Maryland 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
• Data_Quality_Information
• Spatial_Data_Organization_Information
• Spatial_Reference_Information
• Entity_and_Attribute_Information
• Distribution_Information
• Metadata_Reference_Information

Identification_Information:

Citation:

Citation_Information:

Originator: US Environmental Protection Agency

Publication_Date: February 2008

Title:

Maryland 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 Maryland Digital Elevation Model (Environmental Systems Research Institute [ESRI] Grid format) represents an elevation map of the Maryland 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 40 foot NGVD29, 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.  Along the Eastern Shore south of Rock Hall, we used LIDAR developed by the Maryland Department of Natural Resources.

In the rest of the state (as well as the original version of this study) we used United States Geological Survey (USGS) 1:24,000 Digital Line Graphs (DLG), Digitized USGS DLG's (digitized in China by the Henan Institute of Geography) from Digital Raster Graphics and hardcopy 1:24,000 USGS topographic quadrangles), Maryland Department of Natural Resources (MDNR) elevation data, county elevation data provided by Harford, Baltimore, and Anne Arundel Counties, and a map of Kent Island prepared for the National Flood Insurance Rate Map of Queene Anne's County. In addition, the analysis created a supplemental contour representing the elevation of spring high water (SHW), which is generally between 1 and 3 feet above NGVD29 in Maryland. We defined the horizontal position of that contour by extracting the inland limit of the tidal wetland polygons of a separate Coastal Wetlands data set we created for this project (based on data from the US Fish and Wildlife Service National Wetlands Inventory [NWI] and the MD-DNR). 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, the 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).   See the companion dataset demmd_nolidar for the original dataset developed before the LIDAR became available.

We converted the absolute elevation estimates (usually NGVD29) into elevations relative to SHW using the "tidal elevation surface."  For purposes of this data set, we assume that SHW is the upper (inland) 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_MD_Elevation.doc, which provides a brief overview of the relationship between this dataset and related data

2. InterpolationMethods_MEMO.doc

3. MD_Data_Quality.jpg

4. DEM_LidarComparisonTable.doc

5. DEM_Comparison_with_DLG_11_quads.xls

6. Institute_of_Geography_DLG.xls

7. Lidar_dvd_cds.pdf, which is a figure showing location of lidar data used

8. Worc02_03LIDAR.jpg, which is a figure showing 2002 vs. 2003 lidar data used

9. 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 Maryland 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 (except for those areas where LIDAR or 2-ft contour data is available) unless the audience is likely to understand the limitations of the data

Supplemental_Information:

Elevations relative to year 2000.

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: -77.494904

East_Bounding_Coordinate: -74.321993

North_Bounding_Coordinate: 39.874503

South_Bounding_Coordinate: 37.594593

Keywords:

Theme:

Theme_Keyword_Thesaurus: General

Theme_Keyword: Maryland Elevation

Theme_Keyword: DEM

Theme_Keyword: Coastal Elevation

Place:

Place_Keyword_Thesaurus: Geographic Names Information System

Place_Keyword: Maryland MD

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

Cross_Reference:

Citation_Information:

Publication_Date: February 2008

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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 "MD_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.

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 Titus and Wang 2008 technical report that documents this study for information on the publication date for data and procedures used in the development of 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 using two different systems of measurements (meters and feet). We use the two diffent systems to reflect the actual contour intervals used by USGS over the years, which vary on a quadrangle by quadrangle basis.

Completeness_Report:

This elevation data set generally reflects that of 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 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), 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 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.

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.

We did not divide the data into the separate elevation classes in those areas where we had two-foot contours.  Therefore, this issue is inapplicable to Baltimore County, and Kent Island. Similarly, this approach does not apply to areas where lidar data was used (see the index of sources shown in MD_Data_Quality.jpg provided with this data set).

Also see the sections on Positional Accuracy (Horizontal and Vertical) and process steps.

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 procedures used to interpolate between contours 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.

