However, challenges still exist at both methodological and application levels. How to properly acquire images and 3D data in harsh, remote or non-ideal environments? How to process unknown, damaged and/or poorly overlapping digitized analogue photographs? How to assess measurement precision and incorporate this uncertainty in the results and interpretation? How to model complex camera distortions and/or the resulting systematic error? How to deal with large, heterogeneous time series and multi-modal data sets? These questions exemplify situations commonly faced by geoscientists.
In the present session, we invite contributions from a broad range of geoscience disciplines (geomorphology, glaciology, volcanology, hydrology, soil sciences, etc.) to share perspectives about the opportunities, limitations, and challenges that modern 2-4D surface imaging offers across diverse processes and environments. Contributions can cover any aspect of surface imaging and mapping, from new methods, tools, and processing workflows to precision assessments, time series constructions, and specific applications in geosciences. We especially welcome contributions that cover 1) novel data acquisition and processing approaches (including image matching, camera distortion correction, complex signal/image and point cloud processing, and time series construction), 2) data acquisition in complex and fast-changing environments, and 3) innovative applications in geosciences.
Orals: Fri, 8 May, 08:30–10:05 | Room -2.20
Many topographic scenes demonstrate complex dynamic behavior that is difficult to map and understand. A terrestrial laser scanner fixed on a permanent position can be used to monitor such scenes in an automated way with centimeter to decimeter quality at ranges of up to several kilometers. Laser scanners are active sensors, and can continue operation during night. Their independence from surface texture properties ensures in principle that they provide stable range measurements for varying surface conditions.
Recent years have seen an increase in the employment of such systems for different applications in environmental geosciences, including forestry, glaciology and geomorphology. This employment resulted in a new type of 4D topographic data sets (3D point clouds + time) with a significant temporal dimension, as such systems can acquire thousands of consecutive epochs.
However, extracting information from these 4D data sets turns out to be challenging, first, because of insufficient knowledge on error budget and correlations, and second, because of lack of algorithms, benchmarks, and best-practice workflows.
The presentation will showcase recently active systems that monitored a forest, a glacier, an active rockfall site and a sandy beach respectively. Data from these systems will be used to illustrate different systematic challenges that include instabilities of the sensor system, meteorological and atmospheric influence on the data product and the maybe surprising need for alignment of point clouds from different epochs.
In addition, different ways to extract information from these 4D data sets will be discussed, in connection with particular applications. While bi-temporal change detection is often a starting point for exploring 4D data, several methods are being developed that truly exploit the extensive time dimension, including tracking, trend analysis, time series clustering and spatio-temporal region growing.
Lessons learned from experiences with these systems in different domains lead to several recommendations for future employment considering field of view design, auxiliary sensors (e.g. IMU, camera, weather station) and the possible deployment of low-cost alternatives, thereby providing a view on the near future of permanent laser scanning.
Reference
Lindenbergh, R., Anders, K., Campos, M., Czerwonka-Schröder, D., Höfle, B., Kuschnerus, M., Puttonen, E., Prinz, R., Rutzinger, M., Voordendag, A & Vos, S. (2025). Permanent terrestrial laser scanning for near-continuous environmental observations: Systems, methods, challenges and applications. ISPRS Open Journal of Photogrammetry and Remote Sensing, 17, 100094. DOI: 10.1016/j.ophoto.2025.100094
How to cite: Lindenbergh, R., Vos, S., and Hulskemper, D.: Permanent terrestrial laser scanning for environmental monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17484, https://doi.org/10.5194/egusphere-egu26-17484, 2026.
Quantifying pebble size, shape, and roundness is fundamental to
understanding sediment transport and abrasion in fluvial systems, yet
remains challenging in natural, densely packed settings. Most existing
approaches rely on 2D imagery and therefore fail to capture true
three-dimensional morphology. Here, we present a curvature-based instance
segmentation framework for reconstructed surface meshes and demonstrate its
role as a key step enabling 3D roundness and orientation analysis.
In our approach, individual pebbles are detected directly from 3D surface
reconstructions using curvature features, without prior shape assumptions.
Validation against high-resolution reference models yields a high detection
precision of 0.98, with remaining errors mainly due to under-segmentation
in overly smooth reconstructions. Estimates of 3D pebble orientation are
strongly controlled by the represented surface area, highlighting both the
potential and current limitations of orientation retrieval from incomplete
surface segments.
We illustrate how reliable segmentation allow downstream 3D shape and
roundness analyses that are not accessible in 2D, including curvature-based
surface metrics and volumetric descriptors. Example fluvial scenes
demonstrate that segmentation quality directly controls the stability of
roundness estimates and their geomorphic interpretation. Our results
establish curvature-based 3D pebble segmentation as a methodological
foundation for reproducible analyses of pebble shape, roundness, and
orientation in natural river systems.
How to cite: Rheinwalt, A. and Bookhagen, B.: Curvature-based pebble segmentation as a foundation for 3D roundness and orientation analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5922, https://doi.org/10.5194/egusphere-egu26-5922, 2026.
Large-scale infrastructure development in mountain regions produces significant changes in slope morphology and surface processes. However, stability assessments conducted after construction often rely on static or short-duration evaluations. These approaches tend to assume an immediate geomorphic adjustment to human disturbance, which can overlook delayed and nonlinear responses of hillslopes. This study examines terrain adjustments that occur with a time delay following major construction activities in complex mountainous settings. The analysis is based on a series of high-resolution topographic datasets obtained through repeated LiDAR surveys along the Sibiu - Pitești motorway corridor in the Southern Carpathians of Romania. Changes in terrain configuration caused by excavation, filling, drainage alteration, and the unloading of slopes are identified by comparing elevation models and terrain metrics. Instead of focusing solely on deformation located at the site of intervention, the study investigates terrain responses that appear later and in areas situated upslope or laterally from the engineered zones. Findings show that slope instability and surface reorganization often emerge after a measurable time delay, typically reactivating existing geomorphic features such as drainage pathways, slope breaks, and erosional forms. These responses are not random but show a strong dependence on prior landscape conditions and the type of construction-related disturbance. The results emphasize the limitations of early assessments performed shortly after construction, which may fail to capture landscape dynamics relevant for landslide initiation. The study demonstrates the usefulness of repeated LiDAR mapping for detecting evolving terrain responses in engineered mountain landscapes and supports the integration of time-sensitive processes into hazard assessment strategies.
