New York Elevation Data underlying analysis by J.G. Titus and J. Wang entitled "Maps of lands close to sea level along the middle Atlantic coast of the United States."

Metadata:

Identification_Information:

Citation:

Citation_Information:

Originator: US Environmental Protection Agency

Publication_Date: February 2008

Title:

New York Elevation Data underlying analysis by J.G. Titus and J. Wang entitled "Maps of lands close to sea level along the middle Atlantic coast of the United States."

Geospatial_Data_Presentation_Form: raster digital data

Other_Citation_Details:

Data underlying the analysis reported in J.G. Titus and J. Wang, 2008.

Online_Linkage: http://maps.risingsea.net/data.html

Larger_Work_Citation:

Citation_Information:

Originator: US Environmental Protection Agency

Publication_Date: February 2008

Title:

Maps of lands close to sea level along the middle Atlantic coast of the United States

Other_Citation_Details:

Full Citation: Titus, J.G. and J. Wang, 2008: Maps of lands close to sea level along the middle Atlantic coast of the United States: an elevation data set to use while waiting for LIDAR. In: Background Documents Supporting Climate Change Science Program Synthesis and Assessment Product 4.1: Coastal Elevations and Sensitivity to Sea Level Rise [J.G. Titus and E.M. Strange (eds.)]. EPA430R07004, U.S. Environmental Protection Agency, Washington, DC.

Description:

Abstract:

Coastal New York Digital Elevation Model (Environmental
Systems Research Institute [ESRI] Grid format)
represents an elevation map of the New York coastal
zone created for the purposes of analyzing
vulnerability to coastal flooding and rising sea level.
The domain of the data set extends from the upper tidal
wetland boundary up to the 40 foot NGVD29 contour, but
the primary focus of the analytical approach and
quality control has focused on land below the 10-foot
contour. This data set has been derived from several
sources of elevation data, including United States
Geological Survey (USGS) 1:24,000 Digital Line Graphs
(DLG), DLG's created by Henan Institute
of Geography from USGS 1:24,000 Digital Raster Graphics,
US. Corps of Engineer Spot Elevation data, and Monmouth
County elevation data. In addition, the analysis
created a supplemental contour representing the
elevation of spring high water (SHW), which
ranges from  1.1 to 5.3 feet above NGVD29 in New York. We
defined the horizontal position of that contour by
extracting the inland limit of  the tidal wetland
polygons in a separate Coastal Wetlands data set we
created for this project (based on the National
Wetlands Inventory and state wetlands data). . We
defined the vertical position of the supplemental
contour by creating a "tidal elevation surface" using
the National Ocean Service's (NOS) estimated tide
ranges, NOS estimated sea level trends, NOS published
benchmark sheets, and the National Geodetic Survey
North American Vertical Datum Conversion Utility
(VERTCON) program to convert the Mean Tide Level (MTL)
above NAVD88 to NGVD29. All elevation information was
converted to a common vertical reference, usually
NGVD29, and the DEM was generated from that input data
using ESRI's interpolation algorithm TOPOGRID (within
ArcGIS workstation GRID extension). We converted the absolute elevation estimates (usually NGVD29) into elevations relative to SHW using the "tidal elevation surface." For purposes of this data set,
SHW is the upper boundary of tidal wetlands (including
vegetated wetlands and intertidal beaches). Elevation
is expressed in cm.
The zip file associated with this data set should
include:
 
1.   README_NY_Elevation.doc, which provides a brief overview of the
relationship between this dataset and related data
 
2.   InterpolationMethods_MEMO.doc
 
3.   NY_Data_Quality.jpg
 
4.   DEM_LidarComparisonTable.doc
 
5.   DEM_Comparison_with_DLG_11_quads.xls
 
6.   Institute_of_Geography_DLG.xls
 
7.  Titus_and_Wang_2008.pdf
 
However, to speed download, in the online versions, (2) and (9) (which are associated with all of the states) may have been removed
and included in a file called “Common_supplemental_metadata.zip”

Purpose:

The New York Digital Elevation Model provides a base
map layer for assessing the possible influences of
potential sea level rise on coast regions. We recommend against using this data to create maps with scales greater than 1:100,000, regardless of the level of vertical precision portrayed.  Moreover, if the purpose of using this data is to create graphical depictions of risk with contour intervals of 50-100 cm, we recommend a considerably smaller scale unless the audience is likely to understand the limitations of the data.

Supplemental_Information:

Elevations relative to year 2000.

Description:

Purpose:

The New York Digital Elevation Model provides a base
map layer for assessing the possible influences of
potential sea level rise on coast regions. We recommend against using this data to create maps with scales greater than 1:100,000, regardless of the level of vertical precision portrayed.  Moreover, if the purpose of using this data is to create graphical depictions of risk with contour intervals of 50-100 cm, we recommend a considerably smaller scale unless the audience is likely to understand the limitations of the data.

