OPTIMIZED LIDAR-DERIVED TERRAIN IMAGERY FOR LANDFORM IDENTIFICATION, MAPPING AND CLASSIFICATION IN THE APPALACHIANS

Lidar-derived digital elevation models (DEMs) of 1m resolution are now available for most of the Appalachian region through the U.S. Geological Survey 3D Elevation Program (3DEP). Imagery for visualizing topography can be created with modern GIS software to facilitate the recognition and digital mapping of surficial geologic units as well as geomorphic landforms. The DEMs can be analyzed with respect to numerous quantitative terrain parameters such as slope, topographic position index (TPI), and curvature. After experimenting with several combinations of DEM-derived terrain indices, we find an optimal basemap for detailed mapping (1:24,000 scale and larger) is a grayscale image composed of a TPI surface calculated using an annular window of 2m inner radius and 10m outer radius with 40% transparency overlain atop a hillshade surface with azimuth of 315, illumination inclination of 35 degrees, and 2x vertical exaggeration (TPI+HSD). The imagery provides the familiar look of a traditional hillshade, but highlights terrain features often obscured in the shaded areas of a hillshade image. The TPI+HSD is superior to a slope surface because the image provides an immediate visual indicator of vertical direction (up or down), which is ambiguous in a slope surface.

In this presentation, we show examples where this visualization technique has enabled identification and detailed mapping of surficial landforms with subtle relief (on the order of tens of centimeters) and areal extent as small as 1 square meter. Examples include: modern alluvial floodplains and terraces; remnant, ancient alluvial fan surfaces; relative degree of weathering on alluvial terraces and fan surfaces; periglacial solifluction lobes and boulder streams; colluvial lobes; landslides, slumps, debris flow runouts, and scarps; and karst features. Landforms identified in 2D are validated with GIS analysis tools such as surface profiles and interactive contouring of the terrain in a 3D view. In addition, stream channels extracted from lidar-derived DEMs aid in the identification and classification of landforms and provide insights into landform evolution processes. We present both qualitative and quantitative criteria for mapping and classifying surficial landforms with these techniques.