Multi-temporal LiDAR (light detection and ranging) data have provided the opportunity to detect changes in the topography and landscape at resolutions previously not possible. Multi-temporal LiDAR change detection analysis requires data sets to be spatially co-registered. The co-registration is ideally performed using the LiDAR point clouds for each flightline rather than LiDAR-derived rasters to avoid propagation of flightline co-registration errors. A slope-based surface matching of the LiDAR point cloud was previously used to improve the relative accuracy of overlapping flightlines. The slope-based matching method employs the slope of the elevation data to determine the spatial offset between the overlapping surfaces. We extend this technique to further improve the co-registration of the multi-temporal LiDAR data to an accuracy that is applicable for fine-scale change detection. Flightlines are co-registered, first with adjacent flightlines, and then with overlapping flightlines from data collected at different times. The co-registered flightlines are combined to perform change analysis. We evaluate our results using several case studies, ranging from landscape change due to fire, landslides, and disaster managements.