Extraction of thalweg networks from DTMs: application to badlands

Abstract

Abstract. To study gully spatial patterns in the badlands using a continuous thalweg vector network, this paper presents methods to extract the badlands' thalweg network from a regular grid digital terrain model (DTM) by combining terrain morphology indices with a drainage algorithm. This method will delineate a thalweg only where the DTM denotes a significant curvature with respect to DTM accuracy and relies on three major steps. First, discontinuous concave areas were detected from the DTM using morphological criteria, either the plan curvature or the convergence index. Second, the concave areas were connected using a drainage algorithm, which provides a continuous, thick, tree-structured scheme. We assumed that these areas were physically significant and corresponded to a gully floor. Finally, the thick path was reduced to its main course and vectorised to obtain a thalweg network. The methods were applied to both virtual and actual DTM cases. The actual case was a LiDAR DTM of the Draix badlands in the French Alps. The obtained networks were quantitatively compared, both with a network obtained using the usual drainage area criteria and with a reference network mapped in the field. The CI-based network showed the great potential for thalweg network extraction.

An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification

“…The choice of curvature has been done considering that numerous works proved its effectiveness for feature extraction (e.g. Molloy and Stepinski, 2007;Lashermes et al, 2007;Tarolli and Dalla Fontana, 2009;Thommeret et al, 2010;Pirotti and Tarolli, 2010;Passalacqua et al, 2010a,b). The choice of openness is based on the fact that its measure of convergences relies on an averaging procedure: openness values are calculated as averaging angles along azimuths (Yokoyama et al, 2002;Prima et al, 2006).…”

“…Curvature maps derived from LiDAR DTMs have been used by Tarolli and Dalla Fontana (2009) and Pirotti and Tarolli (2010) to assess the capability of high resolution topography for the recognition of convergent hollow morphology of channel heads and for channel network extraction respectively. Thommeret et al (2010) used a datadriven and data-derived threshold based on DTM noise to extract badlands network, identifying convergent areas from a combination of terrain morphology indices and a single flow drainage algorithm. Passalacqua et al (2010a,b) applied nonlinear diffusion filtering combined with a geomorphicallyinformed geodesic cost function to identify automatically channel initiation points and extract channel paths from Li-DAR data.…”

“…The referenced studies dealt with a prior assessment of the input data (Thommeret et al, 2010), calibration of the kernel size by interactively testing its effectiveness related to the investigated features (Pirotti and Tarolli, 2010), fixed arbitrarily chosen scales to evaluate topographic parameters (Tarboton and Ames, 2001;Molloy and Stepinski, 2007;Tarolli and Dalla Fontana, 2009;Passalacqua et al, 2010a,b). Some open questions still remain, as in how to identify thresholds that are not data-driven and how to objectively select scale without calibrating it on results and without considering a previous analysis of the data and of the study area.…”

GIS-Based Detection of Gullies in Terrestrial LiDAR Data of the Cerro Llamoca Peatland (Peru)

“…3D breaklines can be derived by setting a threshold on high plan curvature and subsequent skeletonization of the masked areas. The thalweg is defined as the connecting line of the lowest points inside the gully along the channel course [43]. Several studies such as [31,32] assume concavity along the bottom of the gully, which does not hold for the Llamoca gullies with predominantly U-shaped erosion channels.…”

“…Several studies such as [31,32] assume concavity along the bottom of the gully, which does not hold for the Llamoca gullies with predominantly U-shaped erosion channels. The approach of [43] to extracting thalweg networks combines the morphological criteria of significant curvature (i.e., discontinuous concave areas) and topographic convergence index. To generate the thalweg we use the topographic convergence index (TCI), which is defined as the logarithm of the ratio of flow accumulation and local slope [44].…”