The analysis and interpretation of 3D topographic change requires methods that achieve low uncertainties in change quantification. Many recent geoscientific studies that perform point cloud-based topographic change analysis have used the multi-scale-model-to-model-cloudcomparison (M3C2) algorithm to consider the associated uncertainty. Change measured with the M3C2 approach, however, is difficult to interpret where 1) change occurs in directions different to the direction of change computation or 2) the quantified magnitudes of change are exceeded by the associated uncertainty due to a rough surface morphology. We present a correspondence-driven plane-based M3C2 approach that is tailored to quantifying small-magnitude (< 0.1 m) 3D topographic change of rough surfaces by reducing the uncertainty of quantified change. The approach 1) extracts planar surfaces in point clouds of successive epochs, 2) identifies corresponding planar surfaces between two point clouds using a binary random forest classification, and 3) calculates M3C2 distances and the associated uncertainty between the corresponding planar surfaces. This correspondence-driven plane-based M3C2 does not require recognition or reconstruction of geometrically complex objects but instead quantifies change between less complex, homologous planar surfaces. The approach further allows to relate change directly to a moving object. We apply our approach to a bi-weekly time series of terrestrial laser scanning point clouds acquired at a rock glacier in the Austrian Alps. The approach enables a sevenfold reduction in the uncertainty associated with topographic change compared to standard M3C2. Significant change is therefore detected in around 75% of the area of change analysis, whereas standard M3C2 detects significant change in only 16% (2-week timespan) to 59% (10-week timespan) of the same area. The correspondence-driven plane-based M3C2 complements 3D change analysis in applications that aim to quantify smallmagnitude topographic change in photogrammetric or laser scanning point clouds with low uncertainties in natural scenes which are characterised by overall rough surface morphology and by individual rigid objects with planar surfaces (e.g., rock glaciers, landslides, debris covered glaciers).

This dataset includes six point clouds acquired bi-weekly by terrestrial laser scanning in the summer of 2019. Point clouds have been preprocessed using the following workflow: 1) MSA coregistration within all epochs using RiSCAN PRO (v. 2.11), 2) cropping of point clouds to the area of the rock glacier using a region filter in the software OPALS, 3) tiling into 10 tiles using a region filter in OPALS, 4) point cloud filtering to remove statistical outlier points in CloudCompare (v. 2.11.1) according to https://pointclouds.org/documentation/classpcl_1_1_statistical_outlier_removal.html (parameters: number of points to use for mean distance estimation: 12; standard deviation multiplier threshold (nSigma): 1.00), 5) normal computation in OPALS (parameters: -NormalsAlg robustPlane -searchMode d3 -searchRad 0.5 -neigh 200 -selmode nearest -storeMetaInfo medium -direction upwards)

Based on these datasets, the extraction of planar areas and the identification of plane correspondences were performed as described in Zahs et al. (2021).

Approximated scan positions for the laser scans are: (X/Y/Z in UTM32N) SP1 = [652992.6490, 5189116.7022, 2526.2710], SP2 = [652949.7085, 5189182.2139, 2473.9986], SP3 = [652917.8332, 5189284.5585, 2423.7433], SP4 = [652804.5869, 5189190.5423, 2456.0348], SP5 = [652812.0904, 5189246.1069, 2433.7296], SP6 = [652831.6805, 5189073.5765, 2523.7454], SP7 = [652862.9167, 5189292.7994, 2403.6955]

Time period covered: Start: 2019-06-24; End: 2019-08-30

Dates of collection: Start: 2019-06-24; End: 2019-06-24 Start: 2019-07-06; End: 2019-07-06 Start: 2019-07-19; End: 2019-07-19 Start: 2019-08-03; End: 2019-08-03 Start: 2019-08-16; End: 2019-08-16 Start: 2019-08-30; End: 2019-08-30

Alignment error between point clouds of all dates in stable areas: 0.011-0.013 m.