2018 - 2019 USGS QL1 Lidar: Northern California Wildfires

Create custom data files by choosing data area, product type, map projection, file format, datum, etc. A new metadata will be produced to reflect your request using this record as a base. Change to an orthometric vertical datum is one of the many options.

Bulk download of data files in LAZ format, NAD 1983 2011 Contiguous USA Albers, meters projection and orthometric heights in meters.

Link to the Quantum Spatial lidar report.

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

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.

This data source was used (along with airborne GPS/IMU data) to georeference the LiDAR point cloud data.

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 Quantum Spatial, Inc. 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 TerraScan macro functionality. A buffer of 1 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. All overlap data was processed through automated functionality provided by TerraScan to classify the overlapping flight line data to approved classes. The overlap data was classified using standard LAS overlap bit. These classes were created through automated processes only and were not verified for classification accuracy. Due to software limitations within TerraScan, these classes were used to trip the withheld bit within various software packages. These processes were reviewed and accepted through numerous conference calls and pilot study areas. 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 All Point Cloud Data and the Bare Earth. Quantum Spatial, Inc. 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.

The NOAA Office for Coastal Management (OCM) downloaded this data set in 4 blocks from this USGS site:

https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/

The blocks downloaded were:

CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCal_Wildfires_B5b_QL1_2018/LAZ Number of files: 1799

CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCal_Wildfires_CampFire_2018/LAZ Number of files: 768

CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCAL_Wildfires_PlumasNF_B1_2018/LAZ Number of files: 4298

CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCAL_Wildfires_PlumasNF_B2_2018/LAZ Number of files: 5081

These files were processed to the Data Access Viewer (DAV) and https. The total number of files downloaded and processed was 11,946.

The data were in Albers Equal Area (NAD83 2011), meters coordinates and NAVD88 (Geoid12B) elevations in meters. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 9 - Water, 17 - Bridge Decks, 18 - High Noise, 20 - Ignored Ground, 21 - Snow, 22 - Temporal Exclusion. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 3, 7, 9, 17, 18, 20, 21, 22.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. An internal OCM script was run to check the number of points by classification and by flight ID and the gps and intensity ranges.

2. Internal OCM scripts were run on the laz files to convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid 12B model, to convert from Albers Equal Area (NAD83 2011) coordinates in meters to geographic coordinates, to assign the geokeys, to sort the data by gps time and zip the data to database.

The NOAA Office for Coastal Management (OCM) downloaded this data set from this USGS site:

https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/CA_NoCAL_3DEP_Supp_Funding_2018_D18/CA_NoCAL_Wildfires_TL_QL1_2018/LAZ/

This block of data was added to the USGS rockyweb site after NOAA OCM added the blocks in Process Step 3 to the Data Access Viewer (DAV). This block consisted of 14 laz files.

The data were in Albers Equal Area (NAD83 2011), meters coordinates and NAVD88 (Geoid12B) elevations in meters. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 17 - Bridge Decks, 18 - High Noise. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 3, 7, 17, 18.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. An internal OCM script was run to check the number of points by classification and by flight ID and the gps and intensity ranges.

2. Internal OCM scripts were run on the laz files to convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid 12B model, to convert from Albers Equal Area (NAD83 2011) coordinates in meters to geographic coordinates, to assign the geokeys, to sort the data by gps time and zip the data to database.