In areas where we used lidar (see figures lidar_dvd_cds.pdf and Worc02_03LIDAR.jpg), accuracy is much better. According to the accuracy assessment conducted for the Maryland lidar program,  (Dewberry and Davis, LIDAR 2003 Quality Assurance Report, MD DNR), the lidar for the 2003 data had an RMSE of 16.9 cm.  When the worst 5% of the data is excluded, the RMSE is 14.3 cm over the remaining 95% of the data. In areas where only 2002 data was available, the data is less accurate according to Kevin Boone at MD DNR. However, the DNR did not conduct a vertical accuracy assessment.

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. 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 NGVD29, 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 2 ha 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 in a 0.2 ft elevation increment.

This metadata discusses only the four quads analyzed in Maryland. In Ocean City, the DEM over-estimates the low land by 6%. Examining an overlay of the DEM and the DLG's, the discrepancy appears to be explained by one-cell boundary errors. The plateau exaggeration factors were 2.8, 3.1, and 4.4, for the spring high water, 5-ft, and 10-ft contours, respectively.  The Middle River (quarter quad) understates the 0-2 foot land by 93%. However, this discrepancy does not seem serious for two reasons:  First, we did not split the data between lowland and midland for areas with 2 foot contours, so we did not expect a precise conformance. Second, the 26 ha difference is a very small area, and the DEM calculated another 26 ha between 2.0-2.7 feet. The plateau exaggeration factors were 2.3, 1.5, and 2.7, for the spring high water, 2-ft, and 4-ft contours, respectively.

The discrepancies were more serious, however, for two quads in Maryland:  Broomes and South River. The South River 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 10-15 feet, 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 feet.  For land below 5 feet, 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. The net impact was that the DEM, in effect, estimated the 5-foot contour to be approximately 7.3 feet above the vertical datum for South River. The plateau exaggeration factors were 0.8, 2.3, and 2.9, for the spring high water, 5-ft, and 10-ft contours, respectively.

The Broomes quad had a similar upward bias, effectively treating the 5-foot contour as a 5.8-foot contour.  The plateau exaggeration factors were 7.0, 0.9, and 4.2, for the spring high water, 5-ft, and 10-ft contours, respectively.

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) and Digital Raster Graphic (DRG) data

Online_Linkage: http://edc.usgs.gov/geodata/dlg_large/states/MD.html

Online_Linkage: http://staging.icfconsulting.com/staging/gis/sealevel/MD/GIS%20Data/USGS%2024K%20Contour%20Interval.jpg

Source_Scale_Denominator: 24,000

Type_of_Source_Media: Digital data and paper

Source_Time_Period_of_Content:

Source_Currentness_Reference:

Multiple

Source_Contribution:

Spatial data and attributes. Some contours digitized from Digital Raster Graphics (DRGs) and hardcopy.

Source contours are 1 meter, 5 feet, 10 feet, and 20 feet depending on the 7.5' topographic quadrangle used. The zip file with which this metadata is distributed includes a the file "MD_Data_Quality.jpg" which defines the contour intervals of the input data.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Maryland Department of Natural Resources (MD DNR)

Publication_Date: 1992

Title:

Maryland Department of Natural Resources (MD DNR) Elevation Data

Online_Linkage: NA

Source_Scale_Denominator: 12,000

Type_of_Source_Media: Digital data

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1992

Source_Currentness_Reference:

publication date

Source_Contribution:

Spatial data and attributes. MD DNR staff told the project manager that 90% of the elevation points are within 5 feet of true elevation. Horizontal accuracy, 33 feet. Vertical Position Accurancy:  5 feet.

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: 24000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 1960

Ending_Date: 1978

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 25 tide gages in Maryland.

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

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 more than 150 tide gages in Maryland.

Source_Information:

Source_Citation:

Citation_Information:

Originator: U.S. Fish and Wildlife Service

Publication_Date: Various

Title:

US Fish and Wildlife National Wetlands Inventory (NWI) Data

Online_Linkage: http://wetlands.fws.gov/downloads.htm

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: Various

Source_Currentness_Reference:

publication date

Source_Citation_Abbreviation:

USFWS NWI

Source_Contribution:

Horizontal location of upper and lower limits of tidal wetlands. See the metadata for the wetlands data used in this report.  See also the technical report for this project J.G. Titus and J. Wang. 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. For a graphic depicting the specific counties or USGS 7.5' quadrangles where this data set was used, see the Maryland_DNR_Wetlands_Data_Coverage.jpg. The USFWS data was used wherever that graphic shows no DNR data coverage.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Maryland Department of Natural Resources

Publication_Date: 1991

Title:

Maryland Department of Natural Resources Wetland Coverage

Online_Linkage: http://dnrweb.dnr.state.md.us/gis/data/

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 1988

Ending_Date: 1995

Source_Currentness_Reference:

ground condition

Source_Contribution:

Spatial location of upper and lower limits of tidal wetlands.