Description:

Purpose:

The New York Digital Elevation Model provides a base
map layer for assessing the possible influences of
potential sea level rise on coast regions. We recommend against using this data to create maps with scales greater than 1:100,000, regardless of the level of vertical precision portrayed.  Moreover, if the purpose of using this data is to create graphical depictions of risk with contour intervals of 50-100 cm, we recommend a considerably smaller scale unless the audience is likely to understand the limitations of the data.

Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2000

Currentness_Reference:

ground condition

Status:

Progress: Complete

Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -74.490310

East_Bounding_Coordinate: -70.870849

North_Bounding_Coordinate: 42.169455

South_Bounding_Coordinate: 39.904846

Keywords:

Theme:

Theme_Keyword_Thesaurus: General

Theme_Keyword: New York Elevation

Theme_Keyword: DEM

Theme_Keyword: Coastal Elevation

Place:

Place_Keyword: NY

Access_Constraints: None

Use_Constraints:

None

Point_of_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: James G. Titus

Contact_Organization: U.S. Environmental Protection Agency, Climate Change Division

Contact_Position: Project Manager

Contact_Address:

Address_Type: mailing address

Address:

Mailcode 6207J

City: Washington

State_or_Province: DC

Postal_Code: 20460

Contact_Voice_Telephone: 202-343-9307

Contact_Facsimile_Telephone: 202-343-2338

Contact_Electronic_Mail_Address: Titus.Jim@epamail.epa.gov

Hours_of_Service: 9:00 - 6:00 Eastern

Data_Set_Credit:

Jue Wang, GIS Practice, ICF
Consulting, Inc.

Native_Data_Set_Environment:

Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.3.0.1770

Back to Top

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

The underlying data used in the creation of this layer may contain errors or omissions. The accuracy of this data set generally corresponds to the source data used in the layer development. See "NY_Data_Quality.jpg" for  an index of the source data used (that accompanied this data set in the zip file.)
See the sections on Positional Accuracy for more detailed information.
Additional consideration: The vertical values and their associated positions were generated using the interpolation function "TOPOGRID" within the ESRI GRID module. TOPOGRID uses input elevation data such as contours and elevation point data along with supplemental information such as stream networks, lakes (of known elevation), and bounding areas to generate a hydrologically-correct DEM. Each state DEM was generated using TOPOGRID but the specific parameters were unique to the data sets available and issues related to each state.
 
There are known issues relating to the interpolation algorithm TOPOGRID.
 
TOPOGRID Plateau Problem. The TOPOGRID function generates disproportionately large areas with the same value of the input contour lines, e.g., if we have 5 and 10 foot contour lines, there would be substantially more areas with values between 4 to 6 and 9 to 11, than 6 to 9 feet. At the upper tips of narrow valleys, the cell values tend to be the same as the bounding contours so the valleys become plateaus. The TOPOGRID function within the ESRI GRID module tends to calculate a trend from neighboring contour lines. As a result, TOPOGRID frequently creates areas of erroneous depressions on the plains adjacent to steep slopes, often substantially below the contours between which those depressions lay. It also creates plateaus along contours, which can be problematic because they overstate the amount of land barely above the wetlands and right at the first contour, while understating the amount of land halfway between the wetlands and the first contour. To address these problems, we processed the areas above and below the first contour separately. However, this caused another problem. In narrow valleys in the area below the first contour, the output DEM values were similar or identical to those of the bounding contour lines due to the lack of elevation information that TOPOGRID needs to calculate trend. The most problematic regions occurred where there was a stream valley below the first contour (e.g. between two parallel 5 foot contours), neither open water nor tidal wetlands along most of the length of the valley, but open water or tidal wetlands at one end of the valley (e.g. a typical non-tidal stream flowing into tidal waters). In some cases, the trend from the wetlands or open water at the mouth toward the bounding first contour would provide values even higher than that first contour farther up the valley. And in general, TOPOGRID would be more likely to assume a flat area between the contours, than to characterize it as a valley--except for when stream data showed a stream in the correct location.
 
 
Evaluation of other interpolation methods. 
Several interpolation methods were evaluated before the TOPOGRID function was selected. Specifically, Spline, Inverse Distance Weighting (IDW), and Triangulated Irregular Network (TIN) methods were evaluated and compared to the TOPOGRID function. Statistics and graphical examples of cross sections specific to each interpolation method are presented in the accompanying "InterpolationMethods_MEMO.doc" memo included in the zip file associated with this data set.

Attribute_Accuracy:

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Explanation:

An accuracy assessment was made between the source  DLG's and the DEM for a select number of quads. See "DEM_Comparison_with_DLG_11_quads.xls"  which accompanies this data set in the zip file.
An additional assessment was made between the DEM and lidar data where it was available in Maryland and North Carolina. The results can be found in the "DEM_LidarComparisonTable.doc" that accompanied this data set in the zip file.
See the sections on Positional Accuracy (Horizontal and Vertical).