How to cite: Al-Taha, W., Andra-Topârceanu, A., and Mustățea, S.: Delayed slope response to infrastructure-induced landscape modifications in mountainous terrain revealed by high-resolution LiDAR analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21758, https://doi.org/10.5194/egusphere-egu26-21758, 2026.
Badlands are among the most rapidly developing landscapes and exhibit a significant degree of geomorphological activity. In semi-arid/ sub-humid landscapes, specific precipitation dynamics result in particularly rapid geomorphological development. This applies in particular to land cover and geomorphology. This study employs quantitative, multi-temporal analysis to examine the spatio-temporal changes in a sub-humid calanchi badland in the upper Val d'Orcia (Italy) over a period of ten years (2014-2024). Particular emphasis lies on the dynamics of geomorphological processes and topographical changes, while considering the variables of vegetation and precipitation. The analysis encompasses both extreme events and prolonged rainfall lasting several days, which are the primary factors for surface changes in subhumid badlands. The utilisation of UAS SfM-MVS in conjunction with precise dGNSS measurements facilitates high-resolution change detection and landform analysis across five distinct observation periods, each spanning two years (= five DoD). The interactions between vegetation and geomorphological processes are investigated using a semi-automatic mapping approach based on the Triangular Greenness Index (TGI) and the interpretation of topographical changes (DoD). The vegetation analysis are based on high-resolution orthomosaics with a resolution of 0.05 m, while the geomorphic change detection analysis is carried out on 2.5D rasterised digital surface models with a resolution of 0.25 m. The major results are as follows: The mean slope gradient of the entire study site remained largely stable despite certain areas showing enhanced geomorphic activity. The DoD analysis revealed four 'geomorphic hot spots', areas of enhanced geomorphic activity and sediment contribution from the tributaries to the main valley (the major deposition area). The annual erosion rates vary between -0.4 cm (2018-2022) and -4 cm (2022-2024). The observed topographic changes can be attributed primarily to high-magnitude events (complex landslides and debris-like flows) that occur irregularly. The multi-temporal mapping of landforms has revealed a significant reduction in water erosion, with a 50% decrease observed from 35% in 2014 to 17% in 2024. Furthermore, the combination of 2D-mappings and 2.5D DoD-analysis enabled the documentation of a geomorphological process previously unknown in badland areas, namely gravitational bulging. This describes the deformation of sediments in lower-lying clay layers as a response to water infiltration, high swelling capacities of clays and the pressure exerted by the sediment packages lying above them. A significant increase in vegetation cover has been observed, particularly in areas designated as potentially moist and gentle terrain, often the deposition areas from the previous period. In general, vegetation underwent a gradual transition, evolving from a fragmented to a continuous structure, primarily due to the widespread colonisation of the main valley and the landslide pathways. Although the area affected by erosion processes decreased over the course of the study period, erosion rates remained relatively constant. This indicates a shift from high-frequency to high-magnitude processes in the most recent observation period. Overall, the phase under consideration in this study (2014-2024) can be characterised as a phase of badland stabilisation.
How to cite: Stark, M., Sannino, A., Trappe, M., Rom, J., Forster, J., Kahlenberg, G., Haas, F., and Vergari, F.: From badland to bushland? Analysis of geomorphic process dynamics and vegetation development in a sub-humid calanchi area based on high-resolution UAS data (2014-2024)., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18399, https://doi.org/10.5194/egusphere-egu26-18399, 2026.
Avalanches are critical contributors to the mass balance and spatial accumulation patterns of mountain glaciers. While gravitational snow redistribution models predict high localized accumulation, these predictions lack field validation due to the difficulty of monitoring highly dynamic avalanche cones. Here, we present two years of high-resolution monitoring of a large avalanche cone in the accumulation area of Argentière Glacier (French Alps). To capture these dynamics, we employed a multi-sensor approach: Uncrewed Aerial Vehicle (UAV) surveys and a time-lapse photogrammetry array consisting of 7 low-cost cameras deployed ~1 km away from the cone. The distance of the sensors from the surveyed area, its geometry (>30°), its surface characteristics (smooth snow surface) and the absence of fixed stable terrain due to the surrounding headwalls being episodically covered in snow made this environment particularly challenging for the photogrammetry methods applied. Point clouds and Digital Elevations Models were produced at a two-week resolution using Structure-from-Motion photogrammetry in Agisoft Metashape v1.8.3. with the alignment being constrained with Pseudo Ground Control Points. We could further co-register all point clouds to a September UAV acquisition with the Iterative Closest Point algorithm from the open-source project Py4dgeo, using automatically-derived stable ground from the RGB information of the images.
Methodological validation shows that while side-looking time-lapse photogrammetry captures the overall trend, it tends to underestimate elevation changes compared to UAV data, with biases up to 1.8 m and standard deviations of 2–6 m. Winter-time acquisitions with low light conditions over smooth snow surfaces also lead to reduced correlation over the cone. Despite these uncertainties, our results reveal extreme spatial variability in accumulation. The top of the cone is the most active zone, exhibiting elevation changes of ~30 m annually and a strong accumulation of 60 m w.e. between March 2023 and 2025 when accounting for the ice flow—roughly 15 times the annual mass balance recorded by the GLACIOCLIM program in the nearby accumulation area not affected by avalanche deposits. We identify a topographical threshold for snow storage: the upper cone fills early in the season until reaching a critical slope of ~35°, after which subsequent avalanches bypass the apex to deposit mass at the cone’s base. From May onwards, mass redistribution is further modulated by the development of surface channels. Our findings demonstrate that time-lapse photogrammetry is a viable tool for monitoring dynamic glacier surfaces and provide rare empirical evidence of the dominant role avalanches play in glacier mass budgets.
