Description: Long-term rates of shoreline change were determined by fitting a least squares regression line to all shoreline positions from the earliest (1880s) to the most recent (2006). The rate of change is the slope of the regression line. Negative values indicate erosion (movement of the shoreline away from the established baseline) and positive values indicate accretion (movement of the the shoreline towards the established baseline.) The calculation of linear regression rates requires a minimum of three shoreline years at each transect, and rates calculated with many shoreline positions can increase confidence by reducing potential errors associated with the source data, and fluctuating short-term changes (Dolan et al.and others, 1991).
The linear regression method for determining shoreline change rates assumes a linear trend of change among the shoreline dates. However, in locations where shoreline change rates have not remained constant through time, a linear trend would not exist. For example, a shoreline may exhibit accretion over the first 100 years, but in later years, the shoreline may shift to an erosional trend. In these cases, it is expected that using a linear fit to the data is poorer, and as a result the uncertainty asociated with these shoreline change rates is higher than those whose trend is more linear.
Description: Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone. Transects were converted into points by converting the data "CTShore_Transects1880_2006_RateDate_ln" from line to point (as midpoint.)
Description: Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone. Transects were converted into points by converting the data "CTShore_Transects1880_2006_RateDate_ln" from line to point (as midpoint.)
Description: Long-term rates of shoreline change were determined by fitting a least squares regression line to all shoreline positions from the earliest (1880s) to the most recent (2006). The rate of change is the slope of the regression line. Negative values indicate erosion (movement of the shoreline away from the established baseline) and positive values indicate accretion (movement of the the shoreline towards the established baseline.) The calculation of linear regression rates requires a minimum of three shoreline years at each transect, and rates calculated with many shoreline positions can increase confidence by reducing potential errors associated with the source data, and fluctuating short-term changes (Dolan et al.and others, 1991).
The linear regression method for determining shoreline change rates assumes a linear trend of change among the shoreline dates. However, in locations where shoreline change rates have not remained constant through time, a linear trend would not exist. For example, a shoreline may exhibit accretion over the first 100 years, but in later years, the shoreline may shift to an erosional trend. In these cases, it is expected that using a linear fit to the data is poorer, and as a result the uncertainty asociated with these shoreline change rates is higher than those whose trend is more linear.
Description: Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone. Transects were converted into points by converting the data "CTShore_Transects1880_2006_RateDate_ln" from line to point (as midpoint.)
Description: Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone. Transects were converted into points by converting the data "CTShore_Transects1880_2006_RateDate_ln" from line to point (as midpoint.)
Description: Long-term rates of shoreline change were determined by fitting a least squares regression line to all shoreline positions from the earliest (1880s) to the most recent (2006). The rate of change is the slope of the regression line. Negative values indicate erosion (movement of the shoreline away from the established baseline) and positive values indicate accretion (movement of the the shoreline towards the established baseline.) The calculation of linear regression rates requires a minimum of three shoreline years at each transect, and rates calculated with many shoreline positions can increase confidence by reducing potential errors associated with the source data, and fluctuating short-term changes (Dolan et al.and others, 1991).
The linear regression method for determining shoreline change rates assumes a linear trend of change among the shoreline dates. However, in locations where shoreline change rates have not remained constant through time, a linear trend would not exist. For example, a shoreline may exhibit accretion over the first 100 years, but in later years, the shoreline may shift to an erosional trend. In these cases, it is expected that using a linear fit to the data is poorer, and as a result the uncertainty asociated with these shoreline change rates is higher than those whose trend is more linear.
Name: Positive Linear Regression Rates (meters/year)
Display Field: ProcTime
Type: Feature Layer
Geometry Type: esriGeometryPolyline
Description: Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone.
Name: Negative Linear Regression Rates (meters/year)
Display Field: ProcTime
Type: Feature Layer
Geometry Type: esriGeometryPolyline
Description: Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone.
Description: Transects for historical shorline data spanning ~1880-2006 were created using the USGS Digial Shoreline Analysis software for ArcGIS 10.x. Tansects were cleaned, clipped to the shoreline change envelope (the distance between the shoreline farthest from and closest to the baseline at each transec) and merged with statisitical analyses from DSAS providing rate of chage data, confidence intervals, and other supplementary statistics. Additional desciptors were included to allow users to parse transects for omission (i.e., those that may reflect excessive development, fill, or do not meet certain quality conditions) as well as to group/associate transects by politcal town, drainage basin, or geologic zone.
