Southeastern Section - 61st Annual Meeting (1–2 April 2012)

Paper No. 7
Presentation Time: 10:20 AM


LYONS, Nathan J., MITASOVA, Helena and WEGMANN, Karl W., Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695,

Debris flows are the most prevalent and destructive landslide type in the southern Appalachians emphasizing the need to understand limits upon their extent. Numerous researchers in mountainous streams throughout the world have identified the transition between the dominant channel erosional process, fluvial or debris flow, commonly called the critical drainage area (A*). This threshold area, above which the effects of fluvial power laws upon channel topography are not dominant and below which debris flow scars are not found, may signify the lowest point in the watershed where debris flows initiate. A* can be determined at scaling breaks in log-log plots of a channel’s drainage area versus slope. Using a lidar-derived digital elevation model, we have determined a A* range of 3 to 37 km2 in the Oconaluftee River basin in the Great Smoky Mountains National Park (GSMNP) region of the southern Appalachians. Lithologic erodibility contrasts, between Ocoee Supergroup metasedimentary rocks and Grenville gneiss, induce spatial variability to A*, producing a well suited study area to test an application of this parameter.

Landslide inventories, frequently created by aerial photograph interpretation (API) and field traverses, are often used to produce hillslope hazard maps to characterize past landslides or to evaluate a hazard model. Inventory accuracy continues to improve with new technology and automated techniques, though rarely is A* considered during the inventory process. We compare two inventories created with an automated land classification algorithm: the entire study area and areas that are upstream from A*. In both of these inventories, a map of debris flow candidates is produced by multi-resolution, contextual segmentation and classification and is evaluated with a previously published API landslide inventory of GSMNP. Reducing the inventory to areas that drain to A*, while limited to one landslide type, allowed for a focused approach to statistically characterize the land surface and resulted in more correctly identified landslides in this study area.