Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z)

Abstract

Surveying techniques such as Terrestrial Laser Scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. We introduce a new algorithm performing a direct comparison of point clouds in 3D. Surface normals are first estimated in 3D at a scale consistent with the local surface roughness. The measurement of the mean change along the normal direction is then performed with an explicit calculation of a confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or DEM generation. Application of the method in a rapidly eroding meandering bedrock river (Rangitikei river canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behavior of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behavior of the level of change detection.