2021 CA DWR Lidar DEM: San Joaquin Valley, CA

CA_SanJoaquin_1

Dates of collection for CA_SanJoaquin_1

CA_SanJoaquin_2

Dates of collection for CA_SanJoaquin_2

CA_SanJoaquin_3

Dates of collection for CA_SanJoaquin_3

CA_SanJoaquin_4

Dates of collection for CA_SanJoaquin_4

CA_SanJoaquin_5

Dates of collection for CA_SanJoaquin_5

CA_SanJoaquin_6

Dates of collection for CA_SanJoaquin_6

CA_SanJoaquin_7

Dates of collection for CA_SanJoaquin_7

CA_SanJoaquin_8_Reflight (tile s64825w18700)

Date of collection for CA_SanJoaquin_8_Reflight (tile s64825w18700)

CA_SanJoaquin_9_Reflight (tiles s65150w20125 and s65225w20500)

Dates of collection for CA_SanJoaquin_9_Reflight (tiles s65150w20125 and s65225w20500)

Create custom data files by choosing data area, map projection, file format, etc. A new metadata will be produced to reflect your request using this record as a base.

Bulk download of data files in GeoTIFF format, CA State Plane Zones 3, 4 and 5 NAD83 (2011), US survey feet coordinates and orthometric heights in feet.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Link to the reports, breaklines, metadata, and spatial metadata.

Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer.

Raw Data and Boresight Processing: The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used NV5 Geospatial proprietary and commercial software to calculate initial boresight adjustment angles based on sample areas selected in the lift. These areas cover calibration flight lines collected in the lift, cross tie, and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technicians then analyzed the results and made any necessary additional adjustment until it was acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps matched for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was less than or equal to 7 cm RMSEz within individual swaths and less than or equal to 10 cm RMSEz or within swath overlap (between adjacent swaths). 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed checkpoints after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met.

LAS Point Classification: The point classification was performed as described below. The bare earth surface was manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface was finalized, it was then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) LiDAR data inside of the Lake Pond and Double Line Drain hydro-flattened breaklines were then classified to Water (ASPRS Class 9) using proprietary tools. A buffer of 0.5 meter was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) points were reclassified to the correct classification after the automated classification was completed. Any noise that was identified either through manual review or automated routines was classified to the appropriate class ( ASPRS Class 7 and/or ASPRS Class 18) followed by flagging with the withheld bit. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. Global Mapper was used as a final check of the bare earth dataset. GeoCue was then used to create the deliverable industry-standard LAS files for both the Point Cloud Data and the Bare Earth. NV5 Geospatial proprietary software was used to perform final statistical analysis of the classes in the LAS files, on a per tile level to verify final classification metrics and full LAS header information.

CA_SanJoaquin_8_Reflight (Work Unit 300177, Tile s64825w18700))

Work unit 300177 was created specifically to deal with a data void that was discovered during data processing. The data void impacted a total of 1 tile. The void was filled using an auto-correlated point cloud. NV5G utilized autocorrelated point cloud data. This point cloud data was generated from the from San Joaquin 2021 Imagery acquired by Towill, Inc. using INPHO Match-T DSM version 13 software. The resulting point cloud measured roughly 26 pts/sqm. NV5G utilized proprietary software to merge the auto-correlated point cloud with the lidar point cloud and create as seamless a dataset as possible. All points used from the auto-correlated point cloud have had the synthetic bit applied for easy identification within the LAS file.

CA_SanJoaquin_9_Reflight (Work Unit 300242, Tiles s65150w20125 and s65225w20500)

Work unit 300242 was created specifically to deal with 2 data voids that were discovered during data processing. These data voids impacted a total of 2 tiles. The voids were filled using an auto-correlated point cloud. NV5G utilized autocorrelated point cloud data. This point cloud data was generated from the from San Joaquin 2021 Imagery acquired by Towill, Inc. using INPHO Match-T DSM version 13 software. The resulting point cloud measured roughly 26 pts/sqm. NV5G utilized proprietary software to merge the auto-correlated point cloud with the lidar point cloud and create as seamless a dataset as possible. All points used from the auto-correlated point cloud have had the synthetic bit applied for easy identification within the LAS file.

Hydro-Flattened Raster DEM Processing: Class 2 (Ground) LiDAR points in conjunction with the hydro-breaklines were used to create a 1 foot hydro-flattened raster DEM. Using automated scripting routines in proprietary software, GeoTIFF files were created for each tile. Each surface was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface.

The NOAA Office for Coastal Management (OCM) created references to the USGS GeoTIFF files that were ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. The DAV will access the raster data as it resides on Amazon Web Services (AWS).

These are the GeoTIFF files that are being accessed:

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_1_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_2_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_3_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_4_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_5_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_6_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_7_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_8_Reflight_2021/TIFF/

https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/OPR/Projects/CA_SanJoaquin_2021_A21/CA_SanJoaquin_9_Reflight_2021/TIFF/