INVENTORY MAPPING AND CHARACTERIZATION OF LANDSLIDES USING LIDAR: KENTON AND CAMPBELL COUNTIES, KENTUCKY

The natural geology and topography of northern Kentucky and Cincinnati are susceptible to landslides. Compounded by decades of development and slope modification, this has produced some of the highest costs per capita in the United States for landslide damage. Many landslides damage roads, homes, and other infrastructure, causing financial woes for property owners and struggles for the nearby government agencies. However, the slow nature of some landslides and incremental damage that can span several decades often make people unaware of the problem. Many of the landslides go unreported and citizens do not take advantage of resources to become educated and start to mitigate the problem.

In Kenton and Campbell Counties, Kentucky, Ordovician bedrock geology consists of, in ascending order, the Kope Formation, Fairview Formation, Grant Lake Limestone, and Bull Fork Formation. Although landslides can occur in any of these units, the Kope Formation is especially problematic, and many of the landslides in the area occur in it. The shaly Kope Formation weathers easily, slumping and producing thin to thick colluvial soils. Landslides typically occur within the colluvium or along the colluvial-bedrock contact.

Landslide identification and hazard mapping using LiDAR (light-detection and ranging) has proven successful in other landslide-prone areas of the United States, such as Oregon, Washington, and Pennsylvania. The purpose of this project was to develop a methodology using LiDAR data optimal for the geologic setting of Kenton and Campbell Counties and document landslides as part of an existing inventory. To do this, potential landslides were mapped and data that were previously not visible on existing maps or coarse digital elevation models were digitized. Hillshade DEM maps were the primary data set used for LiDAR visualization and landslide mapping. Locations were also field verified, where possible. Continued use of high-resolution LiDAR to identify potential landslides will provide a framework for analyzing landslide data that is crucial to understanding the nature of the landslide-prone areas and reducing long-term losses.