(Note: photograph dates were from 1988 to 1995, but MD DNR listed the publication date of 1991 on the DNR website.) Areas used:  For a graphic depicting the specific counties or USGS 7.5' quadrangles where this data set was used, see the Maryland_DNR_Wetlands_Data_Coverage.jpg, a graphic file included in the zip file with which this data is distributed. See also the technical report for this project for  specific counties or USGS 7.5' quadrangles where this data set was used. J.G. Titus and J. Wang. 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.

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.

Source_Information:

Source_Citation:

Citation_Information:

Originator: GEOD Surveying and Aerial Mapping Corporation, prepared for Federal Emergency Management Agency's Flood Insurance Administration

Publication_Date: 1981

Title:

Kent Island, Maryland

Geospatial_Data_Presentation_Form: map

Source_Scale_Denominator: 7,200

Type_of_Source_Media: paper

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1981

Source_Currentness_Reference:

publication date

Source_Contribution:

Used to supplement USGS DLGs on Kent Island. Elevation contours (2 - 12 ft NGVD29). Mapped by photogrammetric methods from aerial photographs.

Contour Interval: 2'

This map complies with National Map accuracy standards.

Datum is based on mean sea level.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Photo Science, Inc. (now EarthData International, Inc.) for Anne Arundel County, Department of Public Works

Publication_Date: 1995

Title:

Anne Arundel County 1995 Topographic Mapping

Source_Scale_Denominator: 2,400

Type_of_Source_Media: paper

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1995

Source_Currentness_Reference:

ground condition

Source_Contribution:

Five foot contour interval elevation map was used to generate a DEM for Anne Arundel County. Contours generated from aerial photos. Complies with National Map Accuracy Standards

Source_Information:

Source_Citation:

Citation_Information:

Originator: Harford County Government GIS

Publication_Date: Unknown

Title:

Harford County 5 foot contour elevation maps

Source_Scale_Denominator: 2,400

Type_of_Source_Media: digital tape media

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference:

ground condition

Source_Contribution:

Harford County provided 5 foot (NGVD29) contour interval elevation maps for Harford County in ArcInfo Interchange Format (.e00) in ascii breaklines and mass points from a Digital Terrain Model (DTM). According to metadata, NMAS for 5 foot contours states that 90% of the tested elevations shall be in error by less than 1/2 a contour interval, i.e. 2.5 foot.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Baltimore County OIT/GIS Services Unit

Publication_Date: 1997

Title:

Baltimore County Topo Data

Source_Scale_Denominator: 100

Type_of_Source_Media: digital tape media

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1997

Source_Currentness_Reference:

publication date

Source_Contribution:

Elevation contours were used to generate a DEM. Two foot contour interval contours relative to NAVD88. Complies with standards of the American Society Photogrametry and Remote Sensing, as well as with National Map Accuracy Standards.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Henan Institute of Geography, China

Title:

Coastal Digital Line Graphs (DLG) created from USGS Digital Raster Graphics

Source_Scale_Denominator: 24,000

Type_of_Source_Media: Digital vector data

Source_Time_Period_of_Content:

Time_Period_Information:

Multiple_Dates/Times:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference:

publication date

Source_Contribution:

Spatial data and attributes. Contours below 40 feet, for areas where neither USGS 1:24,000 DLG's nor local data were not available.

Contours digitized from USGS 7.5 minute Digital Raster Graphics (DRGs) or hardcopy. See Institute_of_Geography_DLG.doc, included in the zip file with which this data is distributed, for a list of the 7.5-minute quads where we used the Institute of Geography DLG's. Additionally, the graphic "MD_Data_Quality.jpg" defines the contour intervals of the input data.

