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MosaicToNewRaster SD2017_east_Mosaic.img;FEMA_2015_Mosaic.img;SD2014_West_Mosaic.img L:\gis\LIDAR\SD2017\Testing\Merged_Regionwide_DEM SD_Merged_DEM.img PROJCS['NAD_1983_2011_StatePlane_California_VI_FIPS_0406_Ft_US',GEOGCS['GCS_NAD_1983_2011',DATUM['D_NAD_1983_2011',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Conformal_Conic'],PARAMETER['False_Easting',6561666.666666666],PARAMETER['False_Northing',1640416.666666667],PARAMETER['Central_Meridian',-116.25],PARAMETER['Standard_Parallel_1',32.78333333333333],PARAMETER['Standard_Parallel_2',33.88333333333333],PARAMETER['Latitude_Of_Origin',32.16666666666666],UNIT['Foot_US',0.3048006096012192]],VERTCS['NAVD_1988_Foot_US',VDATUM['North_American_Vertical_Datum_1988'],PARAMETER['Vertical_Shift',0.0],PARAMETER['Direction',1.0],UNIT['Foot_US',0.3048006096012192]] 32_BIT_FLOAT 2.5 1 LAST FIRST
CopyRaster F:\Regional_DEM_and_Contours\SanDiegoRegionDEM\SD_Merged_DEM.img F:\Regional_DEM_and_Contours\SanDiegoRegionDEM\DEMs.gdb\SD_Merged_DEM # # -3.402823e+038 NONE NONE # NONE NONE "Esri Grid" NONE
Clip SD_Merged_DEM "6124363.73673814 1748904.07625166 6639836.99667947 2156159.98026295" E:\DEMs.gdb\DEM_MinusRincon OutsideRincon2019 -3.402823e+38 ClippingGeometry NO_MAINTAIN_EXTENT
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San Diego County
Merged Digital Elevation Model (DEM) data derived from Quality Level 2 LiDAR (USGS Base Specification) acquired through the San Diego regional partnership acquisition projects in 2014 and 2017, and the FEMA acquisition project in 2015.
<div style='text-align:Left;'><div><div><p><span><span>Product: </span></span></p><p><span>Merged Digital Elevation Model (DEM) data derived from Quality Level 2 LiDAR (USGS Base Specification) LiDAR acquired through the San Diego regional partnership acquisition projects in 2014 and 2017, and the FEMA acquisition project in 2015. Class 2 (ground) LiDAR points in conjunction with the hydro breaklines were used to create 2.5-foot hydro-flattened raster DEMs from which contour lines were generated.</span></p><p><span><span>Geographic Extent:</span></span></p><p><span><span>San Diego County in southwestern California, covering approximately 4,475 total square miles.</span></span></p><p><span><span>Dataset Descriptions:</span></span></p><p><span><span>2014: The San Diego, California 2014 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1. The data was developed based on a horizontal projection/datum of NAD83, State Plane California VI, Survey Feet and vertical datum of NAVD1988 (GEOID12A), Survey Feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.2 Files formatted to 1565 individual 5000 ft x 5000 ft tiles, and corresponding Intensity Images and Bare Earth DEMs tiled to the same 5000 ft x 5000 ft schema, and Breaklines in Esri geodatabase format. Ground Conditions: LiDAR was collected in late 2014 and early 2015, 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, Photo Science established a total of 88 QA calibration control points and 80 Land Cover control points that were used to calibrate the LIDAR to known ground locations established throughout the San Diego, California project area.</span></span></p><p><span><span>2015: The San Diego, California 2015 QL2 LiDAR project called for the planning and acquisition of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane California VI, US survey feet and vertical datum of NAVD1988 (GEOID12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 2,045 individual 2,500-foot x 2,500-foot tiles, corresponding 2.5-ft hydro-flattened bare-earth DEMs tiled to the same 2,500-foot x 2,500-foot schema in ERDAS .IMG format, and Breaklines in Esri file geodatabase format. Ground Conditions: LiDAR was collected in late 2015, 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, Quantum Spatial established 39 calibration control points in order to calibrate the LIDAR to known ground locations established throughout the project area. The accuracy of the data was checked with 119 QC points. </span></span></p><p><span>2017: The Eastern San Diego County, California 2017 QL2 Lidar project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane California Zone VI, US survey feet and vertical datum of NAVD88 (GEOID12B), US