The availability of 3D data in geology, especially in geomorphology, has increased tremendously in the past years. Airborne lidar data, large-scale UAV surveys using Structure-from-Motion approaches, or virtual outcrops generated from hand-held cameras allow a much finer quantitative description of the Earth’s surface. New analysis techniques for geomorphology are actively developed to explore and take advantage of data structures and higher resolution.
This presentation will showcase some recent examples of data collection strategies in the field, but also segmentation and feature generation on dense point clouds and high-resolution orthomosaics. Point clouds often sample objects very densely and form nearly continuous surfaces - 3D coordinates can be converted into a network structure to form meshes, which are spatial data structures containing slope and aspect information for every facet that links three points. The view angle of lidar scanners or cameras during Structure-from-Motion processing can be used to orient normals of points and meshes. As an application example, we use curvature measured along mesh surfaces for 3D segmentation of pebbles and grains. Meshes are also data structures for measuring volumetric differences, for example between pre- and post-event data acquisitions. Textured 3D models are not yet used in the geosciences, but provide opportunities to include spectral and roughness information on meshes.
A current challenge in the processing of point clouds is the precise classification of points, such as distinguishing between ground and vegetation points. A common approach is to calculate geometric features in a spherical neighborhood and use these as an input to a classifier such as a random forest or a neural network. Here, we show an alternative approach for deriving point neighborhoods to calculate features for point-cloud classification: Instead of using all points in a neighborhood, we select points based on point attributes such as normal direction, color, or point connectivity. A feature-based classification based on these modified neighborhoods shows improved classification accuracy.
By highlighting two approaches in point-cloud processing - turning point clouds into meshes containing network information and carefully selecting neighborhoods for point-feature calculation - we show the potential that point clouds have in geomorphologic applications. An open research area is to further improve classifications for environmental point clouds to better monitor and quantify processes in the geosciences.
How to cite: Bookhagen, B., Rheinwalt, A., and Hess, M.: Point Clouds, Voxels, Meshes, and Beyond: Examples for 3D Data Processing in the Environmental Sciences, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10879, https://doi.org/10.5194/egusphere-egu25-10879, 2025.
Surface roughness is an important control on a wide range of Earth surface processes. The increasing spatiotemporal availability of topographic point cloud data provides scope for advances in quantifying geomorphic surfaces and topography. Here, bedrock riverbed point clouds were obtained from dry riverbeds using terrestrial laser scanning (TLS) and Structures from Motion (SfM) photogrammetry. These data were processed using a unified workflow to extract the channel morphology and multiple different surface roughness. Metrics were calculated based on vertical and horizontal point spacings, cell area and slope, and incorporated multiscale analysis methods. Principal component analysis and hierarchical clustering revealed the concurrent use of multiple metrics is required to comprehensively describe the diversity in bed topographic characteristics. Multiple metrics are required as riverbed characteristics and features are shown to be represented by differing surface roughness metrics. This work further explores the applications of these metrics to advance the understanding of geomorphic and Earth surface processes, including sediment transport processes and hydrodynamics. It is proposed these metrics and analysis approaches can be applied more widely to landscapes beyond riverbeds, yet the most appropriate metric likely depends on the process that is of interest.
How to cite: Houseago, R., Hodge, R., Ferguson, R., Hackney, C., Hardy, R., Hoey, T., Johnson, J., Rice, S., Yager, E., and Yamasaki, T.: Quantifying riverbed surface roughness from point cloud data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18204, https://doi.org/10.5194/egusphere-egu25-18204, 2025.
The mud volcanoes in Romania are among the most well-preserved and accessible mud volcano fields in Europe. They represent important natural heritage and, at the same time, offer a unique natural laboratory for studying the interplay between tectonics and hydrocarbon fluid escape in an active geological setting. However, these mud volcanoes remain understudied. Here, we present an integrated UAV approach (LiDAR, photogrammetry, thermal imaging) to investigate relevant aspects of the Pâclele Berca, Mici, Mari, and Beciu located in Buzău Land UNESCO Global Geopark, Romania.
The Pâclele Mici mud volcano is an area of complex geology shaped by tectonic phases from the Late Cretaceous and Early Miocene periods. Situated on the Berca–Arbanasi anticline near the seismically active Vrancea zone, this site is geologically significant as it represents an interaction of methane-rich fluids with neotectonic faults and is possibly still influenced by a seismogenic area, creating a dynamic and unique environment.