Logical_Consistency_Report:

Refer to the draft technical report that documents this
study for information on the  procedures used to
develop this layer. See the sections on Positional
Accuracy (Horizontal and Vertical) for additional
information.
Note that the discussions presented in the accuracy
reports refer to contour intervals in multiple units
(meters and feet). This was done purposefully to
reflect the actual contour intervals used by USGS over
the years and which vary on a quadrangle by quadrangle
basis.

Completeness_Report:

This data set generally corresponds to the source data
     used in the layer development. See the sections on
     Positional Accuracy (Horizontal and Vertical) for
     additional information.
     The vertical values and their associated positions were
     generated using the interpolation function "TOPOGRID"
     within the ESRI GRID module. TOPOGRID uses input
     elevation data such as contours and elevation point
     data along with supplemental information such as stream
     networks, lakes (of known elevation), and bounding
     areas to generate a hydrologically correct DEM. Each
     state DEM was generated using TOPOGRID but the specific
     parameters were unique to the data sets available and
     issues related to each state. The specifics to each
     state DEM are described under positional accuracy
     section of the metadata and in process steps.
     There are known issues relating to the interpolation
     algorithm TOPOGRID.
 
     TOPOGRID Plateau Problem. 
 
The TOPOGRID function
     generates disproportionately large areas with the same
     value of the input contour lines, e.g., if we have 5
     and 10 foot contour lines, there would be substantially
     more areas with values between 4 to 6 and 9 to 11, than
     6 to 9 feet.. At the upper tips of narrow valleys, the
     cell values tend to be the same as the bounding
     contours; so the valleys become plateaus.
     The TOPOGRID function within the ESRI GRID module tends
     to calculate a trend from neighboring contour lines.
     As a result, TOPOGRID frequently creates erroneous
     depressions on the plains adjacent to steep slopes,
     often substantially below the contours between which
     those depressions lie. It also creates plateaus along
     contours, which can be problematic because they
     overstate the amount of land barely above the wetlands
     and right at the first contour, while understating the
     amount of land halfway between the wetlands and the
     first contour. To address these problems, we processed
     the areas above and below the first contour separately.
     However, this caused another problem. In narrow valleys
     in the area below the first contour, the output DEM
     values were similar or identical to those of the
     bounding contour lines due to the lack of elevation
     information that TOPOGRID needs to calculate trend. The
     most problematic regions occurred where there was a
     stream valley below the first contour (e.g. between two
     parallel 5 foot contours), no open water or tidal
     wetlands along most of the length of the valley, but
     open water or tidal wetlands at one end of the valley
     (e.g. a typical nontidal stream flowing into tidal
     waters). In some cases, the trend from the wetlands or
     open water at the mouth, toward the bounding first
     contour, would provide values even higher than that
     first contour farther up the valley. And in general,
     TOPOGRID would be more likely to assume a flat area
     between the contours, than to characterize it as a
     valley--except for when stream data was used.
     
     
     Other Issues: The decision to process 3 elevation areas
     separately within TOPOGRID (as described in the process
     step #4 - "Interpolation of Digital Elevation Model")
     and then combine them into a single DEM removes the
     algorithm from its theoretical underpinning, because it
     separates each elevation zone from the context of the
     overall environment that TOPOGRID uses to generate a
     hydrologically-correct DEM. Because the objective of
     this DEM is to estimate elevations of lands close to
     sea level, rather than characterize drainage correctly,
     the ad hoc response to the TOPOGRID plateau problem is
     not as unreasonable as would have been the case were
     this data to be used for analyzing hydrology.
     Nevertheless,  modification of the tolerance values and
     other parameters within TOPOGRID, and inclusion of
     additional vertical data in areas of known errors
     (determined through the use of diagnostic outputs
     within the TOPOGRID function), probably could have
     substantially diminished the plateau problem in the
     vicinity of the first topographic contour. Because the
     plateau problem around the edge of tidal wetlands was
     often caused largely by the relative complexity of the
     wetland supplemental contour compared with other
     contours,  the case for dividing the data as we did is
     probably greater along the wetland boundary than along
     the first contour.
     
     Also see the sections on Positional Accuracy  (Horizontal and Vertical) and process steps.

Positional_Accuracy:

Positional_Accuracy:

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The source data generally were 1:24,000 scale or
better. Therefore our use of 30 meter cells
deteriorated the horizontal accuracy.  Assuming that
90% of well defined points are within 30 meters of the
indicated location would imply a scale of 1:60,000
under National Map Accuracy Standards. (That assumption
may be conservative because 100% of the points in a 30
meter cell are less than 21.2 meters of the center of
the cell.  If the input map has 1:24,000 scale (well
defined points within 12.2 meters)  and errors are
random, then more than 90% of the points will be within
24.5 meters of the indicated location, which would
imply a scale of 1:50,000.)  However, our interpolation
program may further deteriorate the horizontal
accuracy.  Under some circumstances, the horizontal error appears to be as great as the width of a cell.  Given that the diagonal in this case would be 42.4 m, if errors are random, then the scale might be as poor as 1:86,000 in areas where those 1-cell errors are common.

