Multiscale analysis of geomorphological and geological features in high resolution digital elevation models using the wavelet transform

Abstract

At the end of the 1990s the emergence of high resolution (1 m) digital elevation models (DEMs) settled the context of high precision geomorphological analysis. These new elevation models permitted to reveal structures that remained heretofore undetectable. Earth scientists henceforth benefit from a source of data with a textural detail that was never attained before. Despite its richness, this information must be treated efficiently to extract features helping geomorphologists to analyze the processes occurring at the surface. Such processes are complex, localized and naturally multiscale. Recently, space-frequential descriptors as wavelets have been proposed successfully for the analysis of DEMs. The wavelet transform is widely used in image processing since it allows us to decompose a signal into a weighted sum of atoms with joint space-frequency localization. Such a decomposition facilitates a coherent navigation from scale to scale, but also permits to detect heretofore undiscerned phenomena at different scales. This is appealing in geomorphology, where structural components, related to a given phenomenon, are well determined in these sub-spaces specific to the scale continuum. In this paper, we propose a filtering procedure of the wavelet decomposition as an approach for the analysis of geomorphological multiscale structures. This filtering procedure enhances the high pass information contained at each scale. The proposed bottom-up approach is applied here to a case study to detect geomorphological structural elements in a valley of the Swiss Jura. It demonstrates that the proposed filtering procedure is an efficient tool for geomorphological multiscale generalization.

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

“…The wavelet approach has been applied to DEM in geomorphic studies, including terrain analysis and landslide analysis (Bjørke and Nilsen, 2003;Kalbermatten, 2010;Kalbermatten et al, 2012). In this transform, a high-pass filter (a mother wavelet) and a low-pass filter (a father wavelet) are applied to decompose the DEM into four images at each scale: low-pass, high-pass horizontal, high-pass vertical and high-pass diagonal images.…”

“…In this transform, a high-pass filter (a mother wavelet) and a low-pass filter (a father wavelet) are applied to decompose the DEM into four images at each scale: low-pass, high-pass horizontal, high-pass vertical and high-pass diagonal images. The scale is a parameter in the wavelet transform, representing the width of the filter and the scale of topographic variability (Kalbermatten et al, 2012). Depending on the scale of the wavelet transform, the method yields different images, corresponding to different scales of topographic features.…”

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

“…In order to remove artefacts introduced by soil roughness, the DEM was filtered by the multi-resolution analysis (MRA) in 2 dimension based on the wavelet method (Tate et al, 2005). Also, Kalbermatten et al (2012), used the MRA to highlight the different landscape structure from a LiDAR. The MRA was performed in R with the package waveslim (Witcher, 2015).…”

Estimating the Rut Depth by UAV Photogrammetry

“…This makes the field procedure simpler and faster and gives more freedom to choose the UAV flight pattern. A multi-scale approach similar to [24] could be useful, since the point cloud density changes especially at the canopy border. Now, a mono-scale analysis has been used.…”