2004 Denver Annual Meeting (November 7–10, 2004)

Paper No. 11
Presentation Time: 1:30 PM-5:30 PM


WASHINGTON, Paul A. and CHENOWETH, M. Sean, Department of Geosciences, Univ of Louisiana at Monroe, Monroe, LA 71209, chenoweth@ulm.edu

Because digital elevation data only reports elevations in iterated intervals for discrete grid points, hillslopes are approximated by discretized panels with uniform orientations and slopes. This obscures the true orientations of hillslopes on which lineation analysis and other statistical orientation analyses are based. Furthermore, the horizontal spacing of the elevation points excludes direct sampling of cliff faces and mutes short high-angle hillslopes; these are the features that commonly provide the best source for traditional manual slope orientation analysis. Traditional statistical analysis of hillslope orientation using ARCVIEW and similar software packages produces biased orientations because: 1) the orientations of low-slope surfaces that dominate most land surfaces are more coarsely iterated than the higher slope surfaces that provide most of the critical information, and 2) the boundaries between the orientation iterations vary with slope and do not conform to traditional statistical orientation boundaries. To overcome these limitations, we have developed a method for graphically representing the data so that the useful information can be extracted. The information that needs to be represented includes orientation, slope, and frequency. This is best represented in a grid plot of the frequency of poles to the TIN elements. The outer edges of the plot provide the most useful information, but are also the areas with the lowest frequencies. To aid in extraction of the useful orientations, the frequencies of each discrete orientation-slope point are normalized to the measured total slope frequency for the study area. The resulting normalized discretized slope diagrams allow a reasonable approximation of the hillslope orientations needed for lineation analysis.