2007 USFS Lidar: Biscuit Fire

Biscuit Fire project area.

Dates of 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.

Bulk download of lidar files in laz format, geographic coordinates, orthometric heights.

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 displays the footprint for this lidar data set.

Link to the Data Report.

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

Acquisition.

The full survey was conducted on September 19-23, 2007 (Julian Days 262-266). The LiDAR survey uses a Leica ALS50 Phase II laser mounted in a Cessna Caravan 208B. The sensor scan angle was ±14o from nadir1 with a pulse rate designed to yield an average native density (number of pulses emitted by the laser system) of ≥ 4 points per square meter over terrestrial surfaces. The Leica ALS50 Phase II system allows up to four range measurements (returns) per pulse, and all discernable laser returns are processed for the output dataset. It is not uncommon for some types of surfaces (e.g. dense vegetation or water) to return fewer pulses than the laser originally emitted. These discrepancies between ‘native’ and ‘delivered’ density will vary depending on terrain, land cover and the prevalence of water bodies. To accurately solve for laser point position (geographic coordinates x, y, z), the positional coordinates of the airborne sensor and the attitude of the aircraft are recorded continuously throughout the LiDAR data collection mission. Aircraft position is measured twice per second (2 Hz) by an onboard differential GPS unit. Aircraft attitude is measured 200 times per second (200 Hz) as pitch, roll and yaw (heading) from an onboard inertial measurement unit (IMU). To allow for post-processing correction and calibration, aircraft/sensor position and attitude data are indexed by GPS time.

Ground Survey

Simultaneous with the airborne data collection mission, we conduct a static (1 Hz recording frequency) survey of the horizontal and vertical positions of one or more survey control base stations established over monuments with known coordinates.

Indexed by time, these GPS data are used to correct the continuous onboard measurements of aircraft position recorded throughout the mission. Multiple sessions are processed over the same monument to confirm antenna height measurements and reported position accuracy. After the airborne survey, these static GPS data are processed using triangulation with Continuously Operating Reference Stations (CORS) stations, and checked against the Online Positioning User Service (OPUS2) to quantify daily variance. Controls are located within 13 nautical miles of the mission area. Ground truth points are collected using a GPS based real-time kinematic (RTK) survey. For an RTK survey, the ground crew uses a roving unit to receive radio-relayed corrected positional coordinates for all ground points from a GPS base unit set up over a survey control monument. The roving unit records precise location measurements with an error (σ) of ≤ 1.5 cm (0.6 in). 673 RTK ground points were collected in the Biscuit Fire Study Area.

Laser Point Processing

Laser point coordinates are computed using the IPAS and ALS Post Processor software suites based on independent data from the LiDAR system (pulse time, scan angle), and aircraft trajectory data (SBET). Laser point returns (first through fourth) are assigned x, y, z coordinates along with unique intensity values (0-255). The data are output into large LAS v. 1.1 files; each point maintains the corresponding scan angle, return number (echo), intensity, and x, y, z (easting, northing, and elevation) information. These initial laser point files are too large for subsequent processing. To facilitate laser point processing, bins (polygons) are created to divide the dataset into manageable sizes (< 500 MB). Flightlines and LiDAR data are then reviewed to ensure complete coverage of the study area and positional accuracy of the laser points. Laser point data are imported into processing bins in TerraScan, and manually calibrated to assess the system offsets for pitch, roll, heading and scale (mirror flex). Using a geometric relationship developed by Watershed Sciences, each of these offsets is resolved and corrected if necessary.

LiDAR points are then filtered for noise, artificial low points (‘pits’) or non-terrestrial high points (e.g., birds, clouds, vapor, haze) by screening for absolute elevation limits, isolated points and height above ground. Each bin is then inspected for remaining spurious points which are then manually removed. In a bin containing approximately 7.5-9.0 million points, an average of 50-100 points are typically found to be artificially low or high. Where there is dense vegetation and/or at breaks in terrain, steep slopes and at bin boundaries, the delivered density can be significantly less than the native density. In areas where it is determined that the ground surface model has failed, supervised classifications are performed by ‘reseeding’ the ground surface model with ground points. Internal calibration is refined using TerraMatch. Points from overlapping lines are tested for internal consistency and final adjustments are made for system misalignments (i.e., pitch, roll, heading offsets and scale). Automated sensor attitude and scale corrections yield 3-5 cm improvements in the relative accuracy. Once system misalignments are corrected, vertical GPS drift is then resolved and removed per flight line, yielding a slight improvement (<1 cm) in relative accuracy.

The TerraScan software suite is designed specifically for classifying near-ground points (Soininen, 2004). The processing sequence begins by ‘removing’ all points that are not ‘near’ the earth based on geometric constraints used to evaluate multi-return points. The resulting bare earth (ground) model is visually inspected and additional ground point modeling is performed in site-specific areas to improve ground detail. This manual editing of ground occurs in areas with known ground modeling deficiencies, such as bedrock outcrops, cliffs, deeply incised stream banks, and dense vegetation. In some cases, automated ground point classification includes known vegetation (i.e., understory, low/dense shrubs, etc.). These points are manually reclassified as non-ground. Where it is determined that the ground model has failed (usually under dense vegetation and/or at breaks in terrain, steep slopes and at bin boundaries), supervised classifications are performed by ‘reseeding’ the ground model. Ground surface rasters are developed from triangulated irregular networks (TINs) of ground points.

The NOAA Office for Coastal Management (OCM) received 178 lidar point cloud files in laz format from DOGAMI. The files contained lidar elevation and intensity measurements. The data were in UTM Zone 10N, NAD83, meters, coordinates and NAVD88 (Geoid03) elevations in meters. The data were classified as: 1-Unclassified, 2-Ground. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2.

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 Geoid03 model, to convert from UTM Zone 10N, NAD83 coordinates in meters, to geographic coordinates, to assign the geokeys, to sort the data by gps time, and zip the data to database and to http.