Data Management Plan (Deprecated)

This data set consists of Classified LiDAR Point Cloud, Classified LiDAR, Digital Elevation Model, Digital Surface Model, and LiDAR Intensity Images, and extends to a 100 meter buffer around BAA AL 3 County - QL2 Lidar boundary, approximately 2910 square miles. The dataset consists of LiDAR point cloud LAS swath files and tiled Las files. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. Each file represents a separate swath of LiDAR. The three counties included in this dataset are: Cullman, Tuscaloosa, and Walker.

Ground Conditions: water at normal levels; no unusual inundation; no snow; leaf off

How the Withheld Points are Identified: Withheld (ignore) points were identified in the files using the standard LAS Withheld bit.

Class Code:1

Class Item:Undetermined/Unclassified

Class Code:2

Class Item:Bare Earth

Class Code:3

Class Item:Low Vegetation

Class Code:4

Class Item:Medium Vegetation

Class Code:7

Class Item:Low Noise

Class Code:9

Class Item:Water

Class Code:10

Class Item:Ignored Ground

Class Code:17

Class Item:Bridges

Class Code:18

Class Item:High Noise

Class Code:24

Class Item:Reserved for ASPRS Definition

This metadata supports the data entry in the NOAA Digital Coast Data Access Viewer (DAV). For this data set, the DAV is leveraging the Entwine Point Tiles (EPT) hosted by USGS on Amazon Web Services.

Lineage Statement:
The NOAA Office for Coastal Management (OCM) ingested references to the USGS Entwine Point Tiles (EPT) hosted on Amazon Web Services (AWS) into the Digital Coast Data Access Viewer (DAV). The DAV accesses the point cloud as it resides on AWS under the usgs-lidar-public-container.

Process Steps:

• 2016-01-18 00:00:00 - Aerial LiDAR Acquisition: Aerial data collection was acquired, in Fourteen (14) missions, using the Leica ALS80 SN# 8235 at an altitude of 6,100 feet MSL. This was to support a 4.0 ppm^2 LiDAR point cloud. Airborne GPS and IMU data was collected during the acquisition and supported by Leica GS15 & GPS 1200 base stations that occupied temporary monuments in conjunction with CORS stations, Data acquisition started January 19, 2016 and was complete Febuary 16, 2016. Ground Control Survey: A survey was performed to support the acquisition of Light Detection and Ranging (LiDAR). The control network involved a total of 173 check points (107 NVA + 66 VVA). The points were a combination of the following ground cover classification: Open Terrain, Urban Terrain, Bare Earth, High Grass and Low Trees. All field survey observations were conducted between January 20, 2016 and January 26, 2016 with Leica GPS 1200 equipment.
• 2016-02-22 00:00:00 - LiDAR Pre-processing: Airborne GPS and IMU data were merged to develop a Smooth Best Estimate of Trajectory (SBET) of the LiDAR system for each mission. 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. Raw data NVA were checked using surveyed check points.
• 2016-02-29 00:00:00 - RAW Data Processing: The Initial processing of the GPS data was processed using Inertial Explorer producing a solution file for each mission. Leica CloudPro software was then used to generate georeferenced laser returns into LAS 1.2 file format for each flight-line per each mission, this went through an initial Quality Control of the overlap between the flight-line swaths.
• 2016-02-21 00:00:00 - 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.
• 2016-01-08 00:00:00 - LiDAR Classification: The calibrated LiDAR data went through automated classification routines and then manually edited and checked. The LiDAR point cloud data was classified into the following classes: 1-unclassified*, 2-ground, 7-low noise, 9-water, 10-buffer, 17-bridges, and 18-high noise
• 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 GEOID12A; EPSG 5703).
• 2024-06-11 00:00:00 - 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. This is the EPT file being accessed: https://s3-us-west-2.amazonaws.com/usgs-lidar-public/USGS_LPC_AL_3County_2015_LAS_2017/ept.json

Data is available online for bulk and custom downloads.

Data is backed up to cloud storage.