How to cite: Kneib, M., Wagnon, P., Arnaud, L., Balmas, L., Laarman, O., Jourdain, B., Dehecq, A., Le Meur, E., Brun, F., Kneib-Walter, A., Santin, I., Charrier, L., Faug, T., Mazzotti, G., Rabatel, A., Six, D., and Farinotti, D.: Use of time-lapse photogrammetry to capture substantial accumulation rates on an on-glacier avalanche deposit , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9007, https://doi.org/10.5194/egusphere-egu26-9007, 2026.
The Antarctic Peninsula (AP) is a hotspot of global warming, with pronounced atmospheric warming reported during the 20th century. Although it is critical in terms of climate change studies, the mass balance of glaciers prior to 2000 remains poorly constrained. Existing mass balance estimates are further characterized by high uncertainties due to a lack of observations. In contrast, more than 30000 historical images in archives are the sole direct observations to quantify past glacial changes and their contribution to sea-level rise.
In this study, we present a unique, timestamped, high-resolution Digital Elevation Model (DEM) and orthomosaic dataset, derived from aerial imagery that covers about 12000 km2 area on the western Antarctic Peninsula and surrounding islands between 66–68° S. We used a film-based aerial image archive from 1989 acquired by the Institut für Angewandte Geodäsie (IfAG), and is kept in the Archive for German Polar Research at the Alfred Wegener Institute, Germany, to generate the historical DEMs and orthoimages. The historical DEMs were co-registered to the Reference Elevation Model of Antarctica (REMA) mosaic on stable terrain. Our historical DEMs have vertical accuracies better than 6 m and 8 m with respect to modern elevation data, REMA, and ICESat-2, respectively. We have made this dataset publicly available at https://doi.org/10.5281/zenodo.16836526.
Initial mass balance estimates from DEM differencing of our 1989 DEM with recent surfaces from REMA strip DEMs show a near-constant ice mass despite widespread glacier frontal retreat and thinning. We hypothesize that low-elevation ice thickness loss in this period is largely compensated by higher surface mass balance in higher areas. However, this regime appears to be changing, with glaciers transitioning toward increased dynamic activity with enhanced mass loss, and higher ice fluxes.
How to cite: Thota, V. K., Seehaus, T., Knuth, F., Dehecq, A., Salewski, C., Farías-Barahona, D., and H.Braun, M.: Historical aerial imagery–derived Digital Elevation Models and orthomosaics for glacier change assessment in the western Antarctic Peninsula since 1989, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4849, https://doi.org/10.5194/egusphere-egu26-4849, 2026.
The recovery of historical topography from analogue aerial archives has has become a well-established workflow in geosciences, unlocking high-resolution records of topographic change that were previously inaccessible. However, the standard practice for sharing these results relies on static FTP servers or raw file downloads. Consequently, these datasets often remain difficult to discover, particularly for researchers from other disciplines who cannot easily assess the spatial coverage or relevance of the archive through static file lists. Furthermore, existing web-based visualization solutions often require complex database configurations and advanced full-stack development skills, rendering them inaccessible for many geoscience research groups lacking dedicated software engineers.
In this work, we present a lightweight, open-source web application designed to support the publication of historical photogrammetric data. The design prioritizes portability and ease of deployment for non-developers. Unlike complex Content Management Systems (CMS) that rely on heavy database backends, our tool utilizes a streamlined file-based ingestion pipeline. Researchers can deploy a fully interactive instance by populating a directory structure with standard geospatial vector formats (e.g., Shapefiles, GeoJSON) and point cloud data. The Node.js-based backend automatically parses these inputs to configure the visualization interface, thereby eliminating the need for manual database administration.
We demonstrate the capabilities of the website using a dataset from the Antarctic TMA archive with ~ 250.000 images. The resulting interface facilitates spatio-temporal discovery through an interactive map that visualizes survey footprints, including the residuals between metadata-derived and SfM-estimated positions. This allows users to rapidly assess geometric quality and survey coverage. To extend the platform beyond simple 2D mapping, we present the architectural integration of Potree for browser-based 3D visualization. We discuss the workflow for streaming massive point clouds to the client, a feature designed to transform the website from a passive gallery into an active analytical tool for measurement and validation. Finally, we address the challenge of data distribution by outlining the implementation of a bulk-download utility, structured to allow users to filter and request specific subsets of raw imagery, associated metadata and processed data based on their visual selection.
By providing a self-contained, low-dependency solution, we aim to shift the community standard from static archiving to dynamic, interactive exploration. This tool allows geoscientists to easily share their historical images and reconstructions and make their data truly accessible to the broader scientific community without the overhead of custom software development.
How to cite: Dahle, F., Lindenbergh, R., and Wouters, B.: From Static to Dynamic: Modernizing the Sharing of HistoricalPhotogrammetry Datasets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19445, https://doi.org/10.5194/egusphere-egu26-19445, 2026.
Currently available global Digital Elevation Model (DEM) surfaces are either derived from the stereoscopic exploitation of multispectral satellite imagery, point-wise laser altimetry measurements or the interferometric processing of bistatic synthetic aperture radar data, but only radar data allows the acquisition of a global product in a reasonable timeframe. The public private partnership of DLR and Airbus in the TanDEM-X mission paved the ground for the WorldDEM product line and its derivatives such as the Copernicus DEM. Both datasets are based on data acquisitions from December 2010 to January 2015, manual and semi-automated DEM editing procedures and represent a very accurate, very consistent and only pole-to-pole DEM data set. The Copernicus DEM is available with a free-and-open licence.
