Abstract
Abstract. The irregularities of the earth’s surface are quantified by means of roughness measurements using Digital Elevation Models (DEM’s). This article presents a roughness measurement method that is based on the calculation of the difference of altitude existing between a plane passing through the centre of a moving window and the altitude of the DEM surface inside this window. This method differs from the measure of the standard deviation and best fit plane, in the sense that it considers all difference values, positives or negatives. The measurement is done in a 3 × 3 or a 5 × 5 moving window and contemplates inside this window the plane which passes through the centre of the window and the highest pixel located in the border or perimeter of this window. According to the 3D configuration of the DEM surface inside the moving window, the sum of all the differences is positive or negative, allowing to discriminate the local morphology independently of the global roughness. The roughness variable which distinguishes negative and positive values allows to classify accurately landscape units such as watersheds, riverbeds, volcanic assemblages as well as landforms associated with tectonic structures.
Highlights
The notion of roughness is commonly used in quantitative geomorphology studies
The roughness extracted from a Digital Elevation Model (DEM) is defined in terms of the variability of the elevation data
To define the relationships existing between distinct curvature and various roughness characteristics, Hani et al (2011, 2012) proposed to compute individually all the cells describing a Digital Elevation Model (DEM) and realize a multiscale analysis
Summary
The notion of roughness is commonly used in quantitative geomorphology studies. This variable characterizes rock types, performs relief classifications, identifies landslide mechanisms, among others (Barnett et al, 2004, Berti et al, 2013). The main extraction methods are the root mean square applied to elevation and slope grids (Grohman, 2015), eigenvalue ratios (Cloude, 1999; Cloude et al, 2000), structuring elements (Cao et al, 2015), fractal dimension (Taud and Parrot, 2005), discrete Fourier transform (Bingham and Siegert, 2009), continuous wavelet transform and wavelet lifting scheme (Hani et al, 2011, 2012), as well as standard deviation in a fit plane (Hobson, 1972; Evans, 1972; Herzfeld et al, 2000; Haneberg et al, 2005,) The results of these measurements depend on the resolution of the DEM and the size of the moving window (Grohmann et al, 2011).
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