Abstract. Digital elevation models (DEM) are widely used to determine characteristics of mass movement processes such as accumulation and deposition of material, volume estimates or the orientation of discontinuities. To create such DEMs point cloud data is provided by terrestrial laser scanning (TLS) and recently used for analysis of mass movements. Therefore the reliability of TLS data was investigated in a comparative study with tachymetry. The main focus was on the possibility of determining movement patterns of landslides <100 mm. Therefore, several post processing steps are needed and the reliability of those were analyzed. The post processing steps that were investigated include: (1) The registration process is a crucial step considering long term TLS monitoring of an object and can be significantly improved using an iterative closest point (ICP) algorithm; (2) Filtering methods are necessary to create DEMs in order to separate favored laser points on the terrain surface (ground points) from topographically irrelevant points (non-ground-points). Therefore GIS tools were applied. Surfaces with and without vegetation cover were differentiated; (3) Displacement vectors are used to determine slope movement rates. They were created from TLS data after the computation of true orthophotos.Using the methodology presented it was not possible to determine movement rates <50 mm per period. However, if the quality of the point density is described and areas with very low point density are detected, reliable conclusions can be made regarding slope movement patterns and erosion and deposition of material for changes <100 mm for the investigated slope.

Terrestrial laser scanning is of increasing importance for surveying and hazard assessments. Digital terrain models are generated using the resultant data to analyze surface processes. In order to determine the terrain surface as precisely as possible, it is often necessary to filter out points that do not represent the terrain surface. Examples are vegetation, vehicles, and animals. Filtering in mountainous terrain is more difficult than in other topography types. Here, existing automatic filtering solutions are not acceptable, because they are usually designed for airborne scan data. The present article describes a method specifically suitable for filtering terrestrial laser scanning data. This method is based on the direct line of sight between the scanner and the measured point and the assumption that no other surface point can be located in the area above this connection line. This assumption is only true for terrestrial laser data, but not for airborne data. We present a comparison of the wedge filtering to a modified inverse distance filtering method (IDWMO) filtered point cloud data. Both methods use manually filtered surfaces as reference. The comparison shows that the mean error and root–mean-square-error (RSME) between the results and the manually filtered surface of the two methods are similar. A significantly higher number of points of the terrain surface could be preserved, however, using the wedge-filtering approach. Therefore, we suggest that wedge-filtering should be integrated as a further parameter into already existing filtering processes, but is not suited as a standalone solution so far.

Terrestrial laser scanning has become an important surveying technique in many fields such as natural hazard assessment. To analyse earth surface processes, it is useful to generate a digital terrain model originated from laser scan point cloud data. To determine the terrain surface as precisely as possible, it is often necessary to filter out points that do not represent the terrain surface. Examples are vegetation, vehicles, and animals. In mountainous terrain with a small-structured topography, filtering is very difficult. Here, automatic filtering solutions usually designed for airborne laser scan data often lead to unsatisfactory results. In this work, we further develop an existing approach for automated filtering of terrestrial laser scan data, which is based on the assumption that no other surface point can be located in the area above a direct line of sight between scanner and another measured point. By taking into account several environmental variables and a repetitive calculation method, the modified method leads to significantly better results. The root-mean-square-error (RSME) for the same test measurement area could be reduced from 5.284 to 1.610. In addition, a new approach for filtering and interpolation of terrestrial laser scanning data is presented using a grid with horizontal and vertical angular data and the measurement length.