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The increasing precision of ground-based Lidar technologies makes possible to perform more detailed systematic structural and morphological analyses than ever reached before. Using the orientation of each single collected vertex, a point cloud data set can be represented by a 3D image where each single point has a color defined by the local dip and strike direction, which allows a very simple slope analysis. This can also be applied to any surface reconstructed through the data set, making the detection of planar structures within a cliff, i.e. in the presence of overhangs, possible, which is not with classical 2D digital elevation models. Such simple analyses applied to 3D clouds of points make it possible to quickly identify structural features affecting topography. They open new perspectives in relief analysis. Although the principle of the analysis is simple, it is not straightforward to manage and to create surface from clouds of points. This paper describes the basis (kernel) of a software (Coltop-3D) which is dedicated to perform these tasks. Terrestrial laser scanners allow for capturing dense 3-dimensionnal data set (up to millions of points) on the surface of an object, within a few minuts. However, the post-treatment and the standard operating use of such large data set may impair an in-depth analysis for specific applications, such as landslide and rockfall analysis. This is mainly due to computer access time for the data point near a given location and to the required size to store the data in the random access memory (RAM). To solve both problems, a structure based on octrees which allow for fast localization of points and low consumption of RAM is used.
Numerous work have pinpointed that eigenanalysis of the covariance matrix of a local neighborhood can be used to estimate local surface properties, and hence the normal. The direction of the normal vector is the same as the one found by least squares plane fitting, since the two methods are equivalent. Once the normal is known, it is straightforward to compute the dip and strike direction.
One of the main advantage of computing eigenvalues for normal estimation instead of least-squares plane fitting, is that the eigenvectors correspond to the principal components (directions and orientations) of the neighbourhood and the eigen values will represent the variance in each direction. Thus, it is possible to estimate the change of geometric curvature, in the neighbourhood of a single point with simple measure. The change of curvature can be used to semi-automatically remove folliage from data set, as it is excepted that such feature have high change of curvature.
This is illustrated with some examples (Burdon rockfall area and Randa landslide area, both located in the Swiss Alps).
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