Refraction-aware integrated Georeferencing of bathymetric Laser Scanning Data

Accuracy assessment of bathymetric LiDAR using planar reference geometries and total station measurements

Abstract. Airborne laser bathymetry (ALB) is an efficient and accurate tool for mapping submerged environments, particularly shallow water bodies that are difficult to access with surface vessels. Modern ALB systems can achieve accuracies comparable to SONAR. However, multiple factors, including geo-referencing, water surface modelling, and range measurements, influence the resulting point cloud, making analytical error propagation challenging. Empirical evaluation against reference data is therefore essential, but difficult: ALB accuracy is typically in the low centimetre range, requiring reference data of equal or higher accuracy. Robotic total stations enable acquisition of underwater reference data for shallow water depths, e.g., up to 4.5m, with expected accuracies between 3mm to 10mm, depending on water depth, which approaches the inherent accuracy of ALB and limits the evaluation significance. In this study, we assess a UAS-based ALB data set from a mountain lake in Austria using reference planes and points acquired by robotic total stations. We separate the accuracy analysis into trueness and precision to isolate the effects of geo-referencing and water surface modelling from the intrinsic uncertainty of the LiDAR sensor. The results show that geo-referencing introduces the largest systematic bias, while the precision of the ALB data remains approximately 1 cm to 2 cm, even for submerged measurements. These findings demonstrate the high accuracy of state-of-the-art ALB systems and provide a framework for rigorous accuracy assessment in shallow aquatic environments.

Mapping at the Boundary: Simultaneous Above- and Underwater Surveying of Rocky Coastal Environments with an Uncrewed Surface Vehicle

Abstract. Mapping at the air–water interface in shallow coastal environments remains challenging due to the need to integrate heterogeneous datasets acquired under different geometric and operational conditions. This study presents a modular uncrewed surface vehicle (USV)-based system for simultaneous above- and underwater photogrammetric surveying supported by differential GNSS positioning. The system integrates a rigid multi-camera configuration, GNSS time synchronization, and a direct georeferencing workflow based on trajectory interpolation and lever-arm calibration. Experimental results from a rocky coastal site in Sardinia (Italy) show that underwater photogrammetry can achieve centimetric absolute accuracy (2–4 cm horizontally and ~8 cm vertically) without underwater ground control points. The USV enables controlled and repeatable acquisition in very shallow environments, while UAV photogrammetry complements the reconstruction of the emerged area. Limitations related to image quality and refraction effects are discussed. The system represents a flexible and scalable solution for integrated coastal mapping and monitoring.

Airborne Bathymetric LiDAR Outlier Removal using Unsupervised Iterative Refinement for Tidal Flats Topographic Mapping

Supraglacial lake bathymetry from high-resolution airborne imagery using stereophotogrammetry and structure from motion

Assessment of Radial Distortion Parameter Variability in Airborne Photogrammetry: Implications for Atmospheric Refraction

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.

Pathfinder: Efficient Snow Depth, Stratigraphy, and Uncertainty Determination From Radar Measurements

Snow strongly influences the climate system through its albedo and insulating properties, while also representing a critical freshwater resource. Yet, its spatial and temporal variability remain poorly constrained due to limitations of in situ and satellite observations. Unoccupied aerial vehicle (UAV)-mounted surface-penetrating radars provide a solution for high-resolution snow surveys, but their data are often difficult to interpret because of variable flight conditions, which, combined with the complicated microwave interactions with snow, result in complex noise and signal patterns. We present Pathfinder, an open-source algorithm for automatic detection of snow interfaces in radar echograms. The method formulates interface tracking as a path-finding problem, combining cost maps derived from reflection strength, ridge detection, and horizontal continuity, and solves it using an efficient dynamic-programming scheme. Pathfinder retrieves the air–snow and snow–ground (or snow–ice) interfaces, and can additionally identify internal layers when present. Validation against coincident in situ, probe-derived snow depth measurements shows an accuracy of snow depth retrievals with R$^{2}$ = 0.96 and RMSE = 8 cm. The algorithm is computationally efficient, enabling real-time application during UAV surveys. Pathfinder was developed for the ultrawide-band snow sensor (UWiBaSS) from NORCE Research but we show it to be transferable across different UAV-mounted and ground-based radar systems. Case studies from Svalbard, the Alps, and polar sea ice demonstrate its robustness across diverse, but mostly dry snow conditions. Pathfinder advances UAV-borne radar as a practical tool for snow depth and stratigraphy mapping by providing an efficient, and operational interface detection method. Consequently, it can support both scientific research and in-the-field decision-making.