Attribute_Accuracy_Report:
The data provided here consist of estimates of the foreshore slopes of primarily sandy beaches along the U.S. East Coast. The beach slopes were calculated using many different lidar surveys conducted from 1997 to 2018. The accuracy of the lidar point cloud elevation data from these surveys varies, with the oldest surveys being the least accurate. There are several factors that affect the accuracy of the beach slopes. These vary by survey and include: the accuracy of the lidar data, the scatter in the lidar data, the density of lidar data on the foreshore, the width of the foreshore, and the slope of the foreshore. In addition, this methodology assumes that the slope of the foreshore is constant along the transect from the water to the berm. While this assumption is usually valid, we do not know how valid it is at any individual transect. The best way to assess the accuracy of a given slope value is to compare it to adjacent values in both space and time.
Beach slopes were automatically calculated for each transect during the shoreline extraction process. If no shoreline point was found on a transect or if the shoreline point did not pass QA/QC procedures, that transect will not have a value for slope. There was more than one lidar survey available for many transects; shorelines and slopes were calculated for each lidar survey. The beach slopes vary due to both measurement error and natural variability of the system. Transect numbers are unique for each state, or if the state is divided into regions, for each region.
Beach slopes were calculated primarily on open-ocean, sandy beaches. Some slopes on non-sandy beaches and rocky headlands are included. Most locations have more than one estimate of beach slope because multiple lidar surveys were conducted and processed. There are some large gaps in the slope data. These are due to gaps in the lidar data. Additional small gaps were created when data that did not pass our QA/QC procedures were removed. The baselines (and transects) sometimes continue across inlets, rocky headlands or other areas where shorelines were not extracted. All transects are listed, even transects for which no shoreline was found.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters).
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
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Source_Contribution:
The bare-earth lidar point cloud data in LAS format were used to calculate beach slope using methods described in the process steps. The point data were downloaded using the cart feature in the appropriate UTM zone projection with NAD83 horizontal datum and NAVD88 vertical datum (meters), in the most recent Geoid.
Process_Step:
Process_Description:
For each state/region, a coast-following reference baseline was digitized in UTM using Google Earth. The baselines sometimes continued across inlets, rocky headlands, or other areas where shorelines were not extracted. These baselines were loaded into Matlab (version R2007a) and equally-spaced points were defined along the lines at a 20-meter spacing. Transect lines perpendicular the baselines were calculated at each of these points. The transects are defined by their origin point (the point on the baseline), their slope, and the y-intercept using the formula: y = m*x + b, where m is the slope, b is the y intercept, x is the easting of the transect origin and y is the northing of the transect origin. This Process step was completed by Amy S. Farris.
Process_Date: 2007
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Amy S. Farris
Contact_Organization: U.S. Geological Survey
Contact_Position: Oceanographer
Contact_Address:
Address_Type: mailing address
Address: 384 Woods Hole Road
City: Woods Hole
State_or_Province: MA
Postal_Code: 02543
Country: US
Contact_Voice_Telephone: 508-548-8700
Contact_Facsimile_Telephone: 508-457-2310
Contact_Electronic_Mail_Address: afarris@usgs.gov
Process_Step:
Process_Description:
The reference baseline and transects were merged for all states/regions. The transect origins were converted to a geographic coordinate system. However, since the transect lines are defined in UTM (NAD83) and cannot be expressed in geographic, the transect origins are also provided in UTM. The final reference line data matrix has one row for each transect and each row contains the following information: state, region, transect number, latitude and longitude of the transect origin, easting and northing of the transect origin, slope of the transect (m in the formula y=m*x+b) , y-intercept (b in the formula y=m*x+b), mean high water (MHW) elevation (meters above NAVD88), UTM zone. The height of mean high water was determined from Weber and others (2005). We do not use a continuously varying MHW, but rather use one number for a long section of coast. This value is within 15 cm of the 'true' value of MHW.
The final reference line data matrix was output as both a .csv and a shapefile. All points on the baseline are given, even if no shorelines were found on the transect associated with that point. All work was done in Matlab (version R2023a) by Amy S. Farris.
Process_Date: 2023
Process_Step:
Process_Description:
A transect-based method described in Farris and others (2018) was used to estimate the slope of the foreshore of the beach using Matlab (version R2023a). This method utilized the transects created in the first Process Step. All lidar data points within 1 meter (in the alongshore direction) of each transect were associated with that transect. All processing was done on each 2-meter wide transect, working on a single transect at a time. For each transect, points on the foreshore were identified and a linear regression was fit through them. Foreshore beach slope was defined as the slope of the regression line. The regression was evaluated at the elevation of mean high water (MHW) to yield the location of the shoreline.
See second Process Step for information about how the height of mean high water was determined.
The shoreline position on each transect has an estimated horizontal uncertainty associated with it. This uncertainty includes four components: 1) the 95% confidence interval on the linear regression estimate of the shoreline position; 2) the vertical error of the raw lidar data as reported in the lidar data’s metadata; 3) a +/- 15 cm vertical error in our chosen value of MHW, and; 4) the uncertainty due to extrapolation (if the shoreline was determined using extrapolation). These four components of uncertainty were added in quadrature to yield a total error for each shoreline point.
This step was completed by several people: Kathryn M. Weber, Marie K. Bartlett and Amy S. Farris.
Process_Date: 2023
Process_Step:
Process_Description:
The slope data for all surveys for all states were merged into a single data matrix in Matlab (version R2023a). For each survey, the following data were preserved for each transect and added as a row to the matrix of the data: state, region, year and month of survey, transect number, latitude and longitude of shoreline position, foreshore beach slope, distance from shoreline to transect origin, and shoreline uncertainty. The final slope data matrix was output as both a .csv file and a shapefile. This step was completed by Amy S. Farris.
Process_Date: 2023
