Landslide Displacement Monitoring Using 3D Range Flow on Airborne and Terrestrial LiDAR Data

Abstract

An active landslide in Doren, Austria, has been studied by multitemporal airborne and terrestrial laser scanning from 2003 to 2012. To evaluate the changes, we have determined the 3D motion using the range flow algorithm, an established method in computer vision, but not yet used for studying landslides. The generated digital terrain models are the input for motion estimation; the range flow algorithm has been combined with the coarse-to-fine resolution concept and robust adjustment to be able to determine the various motions over the landslide. The algorithm yields fully automatic dense 3D motion vectors for the whole time series of the available data. We present reliability measures for determining the accuracy of the estimated motion vectors, based on the standard deviation of components. The differential motion pattern is mapped by the algorithm: parts of the landslide show displacements up to 10 m, whereas some parts do not change for several years. The results have also been compared to pointwise reference data acquired by independent geodetic measurements; reference data are in good agreement in most of the cases with the results of range flow algorithm; only some special points (e.g., reflectors fixed on trees) show considerably differing motions.

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

“…Almost every technology, including theodolites, GPS, photogrammetry, InSAR, airborne LiDAR (ALS), and ground LiDAR, has its application ranges and restrictions in terms of spatial and time scales when employed to obtain 3-D terrain data [6,7]. By comparing the information with various precision and resolution methods that are collected during different periods, these multi-method, multi-period data of digital terrain models (DTM) have been used in landslide mechanism-related research [8], observations of surficial activity [9][10][11], landslide volume calculations [12][13][14], and volume variation estimations [15], as well as disaster scale assessment and simulation [16].…”

LiDAR and Orthophoto Synergy to optimize Object-Based Landscape Change: Analysis of an Active Landslide

“…Major landslide events occurred in 1927, 1935 and 1988, during which 2-3 × 10 6 m 3 was mobilized as rock-and soil slides, and debris flows. The deformation, movement direction and scarp stability of the landslide have been studied with multi-temporal terrestrial and airborne laser scanning and UAV flights since 2003 [26,27]. Ghuffar et al [26] evaluated the 3D motion of the Doren landslide using a range flow algorithm on airborne and terrestrial LiDAR.…”

“…The deformation, movement direction and scarp stability of the landslide have been studied with multi-temporal terrestrial and airborne laser scanning and UAV flights since 2003 [26,27]. Ghuffar et al [26] evaluated the 3D motion of the Doren landslide using a range flow algorithm on airborne and terrestrial LiDAR. Landslide activity is closely related to the underlying geology, which is formed by a tilted sedimentary sequence of alternating Molasse marls, sandstone and conglomerates [28].…”

Uncertainty in Terrestrial Laser Scanner Surveys of Landslides

“…Moreover, although ALS does not lead to "illumination" shadows cast, in the case of TLS, the stations can be located in the most appropriate positions to avoid them. TLS produces good results both in the detection of morphological features [6] and for deformation monitoring purposes [7,8], thanks to its capability to derive from the acquired point cloud an accurate and regularly-structured digital elevation model therefore, the actual possibilities of using it. During survey design and data processing, the analysis and the suitable modeling of these errors is one major area of research, even though the accuracy of both TLS point clouds and derived DEM must be deepened still further.…”