14-16. Multispectral-Mobile LiDAR for Hazards Research and Rapid Response.
We seek a postdoctoral fellow to perform research that will significantly improve the utility of ground-based LiDAR (Light Detection And Ranging) data for USGS hazards applications. In the hours and days following natural disasters, investigators need the highest-possible resolution data to assess ongoing hazards and capture time-sensitive information that could contain lessons for future preparedness. In particular, LiDAR topographic data are among the most important disaster-related data sets. Acquiring airborne LiDAR data rapidly in the potentially chaotic hours and days following a natural disaster, however, may be practically impossible. Following the 4 April, M7.2 El-Mayor Cucapah earthquake, for instance, ALSM (Airborne Laser Swath Mapping) resurveying occurred 4 months after the event [Oskin et al., 2012.]. Recently, a new class of individually operated ground-based Terrestrial Laser Scanners (TLS), offers the potential for rapid data collection without dependence on third party logistics. TLS sensors are not without their own limitations, however, and an opportunity exists to address these limitations with the goal of standing up a viable system to acquire high-resolution topographic data for use in diverse post-disaster environments.
The three following science topics must be addressed in order for LiDAR data to be incorporated effectively into hazard applications and operations, especially, though not exclusively, if they are to be used in a rapid, disaster-response context: (1) Limitations of static acquisition: Limited-range ground-based LiDAR instruments require multiple, time-consuming, overlapping set-ups to cover regions of interest. (2) Vegetation mitigation: Measures must be taken to alleviate the fact that the ground surface is often shielded from line-of-site LiDAR pulses by varying degrees of vegetative cover. (3) The processing and dissemination bottleneck: There are myriad computational steps required to transform a raw collection of LiDAR returns into a properly georeferenced point-cloud suitable for further analysis and dissemination.
The critical component for solving the static LiDAR platform problem is the use of an Inertial Navigation System (INS) that, when combined with instantaneous GPS-provided positioning information, can provide six-degree-of-freedom (6DOF) motion estimates to accurately georeference a LiDAR point cloud collected from a moving platform [Glennie et al., 2013.]. PI Brooks, Glennie, and Finnegan We operate two different resolution INS devices and exploring their capabilities for accurate positioning beneath the forest canopy is a particularly novel and exciting part of this Research Opportunity. The concept is that, even though the forest canopy might cause gaps in GPS signal reception, the INS 6DOF motion estimates can bridge the gaps and prevent accuracy degradation of sub-canopy point cloud data. With a backpack-mounted LiDAR, for instance, a person in the field could navigate underneath the canopy from position to position, pausing to acquire necessary GPS satellite signals.
The project provides the opportunity to leverage existing LiDAR tools for vegetation removal (e.g. height filtering [Streutker and Glenn, 2006.]) as well as to explore two emerging TLS capabilities, full-waveform and multispectral LiDAR. The full-waveform data available allows penetration of the canopy and thus the potential for an increased number of returns for fine-scale terrain discretization. The combined use of intensity data from the multiple TLS sensors available to this project allows for ‘multispectral’ (of at least 5 different laser frequencies) point cloud returns [Woodhouse et al., 2011].
This Research Opportunity will be the pioneering USGS effort for developing an acquisition-to-point cloud protocol for ground-based LiDAR data. Built on the NSG’s enterprise database, GRiD (Geospatial Repository & Data) is a government-funded, open source solution for dedicated storage/search/retrieval of geospatial data, including LiDAR point clouds. GRiD was developed to provide server-side, real-time or near real-time access to point cloud data to a specific set of analysts in military theaters, hence its use for USGS hazards applications would be a natural extension.
The applicant’s research proposal must focus on Northern California’s San Francisco Bay Area, one of the zones of highest seismic hazard exposure in the United States, in particular, the surface manifestation of the San Andreas and its subsidiary faults (Hayward, Calaveras, Rodgers Creek, etc.). The San Andreas fault system is expressed well in certain areas of clear exposure, but dense coastal and redwood forest and related canopy vegetation can also preclude complete imaging of the faults by airborne LiDAR surveys [Prentice et al., 2009.]. We anticipate, therefore, that this Mendenhall project will not only make contributions in terms of rapid acquisition of ground-based LiDAR data but also in terms of providing new, first order characterization and understanding of surface rupture processes for the San Andreas fault system.
Glennie, C., B. A. Brooks, T. Ericksen, D. Hauser, K. Hudnut, J. Foster, and J. Avery (2013), Compact Multipurpose Kinematic LiDAR Scanning System – Initial Tests and Results, Remote Sensing, 5, 521-538.
Oskin, M. E., et al. (2012), Near-field deformation from the El Mayor-Cucapah earthquake revealed by differential LIDAR, Science, 335(6069), 702-705.
Prentice, C., C. J. Crosby, C. S. Whitehill, J. R. Arrowsmith, K. P. Furlong, and D. A. Phillips (2009), Illuminating Northern California’s Active Faults, EOS, 90(7), 55-56.
Streutker, D., and N. Glenn (2006), LiDAR measurement of sagebrush steppe vegetation heights., Remote Sensing of Environment, 102, 135-145.
Woodhouse, I. H., C. Nichol, P. Sinclair, J. Jack, F. Morsdorf, T. J. Malthus, and G. Patenaude (2011), A Multispectral Canopy LiDAR Demonstrator Project, IEEE Geoscience and Remote Sensing Letters, 8(5).
Proposed duty station: Menlo Park, California.
Areas of Ph.D.: Geodesy, Geomatics, Remote Sensing, Computer Sciences.
(candidates holding Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications: Applicants must meet one of the following qualifications - Research Geologist, Research Geophysicist, Research Civil Engineer.
(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant’s background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)
Research Advisor (s): Ben Brooks, (650) 329-5436, firstname.lastname@example.org.; Carol Prentice, (650) 329-5690,email@example.com.; Steve DeLong, (650) 329-5254,firstname.lastname@example.org.;Ken Hudnut, (626) 583-7232,email@example.com.; Nancy Glenn, (Boise State U), (208) 426-2933, firstname.lastname@example.org.; Craig Glennie, (U Houston), (832) 842-8861, email@example.com.; David Finnegan, (US Army Cold Regions Research and Engineering Lab), (603) 646-4100, David.Finnegan@erdc.dren.mil.
Human Resources Office Contact: Lisa James, (916) 278-9405, firstname.lastname@example.org.
|Summary of Opportunities|