survey feet. Lidar data was delivered as flightline-extent unclassified LAS swaths, as processed Classified LAS 1.4 files formatted to 1,710 individual 5,000-foot x 5,000-foot pilot tiles; as tiled intensity imagery, and as tiled bare earth DEMs, all tiled to the same schema. Continuous breaklines were produced in Esri file geodatabase format. Building footprints were produced in Esri file geodatabase format. Tiled contours with a 2-foot interval were produced in Esri file geodatabase format. Ground Conditions: Lidar was partially collected in fall/winter of late 2016/early 2017, 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, Quantum Spatial, Inc. established a total of 28 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 116 independent accuracy checkpoints, 64 in Bare Earth and Urban landcovers (64 NVA points), 52 in the Forest, Shrubs, and Tall Weeds category (52 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.</span></p><p><span>Special Considerations:</span></p><p><span>The data covering the Borrego Valley and surrounding area (Min X: 6499999.9998755, Min Y: 2014999.99983956, Max X: 6559999.999954, Max Y: 2084999.99993114) was developed by processing the swath LiDAR data from the 2017 Eastern San Diego County project, and merging with the 2015 Borrego Valley LiDAR data to create a new DEM. This was necessary to fill in the gap areas between the two acquisition projects. The four DEM datasets were then merged in the following order: Borrego Valley DEM --> 2017 Eastern San Diego DEM --> FEMA 2015 Central San Diego DEM --> 2014 Western San Diego DEM.</span></p><p><span>SANDAG has determined that this merged regionwide DEM is adequate for cartographic purposes. For analysis purposes, users may elect to use the original source data from each project. Refer to the metadata for each acquisition project for additional details.</span></p><p><span /></p></div></div></div>
San Diego
DEM
Elevation
LiDAR
SANDAG
SANDAG, FEMA, County of San Diego
Pat Landrum
SANDAG 401 B Street, Suite 800 San Diego, CA 92101
Manager of Data Solutions
<div style='text-align:Left;'><div><div><p><span>Data Disclaimer</span></p><p><span /></p><p><span>While the data have been tested for accuracy and are properly functioning, SANDAG disclaims any responsibility for the accuracy or correctness of the data.</span></p><p><span /></p><p><span>THE FOREGOING WARRANTY IS EXCLUSIVE AND IN LIEU OF ALL OTHER WARRANTIES OR MERCHANTABILITY, FITNESS FOR PARTICULAR PURPOSE AND/OR ANY OTHER TYPE WHETHER EXPRESSED OR IMPLIED.</span></p><p><span /></p><p><span>In no event shall SANDAG become liable to users of these data, or any other party, for any loss or damages, consequential or otherwise, including but not limited to time, money, or goodwill, arising from the use, operation or modification of the data. In using these data, users further agree to indemnify, defend, and hold harmless SANDAG for any and all liability of any nature arising out of or resulting from the lack of accuracy or correctness of the data, or the use of the data.</span></p><p><span /></p><p><span>To assist SANDAG in the maintenance of the data, users should provide SANDAG, at the address shown below, information concerning errors or discrepancies found in using the data.</span></p><p><span /></p><p><span>SANDAG</span></p><p><span>Attn: GIS Project Manager</span></p><p><span>401 "B" Street, Suite 800</span></p><p><span>San Diego, CA 92101</span></p><p><span /></p><p><span>gismaster@sandag.org</span></p><p><span /></p><p><span>In using the data, users should be aware that these data are generalized and not parcel based, and were created for use in regional planning projects.</span></p><p><span /></p><p><span>Please acknowledge SANDAG as a source when SANDAG data are used in the preparation of reports, papers, publications, maps, and other products.</span></p><p><span /></p><p><span>To ensure that appropriate documentation and data limitations are provided, these databases should not be redistributed to any other parties.</span></p></div></div></div>
File Geodatabase Raster Dataset
Pat Landrum
SANDAG 401 B Street, Suite 800 San Diego, CA 92101
Manager of Data Solutions
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Pat Landrum
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