Various ecosystems such as the geosphere, biosphere, cryosphere and anthroposphere are subject to continuous changes which demand the monitoring of Earth’s topography in regular updates of global Digital Elevation Model data. The WorldDEM Neo product represents the successor of the aforementioned WorldDEM but is based on a fully-automated editing & production process and newer data: the on-going TanDEM-X mission is expected to operate until 2028 and has created an archive of up-to-date DEM scenes ready for integration into a new global DEM coverage (>90% of global landmass acquired between 2017 and 2021; ~60% of global landmass acquired again between 2021 and 2025). In conjunction with continuous improvements of the fully-automated production processes, a new global DEM coverage of WorldDEM Neo is produced early 2026. DEM applications such as the orthorectification of raw satellite imagery will benefit from the availability of an accurate and up-to-date global DEM dataset. Other applications such as multi-temporal 3D change analysis based on a single satellite mission (TanDEM-X) are possible and support the understanding of environmental changes thanks to the 3rd dimension. The rapid availability of the error-compensated WorldDEM Neo Digital Surface Model (DSM) and bare-ground Digital Terrain Model (DTM) after raw data acquisition serve various applications of global DEMs. Future acquisitions of the on-going TanDEM-X mission (until 2028) allow the processing of final and up-to-date DSM and DTM coverages at the end of the mission lifetime.
The presentation comprises a short look into the history with its manual & semi-automated DEM editing procedures. The main focus will be on the fully-automated production processes for truly global DSM & DTM coverages. Accuracy metrics, 3D change statistics between the different global coverages but also visual impressions of the various global DEM coverages will be addressed, too. On-going challenges with interferometry-based elevation data are part of an outlook and different error compensation strategies (e.g. height reconstruction from radar amplitude data based on machine-learning techniques) are highlighted.
How to cite: Fahrland, E. and Schrader, H.: Updating and upgrading a global Digital Elevation Model - the fully automated production of WorldDEM Neo with acquisitions until 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6660, https://doi.org/10.5194/egusphere-egu26-6660, 2026.
Desert kites are large prehistoric hunting traps typically composed of two long, low stone walls that converge toward an enclosure. These structures are widely distributed across the arid and semi-arid margins of the Middle East and Central Asia, exhibiting substantial variability in size, geometry, construction techniques, and topographic setting. To better understand their functionality from the Neolithic to sub-contemporaneous times, terrestrial laser scanning has increasingly been used to capture high-resolution three-dimensional representations of desert kites, enabling detailed characterization of their construction and local terrain setting. However, the kites’ subtle expression, their large spatial extent, and their progressive blending into the natural surface complicate their detection. These difficulties are further exacerbated by variable point density resulting from the alignment of multiple terrestrial scans, unavoidable occlusions caused by topography or vegetation, and the sheer volume of data produced by high-resolution ground-based surveys. Together, these factors make the reliable identification and analysis of desert kite features within raw terrestrial point clouds a challenge, which requires extensive manual intervention and expert interpretation.
In this study, I present an automated, machine-learning-based approach for highlighting desert kite features directly within 3D point clouds derived from terrestrial laser scanning, without the need for manual annotation or labelled training data. The proposed method is based on the premise that the kites' structures introduce geometric irregularities (anomalies) relative to the surrounding natural surface. Rather than explicitly modelling the kite's form or imposing predefined shape descriptors, the method learns a representation of the underlying terrain surface directly from the point cloud. This learned representation is then used to reconstruct the surface, which is subsequently compared to the original terrestrial measurements. Local deviations between the reconstructed surface and the original point cloud are quantified, with larger reconstruction errors interpreted as potential surface anomalies indicative of the kite's features.
The proposed workflow is fully data-driven and unsupervised. It does not rely on prior knowledge of kite geometry, site-specific heuristics, or expert-defined thresholds. Instead, the learning process adapts to the local surface characteristics captured in the input dataset, making it robust to variations in resolution, occlusions, and terrain complexity commonly encountered in terrestrial laser scanning surveys.
The findings demonstrate that surface-reconstruction-based anomaly detection offers a promising pathway for the automated identification of desert kite features in terrestrial 3D point clouds. More broadly, the approach is applicable to archaeological structures that exhibit weak or subtle geometric signatures. By reducing dependence on manual interpretation and labelled datasets, the method supports more objective, scalable, and reproducible analyses of archaeological landscapes, particularly in complex terrain where anthropogenic features are embedded within natural surfaces.
How to cite: Arav, R.: Detecting desert kites in 3D point clouds by learning anomalies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20499, https://doi.org/10.5194/egusphere-egu26-20499, 2026.
High-resolution 3D mapping of subterranean environments remains challenging due to their complex geometry, low-light conditions, and restricted accessibility. Among these environments, caves represent particularly demanding settings where detailed spatial documentation is essential for monitoring processes, supporting exploration and conservation efforts. Laser scanning has become a key technique for capturing accurate and detailed 3D representations of caves that form the basis for this heritage documentation and multidisciplinary research. Despite these advances, the creation of cave maps still commonly relies on traverse-line measurements and field sketches, later digitized using specialized cave-surveying software. In recent years, LiDAR data have been used for deriving the cave extent. While this method effectively captures the general geometry of cave passages, the delineation of cave-floor units, sediments, speleothems, rock blocks, and other features remains largely manual and relies heavily on the surveyor’s interpretation. As a result, feature boundaries vary between authors, and detailed cave-surface representation lacks reproducibility that is problematic for long-term documentation. In this study, we explore the use of deep-learning semantic segmentation for classifying selected cave-floor surface types based on geometric features derived from LiDAR data. Building on previous work focused on semi-automatic cave-map generation from LiDAR point clouds, we extend the workflow from deriving cave extent and floor morphology toward the automated interpretation of surface materials and forms. The method was tested on several common cave-floor surface types, including clastic sediments, flowstone, and bedrock, as well as artificial surfaces and objects typical in showcaves. The resulting classifications show that deep-learning models can distinguish surfaces with subtle geometric differences and produce consistent, reproducible delineations of units that are traditionally mapped by hand. Compared with manual digitization, the approach reduces subjectivity and provides a scalable way to generate polygonal layers used in speleocartographic workflows.
