Airborne and Terrestrial Laser Scanning Activities at UNAVCO: From GeoEarthScope to INTERFACE and Beyond
UNAVCO leads and supports airborne and terrestrial laser scanning (ALS and TLS) activities in support of a
wide range of earth science applications. UNAVCO acquired nearly 6,000 km2 of high resolution ALS data as
part of GeoEarthScope, a component of the EarthScope Facility construction project funded by the National
Science Foundation. GeoEarthScope ALS targets in most cases were 1- to 2-km wide corridors centered along
active faults including the San Andreas, Hayward, Calaveras, Maacama, Green Valley, Little Salmon, Elsinore,
San Cayetano, Garlock, Calico, Lenwood, Blackwater, Helendale, Panamint Valley, Ash Hill, Owens Valley,
Death Valley-Fish Lake Valley, Wasatch, Teton, Denali and Totschunda faults. Acquisitions were planned and
conducted based on community recommendations with respect to target identification and data collection
practices. Particular care was taken to ensure the highest data quality possible within scope and budget, with
special considerations given to effective ground point density and geodetic control. Data products are freely
available from http://opentopography.org. TLS projects include numerous investigations in polar regions, such
as the first TLS survey of the lava lake at Mount Erebus, Antarctica, in January 2009, and activities related to
INTERFACE (INTERdisciplinary alliance for digital Field data ACquisition and Exploration), a Collaborative
project currently funded by NSF and managed at UNAVCO which includes specialized TLS data processing
and visualization software tools developed specifically for geoscience applications. We will present an
overview of ALS and TLS project highlights; resources for data collection, accessibility and analysis; and
potential use of these data for scientific research and as a framework for future endeavors.
Terrestrial and Airborne LIDAR: Comparison of Coincident Datasets for Measuring Ground Deformation and Topographic Change
We present the results from a controlled study on the use of pulse-based terrestrial lidar and phase-based airborne lidar to detect topographic change and ground deformation in areas of earthquake- and storm- induced landslides. Terrestrial and airborne lidar scans were performed at three sites in Los Angeles County and their accuracy was gauged using coincident total station survey measurements as the control. The study was supported by the Multidisciplinary Center for Earthquake Engineering Research (MCEER), the National Science Foundation (NSF), and the Los Angeles Department of Water and Power (LADWP). Horizontal accuracy was evaluated through the measurement of Northing and Easting residuals, standardized to WGS84. Assessment of accuracy was made on lengths and heights of well-defined objects in the lidar scans, such as LADWP buildings and water tanks. The bias and dispersion of lidar height measurements, standardized to NGVD88, were assessed at the Mulholland Tank adjacent to Hollywood Reservoir, the Owens Aqueduct Penstock at Power Plant 2 (PP2) in San Francisquito Canyon, and a flat un-vegetated site near the Los Angeles Reservoir before and after carefully measured trenching. At the vegetated slopes near PP2 and the Hollywood Reservoir site, airborne lidar showed minimal elevation bias and a standard deviation of approximately 50 cm, whereas terrestrial lidar demonstrated large bias and dispersion (on order of meters) due to the inability of ground-based lidar to penetrate heavy vegetation. Both systems were able to assess heights and lengths on unobstructed man made structures at the sub-decimeter scale. At the trench site, airborne lidar showed decimeter scale bias of -23.6 cm for flat ground to -8.7 cm for trenched ground, and dispersion of 5.6 for flat ground to 20 cm for trenched ground. Terrestrial lidar was nearly unbiased (~0 cm for flat or trenched ground) and with very low dispersion of 4.1 and 6.5 cm for flat and trenched ground, respectively. Pre- and post-trench bias-adjusted normalized residuals are essentially randomly scattered, but elevation change was affected by relative bias within epochs. With careful calibration, both terrestrial and airborne lidar are capable of measuring centimeter-to decimeter level ground displacements, respectively, for large features in areas of minimal vegetation.
To the Application of LiDAR to Detect the Geological Structures in Sulphurets Property, British Columbia, Canada
The Kerr Sulphurets property in North Western British Columbia has been explored primarily as a placer gold holding since the 1880s; and, potentially includes one of Canada's largest gold deposits (e.g. the Mitchell Zone). The Sulphurets camp has been classified by Taylor in 2007 as a prominent global epithermal high-sulphidation subtype with 10 million tonnes of ore (reserves + production) containing approximately 10 g/t gold. The geological and geophysical observations of this deposit indicate intrusion- related mineralized veins which are known to overlap as the result of structural complexities. Faulting predates mineralization and alteration and dramatically dominates the location of the mineralization for this porphyry- epithermal high-sulphidation deposit (Britton and Alldrick 1988, British Columbia Ministry of Energy, Mines and Petroleum Resources, 1992; Margolis, 1993). However, the surface trace of these structures and lineaments within the site is obscured by vegetation, glacial cover and steep topographic relief. We used high resolution LiDAR airborne bare-earth sensing (vegetative data deleted) in an effort to detect the surface geological features and lineaments in the Kerr Sulphurets site. The LiDAR flight was designed to acquire high density data with 2 points per square meter using a 150 kHz multipulse system. High resolution LiDAR data provides a level of detail not achievable by other digital terrain modelling techniques, whether extracted from aerial photography, low-resolution topographic contour maps, 10-30 meter USGS, or SRTM digital elevation models. LiDAR bare-earth data spectacularly revealed hidden geological structures within the property district, which in turn assisted in identifying the high potential zones for mineralization in Sulphurets.