Source_Information:

Source_Citation:

Citation_Information:

Originator: U.S. Environmental Protection Agency

Title:

Coastal Wetlands Data: Maryland

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Multiple_Dates/Times:

Single_Date/Time:

Calendar_Date: Mostly 1988-95, 1980s for a few counties.

Source_Currentness_Reference:

publication date

Source_Contribution:

Horizontal location of upper and lower limits of tidal wetlands. Derivative product based on MD-DNR and NWI wetland data sources cited in this metadata.  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. 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. See Readme.doc for additional information.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Spatial Systems Associates, Inc.

Publication_Date: 11/30/2003 (and 2002 - most of Worcester and small portion of Wicomico counties)

Title:

Elevation Data - LiDAR Bare Earth Gridded DEM

Publication_Information:

Publication_Place: Annapolis, Maryland

Publisher: Maryland Department of Natural Resources

Other_Citation_Details:

1. Airborne 1 (flight firm) 2. Computational Consulting Services, LLC (processing firm) 3. Spatial Systems Associates, Inc. (post-processing firm) 4. Dewberry & Davis (QA/QC firm) Note: 2002 Lidar data was provided by Joe Gavin (USACOE)

Online_Linkage: http://www.msgic.state.md.us/techtool

Source_Scale_Denominator: 24,000

Type_of_Source_Media: DVD

Source_Time_Period_of_Content:

Time_Period_Information:

Multiple_Dates/Times:

Single_Date/Time:

Calendar_Date: 2002

Single_Date/Time:

Calendar_Date: 11/30/2003

Source_Currentness_Reference:

ground condition

Source_Contribution:

Horizontal coordinates and vertical elevations. Note that data for Worcester and along the border of Worcester/Wicomico counties the data is of less quality and is from 2002.

Source_Information:

Source_Citation:

Citation_Information:

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

Publication_Date: 1994

Publication_Time: Unknown

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/>

Online_Linkage: For further documentation and reference to EPA's River Reach Files <http://www.epa.gov/owowwtr1/monitoring/rf/rfindex.html>

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 scale DLG's from either USGS or the Institute of Geography. They were appended and then projected into Albers projection except for Baltimore, Anne Arundel, and Harford Counties, and Kent Island in Queen Anne's County (listed below).

2) MD-DNR data were only used in areas where the (a) USGS contour interval was larger than 5 feet and (b) we had no local elevation data. Maryland Department of Natural Resources elevation data had a horizontal resolution of 100 by 100 feet and vertical resolution of 0.1 foot. The data were imported from text files and interpolated into DEMs with 30 meter resolution using the TOPOGRID function (ESRI GRID extension). The vertical datum of DNR elevation data was NAVD88, while the vertical datum of USGS DLG data was NGVD29. Therefore, Wang generated a grid representing the difference between the two datums by obtaining difference values for each point with a 0.1 decimal degree resolution (horizontal) over the entire study area from VERTCON program. The data were projected into Albers projection and interpolated into a grid of 30 meter resolution,  which he used to convert the DNR DEM into NGVD29. He then converted the DEM into 5 foot interval contour lines (5, 10, and 15 foot contours), which he used as supplementary input to the final DEM creation. The DNR contours were not used if (a) there was an elevation source other than USGS maps (e.g., Baltimore, Anne Arundel, and Harford Counties) or (b) USGS maps had a contour of 5 feet or 1 meter.  If USGS had a 10 foot contour, the 5 and 15 foot contours from the DNR data were used.  If the USGS map had a 20 foot contour interval, then all three contours were used.

3) Kent Island: Contour lines from Dewberry & Davis flood line elevation maps developed for FEMA in 1981 were digitized. The contour lines were from 2 feet to 12 feet. When compared with USGS DLG as well as MD DEP elevation data, the vertical datum was determined to be NGVD29. These contour lines were used in lieu of USGS DLG contours and combined with the point covers representing spring high water level and mean tide level to generate the DEM.