Quantitative_Horizontal_Positional_Accuracy_Assessment:

Horizontal_Positional_Accuracy_Value: 40-42.4 meters

Horizontal_Positional_Accuracy_Explanation:

In the event the horizontal error is as great as the width of a cell, the diagonal would be 42.4 m.

Vertical_Positional_Accuracy:

Vertical_Positional_Accuracy_Report:

The vertical accuracy of this data set generally corresponds to that of the source data (described below) used in the layer development, plus errors induced through the various processing steps.
 
The most important processing errors
probably concern the procedures used to interpolate
between contours, which do not necessarily correspond
to the actual geometry of the land surfaces. Therefore,
points that are near a contour have greater accuracy
than points that are farther away from a contour.
 
In order to assess the vertical accuracy of  DEMs generated by ICF Consulting, Russ Jones of Stratus Consulting Inc. compared DEMs with LIDAR data in two areas: 1) an area south of Rock Hall along the eastern shore of Maryland, and 2) portions of North Carolina. Table 1 within DEM_LidarComparisonTable.doc summarizes the comparison. The analysis suggests a Root Mean
Square (RMS) discrepancy between LIDAR and this DEM
approximately one-half of the input contour interval in cases
where the contour interval was 1 meter, 5 feet, or 2
meters.
 
In areas where the USGS contour interval was 20 feet
and we used MD DNR data for supplemental contours, the
mean discrepancy (LIDAR-DEM) was -2.4 feet with a RMS
discrepancy of 6 feet for DEM observations less than 10
feet. The error was much less (mean -1.1 feet, RMS 3.9
feet) for DEM values between 10 and 20 feet NGVD29.
Most of the errors appear to be centered in Caroline
County, where the Maryland DNR data incorrectly showed
a large area below 5 feet NGVD29. 
 
Jones also provided histograms showing the relationship between input contour
intervals and the DEM values, for 11 USGS 7.5'
topographical quadrangles in the study area from New
York to North Carolina, including Marcus Hook. The
technical paper by Titus and Wang (2008, listed in the
citation section above) analyzes the results of that
comparison. Note that this comparison was conducted on the initial DEM generated with TOPOGRID. As a result of this analysis, the minimum and maximum elevation limits were constrained to ensure that the resulting elevations were in accordance with the input data. (See "First-contour truncating" in the process step on interpolation of Digital Elevation Model). That paper compares the area of land below
the first, second, and third contour according to the
DEM, with the area of the input polygons.  That error
can be considered both in terms of the difference in
area estimates, and as a vertical error.  As a measure
of the vertical error, Titus and Wang consider the
effective elevation of the DLG contour that the DEM
estimates, that is, at what elevation does the DEM find
the same amount of land that the DLG polygons show to
be below the first contour.
The technical paper also calculates a plateau
exaggeration factor:  The ratio of the area (according
to the DEM) within 0.1 feet above or below a contour,
to the area that one would expect if elevations were
uniformly distributed between the contour above and (if
it exists) the contour below.  Suppose for example,
spring high water is 2 ft NGVD, the contour
interval is 5 feet, there are 3 ha between spring
high water and the 5 ft contour, and 5 ha between the 5-
and 10-foot contours, and the DEM finds 2ha between 4.9
and 5.1 ft.  The plateau exaggeration factor would be
10, because a uniform elevation distribution would
imply 1 ha per foot of elevation change; but around the
plateau we have 2 ha within a 0.2 ft elevation
increment.
 
This metadata discusses only the one quad in New York,
plus two quads in Maryland that were particularly
problematic.
For the Central Park  quad, the DEM
underestimated the low land (below 10ft) implied by the
DLG by 7%.  Examining an overlay of the DEM and the
DLG's, the discrepancy for land below the 10-foot
contour appears to be largely explained by places where
the cell size was  larger than the distance between
contours. The plateau exaggeration factors were 0.1,
5.3, and 4.0 for the tidal wetland, 10-ft, and 20-ft
contours, respectively.  The  plateau at the 10-ft
contour had  an area three times the size of the
discrepancy between the DEM and polygon estimates of
the amount of land below the 10-ft contour.  As a
result, in effect, the DEM estimates the DLG's 10-ft
contour to be between 10 and 10.1 feet.  The
discrepancy for lands between 10-20 and 20-30 feet are
less than 2%, which further supports the conclusion
that part of the problem for the lowest land may be
explained by areas where the dry land below 10 ft is
too narrow to take up an entire cell (see discussion of
South River, Maryland, below).
The discrepancies were more serious, however, for
two quads in Maryland:  Broomes and South River.  For
South River the DEM under estimates the amount of land
below 5 ft by 50%.  However, it is within 5% and 1% for
the areas between 5-10 and 10-15ft, respectively.  This
error appears to have resulted because the land is
sufficiently steep that particular cells will have more
than one contour crossing them.  The DEM assigns an
average elevation to the cell.  Assuming that the
contours cross the centers of cells randomly, one would
normally expect that the amount of higher and lower
ground being "averaged in" would approximately offset
each other, so that the DEM should find the same amount
of land within a given elevation range as the input
DLG's.  Indeed, this appears to be the case for land at
10-15 ft.  For land below 5ft, however, there is higher
ground but no lower ground to be "averaged in."
Therefore, an upward bias is created for the lowest
areas.  Such an upward bias in the lowest contour range
could have been avoided with an algorithm that
calculated elevations for points rather than cells or
using a much smaller cell size.  Doing so, however,
would have increased the costs of this study several
fold. The net impact was that the DEM, in effect,
estimated the 5-ft contour to be approximately 7.3 ft
above the vertical datum for South River.  The Broomes
quad had a similar upward bias, effectively treating
the 5-ft contour as a 5.8-ft contour.  
 