How to cite: Nováková, M., Šupinský, J., and Širotník, J.: Deep-learning classification of cave-floor surface types from LiDAR data for detailed cave mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-807, https://doi.org/10.5194/egusphere-egu26-807, 2026.
Beachrocks are cemented coastal deposits formed within the intertidal zone by the precipitation of magnesium-rich calcium carbonate. They constitute important paleogeographic and paleoclimatic markers, as they allow the reconstruction of past shoreline evolution. In addition, beachrocks influence current coastal dynamics and represent valuable geological heritage and ecological reservoirs that require preservation.
This study focuses on a sequence of multiple beachrock levels located along the Catalan Coast (NE Iberian Peninsula). The system consists of a complex sequence of submerged beachrocks with a wide formation range, situated at water depths between −0.25 m and −48 m below the current sea level. These deposits exhibit lateral continuity of up to 4.5 km and are characterized by reduced thicknesses and low geomorphic expression. The underlying substrate is composed of unconsolidated marine sediments. In certain sectors, a spatial overlap with Posidonia oceanica meadows occurs.
The aforementioned characteristics hinder their cartographic representation using traditional methods, such as aerial image interpretation and hillshade maps derived from bathymetric data, particularly for thin structures located at greater depths and in areas where Posidonia oceanica meadows are present.
The aim of this study is to evaluate the usefulness of the Red Relief Image Map (RRIM) method as an alternative quantitative terrain visualization tool for the cartography of submerged beachrocks. This method is based on the quantitative attribute openness, which expresses the degree of dominance or enclosure of a location on an irregular surface and enhances concave (negative openness) and convex (positive openness) features. Using this attribute, the RRIM method combines three main elements: topographic slope, positive openness and negative openness, allowing the visualization of subtle, low-relief topographic structures on apparently flat surfaces.
Using this approach, this study aims to improve the identification and cartographic delineation of submerged beachrock levels and to define optimal visualization parameters that contribute to a better understanding of the beachrock sequence.
How to cite: Vicente, M.-A., Mencos, J., and Roqué, C.: Testing the Red Relief Image Maps methodology to enhance the beachrock cartography in Torredembarra coast (Catalan coast, West Mediterranean Sea), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17571, https://doi.org/10.5194/egusphere-egu26-17571, 2026.
Despite significant advancements in landslide monitoring, landslides occurring on densely forested slopes remain largely unexplored. While conventional subsurface characterization methods (e.g., DPH, CPT, percussion drilling) are often impractical due to limited accessibility and steep rugged terrain, surficial analyses using remote sensing techniques frequently face challenges in capturing high-resolution ground surface data due to occlusion caused by dense vegetation cover as well as technical limitations.
Although trees and forests are generally acknowledged to reduce the probability of landslide occurrence, they are unlikely to prevent or substantially mitigate deep-seated landslides or failures on very steep slopes. Instead, trees may serve as proxies of landslide activity, potentially improving the understanding and monitoring of densely forested slopes. Affected by slope movements, trees experience external growth disturbances and develop characteristic growth anomalies that can be partly attributed to underlying landslide processes.
Multiple studies have demonstrated the feasibility of extracting such external growth disturbances, primarily stem tilting, by assessing the inclination and curvature of tree stems in LiDAR point clouds, greatly building upon previous forestry-related studies exploring the mapping, classification, and derivation of stem parameters such as height and diameter from digital twins. However, the potential to extract externally visible eccentric growth patterns in stem cross-sections at heights of maximum bending, analogous to dendrogeomorphologic tree-ring analyses, as a proxy for landslide activity has not yet been explored. Additionally, the classification of overall tree shape may provide valuable insights into the characteristics of underlying slope movements, but, to the best of the author’s knowledge, this has not been addressed in previous research.
To investigate the potential of automatically extracting tree shape and stem eccentricity from LiDAR data, and to evaluate their suitability as proxies of landslide activity, we introduce an improved two-stage processing pipeline for tree identification and extraction, along with a dedicated framework for digital dendrogeomorphology. Building upon previous work, we compute normal vectors of locally fitted planes and projected point densities to separate trees from the point cloud. To enhance the extraction of complex shaped trees (e.g., S-shaped or pistol-butted) characteristic of landslide-prone slopes, we introduce dynamically adjusted normal vector thresholds derived from estimated stem inclination. After segmenting tree stems from the point cloud, ellipses are fitted at configurable height intervals to determine cross-section centroids. These centroids are then connected as vertices of a 3D polyline, which is subsequently smoothed using a natural spline to represent the generalized stem geometry. Based on the curvature of the resulting polyline, the height of maximum bending is identified, and the corresponding cross-section eccentricity is extracted. In addition, the curvature of the polyline is used to categorically classify overall tree shape.
Our digital dendrogeomorphology approach applied to 3D point clouds enables accurate extraction of stem eccentricity, even for complex tree shapes typical of landslide-prone slopes. When paired with automated tree-shape classification, these data offer insights into slope movement and improve understanding of landslide processes in densely forested environments.
How to cite: Kamaryt, T.-H. and Müller, B.: Tree Geometry as a Potential Proxy for Landslide Activity in Densely Forested Slopes: A LiDAR-Based Digital Dendrogeomorphology Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21238, https://doi.org/10.5194/egusphere-egu26-21238, 2026.
In recent years, forest management and inventory have increasingly relied on handheld personal laser scanners (H-PLS) for capturing flexible three-dimensional data. These systems have become essential for extracting critical tree attributes, such as diameter at breast height (DBH) and tree height. Most traditional H-PLS systems utilize Simultaneous Localization and Mapping (SLAM), which fuses LiDAR and Inertial Measurement Unit (IMU) data to reconstruct environments. However, SLAM is based on relative sensor measurements, which inherently causes accumulated errors and trajectory drift. In complex forest environments, similar-looking stems and moving vegetation can further confuse the mapping process, resulting in distorted point clouds or duplicated stems that reduce the accuracy of extracted tree attributes.
While Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK) positioning provides centimetre-level absolute accuracy and usually drift-free trajectories, its application in forestry is critically hindered by signal obstruction in dense canopies. The integration of GNSS-RTK and SLAM offers a robust and synergetic solution to these challenges, allowing one method to compensate for the failures of the other. A promising development in this field is an H-PLS system that integrates GNSS-RTK, IMU, LiDAR, and camera measurements to generate georeferenced point clouds directly in the field. This hybrid approach utilizes LiDAR and camera data to maintain positioning during GNSS outages and utilizes RTK information to re-initialize and correct the trajectory once the signal is restored.
Our study evaluates whether this integrated GNSS-RTK SLAM approach improves point cloud geometry and tree attribute extraction compared to traditional SLAM methods without GNSS integration. We conducted a field campaign in a mixed forest stand during the leaf-off period to simulate realistic operating conditions with alternating GNSS visibility. The performances of a SLAM-only and a SLAM + GNSS-RTK H-PLS were validated against highly accurate terrestrial laser scanning (TLS) reference data. The analysis involved tree segmentation to assess individual tree identification and the derivation of DBH, stem positions, and tree heights. Furthermore, we investigated internal geometric quality by analysing local noise levels using cross-sectional residuals relative to fitted circles and assessed spatial homogeneity to identify artifacts like duplicated stems or gaps.
Initial results indicate that the SLAM + GNSS-RTK H-PLS system provides DBH estimates comparable to TLS, with observed differences of 6.3 mm and 1.17 cm for major and minor axes, respectively. Despite slight overestimations due to scattering, the significantly reduced acquisition time makes this integrated system an efficient alternative for forestry applications. These findings contribute to a better understanding of how integrated positioning systems can enhance mobile laser scanning workflows and support the development of autonomous, high-precision forest mapping solutions.
How to cite: Rünger, C., Binapfl, S., Maiwald, F., Krüger, R., and Eltner, A.: High-precision point cloud generation for forest inventory: Integrating GNSS-RTK and SLAM for handheld laser scanning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17031, https://doi.org/10.5194/egusphere-egu26-17031, 2026.
Digital Elevation Models (DEMs) are fundamental to hydrological modelling, watershed delineation, flood hazard assessment, and resource management. However, the reliability of these applications depends heavily on the vertical accuracy of the DEMs. Although several global DEM products with 30-m spatial resolution are widely available, variations in sensor technology, data acquisition methods, and surface characteristics can significantly influence their accuracy and suitability for hydrological studies. This research provides a comparative evaluation of five commonly used global DEMs—TanDEM-X, ASTER GDEM, SRTM, Copernicus DEM, and ALOS World 3D—by assessing their vertical accuracy against high-resolution airborne LiDAR data and ICESat-2 ATL06 measurements. The findings aim to inform best practices for selecting DEMs in hydrological modelling and catchment-scale applications, particularly in data-scarce regions.
The Flinders River catchment in northern Queensland was selected as the critical test area for evaluating how DEM errors propagate into hydrological calculations. This region is characterised by low rainfall and pronounced topographic variability, encompassing flat lowland plains, dissected upland terrain, and localised areas of steep slopes. All DEMs were standardised to a common horizontal and vertical reference framework and co-registered with the test datasets to eliminate systematic discrepancies. ICESat-2 ATL06 data were rigorously filtered to retain only the highest-quality measurements, based on a combination of quality flags, topographic slope thresholds, and signal strength criteria in vegetated areas.
Elevation differences were computed at matched locations, and DEM performance was evaluated using key statistical metrics, including bias, root mean square error (RMSE), mean absolute error (MAE), median error, and standard deviation. To provide a more comprehensive assessment, error behaviour was analysed in relation to terrain slope and catchment characteristics, highlighting zones most vulnerable to error propagation in flow routing and watershed delineation. Systematic patterns in DEM error were further examined with respect to sensor characteristics under varying landscape conditions.
Results indicate that TanDEM-X and Copernicus DEM exhibit the highest vertical accuracy, closely aligning with ICESat-2 and LiDAR observations, whereas ASTER GDEM and SRTM show larger mean errors, particularly in dissected or mountainous terrain. These findings suggest that TanDEM-X and Copernicus DEM are preferable for hydrology-focused applications in semi-arid basins, while ASTER and SRTM should be used cautiously where precise modelling is required. The study underscores the importance of DEM accuracy evaluation in relation to basin characteristics, as errors can significantly influence hydrological modelling outcomes.
How to cite: Jafari, L., Jarihani, B., Koci, J., Sanislav, I., and Duce, S.: Comparative Analysis of 30-m DEM Products for Hydrological Applications: A Case Study in the Flinders Catchment Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1968, https://doi.org/10.5194/egusphere-egu26-1968, 2026.
As part of the DFG research group “Sensitivity of high alpine geosystems to climate change since 1850” (SEHAG), high-resolution multibeam sonar data was collected from the proglacial Grastallake in the Ötztal valley during a boat survey in the summer of 2025. The Grastallake has an area of approximately 63,000 m², a maximum depth of approximately 16 m, and lies at an altitude of 2,584 m. The lake is situated in a former cirque, and its shores and the surrounding are partly composed of loose material and partly of solid rock. In the western part, there is a large whaleback with already known Egesen-moraines on top. On the southern and eastern shores, larger active debris flow cones are coupled to the lake, with meltwater runoff from the higher Grastalferner glacier flowing into the lake as a perennial stream via the eastern debris flow cone. Due to the permanent inflow from the glacier and the topographic conditions of the catchment area, the eastern debris flow cone is very active and has intensively been reshaped by several extreme debris flow events during the last years.
