HR: 11:20h
AN: H52E-05    [Abstracts]
TI: Spectral Analysis for Characterizing Landslide-Prone Terrain Using High Resolution Topographic Data
AU: * Booth, A M
EM: abooth@uoregon.edu
AF: University of Oregon, Department of Geological Sciences 100 Cascade Hall, Eugene, OR 97403-1272, United States
AU: Mackey, B
EM: bmackey@uoregon.edu
AF: University of Oregon, Department of Geological Sciences 100 Cascade Hall, Eugene, OR 97403-1272, United States
AU: Roering, J
EM: jroering@uroegon.edu
AF: University of Oregon, Department of Geological Sciences 100 Cascade Hall, Eugene, OR 97403-1272, United States
AU: McKean, J
EM: jmckean@fs.fed.us
AF: USDA Forest Service, Rocky Mountain Research Stn. 322 E. Front St., Suite 401, Boise, ID 83702, United States
AU: Perron, T
EM: perron@eps.harvard.edu
AF: Harvard University, Department of Earth and Planetary Sciences 20 Oxford St., Cambridge, MA 02138, United States
AB: Analyses of surface features in landslide-prone terrain can provide insight into spatial and temporal patterns of mass movement as well as landslide mechanics. Traditional analyses involving topographic maps, air photos, and field observations are often subjective and limited by time constraints, vegetative cover, and low-resolution topographic data. Various statistical measures of surface roughness developed and automated in recent years have improved landslide detection and characterization, but an objective, comprehensive technique remains elusive. Here, we apply a two-dimensional discrete Fourier transform (DFT) to 1m resolution LiDAR data from the Eel River catchment in northern California to analyze the spatial extent and internal structure of large earthflows prevalent throughout this region. The 2D DFT provides information about the spatial frequency, amplitude, periodicity, and orientation of topographic features, such as headscarps, lateral levees, and compressional folds, through a range of spatial scales. To highlight patterns in the meter-scale roughness typical of earthflows, we implement a moving window algorithm that computes the DFT periodogram for a window of specified width at each point in the DEM. By filtering the resulting spectral power matrices according to spatial frequency and/or orientation, we can identify several key features of earthflows. Parallel levees and gullies of ~10m width typically characterize the boundaries of an earthflow's main body, whereas sharp scarps of similar scale typify the boundary at the head of the flow. The spectral power of elements in periodograms oriented perpendicular to these boundaries tends to be orders of magnitude greater than in other portions of the terrain. Blocky, hummocky terrain within the earthflow has a unique spectral signature when the DFT periodogram is filtered to include spatial frequencies of comparable scale. In addition, we use the DFT in our moving window algorithm to generate one-dimensional power spectra for ~30000m² patches of terrain representative of active and dormant earthflows and unfailed terrain. The upper envelopes of these spectra are lowest for the unfailed terrain, up to an order of magnitude higher for the dormant earthflows, and up to two orders of magnitude higher for the active earthflows. This indicates that once an active earthflow becomes dormant, erosional processes systematically subdue its roughest meter-scale features through time. Use of the 2D DFT in a moving window algorithm will enable us to map spatial and temporal patterns of instability in landslide-prone terrain with improved efficiency, objectivity, and precision.
DE: 1625 Geomorphology and weathering (0790, 1824, 1825, 1826, 1886)
DE: 1810 Debris flow and landslides
DE: 1826 Geomorphology: hillslope (1625)
DE: 9350 North America
SC: Hydrology [H]
MN: 2007 Fall Meeting