20201022 14090400 1.0 FGDC CSDGM Metadata FALSE Rast_SJ_DEM.img 189722.000000 623544.000000 34292.000000 488014.000000 1 002 Projected GCS_NAD_1983_2011 Linear Unit: Foot_US (0.304801) NAD_1983_2011_StatePlane_New_Jersey_FIPS_2900_Ft_US <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.7'><WKT>PROJCS[&quot;NAD_1983_2011_StatePlane_New_Jersey_FIPS_2900_Ft_US&quot;,GEOGCS[&quot;GCS_NAD_1983_2011&quot;,DATUM[&quot;D_NAD_1983_2011&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,492125.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-74.5],PARAMETER[&quot;Scale_Factor&quot;,0.9999],PARAMETER[&quot;Latitude_Of_Origin&quot;,38.83333333333334],UNIT[&quot;Foot_US&quot;,0.3048006096012192]],VERTCS[&quot;NAVD88 height - Geoid12B (feet)&quot;,VDATUM[&quot;North_American_Vertical_Datum_1988&quot;],PARAMETER[&quot;Vertical_Shift&quot;,0.0],PARAMETER[&quot;Direction&quot;,1.0],UNIT[&quot;Foot_US&quot;,0.3048006096012192]]</WKT><XOrigin>-17954600</XOrigin><YOrigin>-46918100</YOrigin><XYScale>137255069.87923574</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>103106</WKID><LatestWKID>6527</LatestWKID></ProjectedCoordinateSystem> 32 FALSE None 1 IMAGINE Image TRUE continuous floating point -3.4028235e+38 20201207 19160700 20201207 19160700 FGDC 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 Sanborn Map Company, Inc. (866)726-2676 Suite 100 1935 Jamboree Drive Colorado Springs CO 80920 US 20201207 ArcGIS Metadata 1.0 Rast_SJ_DEM.img 2020-01-18 Sanborn Map Company, Inc. raster digital data Product: Bare earth Digital Elevation Model (DEM) data. Geographic Extent: Atlantic, Cape May, Cumberland, Ocean, Salem counties New Jersey, covering approximately 2,545 square miles. Dataset Description: USGS South NJ Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.5 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.3. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane New Jersey, feet and vertical datum of NAVD88 (GEOID12B), feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 3,079 individual 5000 x 5000 foot tiles, as tiled Intensity Rasters, and as tiled bare earth DEMs; all tiled to 3,079 individual 5000 x 5000 foot schema. Ground Conditions: Lidar was collected in early 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc. established a total of 30 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 142 independent accuracy check points, 80 in Open Terrain/Bare-Earth and Urban landcovers (80 NVA points), 62 in Grass, Brush and Trees categories (62 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data. To acquire detailed surface elevation data for use in conservation planning, design, research, floodplain mapping, dam safety assessments and elevation... New Jersey Cumberland Salem Atlantic Ocean Cape May Elevation Data Model Remote Sensing Raster DEM Lidar Elevation Data Model New Jersey Cumberland Remote Sensing Salem Atlantic Raster DEM Lidar Ocean Cape May No restrictions apply to these data. None. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data. ground condition 2019-03-08 2019-04-23 -75.5736209863 -74.0301827626 38.926721708 40.1731865495 Raster File Type = IMG Bit Depth/Pixel Type = 32-bit float Raster Cell Size = 2 Feet Interpolation or Resampling Technique = Triangulated Irregular Network Required Vertical Accuracy = 19.6 cm NVA Version 6.2 (Build 9200) ; Esri ArcGIS 10.7.1.11595 1 -75.582139 -74.029689 40.173227 38.922656 NJOGIS, NJDEP Data covers the full area specified for this project. Datasets contain complete coverage of tiles. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. There are no void areas or missing data. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications. Hydro-Flattened Bare Earth DEM Process: Class 2 (ground) and hydro-flattened breaklines were used to create a 2 foot Raster DEM. Using automated scripting routines within LP360, a IMG file was created for each tile. Each terrain is reviewed using Global Mapper to check for any surface anomalprocessing was compleies or incorrect elevations found within the surface. 2020-01-18 2 226861 2.000000 216911 2.000000 1 0 189722.000000 34292.000000 189722.000000 488014.000000 623544.000000 488014.000000 623544.000000 34292.000000 406633.000000 261153.000000 Raster Dataset dataset EPSG 8.2.10(10.3.1) Layer_1 230.199997 -9999.001953 32