Climate change is driving an increase in the frequency and intensity of extreme events in mountainous environments, amplifying geomorphological hazards and the need for accurate multi-temporal topographic monitoring. However, the integration of multi-source datasets remains challenging due to geolocation inconsistencies, heterogeneous data quality, and complex terrain conditions.<br />This study presents a systematic benchmarking framework to evaluate the performance of local point cloud registration algorithms and their impact on geomorphological change detection. Three widely used methods—Iterative Closest Point (ICP), Point-to-Plane ICP, and Generalized ICP (GICP)—were tested across two alpine case studies in Italy (Rio Cucco catchment and Belvedere Glacier), considering different surface types and initial alignment conditions.<br />Results demonstrate that registration performance is strongly controlled by surface morphology, with rocky areas ensuring stable and accurate alignment, while vegetated surfaces introduce significant uncertainties. Point-to-Plane ICP emerges as the most computationally efficient method, whereas GICP provides improved robustness under complex conditions.<br />The study further highlights that integrating robust outlier rejection significantly improves statistical consistency and reduces LoD95. The proposed approach provides a reproducible framework for optimizing co-registration strategies and improving the accuracy of geomorphological monitoring in high-relief environments.