4) Anne Arundel County: The county's 5 foot contour interval elevation map was used to generate the DEM. First, the contour layer was projected into Albers projection. Then to resolve the conflicts between the five foot contour and upper limit of tidal wetland, the 5 foot contour was reconstructed using a series of editing processes (such as union and relate within ArcInfo). A 30 meter DEM above 5 feet elevation over NAVD88 was first generated using TOPOGRID. The grid representing the difference between NAVD88 and NGVD29 (described in #2 above) was used to convert the DEM relative to the NGVD29 datum. The new DEM was then converted into a point cover. This point cover was then combined with the point covers representing spring high water level and mean tide level to interpolate the final DEM.

5) Baltimore County: 2 foot interval county digital line graph elevation maps based on high resolution aerial photography were projected into Albers projection.Wang used the grid representing the difference between NAVD88 and NGVD29 (described in #2 above) to convert the contours from NAVD88 to NGVD29.  Unlike the other counties, the 2-foot contours obviated the need to split the simulation into lowland and midland.

6)  Harford County.  The County's 5 foot contour interval elevation map was used to generate the DEM. First, the contour layer was projected into Albers projection. Then to resolve the conflicts between the five foot contour and upper limit of tidal wetland, the 5 foot contour was reconstructed using a series of editing processes (such as union and relate within ArcInfo).  Otherwise, the processing was the same as with areas where we used the Maryland DNR data.

7) Light Detection and Ranging (Lidar) data. Jones acquired Lidar data representing a bare-earth model in 1,800 x 1,200 meter tiles for portions of Worcester, Wicomico, Somerset, Dorchester, Talbot, Kent, and Queen Annes counties (see figure "lidar_dvd_cds.pdf" for graphic of spatial distribution of lidar data). According to Kevin Boone (MD DNR), DNR acquired data for Worcester and along the border of Worcester/Wicomico counties in 2002, and the data is of less quality than all other areas that they acquired in 2003 (see figure "Worc02_03lidar.jpg" for areas where 2002 vs. 2003 data were used). Data tiles were provided as either ESRI export format GRIDS (.e00) or as raw ASCII files with horizontal and vertical (x,y,z) coordinates, and imported into ESRI GRID format. Jones combined the Lidar tiles into raster layers by county using the MOSAIC function and then resampled from a 2 meter cellsize to 30 meters using bilinear interpolation. The resampled county grids were combined into a single seamless raster layer using the MOSAIC function and projected into the Albers projection to match the other data sets. According to the metadata provided with the lidar data, values of zero represented open water. These were removed from the data set prior to further processing. The raster data was exported into a point layer (ESRI coverage format) and elevation units converted from meters to feet. Using the US Army Corps of Engineeers Corpscon (v. 6.0.1) program, the full location information (x,y,z values) were exported into an ASCII file and the elevations were converted from NAVD88 to NGVD29 . The converted ASCII file was reimported into ESRI GRID format.

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

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 25 tide gages in Maryland were downloaded from the NOS website. The elevations of mean tide level relative to NAVD88 were calculated and then converted to a NGVD29 using the difference surface between NAVD88 and NGVD29. 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 TOPOGRID. 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 more than 150 tide gages 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 TOPOGRID 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 of Tidal Wetland Data from US FWS NWI and MD DNR Wetland Data to Generate a Supplemental Contour Representing the Elevation of Spring High Water.

1) NWI data were imported into ArcInfo coverage format and projected into Albers projection. A data set representing tidal wetlands was generated using the subtidal and intertidal subsystems for marine and estuarine categories, tidal subsystem for riverine category, and the tidal or non-tidal water regimes for lacustrine and palustrine categories. The upper and lower limits of tidal wetlands were then extracted from this tidal wetland data set.

For the marine and estuary categories, the boundaries between M1 (subtidal) and M2 (intertidal),  E1 (subtidal) and E2 (intertidal) determined the lower boundaries of tidal wetland, and the boundaries of M2 and E2 with upland and other non-tidal wetland or non-tidal open water, determined the upper boundaries of tidal wetland.  For the riverine category, the boundaries between R1 (tidal) and upland as well as other non-tidal wetland or non-tidal open water determined the upper limit. For Lacustrine and Palustrine categories, tidal or non-tidal water regimes determined the upper boundaries of tidal wetland.