An Algorithm for Rapid Determination of Near-Fault Earthquake Deformation using LIDAR
The recently-completed airborne lidar swath mapping (ALSM) survey of the southern San Andreas, San Jacinto and Banning faults (the "B4 survey") has delivered a high-resolution digital elevation model of 1100 km of the most seismically active fault systems in southern California for the express purpose of providing a baseline for post-earthquake slip determination. We used the B4 survey as a testbed to develop a processing algorithm that rapidly estimates near-fault coseismic ground deformation using simultaneous cross correlation of topography and backscatter intensity from pre/post-earthquake LIDAR datasets. We show robust recovery of the direction and magnitude of an applied synthetic slip of 5 m in the horizontal and 0.5 m in the vertical, with excellent discrimination between areas with and without applied slip. Our results indicate that we should be able to accurately recover horizontal slip ≥0.5 m and vertical slip ≥3 cm. We also used this algorithm to investigate misfit between overlapping B4 survey swaths in both the horizontal and vertical; our previous work in this area focused only on the vertical component of error. Significant calibration errors in geolocation resulted in across-swath elevation errors throughout the B4 survey of equal or greater magnitude than the expected vertical GPS error. This suggests that further research in calibration methods would have at least as much impact on survey accuracy as improving GPS trajectories and could greatly improve the recovery of the small vertical coseismic displacements expected in a strike-slip regime such as the San Andreas.
A Novel Approach to Find Anomalies in Lidar Data
A technique to automatically detect anomalies in data from mobile terrestrial LIDAR systems is proposed. Terrapoint's TITAN system uses multiple LIDAR scanners mounted on a vehicle, which scan the same object at different times as the system moves past. By automatically finding anomalies in these scans a human operator can be alerted that editing of the data may be required. Anomalies include drift that occurs when positional accuracy of the system is degraded, mis-matches in data that occur when objects (such as other vehicles or trees blown by wind) have moved between consecutive scans, and sudden jumps in position that occur when the positional accuracy of the system is restored. This contribution describes a methodology for detecting these anomalies. First, the scanned data is tessellated into segments based on the vehicle's path. Then, an Iterative Closest Point (ICP) algorithm is applied to pairs of overlapping segments that were scanned at different times. Tracking the ICP results over time should reveal anomalies as they happen. The position estimates of the vehicle can also be used in combination with the ICP results to identify what kind of error has occurred. The methodology will be tested with ground-truth data, which are produced by applying a known drift to data that have no positional errors. The system should be able to detect the slow drift in position and eventual jump when the positional data return to normal. As well, the system will be tested with scans that include moving objects in natural and urban scenes, to see if they are indeed detected.
Optical Navigation System for Mobile Terrestrial LiDAR System
TITAN is a mobile terrestrial LiDAR system operated by Terrapoint Canada Inc. of Ottawa, Ontario. This system consists of four LiDAR scanners for complete 360 degree coverage: two forward-facing LiDARs that scan objects on either side of the vehicle; and two rear-facing LiDARs that scan objects above and below the vehicle. The LiDAR scanners operate continuously as the vehicle moves through target areas. Continuous scanning provides regions of overlap in which objects are scanned twice: first by the forward-facing LiDARs, then by the rear-facing LiDARs. The primary method to acquire positional information for TITAN is GPS. However, relying mainly on GPS limits the performance of the system in locations where GPS signal is unavailable or intermittent (i.e. underground and in urban canyons). In such areas, the use of a supplemental Inertial Navigation System (INS) reduces the error associated with GPS signal loss, however, the positional accuracy of the INS degrades exponentially when GPS is not available. This research project explores the possibility of complementing the current position sensors with an Optical Navigation System (ONS). In this approach, additional positional information is extracted directly from point cloud data acquired by the mobile terrestrial LiDAR system. The method uses both geometry and intensity features, and involves the following steps: (1) tessellating the overlapping LiDAR point clouds into smaller segments; (2) quantitatively assessing each pair of overlapping point clouds with respect to intensity and geometric variation to select good candidates for alignment; (3) aligning the overlapping point clouds; (4) using the offsets produced by the alignment to determine the positional correction to be applied to the vehicle's trajectory. A successful positional correction technique, that is independent from external signals such as GPS greatly increases the versatility of mobile terrestrial LiDAR systems and has applications for a variety of other survey systems.