Pre-processing algorithms and landslide modelling on remotely sensed DEMs

Abstract

Terrain analysis applications using remotely sensed Digital Elevation Models (DEMs), nowadays easily available, permit to quantify several river basin morphologic and hydrologic properties (e.g. slope, aspect, curvature, flow path lengths) and indirect hydrogeomorphic indices (e.g. specific upslope area, topographic wetness index) able to characterize the physical processes governing the landscape evolution (e.g. surface saturation, runoff, erosion, deposition). Such DEMs often contain artifacts and the automated hydrogeomorphic characterization of the watershed is influenced by terrain analysis procedures consisting in artificial depression (pit) and flat area treatment approaches combined with flow direction methods. In shallow landslide deterministic models, when applied using topographic dataset at medium scale (e.g. 30 m of resolution), the choice of the most suitable DEM-processing procedure is not trivial and can influence model results. This also affects the selection of most critical areas for further finer resolution studies or for the implementation of countermeasures aiming to landslide risk mitigation. In this paper such issue is investigated using as topographic input the ASTER DEMs and comparing two different combinations of DEM correction and flow routing schemes. The study areas comprise ten catchments in Italy for which hydrogeomorphic processes are significant. Aims of this paper are: 1) to introduce a parameter estimation procedure for the physically-based DEM correction method PEM4PIT (Physical Erosion Model for PIT removal); 2) to investigate the influence of different terrain analysis procedures on results of the slope stability model SHALSTAB (SHAllow Landsliding STABility) using remotelysensed ASTER DEMs; 3) trying to assess which of terrain analysis methods is more appropriate for describing terrain instability.

Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search

“…In order to alleviate the problem of routing surface flow through the flat areas created by sink filling, efforts have been made to add a gradient to these flat areas (e.g. Martz and Garbrecht, 1998;Wang and Liu, 2006;Grimaldi et al, 2007;Santini et al, 2009) Nevertheless, as modern high resolution data such as LiDAR demonstrate, actual terrain includes many true depressions, especially in riffle-and-pool portions of mountainous rivers, and on floodplains and coastal plains (Notebaert et al, 2009). Therefore, sink filling is not always an appropriate approach for hydrological conditioning of a DEM because the measured elevation data are replaced with new elevation values based on the rather unrealistic assumption that the terrain has no depressions (Jenson and Domingue, 1988;Tarboton, 1997), be they artificial or real.…”

“…Improved sink filling methods (e.g. Garbrecht and Martz, 1997;Grimaldi et al, 2007;Santini et al, 2009) first fill sinks, and then introduce a gradient to all flat areas to provide non-zero gradients for flow routing. The method complementary to sink filling is carving or breaching (Rieger, 1998;Martz and Garbrecht, 1998) where a channel is carved out of each sink, breaking through the (artificial) obstacle.…”

Recent Landslide Movement in Tsaoling, Taiwan Tracked by TerraSAR-X/TanDEM-X DEM Time Series

“…During the past decades, DEM has been developed from low resolution aerial photo or optical images to high resolution images obtained through airborne LiDAR or Unmanned Aerial Vehicle (UAV), providing more possibilities for monitoring land changes [15,16]. However, these measurements have their own limitations, e.g., high cost, sensitive to working conditions or small coverage.…”

Historical analysis of rainfall-triggered rockfalls: the case study of the disaster of the ancient hydrothermal Sclafani Spa (Madonie Mts, northern-central Sicily, Italy) in 1851

“…Gravity-induced hydrogeomorphic movements, called landslides (WP/WLI, 1990), depend on a combination of multiple controlling factors (Santini et al, 2009): (i) predisposing factors (e.g. lithology, morphology, tectonics, weathering); (ii) triggering factors (e.g.…”