2) Maryland Department of Natural Resources (DNR) wetland data were projected into Albers projection. The tidal wetlands were identified using methods similar to those described above for the NWI data and used to generate a layer representing the upper and lower limits of tidal wetlands.

3) The upper and lower limits of tidal wetlands from the Maryland DNR and US FWS NWI wetland data were then combined, with priority given to the former where available and the combined data set was used to generate supplemental contours. Elevations assigned to these contours was 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 Maryland Wetland polygon datasets.

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) With the exception of those areas with two foot contours, 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 topographic contour available that 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, 20 feet, or 1 meter -- in all cases relative to NGVD29 where we used USGS maps -- or 5 feet NAVD88 in the case of Anne Arundel County, where the 5 foot contours were measured relative to NAVD88.

Midland represents the area above the lowest contour we used and below the highest USGS contour lines (40 foot or 12 meters NGVD29) used.

Upland represents land above the midland contour (i.e., above the 40 foot or 12 meter 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 gridded (and a polygon) wetland dataset so that the user can distinguish tidal wetlands from open water for cells with no data. In the case of Baltimore County and Kent Island, where the underlying topographic information had a two-foot contour interval, the lowland and midland were treated together as a single category.

2) The DEMs were interpolated with a predetermined cellsize of 30 meters for the lowland, midland, and upland areas using contour lines described in the section "Process of Input Elevation Data" and supplemental contours from the combination of NWI and MD DNR wetland data and NOAA tidal record contours. 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", #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 1602000 # 1833000 # 5.0001

boundary topo_bnd_up

stream stream

iterations 40

tolerances # 2

enforce on

end

3) The region with vertical contour interval units in meters was interpolated separately. The contour values were first converted from meters into feet, interpolated into DEMs for both midland and lowland, and then merged together with the region having vertical contour units in feet.

4) 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.

5) 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. 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. Nevertheless, Dr. Wang was unable to apply our preferred approach in areas where the USGS maps had a 10-foot contour, because of our use of the MD DNR data.  In some places, the MD DNR 5-foot contour was landward of the USGS 10-foot contour.  In those areas, we simply did not use the 5-foot contour, due to the superior accuracy of the USGS maps.  The lack of a 5-foot contour meant that the lowland/midland boundary was sometimes 5 feet and sometimes 10 feet.  Therefore, the first-contour truncating set all midland values lower than 5 feet to 5.001 feet, while setting midland values greater than 10 feet to precisely 10 feet.  This data limitation is inapplicable to areas where the USGS contour interval is either 5 or 20 feet, so we could have applied our general approach to those quadrangles. However, Dr. Wang did not do so because he ran TOPOGRID on all areas with 5-, 10-,  or 20-foot USGS contour intervals as a single simulation (because he usually had 5-foot contours from MD-DNR).

6) The DEMs of each separate part were eventually merged into the final DEM with the MERGE function within the ESRI GRID module.

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:

Incorporation of Lidar data with DEM data.

1) We created a mask by setting all values in the area covered by LIDAR data to 1.

2) We shrunk the mask by 30 cells which is 900 meters on each side.

3) We used the ISNULL function to set values of the interpolated DEM to null over the mask area.

4) We reprocessed the LIDAR data by extracting the values less than or equal to 40 feet.

5) We mosaiced the LIDAR DEM and DEM from USGS data together using blend option within Spatial Analyst extension.

6) We substracted the Spring High Water Level elevation from the mosaiced DEM to create a DEM above the Spring High Water Level.

7) We changed the unit of measure from feet to meters.

8) We set the values to null over the areas of tidal wetland and tidal open water.

9) We changed the mosaiced DEM's unit of measure to centimeters and also its type from floating point to integer.

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

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: 7038

Column_Count: 7805

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: Attribute values

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Entity_and_Attribute_Information:

Detailed_Description:

Entity_Type:

Entity_Type_Label: dem_md_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_Definition:

Elevation

Attribute_Definition_Source:

Interpolated from input data sets

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: U.S. 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

Hours_of_Service: 9:00 - 6:00 Eastern

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: 12.213

Custom_Order_Process:

Data available 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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