The experience
with those two quads in Maryland should serve as a
caution that in New York, our results may be much
less accurate in areas with slopes steep enough to have
two contours within 30 meters (e.g. slopes greater than
6% with 5-ft contours, or 12% with 10ft contours),
especially in the area above the lowest contour.  (Our
first-contour truncating mitigates this upward bias
below the first contour; for reasons explained in the
metadata for the Maryland study (dem_MD_shw_cm).   In
New York, this caution may be applicable along the
Hudson River.
 
The results of the "11 quadrangle" analysis are shown in
DEM_Comparison_with_DLG_11_quads.xls., which is
included in the zip file distributed with this dataset.

Quantitative_Vertical_Positional_Accuracy_Assessment:

Vertical_Positional_Accuracy_Explanation:

See vertical accuracy report.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: US Geological Survey

Publication_Date: Multiple

Title:

Large Scale USGS Digital Line Graph (DLG)

Online_Linkage: <http://edc.usgs.gov/geodata/dlg_large/states/PA.html>

Source_Scale_Denominator: 24,000

Type_of_Source_Media: Digital data

Source_Time_Period_of_Content:

Source_Currentness_Reference:

Multiple

Source_Contribution:

Elevation Contours used in Spatial data and attributes.
Some contours digitized from Digital Raster Graphics
(DRGs). Source contours are 5, 10 , and 20 feet depending on
the 7.5' topographic quadrangle used. The zip file with
which this metadata is distributed will include a
graphic  defining the contour intervals of the input
data, NY_Elevation_Data_Quality.jpg

Source_Information:

Source_Citation:

Citation_Information:

Originator: Hunan Institute of Geograophy

Publication_Date: Multiple

Title:

Coastal Digital Line Graphs

Online_Linkage: <http://edc.usgs.gov/geodata/dlg_large/states/PA.html>

Online_Linkage:

Source_Scale_Denominator: 24,000

Type_of_Source_Media: Digital data

Source_Time_Period_of_Content:

Source_Currentness_Reference:

Multiple

Source_Contribution:

Elevation Contours used in Spatial data and attributes
for USGS quads where USGS DLG was unavailable.  See
"Institute_of_Geography_DLG.doc" for a list of quads
where we used these DLG's. Source contours are 5, 10 feet, and 20 feet depending
on the 7.5' topographic quadrangle used. The zip file
with which this metadata is distributed will include a
graphic defining contour intervals of the input data:
NY_Elevation_Data_Quality.jpg

Source_Information:

Source_Citation:

Citation_Information:

Originator: National Oceanic Service

Publication_Date: Unknown

Publication_Time: Unknown

Title:

NOS Tide Observation Data

Online_Linkage: <http://co-ops.nos.noaa.gov/bench.html>

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 1981

Ending_Date: 2000

Source_Currentness_Reference:

Relative to 1960-1978 Tidal Epoch

Source_Contribution:

Spatial coordinates and elevations of MTL relative to
mean lower low water (MLLW), the elevation of MLLW
relative to several benchmarks nearby, and the
elevations of these benchmarks above NAVD88 for 22 tide
gages in or around New York

Source_Information:

Source_Citation:

Citation_Information:

Originator: National Oceanic Service

Publication_Date: 2000

Title:

NOS Tide Estimation Data. Tide Tables 2000, High and Low Water Predictions, East Coast of North and South America including Greenland

Geospatial_Data_Presentation_Form: document

Source_Scale_Denominator: NA

Type_of_Source_Media: paper report, whose tables contained latitude, longitude, and tide range.

