ENHANCING LIDAR DIGITAL ELEVATION MODELS TO IDENTIFY AND CHARACTERIZE THE SURFICIAL EXPRESSION OF FAULTS

A variety of techniques can be used to enhance LiDAR (also known as airborne laser scanning or airborne laser swath mapping) digital elevation models (DEMs) for geologic mapping, particularly the delineation of scarps and other landforms associated with young faults. If x-y-z point cloud data are available, geologists can experiment with interpolation algorithms and grid spacing to create DEMs that are optimized for the terrain they will be mapping. Polynomial interpolation schemes, for example, are useful for eliminating the triangular facets that plague some DEMs produced by linear interpolation of triangulated irregular networks (TINs). Once an optimal DEM is created, shaded relief maps with variable or multiple illumination sources can be used to highlight surficial features. Draping a second data set, for example color-coded elevation or slope angle, over a shaded relief image can also help accentuate surficial features. Standard low-pass or moving average image processing filters can be used to smooth the DEM and remove extraneous noise, which is inherent in LiDAR data, and edge detection filters can accentuate features such as fault scarp segments that might not be easily visible in shaded relief images. Residual topography maps, calculated by subtracting a moving mean or regional trend from the DEM, can also be used to identify subtle scarps. Topographic roughness mapping, which is a measure of local elevation variability that has been used in LiDAR-based landslide mapping projects, has the potential to discriminate among different surficial units, faulted geomorphic surfaces, and perhaps even fault scarps of different ages. The application of these techniques will be illustrated using public domain LiDAR coverage of growth faults in coastal Louisiana and the San Andreas fault in northern California.