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.

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The retrieval of the backscatter cross section in lidar data is of great interest in remote sensing. For the numerical calculation of the backscatter cross section, a deconvolution has to be performed; its determination is therefore an ill-posed problem. Most of the common techniques, such as the well-known method of Gaussian decomposition, make implicit assumptions on both the emitted laser pulse and the scatterers. It is well understood that a land surface is quite complicated, and in many cases it cannot be composed of pure Gaussian function combinations. Therefore the assumption of Gaussian decomposition of waveforms may be invalid sometimes. In such cases an inversion method might be a natural choice. We propose a regularizing method with a posteriori choice of the regularizing parameter for solving the problem. The proposed method can alleviate difficulties in numerical computation and can suppress the propagation of noise. Numerical evidence is given of the success of the approach presented for recovering the backscatter cross section in lidar data.