The bathymetric data was collected using a Norbit multibeam sonar (WBMS), which was supplemented by an SBG INS system (dual GNSS patch antenna system, SBG Eclipse D) by Kalmar Systems. Since the underwater topography of the lake was unknown and its high turbidity due to the glacier inflow, the first step was to conduct a rough survey of the lake. This step made it possible to create a coarse depth map on site in order to identify spots with shallow water, determine the system settings, and draw up a navigation plan along strips. After field work the recorded data was processed using Quinertia for trajectory calculation and Opals for strip adjustment. This resulted in a final 3D point cloud with an average point density of 400 points per square meter, which was converted to raster data in order to perform spatial analyses.
Using the data, geomorphological forms were mapped in a first step. In addition to a previously unknown late glacial moraine section, the underwater deposits of recent debris flows became visible. In addition to mapping, geomorphological structures were used for spatial analysis, such as comparing the depositions of debris flows above and below the water. Since the data is very well suited for mapping underwater structures, this case study demonstrates the enormous potential of bathymetric data acquired by multibeam sonar measurements, that has rarely been used for geomorphological studies to date. Multitemporal analysis in the sense of a 4D analysis could only be carried out to a limited extent in this case study. However, with the data now available, multitemporal analysis, i.e., quantification of sediment input into lakes, will also be possible in the future. This would then enable assessments to be made of the hazard potential of newly formed lakes in the proglacial area and of their lifespan.
How to cite: Haas, F., Stark, M., Rom, J., Dammert, L., Kohlhage, T., Himmelstoss, T., Kara-Timmermann, D.-E., Altmann, M., Surrer, C., Baumgartner, K., Fischer, P., Betz-Nutz, S., Heckmann, T., Pfeifer, N., Mandlburger, G., and Becht, M.: Using high-resolution bathymetric data from a multibeam sonar acquisition to map and analyse geomorphical underwater structures in the proglacial Grastallake in the Horlachtal valley/ Ötztal Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13875, https://doi.org/10.5194/egusphere-egu26-13875, 2026.
As glaciers retreat, permafrost degrades, and mountains destabilize, modern landscape evolution is increasing the potential for catastrophic events, such as the Birch Glacier collapse on May 28, 2025. To improve our understanding of mass movements in mountainous regions and support future hazard assessment and risk mitigation efforts, we are generating time series of glacier surface elevation change from historical aerial photography provided by the Swiss Federal Office of Topography (Swisstopo).
In this case study, we leveraged multi-temporal photogrammetric reconstruction and Digital Elevation Model (DEM) coregistration techniques, implemented in the Historical Structure from Motion (HSfM) pipeline, to generate an ~80-year record of self-consistent DEMs and orthoimage mosaics from analog film imagery collected over the Birch Glacier between 1946 and 2010. From 1985 until 2010 we generated nearly annual surface measurements, making this a unique and remarkably dense historical time series. The time series is augmented with modern surface measurements generated from linescan and UAV imagery collected during the period of 2010 to 2025. To quantify the uncertainty of elevation change measurements we compute residuals with respect to the swissSURFACE3D elevation over stable ground, defined by the swissTLM3D land surface classification. The reconstructed time series provides geometric constraints to precisely model the preconditioning phase leading up to the May 2025 Nesthorn-Birchglacier hazard cascade, which may help mitigate future risks in mountainous terrain (see Jacquemart et al. 2026 in GM3.1)
How to cite: Knuth, F., Hodel, E., Heisig, H., Marty, M., Jacquemart, M., Bauder, A., Simmen, J.-L., and Farinotti, D.: Automated photogrammetric reconstruction of Birch Glacier, Switzerland (1946–2025): A high-density time series of topographic change preceding catastrophic glacier collapse, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22072, https://doi.org/10.5194/egusphere-egu26-22072, 2026.
Long-term observations of glacier mass change provide a key indicator of atmospheric warming and are essential for understanding glacier behaviour and responses to climate forcing. Archived aerial photographs represent an underutilised source of historical information from which three-dimensional surface geometry can be reconstructed to quantify past glacier change. This approach is particularly valuable in Antarctica, where surface-elevation change prior to the 1990s remains poorly constrained due to limited pre-satellite altimetry and a scarcity of reliable Ground Control Points (GCPs). As a result, historic mass-balance estimates have largely relied on climate reanalysis and modelling.
Advances in photogrammetric techniques have substantially improved the efficiency and accuracy of Digital Elevation Models (DEMs) derived from historical aerial imagery. Here, we present a newly compiled inventory of Antarctic aerial surveys conducted throughout the twentieth century, documenting their spatial and temporal coverage to identify regions suitable for DEM reconstruction. Then, building on established workflows, we show newly constructed DEMs for three glaciers that formerly fed the Larsen A Ice Shelf on the Antarctic Peninsula, capturing surface geometry both before and after its collapse in 1995. These reconstructions reveal heterogenous glacier responses to reduced buttressing, controlled by local morphology and consistent with previous regional observations.
How to cite: Rowe, E., Willis, I., and Fenney, N.: Compiling an Inventory of Historic Antarctic Aerial Photographs to Measure Long-Term Glacial Mass Balance Change from Digital Elevation Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13015, https://doi.org/10.5194/egusphere-egu26-13015, 2026.
Scanned historical aerial photographs acquired with film cameras from the early twentieth century to the early 2000s are the longest and richest archive of Earth observation data for reconstructing past topography. Those with stereoscopic acquisition enable the generation of Digital Elevation Models (DEMs) and orthoimages when processed with photogrammetric techniques, extending the assessment of environmental change beyond the time scale of modern satellite observations.
