2016 ADECA Lidar: 6 County (Bibb, Chilton, Coosa, Dallas, Hale, Perry), AL

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 16N NAD83 (2011), meters coordinates and NAVD88 (Geoid12B) elevations in 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 additional information available for this data set from the USGS. This information includes reports, tile index shapefiles, and hydro breaklines.

Link to the lidar report.

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.

Aerial Lidar Acquisition: Aerial data collection was acquired in thirty-eight (38) flight missions, using the ALS70 SN#7123/7225 at an altitude of 2300 meters AGL. During flight operations, the flight crew monitored weather and atmospheric conditions. Lidar missions were flown only when no condition existed below the sensor that would affect the collection of data. The pilot constantly monitored the aircraft course, position, pitch, roll, and yaw of the aircraft. The sensor operator monitored the sensor, the status of the GNSS constellations, and performed the first QC review during acquisition. The flight crew constantly reviewed weather and cloud locations. Data acquisition was completed on March 15, 2017.

Ground Control Survey: This GNSS survey consisted of twelve (12) contiguous Alabama counties which due to geographic proximity were combined into one Area of Interest (AOI) the five hundred twenty-four (524) checkpoints were subdivided into 3 main categories: NVA, VVA, and LCP. A survey crew mobilized to the AOI to perform the RTN –RTK survey to support the aerial LiDAR collection October 20th through November 19th 2016. The RTN –RTK survey consisted of independent GNSS observations on Non-Vegetated Vertical Accuracy, Vegetated Vertical Accuracy (NVA-VVA), and LiDAR Calibration Points (LCP).

Lidar Pre-processing: Airborne GPS and IMU data were merged to develop a Smoothed Best Estimate Trajectory (SBET) of the lidar system for each lift. Lidar ranging data were initially calibrated using previous best parameters for this instrument and aircraft. Relative calibration was evaluated using advanced plane-matching analysis and parameter corrections derived. This process was repeated interactively until residual errors between overlapping swaths, across all project lifts, was reduced to 2 cm or less. Data were then block adjusted to match surveyed calibration control. Raw data NVA were checked using independently surveyed check points. Swath overage points were identified and tagged within each swath file

Initial processing of the GPS data was processed using Inertial Explorer. The solution file was generated and CloudPro software was used to generate georeferenced laser returns which were then processed in strip form allowing for the QC of the overlap between strips (lines). The data from each line were combined and automated classification routines run to determine the initial surface model. This initial surface model was then verified to the surveyed test points.

Lidar Post-Processing: The calibrated and controlled lidar swaths were processed using automatic point classification routines in TerraSolid software. These routines operate against the entire collection (all swaths, all lifts), eliminating character differences between files. Data were then distributed as virtual tiles to experienced Lidar analysts for localized automatic classification, manual editing, and peer-based QC checks. Supervisory QC monitoring of work in progress and completed editing ensured consistency of classification character and adherence to project requirements across the entire project. All classification tags are stored in the original swath files. After completion of classification and final QC approval, the NVA and VVA for the project are calculated. Sample areas for each land cover type present in the project were extracted and forwarded to the client, along with the results of the accuracy tests. Upon acceptance, the complete classified Lidar swath files were delivered to the client.

Lidar Classification: The calibrated Lidar data was run through automated classification routines and then manually checked and edited. The data was classified into the following classes: 1-unclassified*, 2-ground, 7-low noise, 9-water, 10-ignored ground, 17-bridges, and 18-high noise

Lidar Intensity Imagery Creation: All lidar intensity imagery was created from the final calibrated and classified lidar point cloud. Intensity images were produced from all classified points and are posted to a 1 meter cell size. Intensity images were cut to match the tile index and have corresponding names to match tile names.

Hydro line Collection: Hydro break lines were compiled using the lidar intensity data and surface terrain model of the entire project area. After the collection of hydro lines all features were validated for monotonicity and vertical variance, to ensure that no points were floating above ground. The hydro break lines were encoded into the lidar surface and used to hydro-enforce/flatten all significant water bodies. These final hydro lines were then used in the production of bare Earth digital models to hydro flatten significant water bodies.

Bare-Earth DEM Creation: Bare-Earth digital elevation models (DEMs) were derived using the hydro break line and bare-earth (ground) lidar points, all DEMs were created with a grid spacing of 1 meter. The DEMs were cut to tiles of 1500 meters X 1500 meters index.

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). Vertically, no changes were made to the vertical datum (NAVD88 GEOID12B; EPSG 5703).

References to the entwine point tiles and data reports were ingested into the Digital Coast Data Access Viewer. No changes to the data were made at this point. The Data Access Viewer will access the point cloud as it resides on AWS under the usgs-lidar-public container.