HR: 0800h
AN: P41A-03    [Abstracts]
TI: Fine-Scale Topographic Analysis of Rock Size Distributions Derived from High-Resolution Ground-Based LiDAR
AU: * Finnegan, D C
EM: david.finnegan@erdc.usace.army.mil
AF: Cold Regions Research & Engineering Lab, 72 Lyme Rd, Hanover, NH 03755, United States
AU: Arcone, S A
EM: steven.a.arcone@erdc.usace.army.mil
AF: Cold Regions Research & Engineering Lab, 72 Lyme Rd, Hanover, NH 03755, United States
AU: Bulmer, M H
EM: mbulmer@umbc.edu
AF: University of Maryland Baltimore County, Suite 320 5523 Research Park Drive, Baltimore, MD 21228, United States
AU: Anderson, S W
EM: steveanderson@bhsu.edu
AF: Black Hills State University, 1200 University Street, Spearfish, SD 57799, United States
AB: Quantitative factors such as RMS height, correlation lengths and surface slope derived from fine-scale topographic datasets hold the potential for characterizing surface morphology in relation to its underlying geologic processes. In an attempt to better understand the relationships between topographic roughness characteristics and geologic processes responsible for creating a distinct surface morphology, we utilize ground-based terrestrial LiDAR and coincidental orthorectified imagery to quantify the variability in RMS heights and correlation lengths. The purpose of this study is to understand directly how various topographic data collection techniques such as LiDAR and manual field-based measurements compare to one another and which techniques are most appropriate for characterizing topography at various scales. Topographic data from several platforms were acquired over desert surfaces in the Mojave Desert near Palm Springs, California and southwestern Arizona. The desert surfaces imaged in the Mojave contained average rock sizes ranging from decimeters to a maximum size near one meter and revealed wide variations in RMS heights and correlation lengths, in keeping with the highly variable surface. Alternately, the Arizona site exhibits less topographic variability and consistent statistics. The data are useful for characterizing the roughness of surfaces for a variety of disciplines, such as penetration of remote sensing signals, upwelling of radiation and characterizing the genetic origin of surfaces. Furthermore, these data become essential to airborne and ground-based imaging sensors and understanding how topographic irregularities affect data fidelity.
DE: 5464 Remote sensing
DE: 5470 Surface materials and properties
DE: 5494 Instruments and techniques
DE: 6964 Radio wave propagation
SC: Planetary Sciences [P]
MN: 2007 Joint Assembly