2008 Joint Meeting of The Geological Society of America, Soil Science Society of America, American Society of Agronomy, Crop Science Society of America, Gulf Coast Association of Geological Societies with the Gulf Coast Section of SEPM

Paper No. 9
Presentation Time: 4:05 PM

Global-Scale Comparative Geomorphology Using Computer-Generated Physiographic Maps

STEPINSKI, Tomasz F., Lunar and Planetary Institute, 3600 Bay Area Blvd, Houston, TX 77058 and LUO, Wei, Department of Geography, Northern Illinois University, DeKalb, IL 60115, tom@lpi.usra.edu

Satellite altimetry measurements result in increasingly detailed global scale topography datasets for both land and ocean areas as well as for surfaces of other planets. These measurements make possible a qualitatively new approach to geomorphology that relies on automatic parsing and manipulating the massive topographic datasets. We demonstrate the potential of such an approach by generating maps of terrain types for terrestrial landmass, ocean floor and surface of Mars using unsupervised classification based on three taxonomic criteria, slope, texture, and convexity – all derived from satellite-based DEM. A map for each surface has 16 terrain classes that have common meaning throughout the three surfaces. The spatial frequencies of terrain types reveal differences between the surfaces. Although all three surfaces are dominated by fine-textured terrain, dominance of such terrain is most pronounced in land areas and least pronounced on Mars. Both land and ocean are dominated by terrain with high convexity, but Mars is dominated by low convexity terrain. We have also calculated latitude-elevation (LE) diagrams that show predominant terrain type from amongst all location at a given latitude and elevation. The LE diagram for landmass shows equatorial symmetry and dependence on elevation – a sign that climate influences that terrain type. The LE diagram for the ocean shows strong dependence on depth but no latitudinal variation. The diagram for Mars show no latitudinal symmetry and weak dependence on elevation. This type of automated analysis enables quantitative geomorphologic studies on large spatial scale. It can also be applied on smaller scales using higher resolution topographic data.