Accurate 3D surface reconstruction and change analysis relies on point clouds representing persistent solid surfaces and should neglect very small (< laser footprint size) and temporary objects that create outliers. Terrestrial and Permanent Laser Scanning (TLS/PLS) data often contains transient or detached points, which violate assumptions of common cloud-, mesh-, and surface-based 3D change analysis methods. Those points cause wrong correspondences and affect change values in multi-temporal point cloud comparison. We address this with the Scan Outlier Ratio (ScOR) filter, a LiDAR scanning and survey-aware descriptor designed to identify points unsuitable for most point cloud-based change analysis methods. ScOR compares the measured point spacing with the expected spacing, assuming the surface is locally planar and orthogonal to the incoming laser beam. ScOR works with a single scan or multiple scans acquired from the same position, enabling multi-temporal neighborhoods for filtering. Using data from natural and urban environments, we analyze ScOR across different surfaces, neighborhood sizes, temporal neighborhoods, and compare it with the Statistical Outlier Removal (SOR) algorithm. Results show that ScOR successfully removes non-surface points, while preserving surface information. In our experiments, the true positive rate exceeds 95% in all but one case, while the false positive remains below 10% throughout. With neighborhoods from subsequent and aggregated epochs, the method automatically detects and removes large temporary objects (e.g., a person). Due to its interpretability, efficiency, and range-aware design, ScOR provides an effective pre-processing method for automated and near real-time 3D surface change analysis with TLS/PLS.