LiDAR data (August 2008) for the Andrews Experimental Forest and Willamette National Forest study areas

AUTHOR: Theresa J. Valentine
ORIGINATOR: Thomas A. Spies
METADATA CONTACT: Theresa J. Valentine
ABSTRACTOR: Theresa J. Valentine
Data were provided by the HJ Andrews Experimental Forest research program, funded by the National Science Foundation’s Long-Term Ecological Research Program (DEB 08-23380), US Forest Service Pacific Northwest Research Station, and Oregon State University. LiDAR data was flown and processed by: Watershed Sciences 257B SW Madison Street Corvallis, OR 97333 215 SE 9th Ave, Suite 106 Portland, Oregon 97214
13 Dec 2012
1 Nov 2013
Spatial data, Disturbance, Organic matter, stand structure, geology, geomorphology, spatial properties, geographic information systems, disturbance, landscape change, organic matter
The data was collected to provide a highly accurate and comprehensive base layer of elevation data and vegetation cover for the Andrews Experimental Forest, and several timber units in the adjacent area of the Willamette National Forest.The purpose of the data is to provide users with a very accurate view of the topography and vegetation of the study area. The data are suitable for creating visualizations, deriving watershed boundaries, creating stream networks, identifying structures such as roads and water features, vegetation modeling and calculating biomass, and for identifying landslides and geological features.
Field Methods - GI010:

2.1 Airborne Survey – Instrumentation and Methods

The LiDAR survey uses a Leica ALS50 Phase II laser system. For the HJ Andrews and Willamette NF study areas, 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 (Nadir refers to the perpendicular vector to the ground directly below the aircraft. Nadir is commonly used to measure the angle from the vector and is referred to a “degrees from nadir”.) 8 points per square meter over terrestrial surfaces. All study areas were surveyed with an opposing flight line side-lap of =50% (=100% overlap) to reduce laser shadowing and increase surface laser painting. The Leica ALS50 Phase II system allows up to four range measurements (returns) per pulse, and all discernable laser returns were 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 were recorded continuously throughout the LiDAR data collection mission. Aircraft position was measured twice per second (2 Hz) by an onboard differential GPS unit. Aircraft attitude was 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.

Citation: HJ_andrews_report.pdf
Field Methods - GI010 (1):

2.2.1 Survey Control

Simultaneous with the airborne data collection mission, we conducted multiple static (1 Hz recording frequency) ground surveys over monuments with known coordinates (Table 1). Indexed by time, these GPS data are used to correct the continuous onboard measurements of aircraft position recorded throughout the mission. Multiple sessions were processed over the same monument to confirm antenna height measurements and reported position accuracy.

After the airborne survey, these static GPS data were then processed using triangulation with Continuously Operating Reference Stations (CORS) stations, and checked against the Online Positioning User Service (OPUS2) to quantify daily variance. Controls were located within 13 nautical miles of the mission area(s).

Citation: HJ_andrews_report.pdf
Processing Procedures - GI010:

Applications and Work Flow Overview

  1. Resolve kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data.
  2. Develop a smoothed best estimate of trajectory (SBET) file that blends post-processed aircraft position with attitude data (sensor heading, position, and attitude are calculated throughout the survey).
  3. Calculate laser point position by associating SBET position to each laser point return time, scan angle, intensity, etc. Create raw laser point cloud data for the entire survey in *.las (ASPRS v1.1) format.
  4. Import raw laser points into subset bins (less than 500 MB, to accommodate file size constraints in processing software). Perform manual relative accuracy calibration and filter for pits/birds. Classify ground points for individual flight lines (to be used for relative accuracy testing and calibration).
  5. Test relative accuracy using ground classified points per each flight line. Perform automated line-to-line calibrations for system attitude parameters (pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Perform calibrations on ground classified points from paired flight lines. Every flight line is used for relative accuracy calibration.
  6. Import position and attitude data. Classify ground and non-ground points. Assess statistical absolute accuracy via direct comparisons of ground classified points to ground RTK survey data. Convert data to orthometric elevations (NAVD88) by applying a Geoid03 correction. Create ground model as a triangulated surface and export as ArcInfo ASCII grids at the specified pixel resolution.
Instrumentation: Software: Waypoint GPS v.7.60, IPAS v.1.4, ALS Post Processing Software, TerraScan v.7.012, TerraMatch v.7.004, TerraScan v.7.012, ArcMap v9.2
Citation: HJ_andrews_report.pdf
Processing Procedures - GI010 (1):
Description: Aircraft Kinematic GPS and IMU Data LiDAR survey datasets were referenced to the 1 Hz static ground GPS data collected over presurveyed monuments with known coordinates. While surveying, the aircraft collected 2 Hz kinematic GPS data, and the onboard inertial measurement unit (IMU) collected 200 Hz aircraft attitude data. Realm Survey Suite was used to process the kinematic corrections for the aircraft. The static and kinematic GPS data were then post-processed after the survey to obtain an accurate GPS solution and aircraft positions. POSPAC was used to develop a trajectory file that includes corrected aircraft position and attitude information. The trajectory data for the entire flight survey session were incorporated into a final smoothed best estimated trajectory (SBET) file that contains accurate and continuous aircraft positions and attitudes.
Citation: HJ_andrews_report.pdf
Processing Procedures - GI010 (2):
Description: Laser Point Processing Laser point coordinates were computed using the REALM software based on independent data from the LiDAR system (pulse time, scan angle), and aircraft trajectory data (SBET). Laser point returns (first through fourth) were assigned an associated (x, y, z) coordinate along with unique intensity values (0-255). The data were 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. Laser point data were imported into processing bins in TerraScan, and manual calibration was performed 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 was resolved and corrected if necessary. LiDAR points were then filtered for noise, pits (artificial low points) and birds (true birds as well as erroneously high points) by screening for absolute elevation limits, isolated points and height above ground. Each bin was then manually inspected for remaining pits and birds and spurious points were 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. Common sources of non-terrestrial returns are clouds, birds, vapor, haze, decks, brush piles, etc. Internal calibration was refined using TerraMatch. Points from overlapping lines were tested for internal consistency and final adjustments were made for system misalignments (i.e., pitch, roll, heading offsets and scale). Automated sensor attitude and scale corrections yielded 3-5 cm improvements in the relative accuracy. Once system misalignments were corrected, vertical GPS drift was then resolved and removed per flight line, yielding a slight improvement (less than 1 cm) in relative accuracy.
Citation: HJ_andrews_report.pdf
Andrews Experimental Forest and adjacent Blue River Watershed units within the Willamette National Forest, western Cascades, Oregon, USA.
As needed and when funding is available
Ground condition