal Topography-Northern Gulf of Mexico, 2007
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
EAARL Coastal Topography-Louisiana, Alabama, and Florida, June 2008
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
EAARL Coastal Topography-Mississippi and Alabama Barrier Islands, Post-Hurricane Gustav, 2008
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
2010 US Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) Topobathy Lidar: Alabama Coast and Florida Gulf Coast
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
Topographic Lidar Survey of the Alabama, Mississippi, and Southeast Louisiana Barrier Islands, from September 5 to October 11, 2012
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
Topographic Lidar Survey of Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana, July 12-14, 2013
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
2014 Mobile County, AL Lidar
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
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
20170616
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