Abstract
Abstract. In many locations, our ability to study the processes which shape the Earth are greatly enhanced through the use of high-resolution digital topographic data. However, although the availability of such datasets has markedly increased in recent years, many locations of significant geomorphic interest still do not have high-resolution topographic data available. Here, we aim to constrain how well we can understand surface processes through topographic analysis performed on lower-resolution data. We generate digital elevation models from point clouds at a range of grid resolutions from 1 to 30 m, which covers the range of widely used data resolutions available globally, at three locations in the United States. Using these data, the relationship between curvature and grid resolution is explored, alongside the estimation of the hillslope sediment transport coefficient (D, in m2 yr−1) for each landscape. Curvature, and consequently D, values are shown to be generally insensitive to grid resolution, particularly in landscapes with broad hilltops and valleys. Curvature distributions, however, become increasingly condensed around the mean, and theoretical considerations suggest caution should be used when extracting curvature from landscapes with sharp ridges. The sensitivity of curvature and topographic gradient to grid resolution are also explored through analysis of one-dimensional approximations of curvature and gradient, providing a theoretical basis for the results generated using two-dimensional topographic data. Two methods of extracting channels from topographic data are tested. A geometric method of channel extraction that finds channels by detecting threshold values of planform curvature is shown to perform well at resolutions up to 30 m in all three landscapes. The landscape parameters of hillslope length and relief are both successfully extracted at the same range of resolutions. These parameters can be used to detect landscape transience and our results suggest that such work need not be confined to high-resolution topographic data. A synthesis of the results presented in this work indicates that although high-resolution (e.g., 1 m) topographic data do yield exciting possibilities for geomorphic research, many key parameters can be understood in lower-resolution data, given careful consideration of how analyses are performed.
Highlights
Geomorphologists have always made use of topographic data, from initial qualitative observations of surface morphology and its link to process (e.g., Gilbert, 1909) to directly measuring landscape geometries from contour maps, constraining river dynamics and morphometric relationships (e.g., Horton, 1932, Schumm, 1956, and Chorley, 1957)
Decreasing the grid resolution of digital elevation models (DEMs) acts as a low-pass filter on this topographic signal, which preferentially degrades features in the topography that have significant amplitude at small wavelengths, such as sharp ridgelines, narrow valley bottoms, and local topographic roughness generated by, for example, landslides, tree throw, and rock exposure (Figs. 1 and 3)
Through the generation of topographic data spanning the range of grid resolutions currently used in much of geomorphic research, a number of key metrics have been evaluated for their sensitivity to grid resolution
Summary
Geomorphologists have always made use of topographic data, from initial qualitative observations of surface morphology and its link to process (e.g., Gilbert, 1909) to directly measuring landscape geometries from contour maps, constraining river dynamics and morphometric relationships (e.g., Horton, 1932, Schumm, 1956, and Chorley, 1957). Further quantitative analyses of the Earth’s surface were facilitated through the advent of gridded topographic data. Work to generate digital elevation models (DEMs) from photogrammetry, contour maps, and active remote sensing platforms (Yamaguchi et al, 1998; Wolock and McCabe, 2000; Rabus et al, 2003; Walker and Willgoose, 2006) produced datasets at tens to thousands of meters’ grid resolution, along with geomorphic analyses designed for such datasets (O’Callaghan and Mark, 1984; Tarboton et al, 1991; Montgomery and Dietrich, 1994; Burbank et al, 1996; Tarboton, 1997). Grieve et al.: How does grid-resolution modulate geomorphic processes?
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