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2000

Source_Currentness_Reference:

publication date

Source_Contribution:

Horizontal location, and spring high tide ranges for
198 tide gages in or around New York

Source_Information:

Source_Citation:

Citation_Information:

Originator: U.S. Environmental Protection Agency

Publication_Date: 2006

Title:

Coastal Wetlands Data: New York

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2006

Source_Currentness_Reference:

publication date

Source_Contribution:

Horizontal location of upper and lower limits of tidal
wetlands. See Readme.doc (distributed in the zip file with this
data set), for directions on how to download the
Coastal Wetlands Data .  See also the technical report for this project J.G. Titus and J. Wang, 2008. Maps of lands close to sea level along the middle Atlantic coast of the United States: an elevation data set to use while waiting for LIDAR. In: Background Documents Supporting Climate Change Science Program Synthesis and Assessment Product 4.1: Coastal Elevations and Sensitivity to Sea Level Rise [J.G. Titus and E.M. Strange (eds.)]. EPA430R07004, U.S. Environmental Protection Agency, Washington, DC.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Proudman Oceanographic Laboratory (POL)

Publication_Date: 2000

Title:

Permanent Service for Mean Sea Level (PSMSL)- Sea Level Rise Trend Data

Other_Citation_Details:

The PSMSL is a member of the Federation of Astronomical and Geophysical Data Analysis Services (FAGS) established by the International Council of Scientific Unions (ICSU).

Online_Linkage: <http://www.nbi.ac.uk/psmsl/datainfo/rlr.trends>

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference:

publication date

Source_Contribution:

Spatial location and estimates of the rate of sea level rise.
Elevation contours were used to generate a surface of sea level rise rates.

Source_Information:

Source_Citation:

Citation_Information:

Originator: U.S. Environmental Protection Agency, Office of Science and Technology

Publication_Date: 1994

Title:

U.S. EPA Reach File 1 (RF1) for the Conterminous United States in BASINS

Edition: Version 2.0

Online_Linkage: For BASINS model and data <http://www.epa.gov/waterscience/basins/>

Source_Scale_Denominator: 250,000 to 500,000

Type_of_Source_Media: CD-ROM

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1994

Source_Currentness_Reference:

publication date

Source_Contribution:

Spatial data and attributes

Process_Step:

Process_Description:

Process of Input Elevation Data
 
     1) Input elevation contour lines were either 1:24,000
     USGS DLGs or 1:24,000 DLG's created by the Institute of
     Geography (using USGS DRG's)  They were appended and
     then projected into Albers projection..

Process_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Jue Wang

Contact_Organization: GIS Practice, ICF Consulting, Inc.

Contact_Position: Senior GIS Analyst

Contact_Address:

Address_Type: mailing and physical address

Address:

9300 Lee Highway

City: Fairfax

State_or_Province: VA

Postal_Code: 22031

Contact_Voice_Telephone: 703-218-2766

Contact_Facsimile_Telephone: 703-934-3974

Contact_Electronic_Mail_Address: jwang@icfconsulting.com

Hours_of_Service: 9:30 - 5:30 EST

Process_Step:

Process_Description:

Process of Tidal Record: Calculating the Elevation of
Spring High Water Supplemental Contour
 
1) Creation of Mean Tide Level Surface. National
Oceanic Service (NOS) tide observation data, i.e., the
latitudes and longitudes, the elevations of mean tide
level (MTL) above mean lower low water (MLLW), the
elevations of MLLW relative to several benchmarks, and
the elevations above NAVD88 of these benchmarks of 22
tide gages in or near New York were downloaded from the
NOS website. The elevations of mean tide level above
NAVD88 were calculated and then converted to above
NGVD29 using USGS VERTCON program. A point coverage was
then created with such data and projected into Albers
projection. Using the point coverage as a reference,
artificial contour lines were created with
consideration of shorelines via heads-up digitizing and
then used to interpolate the mean tide level surface
using a Triangular Irregular Network (TIN).  As the
tidal epoch used for the MTL data was 1960-1978, a
separate sea level rise rate surface was created by
interpolating actual sea level rise data (trend) from
the Proudman Oceanographic Laboratory website and was
used to adjust the mean tide level to years
corresponding to other data sets, such as NWI data, so
that the wetland boundary would represent spring high
tide for the year the map imagery was taken.
 
2) Creation of Spring Tide Range Surface. From Table 2
of "Tide Tables 2000, High and Low Water Predictions,
East Coast of North and South America including
Greenland", the latitudes, longitudes and spring high
tide ranges of 198 tide gauges in or near New York were
obtained and used to create a point coverage. Using the
point coverage as a reference, artificial contour lines
were created with consideration of shorelines and then
used to interpolate the spring high tide range surface
with the TIN method.
 
3) Creation of a Spring High Water Level Surface. A
spring high water level surface was created by adding
half of spring tide range onto mean tide level surface.

Process_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Jue Wang

Contact_Organization: GIS Practice, ICF Consulting, Inc.

Contact_Position: Senior GIS Analyst

Contact_Voice_Telephone: 703-218-2766

Contact_Facsimile_Telephone: 703-934-3974

Contact_Electronic_Mail_Address: jwang@icfconsulting.com

Process_Step:

Process_Description:

Processing Tidal Wetlands:
 
Calculating the Horizontal Position of Spring High
Water Supplemental Contour
 
1) We identified the upper limit of tidal wetland by
extracting the boundaries of tidal polygons (consisting
of tidal wetland and tidal open water) and non-tidal
polygons (consisting of dry land, non-tidal wetland,
and non-tidal open water).
 
