Coastal New Jersey Digital Elevation Model (Environmental Systems Research Institute [ESRI] Grid format) represents an elevation map of the New Jersey coastal zone created for the purposes of analyzing vulnerability to rising sea level. The domain of the data set extends from the upper tidal wetland boundary up to the 40 foot (NGVD29) contour line, but the primary focus of the analytical approach and quality control has focused on land below the 10 foot contour line. 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 the Henan Institute of Geography from Digital Raster Graphs and hardcopy 1:24,000 USGS topographic quadrangles. In addition, the analysis created a supplemental contour line representing the elevation of spring high water (SHW), which is generally between 1.2 and 5.8 feet above the National Geodetic Vertical Datum of 1929 (NGVD29) in New Jersey. We defined the horizontal position of the supplemental contour line by extracting the inland limit of the tidal wetlands area from the spatial data set created by the US Fish and Wildlife Service National Wetlands Inventory (NWI). 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 the North American Vertical Datum of 1988 (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_NJ_Elevation.doc, which provides a brief overview of the relationship between this dataset and related data 2. InterpolationMethods_MEMO.doc 3. NJ_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
The New Jersey 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 2-ft contour data is available) unless the audience is likely to understand the limitations of the data.
Elevations relative to year 2000.
ground condition
None
Mailcode 6207J
Jue Wang, GIS Practice, ICF Consulting, Inc.
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 "NJ_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. 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.
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).
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.
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. Omissions: Neither stream networks nor lake features were used as inputs into the TOPOGRID function in creation of the DEM for New Jersey. The absence of a hydrologic network explains a significant proportion of the greatest errors in the vicinity of non-tidal streams at elevations below the first contour. TOPOGRID infers stream valleys above the first contour by the pattern of a valley without the stream data. The decision to split the data into land below and above the first contour (discussed in process step #4 "Interpolation of Digital Elevation Model ") had the effect of increasing the potential errors resulting from the absence of stream data. However, the magnitude of any errors induced by this problem was limited by the "first contour truncating" (also discussed in process step #4). The net effect was to put back some of the plateaus eliminated by dividing the data, but not all of those plateaus. Moreover, errors introduced by of omitting stream were avoided in areas with tidal streams or tidal wetlands, by the use of tidal wetlands data, which served the same function. 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, the inclusion of an accurate stream network, 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, and because the tidal wetlands and open water data which we used in effect provide the stream data, the case for dividing the data as we did is probably greater along the wetland boundary than along the first contour. The data was not divided into the separate elevation classes in those areas where we had two-foot contours (Monmouth County) or spot elevation data (land east of US-9 in Ocean, Atlantic, and Cape May Counties). Therefore, this problem with our approach is largely confined to the Delaware Estuary and Northern New Jersey. See NJ_Elevation_Data_Quality.jpg , included in the zip file in which this data set was distributed. Also see the sections on Positional Accuracy (Horizontal and Vertical) and process steps.
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. Neither stream networks nor lake features were used as inputs into the TOPOGRID function in creation of this data set for reasons discussed in the completeness report. The addition of a hydrologic network would increase the accuracy of the resulting DEM.
In the event the horizontal error is as great as the width of a cell, the diagonal would be 42.4 m.
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. If the LIDAR comparison is applicable to New Jersey, one would expect an RMS error of approximately one-half the USGS contour interval, with a mean error less than 1/10 the contour interval. See NJ_Elevation_Data_Quality.jpg, included in the zip file with which this data is distributed, for a graphic depicting the contour intervals for the USGS 7.5-minute quads in New Jersey. 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 Atlantic City and Port Norris. The results of the this analysis are shown in DEM_Comparison_with_DLG_11_quads.xls., which is included in the zip file distributed with this dataset. 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). The technical paper by Titus and Wang (2008, listed in the citation section above) analyzes the results of that comparison. 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 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 2ha in a 0.2 ft elevation increment. This metadata discusses only the two quads in New Jersey, plus two quads in Maryland that were particularly problematic. For the Port Norris quad, the DEM underestimated the low land (below 5ft) implied by the DLG by 4%, while over-estimating the land between 5-10 ft by 3%. The plateau exaggeration factors were 0.13, 8.7, and 1.5 for the tidal wetland, 5-ft, and 10-ft contours, respectively. In the case of the Atlantic City quad, the comparisons reflects the difference between the USGS contours and the Corps of Engineers' spot elevation data. The Corps data (which we use) finds 24% more land below the 5ft contour, and 5% less land between 5-10 ft (NGVD29) than the polygons based on USGS DLG's. The discrepancies were more serious, however, for two quads in Maryland: Broomes and South River quads, respectively. 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. (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)). The experience with those two quads in Maryland should serve as a caution that for New Jersey, 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. In New Jersey, that caution may be applicable to some lands along the Delaware River, particularly Burlington County.
See vertical accuracy report.
Spatial data and attributes. The DLGs provided were the single most important source of elevation data for the DEM creation. Source contours are 5 feet, 10 feet, and 20 feet depending on the 7.5' topographic quadrangle used. The attached zip file includes graphic a spreadsheet defining the contour intervals of the input data. NJ_Elevation_Data_Quality.jpg
Spatial data and attributes. Contours below 40 feet, for areas where USGS 1:24,000 DLG's were not available. Contours were digitized from USGS 7.5 minute Digital Raster Graphics (DRGs). 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. The attached zip file includes graphic a spreadsheet defining the contour intervals of the input data. NJ_Elevation_Data_Quality.jpg
Spot Elevations East of US-9 for Ocean, Atlantic, and Cape May. Vertical Position Accuracy: 1 foot. Horizontal Accuracy, 5 feet.
