Automatic extraction of surface activity from near-continuous 3D time series is essential for geographic monitoring of natural scenes. Recent change analysis methods leverage the temporal domain to improve the detection in time and the spatial delineation of surface changes, which occur with highly variable spatial and temporal properties. 4D objects-by-change (4D-OBCs) are specifically designed to extract individual surface activities which may occur in the same area, both consecutively or simultaneously. In this paper, we investigate how the extraction of 4D-OBCs can improve by considering uncertainties associated to change magnitudes using Kalman filtering of surface change time series. Based on the change rate contained in the Kalman state vector, the method automatically detects timespans of accumulation and erosion processes. This renders change detection independent from a globally fixed minimum detectable change value. Considering uncertainties associated to change allows detecting and classifying more occurrences of relevant surface activity, depending on the change rate and magnitude. We compare the Kalman-based seed detection to a regression-based method using a three-month tri-hourly terrestrial laser scanning time series (763 epochs) acquired of mass movements at a high-mountain slope in Austria. The Kalman-based method successfully identifies all relevant changes at the example location for the extraction of 4D-OBCs, without requiring the definition of a global minimum change magnitude. In the future, we will further investigate which kind of change detection method is best suited for which types of surface activity.