2023 Maryland Lidar: Charles County, MD

Date of data collection

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, geographic coordinates, orthometric heights. Note that the vertical datum (hence elevations) of the files here are different than described in this document. They will be in an orthometric datum.

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.

This graphic shows the lidar coverage for the 2023 Charles County, MD lidar project.

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.

Link to custom download, from the Data Access Viewer (DAV), the raster Digital Elevation Model (DEM) data that were created from these lidar point data.

The boresight was completed prior to project execution. The following steps describe the Raw Data Processing process:

1) Technicians processed the raw data to LAS format flight lines using the final GNSS/IMU solution. This LAS data set was used as source data for lidar matching.

2) Technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps match for the entire lift and adjusted as necessary until the results met the project specifications.

3) Once all lifts were completed with lidar matching, 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 6 cm RMSEz within individual swaths and less than or equal to 8 cm RMSEz or within swath overlap (between adjacent swaths).

4) The technicians ran a final vertical accuracy check of the flight lines against the surveyed check points after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met. Point classification was performed according to USGS Lidar Base Specification v2.1, and breaklines were collected for water features. Bare-earth DEMs were exported from the classified point cloud using collected breaklines for hydroflattening.

LAS Point Classification: The point classification is performed as described below. The bare-earth surface is then manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface is finalized, it is then used to generate all hydro-breaklines through heads-up digitization. All Class 2 (Ground) lidar data inside of the Lake Pond and Double Line Drain hydro-flattened breaklines were then classified to Class 9 (Water) using LP360 functionality.

A buffer of 2 ft was also used around each hydro-flattened feature to classify these Class 2 (Ground) points to Class 20 (Ignored Ground). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the Class 2 (Ground) points were reclassified to the correct classification after the automated classification was completed.

All data was manually reviewed and any remaining artifacts removed using functionality provided by LP360, 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. Sanborn Map Company, 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.

Data was tested at 0.5 meter aggregate nominal pulse spacing and at 5.3 aggregate points per meter. The aggregate nominal pulse spacing was tested on classified tiled LAS using geometrically reliable first-return points without overlap. ANPS was tested using Delaunay Triangulation that produced average point spacing between all nearest neighbors.

The NOAA Office for Coastal Management (OCM) downloaded laz point data files from the Maryland Department of Information Technology (DoIT).

The data were in Maryland State Plane (NAD83 2011), US feet coordinates and NAVD88 (Geoid18) elevations in feet. The data were classified as: 1 - Unclassified, 2 - Ground, 7 - Low Noise, 9 - Water, 17 - Bridge Decks, 18 - High Noise, 20 - Ignored Ground. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV), the classes available on the DAV are: 1, 2, 9, 17, 20.

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

1. Internal OCM scripts were run to check the number of points by classification and by flight ID and the gps, elevation, and intensity ranges.

2. Internal OCM scripts were run on the laz files to:

a. Convert from orthometric (NAVD88) elevations to NAD83 (2011) ellipsoid elevations using the Geoid18 model

b. Convert the laz files from Maryland State Plane (NAD83 2011), US feet coordinates to geographic coordinates

c. Convert the laz files from elevations in feet to elevations in meters.

d. Remove Class 7 and 18 points because the withheld bit flag was set.

e. Assign the geokeys, sort the data by gps time and zip the data to database and to S3 AWS.