SCCD: A slicing algorithm for detecting geomorphic changes on topographically complex areas based on 3D point clouds

Three-dimensional (3D) point clouds are widely used for geomorphic change detection. However, the lack of efficient change-detection algorithms for complex terrain limits the use of 3D point clouds in area-wide morphological change studies. In this study, a complex terrain development process was simulated on a natural slope in the hilly and gully Loess Plateau in China using 28 runoff scouring experiments conducted in two plots. Highly precise point clouds were obtained using terrestrial laser scanning (TLS) before and after each experiment. A slice contraction change detection (SCCD) algorithm was developed based on slicing, Laplacian-based contraction, and differential principles for detecting geomorphic and volumetric changes on complex terrain, and the level of detection (LoD) was derived with respect to that of the multiscale model to model the cloud comparison (M3C2) algorithm. The accuracy of SCCD was compared with that of the 3D-M3C2 algorithm (i.e., a 3D volumetric change calculation algorithm based on M3C2) and the digital elevation model (DEM) of difference (DoD) algorithm based on the measured sediment yield from the plots. The comparison was performed also under different point cloud densities and morphologies. Results showed the following: (1) The precisions of SCCD and 3D-M3C2 were comparable and considerably higher than that of DoD. The mean relative errors of SCCD, 3D-M3C2, and DoD for the two plots were 13.32% and 10.37%, 10.07% and 10.84%, and 35.30% and 27.23%, respectively. The relative error fluctuations of the three algorithms for the individual experiments followed the sequence of DoD (standard deviation (std.): 10.18) > 3D-M3C2 (std.: 8.29) > SCCD (std.: 5.79). (2) The sensitivity to point cloud density changes followed the sequence of 3D-M3C2 > SCCD > DoD as the point cloud density varied between 10,000 and 1000 points m−2. The mean relative errors of 3D-M3C2, SCCD, and DoD for the two plots were 10.07–18.59% and 10.84–13.62%, 13.32–16.83% and 10.37–15.50%, and 35.30–38.33% and 26.52–27.26%, respectively. (3) The accuracy of 3D-M3C2 decreased significantly (p < 0.05), whereas those of SCCD and DoD either changed insignificantly (p > 0.05) or increased significantly for substantial morphologic changes. As the experiments progressed, the relative errors of 3D-M3C2, SCCD, and DoD for the two plots were 10.87–93.77% and 30.76–167.89%, 20.04–9.95% and 5.54–7.96%, and 42.49–11.94% and 3.89–4.96%, respectively. Overall, the SCCD algorithm provides a reliable means of conducting geomorphic change detection in complex terrain and thus facilitates future studies on detecting and characterizing land surface processes.