20201022 17061800 1.0 FGDC CSDGM Metadata FALSE Rast_SJ_HSD.img 002 189722.000000 623544.000000 34292.000000 488014.000000 1 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> 8 FALSE None 1 IMAGINE Image TRUE continuous unsigned integer 255 20201207 18275900 20201208 18551700 150000000 5000 FGDC Sanborn Map Company, Inc. (866)726-2676 Suite 100 1935 Jamboree Drive Colorado Springs CO 80920 US 20201208 ArcGIS Metadata 1.0 Rast_SJ_HSD.img 2020-01-18 Sanborn Map Company, Inc. raster digital data <div style='text-align:Left;'><div><div><p><span>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.</span></p></div></div></div> To acquire detailed surface elevation data for use in conservation planning, design, research, floodplain mapping, dam safety assessments and elevation... Ocean Atlantic Salem Cape May Cumberland New Jersey Lidar Remote Sensing Raster Model DEM Elevation Data Ocean Lidar Remote Sensing Raster Model Atlantic Salem Cape May DEM Cumberland Elevation Data New Jersey No restrictions apply to these data. <div style='text-align:Left;'><div><div><p><span>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.</span></p></div></div></div> -75.5736209863 -74.0301827626 38.926721708 40.1731865495 ground condition 2019-03-08 2019-04-23 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 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 anomalies 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) Rast_SJ_HSD.img.vat Table 255 OID OID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Value Value Integer 10 10 0 Count Count Double 19 0 0 Layer_1 254.000000 0.000000 8 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