2) We used the upper and lower limits of tidal wetlands
to generate supplemental contours. We assigned these
contours elevations derived from spring high tide level
and mean tide level surface grids respectively. The
upper wetland boundary was used as a supplemental
contour. The lower boundary was used for reporting the
area of wetlands but not for elevations, because the
project manager decided not to report wetland
elevations.
See also the metadata accompanying the Coastal Wetlands
Data: New York"  polygon  dataset

Process_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Jue Wang

Contact_Organization: ICF Consulting, Inc., GIS Practice

Contact_Position: Senior GIS Analyst

Contact_Voice_Telephone: 703-218-2766

Contact_Facsimile_Telephone: 703-934-3974

Contact_Electronic_Mail_Address: jwang@icfconsulting.com

Process_Step:

Process_Description:

Interpolation of Digital Elevation Model
 
1) The study area was divided into four parts (tidal
wetlands, lowland, midland, and upland). 
"Tidal wetlands" represents the tidal wetlands as classified from the wetland layer.
Lowland represents the area between the tidal wetlands and the
lowest USGS contour available, which is generally above
the tidal wetlands. Depending on the contour interval
of the input data, this lowest contour may have been 5
feet, 10 feet, or 20 feet -- in all cases relative to
NGVD29.
Midland represents the area above the lowest contour we
used and below the highest USGS contour lines (40 foot
NGVD29) used.
Upland represents land above the midland contour (i.e.,
above the 40 foot NGVD29 contour).
Boundary coverages were created for tidal wetlands,
lowland, and midland using appropriate elevation
contours or upper and lower limits of tidal wetland.
However, we are not making the tidal wetland
interpolations available in this dataset due to the
lack of a theoretical justification for believing that
interpolation to have any information content. (At
best, the wetland elevation interpolations might be
used for graphical representations of the impact of sea
level rise.) We will retain a companion dataset with
elevations stored as floating point double precision,
which may be made available for the sole purpose of
evaluating any graphical representations that use
wetland elevations. We provide a  a polygon wetland
dataset so that the user can  distinguish tidal
wetlands from open water for cells with no data.
 
2) The DEMs were interpolated with a predetermined
cell size of 30 meters for the lowland, midland, and
upland areas using contour lines described in Process
step 1 (the section "Process of Input Elevation Data")
and the supplemental contours explained in process step
3. A common starting coordinate was set for all three
DEM interpolation processes to ensure alignment of the
separate layers after processing. The minimum and
maximum limits were set for each process according to
the input elevation data to ensure the resulting
elevations were in accordance with the input data. See
"first contour truncating", paragraph 5, below. 
The iteration was set to 40, the
horizontal standard error tolerance was set to 2 to
minimize the depression caused by inappropriate tend
calculation, and the drainage enforcement option was
turned on to remove isolated depressions. In addition, we used stream data, which takes priority over the other elevation inputs, to add additional topographic detail.
 
The following represents the options used in TOPOGRID:
 
topogrid dem_mid_ft 30
 
contour topo_cntr elevation
 
xyzlimits 1712800 # 1960000 # 5.0001
 
boundary topo_bnd_mid
 
stream stream
 
iterations 40
 
tolerances # 2
 
enforce on
 
end
 
3) The interpolated DEMs were displayed against input
elevation data and visually checked. These visual
checks would show gross errors, but not necessarily
errors in which the amount of low land is over- or
underestimated by 10-40 percent (additionally, see Data
Quality, Positional Accuracy section). If obvious
errors such as artificial depressions occurred,
supplemental elevation lines were added by heads-up
digitizing to the input contour lines and the
interpolation was repeated.
 
4) First-contour truncating. As a result of the comparison between the initial DEM and the source contours for 11 USGS quadrangles (see vertical accuracy report), we decided to reset the DEM values to coincide with source contours. Our general approach was
that whenever TOPOGRID calculated a value greater than
the first contour within the "lowland", we reset the
value to 0.001 less than the first contour. Whenever
TOPOGRID calculated a midland value less than the first
contour, we reset the value to 0.001 greater than the
first contour. Given the rounding of this integer
dataset, all such values are effectively rounded to the
bounding contour value between midland and lowland
(e.g. 5 feet). Although this approach leaves us with
some plateaus, we have fewer plateaus than we had when
we did not divide the data; and dividing the data left
us with fewer cases of midland and lowland values being
outside of their appropriate ranges
 
5) The DEMs of each separate part were eventually
merged into the final DEM with the MERGE function
within the ESRI GRID module.
 
6) Elevations for Long Beach Island were found to be 
inaccurate due to the dearth of 10ft contours in the area. 
More detailed contours were heads-up digitized from benchmarks 
and 10ft contours found on the 1:24,000 USGS Lawrence, NY 
quadrangle. We then (as described above) used TOPOGRID to 
interpolate our contour lines to a grid and MERGE to update 
our existing DEM.

