Abstract. 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.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.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.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.
Tommaso Mainiero, Jad Ghantous, N. Grasso et al.· The International Archives o...· 0 citations
Abstract. The Valpelline Valley, located in the northern Aosta Valley (Italy) along the Swiss border, is a typical Alpine valley shaped by glacial and fluvial processes. Characterized by a large altitudinal range (900-4000 m a.s.l.) and hosting glaciers feeding the Place Moulin reservoir, the area plays a key role in regional hydroelectric production. Since 2020, GlacierLAB has been conducting glacier monitoring activities through biannual aerial photogrammetric surveys, overcoming the logistical constraints imposed by the steep and inaccessible morphology of the valley.The surveys were performed using a medium-format camera mounted under an aircraft wing and equipped with GNSS and IMU systems. Due to the lack of synchronization between the camera shutter and GNSS receiver, georeferencing relied on Ground Control Points (GCPs), whose spatial distribution is often limited in high-mountain environments. This condition makes camera calibration a critical factor for ensuring reliable multi-temporal analysis.This study investigates the behavior of the radial distortion parameter k1 using images previously corrected for optical distortion. A multi-run bundle adjustment strategy was applied in Agisoft Metashape, including baseline configurations, global and image-wise estimation of k1, and robustness tests under different GCP setups. Statistical analyses reveal a systematic and significant dependence of k1 on the vertical camera–terrain distance.However, comparison with a theoretical atmospheric model based on the Saastamoinen formulation shows weak correlation, indicating that the observed effect cannot be attributed solely to atmospheric refraction. Instead, k1 acts as a compensatory parameter absorbing depth-dependent systematic effects related to block geometry and acquisition conditions.
M. Macelloni, N. Grasso, Alberto Cina· The International Archives o...· 0 citations
