2021 USGS QL2 Lidar: San Bernardino, CA

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, UTM Zone 11N NAD83(2011), meters coordinates and elevation values in NAVD88(GEOID18) meters.

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.

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

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

Link to the lidar report.

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

Lidar data was collected using Optech Galaxy Prime sensor. The raw data was verified in MARS software for complete coverage of the project area, and boresighted to align the flightlines. Raw data files were parsed into manageable client-specific tiles. These tiles were then processed through automated filtering that separates the data into different classification groups: unclassified points, ground points, low noise, water, bridge decks, high noise and ignore ground. The data was next taken into MARS to reclassify the erroneous points that may remain in the lidar point cloud after auto filter. Two points per square meter (QL2).

LiDAR swath data was measured to have an average of 0.558 meter nominal pulse spacing and 3.22 pulses per meter. The method used was counting first return points within each swath, then dividing by the area of the extent of the corresponding swath to get density. NPS was mathematically derived from this density number. This was performed on each swath then averaged to generate the two numbers above. This USGS method does not consider swath sidelap in these calculations.

Original point clouds in LAS/LAZ format were restructured as Entwine Point Tiles and stored on Amazon Web Services. The data were re-projected horizontally to WGS84 web mercator (EPSG 3857) and and the vertical units of meters (NAVD88 GEOID18) were retained.

The NOAA Office for Coastal Management (OCM) created references to the Entwine Point Tile (EPT) that was ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. The DAV will access the point cloud as it resides on Amazon Web Services (AWS) under the usgs-lidar-public container.

This is the AWS URL being accessed:

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/CA_SanBern_EMRI_2_2021/ept.json