Process_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Thomas Hodgson

Contact_Organization: Stratus Consulting Inc.

Contact_Position: Senior Associate

Contact_Voice_Telephone: 303-381-8000

Contact_Facsimile_Telephone: 303-381-8200

Contact_Electronic_Mail_Address: thodgson@stratusconsulting.com

Cloud_Cover: NA

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Spatial_Data_Organization_Information:

Direct_Spatial_Reference_Method: Raster

Raster_Object_Information:

Raster_Object_Type: Grid Cell

Row_Count: 6526

Column_Count: 8739

Vertical_Count: 1

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Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name: Albers Conical Equal Area

Albers_Conical_Equal_Area:

Standard_Parallel: 29.500000

Standard_Parallel: 45.500000

Longitude_of_Central_Meridian: -96.000000

Latitude_of_Projection_Origin: 23.000000

False_Easting: 0.000000

False_Northing: 0.000000

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: row and column

Coordinate_Representation:

Abscissa_Resolution: 30.000000

Ordinate_Resolution: 30.000000

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: North American Datum of 1983

Ellipsoid_Name: Geodetic Reference System 80

Semi-major_Axis: 6378137.000000

Denominator_of_Flattening_Ratio: 298.257222

Vertical_Coordinate_System_Definition:

Altitude_System_Definition:

Altitude_Datum_Name: SHW

Altitude_Resolution: 1 cm

Altitude_Distance_Units: cm

Altitude_Encoding_Method: Explicit elevation coordinate included with horizontal coordinates

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Entity_and_Attribute_Information:

Detailed_Description:

Entity_Type:

Entity_Type_Label: dem_ny_shw_cm.vat

Attribute:

Attribute_Label: Rowid

Attribute_Definition:

Internal feature number.

Attribute_Definition_Source:

ESRI

Attribute_Domain_Values:

Unrepresentable_Domain:

Sequential unique whole numbers that are automatically generated.

Attribute:

Attribute_Label: VALUE

Attribute:

Attribute_Label: Value

Attribute_Definition:

Elevation

Attribute_Definition_Source:

Interpolated from input data sets

Attribute_Value_Accuracy_Information:

Attribute_Value_Accuracy: 1 cm

Attribute_Value_Accuracy_Explanation:

Interpolated from source data sets and rounded to nearest 1 cm

Attribute:

Attribute_Label: COUNT

Attribute_Definition:

Count of cells with common elevation

Attribute_Definition_Source:

ESRI

Overview_Description:

Entity_and_Attribute_Overview:

Elevations generated from input data sets (contours and
spot elevations) and interpolated into a raster DEM and
rounded to nearest cm.

Entity_and_Attribute_Detail_Citation:

See process
steps.

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Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: US Environmental Protection Agency, Climate Change Division

Contact_Address:

Address_Type: mailing address

Address:

USEPA (6207-J)

Address:

1200 Pennsylvania Ave. NW

City: Washington

State_or_Province: DC

Postal_Code: 20460

Contact_Voice_Telephone: 202-343-9990

Contact_Facsimile_Telephone: 202-343-2338

Contact_Electronic_Mail_Address: climatechange@epa.gov

Resource_Description: The dataset is being distributed by the US Environmental Protection Agency.

Distribution_Liability:

Although this data was created under the direction of
the EPA, no warranty expressed or implied is made
regarding the accuracy or utility of the data. Neither
EPA nor the data developers shall be held liable for
any use of the data and information
described and/or contained herein.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Information:

Transfer_Size: 4.197

Custom_Order_Process:

Data available to CCSP collaborators from Alan Cohn at 202-343-9814.

Technical_Prerequisites:

Requires software capable of displaying raster data.

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Metadata_Reference_Information:

Metadata_Date: 20080902

Metadata_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Russ Jones, Jim Titus, and Jue Wang

Contact_Organization: Stratus Consulting Inc. (Jones)

Contact_Position: Managing Analyst (Jones)

Contact_Address:

Address_Type: mailing and physical address

Address:

1881 9th St. Suite 201 (Jones)

City: Boulder

State_or_Province: CO

Postal_Code: 80306

Contact_Voice_Telephone: 303-381-8000 (Jones)

Contact_Voice_Telephone: 202-343-9307 (Titus)

Contact_Facsimile_Telephone: 303-381-8200 (Jones)

Contact_Electronic_Mail_Address: rjones@stratusconsulting.com

Contact_Electronic_Mail_Address: Titus.Jim@epamail.epa.gov

Hours_of_Service: 9:00 - 5:00 MST

Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata

Metadata_Standard_Version: FGDC-STD-001-1998

Metadata_Time_Convention: local time

Metadata_Extensions:

Online_Linkage: http://www.esri.com/metadata/esriprof80.html

Profile_Name: ESRI Metadata Profile

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