In 2024, UAV-mounted LiDAR, photogrammetry, and thermal sensors were deployed to collect high-resolution spatial and thermal data over the Pâclele Mici site. The LiDAR system provided precise digital elevation models, revealing subtle patterns of surface deformation, including subsidence and uplift, that evolve with mud volcanic activity. Photogrammetry generated detailed orthomosaics, allowing for an in-depth assessment of surface morphology, textures, and fissures. Thermal imaging highlighted temperature anomalies linked to active venting and subsurface fluid movement, offering insights into the system's thermal behavior and energy flux. These datasets revealed significant surface deformation and distinct thermal anomalies concentrated around active vents when analyzed together. Subsidence and uplift patterns correlated with zones of intense fluid discharge, aligning with findings from previous deep geoelectrical surveys.
By combining spatial, morphological, and thermal datasets, this research provides a holistic view of the Buzau Land Geopark mud volcanos, enhancing our understanding of their evolution and the mechanisms driving their activity. The findings underscore the importance of remote sensing technologies in studying dynamic geological systems and contribute valuable insights into the broader implications of mud volcanism, including its role in methane emissions, landscape evolution, and geological hazards. This multidisciplinary approach sets a foundation for future studies and monitoring efforts at Pâclele Mici and globally in similar active settings.
Keywords: Remote Sensing, UAV Technologies, Mud Volcano
Acknowledgments: This work was done in the framework of the National Research Program, project SOL4RISC no. PN 23360301
How to cite: Nastase, I. E., Cerbu, B., Petrescu, A., Gerea, A., Mihai, A. E., and Tataru, D.: Geomorphological and Thermal Monitoring of Mud Volcano Landscape with UAV Technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6529, https://doi.org/10.5194/egusphere-egu25-6529, 2025.
Landslides are among the most frequent natural hazards, posing threats to human lives, infrastructure, and the environment. Effective mitigation of the associated risks requires improved landslide understanding based on high-resolution data.
Digital twins, created using optical or LiDAR-based remote sensing techniques alongside the derived digital terrain models (DTMs), offer significant potential to enhance landslide modeling and therefore deepen our understanding of their dynamics. However, the generation of such 3D models remains challenging in forested areas as most remote sensing techniques, particularly those utilizing aerial platforms, lack the ground point density needed to capture the surface precisely. Additionally, ground point classification (GPC) becomes more difficult at increasing resolution and forest cover.
To address these challenges, we explore novel under-canopy ground data acquisition methods for digital twin generation and present innovative approaches for DTM generation and refinement, along with digital dendrogeomorphology.
Conducted at a representative study site, close-range panoramic terrestrial photogrammetry (crpTP) and terrestrial laser scanning (crpTLS) were employed and evaluated for their effectiveness in generating high-resolution 3D models. We utilized a custom R-tool applied to the derived point cloud for ground surface extraction and refinement. The presented script overcomes challenges in DTM generation from high-resolution point clouds by correcting misclassifications typical in the GPC process at fine scales, using a combination of tree detection and generalized additive modeling. Additionally, we analyse the morphology of detected trees by automatically fitting least square ellipses to stem segments and deriving the shape, eccentricity, inclination, and tilting direction of tree stems. This data-driven, digital approach to dendrogeomorphology offers potential solutions to current challenges in conventional dendrogeomorphological surveys, such as time and labour intensiveness or bias in the selection of sample and reference trees.
Our study demonstrates that both crpTP and crpTLS are capable of producing highly accurate digital twins of forested landslides. The models generated through photogrammetric data acquisition for two small-scale test plots are characterized by low check point RMSE values ranging from 0.16 to 0.19 cm, indicating high model accuracy. In comparison, the crpTLS digital twin of the entire study area showed an RMSE value of 1.87 cm. The accuracy of the derived DTMs, validated using independent sets of ground reference points, ranged from 0.92 to 1.90 cm. The high filtering accuracy demonstrated the capability of the presented approach to reduce misclassification and propagation errors in the generated DTMs.
By employing digital dendrogeomorphology, we were able to fit least square ellipses to stem segments at RMSE accuracies close to the voxel size of the point cloud. Based on these accurately fitted ellipses, their respective centroids and shift across stem segments, we demonstrate the feasibility of automatically extracting tree morphology indicators.