In this study, we present a long-term (1945-2020) dataset of glacier surface elevation for the Gran Campo Nevado ice field in southern Chile. The dataset is based on aerial photographs acquired in 1945 using a Trimetrogon camera and in the 1980s and 1990s using nadir-looking film cameras from the Chile60 and Geotec flight campaigns, complemented by a 2020 Pléiades satellite–derived DEM made available through the Pléiades Glacier Observatory program (Berthier et al., 2023). To process the historical photographs, we developed an open-source pipeline that builds on structure-from-motion (SfM) principles and incorporates learning-based feature-detection and matching algorithms, such as SuperPoint and LightGlue. Absolute image orientation is achieved through automated detection of ground control points derived from the Pléiades DEM and orthoimage. DEMs accuracy was evaluated over stable terrain by comparing them with the Pléiades reference DEM. As well, the reconstructed DEMs are compared with those obtained using an established SfM processing workflow (HSfM; Knuth et al., 2023). The resulting DEMs provide a reconstruction of glacier surface elevation spanning more than seven decades, and glacier elevation changes are quantified from the DEM time series. By using reproducible, open-source methodologies, this presentation demonstrates opportunities for the research community to leverage other historical datasets and extend analyses beyond what is possible with modern satellite observations alone.
How to cite: Kugler, L., Rada, C., Webster, C., Wegner, J. D., Berthier, E., and Piermattei, L.: Long-term glacier elevation change at Gran Campo Nevado since 1945 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17927, https://doi.org/10.5194/egusphere-egu26-17927, 2026.
River bank erosion represents a dynamic geomorphic hazard, particularly in meandering channels where migration rates threaten critical infrastructure and agricultural land. While our previous work on the Sajó River (Hungary) established a novel, low-cost monitoring framework utilizing Raspberry Pi (RPi) cameras for near-continuous observation, the reliability of photogrammetric reconstruction under uncontrolled outdoor conditions remains a critical challenge. This study presents a systematic evaluation of the accuracy constraints inherent in automated Structure-from-Motion (SfM) processing pipelines, with a specific focus on optimizing image alignment across a wide range of scene conditions.
To determine the robustness of RPi imagery, we conducted a comprehensive sensitivity analysis of the SfM-based image alignment phase. We systematically tested over 120 variations of processing parameters, manipulating keypoint and tie-point limits, upscaling factors, and masking strategies. The implementation of rigorous masking was critical, as the imagery is geometrically challenging: the moving river surface in the foreground and the sky in the background occupy the majority of the field of view, leaving only a narrow, static fraction of the image relevant for reliable 3D reconstruction. These combinations were evaluated against a dataset representing the full range of environmental variability, including clear, cloudy, dark, foggy, overexposed, and rainy conditions, as well as distinct hydrological states such as low flows, flood events, and snow cover.
Preliminary results indicate that a specific balance of 30,000 keypoints and 5,000 tie points (ratio 6.0) optimizes reconstruction fidelity, achieving an RMS error of 0.75 pixels under clear weather conditions. Notably, the system demonstrated unexpected robustness in low-light scenarios, maintaining consistent error margins of 1.17–1.18 pixels across various configurations. Conversely, scaling up these limits beyond the optimum yielded diminishing returns, confirming that higher computational loads do not necessarily equate to improved geometric accuracy. Furthermore, we applied gradual selection algorithms to filter sparse point clouds, removing unreliable points based on reconstruction uncertainty to isolate the most geometrically valid features.
The crucial final phase of this research bridges the gap between digital reconstruction and physical reality. We validate the optimized SfM-based point clouds by comparing them directly against high-precision Terrestrial Laser Scanning (TLS) data acquired during two previous campaigns and upcoming field surveys. This multi-temporal comparison allows us to quantify specific error margins for volumetric and horizontal material displacement calculations. By defining these accuracy constraints, we establish a validated protocol for calculating erosion volumes during high-flow events, ensuring that automated, low-cost monitoring systems can provide actionable, high-precision data for river management even under adverse environmental conditions.
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The research was funded by the DAAD-2024-2025-000006 project-based research exchange program (DAAD, Tempus Public Foundation).
How to cite: Bertalan, L., Kovács, L., Duran Vergara, L. C., Abriha, D., Krüger, R., Blanch Gorriz, X., and Eltner, A.: Optimizing SfM workflows for continuous river bank monitoring: evaluating image alignment accuracies across diverse environmental conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9167, https://doi.org/10.5194/egusphere-egu26-9167, 2026.
Historical film-based images, acquired during aerial campaigns since the 1930s and from satellite platforms since the 1960s, provide a unique opportunity to document changes in the Earth’s surface over the 20th century. Yet, these data present significant and specific challenges, including complex distortions in the scanned image and poorly known exterior and/or interior camera orientation. In recent years, semi- or fully-automated approaches based on photogrammetric and computer vision methods have emerged (e.g., Knuth et al., 2023; Dehecq et al., 2020; Ghuffar et al., 2022), but their performance and limitations have not yet been evaluated in a consistent way.
The ongoing “Historical Images for Surface Topography Reconstruction Intercomparison eXperiment (Historix)” project aims at comparing existing methods for processing stereoscopic historical images and harmonizing processing tools.
Within this experiment, participants are provided with a set of historical images and available metadata and invited to return a point cloud and estimated camera parameters. We selected two study sites near Casa Grande, Arizona, and south Iceland, chosen for their good availability of historical images and variety of terrain types. For each site, we selected 3 sets of film-based images acquired in the 1970s or 80s, overlapping in space and time: aerial images with fiducial marks from publicly available archives and 2 image sets from the American Hexagon (KH-9) reconnaissance satellite missions acquired by the mapping camera (KH-9 MC) and panoramic camera (KH-9 PC). The submitted elevation data will be cross-validated across different image sets and participant submissions, as well as against reference elevation data over stable terrain. The spread in the retrieved elevations will be analysed with respect to image type, terrain type and processing methods to highlight the strengths and limitations of the different approaches.
In this presentation, we will introduce the experiment design, the selected benchmark dataset, the current methodologies and the preliminary results of the intercomparison. Finally, we will present some of the open-source code that exist or are being developed to process historical images.
How to cite: Dehecq, A., Knuth, F., Belart, J., Piermattei, L., Ressl, C., McNabb, R., and Godin, L.: Historical Images for Surface Topography Reconstruction Intercomparison eXperiment (Historix), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3936, https://doi.org/10.5194/egusphere-egu26-3936, 2026.