Description: A compiliation of historic data for CT from the late 1800s to the late 2000s from several sources: US Coast & Geodetic Survey/NOAA Topographic Survey Sheets, or T-Sheets (1870s, 1880s, 1900s, 1910s, 1930s, 1940s, 1950s, 1960s, 1980s & 2000s); USGS Topgraphic Maps (1950s, 1960s, 1970s, 1980s) & NOAA Environment Sensitivity Index (ESI) data (1990). Only data from the late 1800s, 1990, and the late 2000s provides complete coverage for the entire coast over a reasonably set period of time. All other data sources address sections of the coast, but not the entire state. As such, there are varying amounts of time-series data at any location along the coast. All data has been combined to utilize a uniform system of attributes - since the NOAA T-Sheet data was the most voluminous source of information their schema was used as the default; other data sources transferred similar data into the NOAA schema. There were various ways different source materiall defined and classified the shoreline; this descriptive information was retained in the field called "ATTRIBUTE" and to a lesser extent "INFORM." The "SOURCE_ID" field is used to track the varying source data types. Several temporally related fields exist: "SRCE_DATE" in YYYYMMDD format is the best representation of the source material date and is suitable for sorting chronologically; "DSAS_DATE" uses a format predifined for the DSAS software package and replicates the information from "SRC_DATE." The field "DECADE" allows for aggregation/sorting by decade. The field "UNCERT_M" is required by the DSAS software and represents a cummulative positional uncertainty estimate (in meters) for eaxh shoreline segment. The value combines best estimates for common sources of error - source survey/compilation, map georeferencing, map digitizing, and water level interpretation/location. The estimates are derived from serveral published papers from USGS when conducting similar studines witnin the northeast, and the total value represents the square root of the sum of squares. The field "CONCERN" identifies sections of shoreline that, under professional review, are deemd to be incorrectly located or eroneously depict shoreline. The review process entailed creating buffers based on the uncertainty values and comparing shorelines to assorted aerial photography. While the photo vintages and the shorelines were not complimentary in all cases, there are enough areas of immobility (e.g., rocky shorelines, exposed bedrock, certain manmade structures, etc.) that persist in stable enough positions to enable a general determination of goodness of fit. Shorelines that matched these features within the appropriate uncertainty buffers were identified as "N" for no concern: shorelines that exceeded the uncertainty bounds due to spatial misalignment issues (e.g., out of reference) or were likely misinterpreting the best location of shoreline (i.e., tidal flats instead of a higher water line) were identified as "Y." In cases were uncertainty bounds were exceeded, but there was no conclusive way to determine the reason, or if they were in an areas of high shoreline variability, they were assumed to be correct and coded as "N." Rather than being removed, they were coded as such to either (a) easily be omitted from any analysis, or (b) subject to a reassessment of uncertainty in order to be more appropriately used. The data is preconfiguered to be compatible with the USGS Digital Shoreline Analysis System (DSAS) and is suitable for both cartographic displays and more rigorus shoreline change analysis studies.
Copyright Text: NOAA, USGS, CT DEEP
National Assessment of Shoreline Change: Historical Shoreline Change along the New England and Mid-Atlantic Coasts, by Cheryl J. Hapke, Emily A. Himmelstoss, Meredith G. Kratzmann, Jeffrey H. List, and E. Robert Thieler, Open-File Report 2010-1118.
National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coastal Land Loss Along Sandy Shorelines of the California Coast, by Cheryl J. Hapke, David Reid, Bruce M. Richmond, Peter Ruggiero and Jeff List, Open-File Report 2006-1219.
The Predictive Accuracy of Shoreline Change Rate Methods and Alongshore Beach Variation on Maui, Hawaii
Ayesha S. Genz†, Charles H. Fletcher†, Robert A. Dunn†, L. Neil Frazer†, and John J. Rooney‡
Journal of Coastal Research 23 1 87–105 West Palm Beach, Florida January 2007
†Department of Geology and Geophysics
School of Ocean and Earth Science and Technology
University of Hawaii
‡Joint Institute of Marine and Atmospheric Research
University of Hawaii Pacific Islands Fisheries Science Center
Improving Accuracy and Statistical Reliability of Shoreline Position and Change Rate Estimates
Journal of Coastal Research 25 5 1069–1081 West Palm Beach, Florida September 2009
Peter Ruggiero† and Jeffrey H. List‡
†Department of Geosciences Oregon State University
‡Coastal and Marine Geology Program U.S. Geological Survey