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 82 tide gages in or around New Jersey.
Horizontal location, and spring high tide ranges for more than 312 tide gages in or around New Jersey.
Horizontal location of upper and lower limits of tidal wetlands. See the metadata for the wetlands data used in this report. See also Titus and Wang 2008.
Spatial location of upper and lower limits of tidal wetlands. Areas used: Delaware River above Delaware Memorial Bridge
Horizontal location of upper and lower limits of tidal wetlands. See the metadata for the wetlands data used in this report. Titus and Wang 2008.
Spatial location and estimates of the rate of sea level rise.
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 Titus and Wang 2008.
Two foot contour interval elevation map was used to generate a DEM for Monmouth County. Complies with National Map Accuracy Standards.
Process of Input Elevation Data 1) Input elevation contour lines were either 1:24,000 scale USGS or Institute of Geography DLGs. They were appended and then projected into Albers projection except for Monmouth County and the area with Spot Elevations (listed below). 2) Spot elevation shape files were first converted into coverages, appended together, and then projected into Albers projection. In addition, a boundary cover delineating the extent of spot elevations, was created by digitizing lines around the existing points. A 30 meter DEM above 5 feet elevation over NAVD88 was first generated using TOPOGRID. Jue Wang of ICF Consulting generated a grid representing the difference between the NGVD29 and NAVD88 datums by obtaining difference values for each point with a 0.1 decimal degree resolution (horizontal) over the entire study area using the VERTCON program. The difference data were then projected into Albers projection and interpolated into a grid of 30 meter resolution, which he then used to convert the DEM relative to the NGVD29 datum. 3) Monmouth County: The County's 2 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 2 foot contour and upper limit of tidal wetland, the upper limit of tidal wetland was reconstructed using a series of editing processes (such as union and relate within ArcInfo). Using the edited contour data, Wang generated a 30 meter DEM relative to NAVD88 using TOPOGRID. Finally, he used the grid representing the difference between NAVD88 and NGVD29 (described in #2 above) to convert the DEM relative to the NGVD29 datum.
9300 Lee Highway
Calculating the elevation of Spring High Water Supplemental Contour Processing of Tidal Record Data 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 82 tide gages in or around New Jersey were downloaded from the NOS website. The elevations of mean tide level relative to NAVD88 were calculated and then converted to elevations relative to a 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. This 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 312 tide gages in or around New Jersey 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 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.
Processing of Tidal Wetland Data: Calculating the Horizontal Position of Spring High Water Supplemental Contour. Processing of Tidal Wetlands Data (New Jersey 1995 Level 3 Land Cover Classification, New Jersey Department of Environment Protection (NJ DEP) Wetland, and NJ DEP Tideland for New Jersey) to Generate a Supplemental Contour Representing the Elevation of Spring High Water. 1) We first projected the New Jersey 1995 Level 3 Land Cover Classification (NJLC) dataset into Albers projection and then converted it to ArcInfo coverage format. Based on the original attributes in the dataset, we reclassified the coverage into Tidal Open Water where original attribute was Marine/Estuarine Open Water; Tidal Wetland where the original attributes were Marine/Estuarine Unconsolicated Shore, Estuarine Emergent Marsh, Brackish Tidal/Fresh Tidal Marsh; Non-Tidal Open Water where the original attribute was Riverine/Lacustrine/Palustrine Open water; Non-Tidal Wetland where the original attributes were Riverine/Lacustrine/Palustrine Unsolicated Shore, Riverine/Lacustrine/Palustrine Emergent Marsh, Wetland Forest, and Wetland Scrub/Shrub; and Dryland for the rest of the area. We also determined the boundaries between high marsh and low marshes based on vegetation. 2) We imported the NJ DEP wetland dataset and projected it into Albers projection. Only estuarine, palustrine, and lacustine wetland categories existed in this dataset (i.e. no open water category). The estuarine categories were classified as tidal wetland. For lacustrine and palustrine categories, tidal or non-tidal water regimes determined if the wetland is tidal or non-tidal. For the polygons where no formal attributes were present, descriptions in the LABEL field were used to assist the classification. 3) We converted the NJ DEP Tideland shape file into a coverage and extracted the tideland limit line. 4) We substituted the upper part of Delaware River with NJ DEP wetland and tideland data, because the NJLC data does not identify the tidal river. Because the NJ DEP wetland data set does not include open water and the NJ DEP Wetlands Boundary does not include wetland, we combined these two datasets and used the resultant data in the upper part of Delaware River. 5) 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 EPA project manager decided not to report wetland elevations. See also the metadata accompanying the New Jersey Wetland polygon datasets.
Interpolation of Digital Elevation Model 1) With the exception of those areas with spot elevations or 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, 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 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 Monmouth County, 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 using the ESRI GRID function TOPOGRID with a predetermined cell size 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 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. This means whenever the algorithm estimated a lowland value higher than the lowest contour, we reset it to the precise elevation of that contour; and whenever the algorithm estimated a midland value lower than the lowest contour, we reset it to the precise value of that contour. The iteration was set to 40, the horizontal standard error tolerance was set to 2 to minimize the depression caused by inappropriate trend calculation, and the drainage enforcement option was turned on to remove isolated depressions. 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 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. 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.
Internal feature number.
ESRI
Elevation
Interpolated from input data sets
Count of cells with common elevation
ESRI
Elevations generated from input data sets (contours and spot elevations) and interpolated into a raster DEM and rounded to nearest cm.
See process steps.
USEPA (6207-J)
1200 Pennsylvania Ave. NW
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.
Data available from Alan Cohn at 202-343-9814.
1881 9th St. Suite 201 (Jones)