Our study shows that the combined extraction of DTMs and tree morphology indicators from digital twins can provide valuable data on both surface and subsurface processes, which, when applied over multiple time periods, can enhance landslide understanding. However, as the study site is characterized by little understory, the applicability at other sites with different forest structures has yet to be explored.
How to cite: Müller, B. and Kamaryt, T.-H.: Advancing Landslide Understanding in Forested Areas: High-Resolution Digital Twins for Novel DTM Extraction and Digital Dendrogeomorphology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15955, https://doi.org/10.5194/egusphere-egu25-15955, 2025.
Physical scale experiments of natural systems such as debris flows, rivers, estuaries and deltas are conducted within the geosciences to enhance our understanding of the physical processes on Earth. Methods in these experiments are under continuous development, often inspired by the advancements in remote sensing, making use of e.g. overhead fixed position cameras, movable gantry mounted cameras and line laser scanners, which are analogous to fixed-camera timelapses, drone surveys, and LiDAR, respectively. However, physical scale experiments come with additional challenges, as small uncertainties in positioning and orientation of the sensors can significantly bias the experimental results, especially because the small-scale morphology is typically in the order of a few centimeters. Moreover, photogrammetric data processing is prone to doming which can vary for each timestep, impeding change detection studies.
We will present the advancements in the data processing of our 20 by 3 m laboratory facility that is used to emulate tidal cycles to induce morphological development in coastal systems (www.uu.nl/metronome). Mounted ~4 m above our facility are 7 lower-grade overhead cameras (pixel resolution ~1.5 mm, overlap ~20%) which are simultaneously triggered at each tidal cycle. Additionally, when an experiment is paused, we conduct both DSLR surveys (pixel resolution ~0.5 mm, overlap ~80%) and 3D laser triangulation system surveys producing gridded data (planimetric resolution ~1 mm), along a movable gantry system. We have built a large dataset of >220.000 experimental tidal cycles and >2.000.000 unique images, which requires fully automated data processing that results in morphology which is consistently accurate in both the spatial and temporal domains of our timeseries.
We developed an extensive data processing workflow that incorporates a base model of our facility under idealized conditions. This base model was photogrammetrically constructed in Agisoft Metashape by aligning a total of 169 images from all cameras without downscaling. Through an automated and fast-performing python script, we are able to successfully align this base model to our complete dataset of DSLR-gantry surveys to generate orthomosaics and DEMs, as well as overhead imagery to generate timelapses of orthomosaics. This method shows a striking degree of robustness, because it has no difficulty with aligning 7 unique overhead cameras with limited overlap, as well as imagery of increasingly different morphology compared to the base model.
Finally, positions (X,Y,Z) and orientations (ω,φ,κ) of the cameras along the gantry were extracted from the base model, which were implemented in a new workflow that processes the raw laserscan data using vector algebra and transformation matrices. This results in DEMs that are geometrically aligned to the base model, without the use of a photogrammetric method. Accordingly, these DEMs have no variability in doming throughout the timeseries and therefore a higher temporal accuracy. Moreover, the implementation of variability in camera positions and orientations results in an improvement in altimetric accuracy from 7 to 2 mm (99.7% confidence), significantly reducing the bias in small-scale morphology. Our methods can be partially or fully implemented in research and industry using small to medium scale setups of both fixed and gantry-mounted camera systems.
How to cite: Nota, E., van Amstel, B., Nijland, W., van Maarseveen, M., and Kleinhans, M.: Multiple-sensor photogrammetric base model alignment of large timeseries in physical scale experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19352, https://doi.org/10.5194/egusphere-egu25-19352, 2025.
Around 50 years ago, David Sugden and Brian John proposed a classification scheme for landscapes formed by glacial erosion, based on the geomorphic evidence from the beds and peripheries of Quaternary ice sheets. This evidence was interpreted qualitatively using aerial photography and limited field visits. In the current era of ubiquitous, freely available, high-resolution elevation data, limited attempt has been made to update this classification using quantitative measurements of landscape form (i.e., morphometry), and no such scheme has been applied to the Northern Hemisphere regions where the scheme was originally developed. This is despite landscapes of glacial erosion containing a wealth of information relating to past ice behaviour. This study therefore aims to create a new classification method which: (1) has a robust quantitative basis allowing it to be reproducibly applied to new land surfaces, including those currently buried beneath modern ice sheets; and (2) takes advantage of the power of machine learning approaches to interpret patterns at scale and provide estimates of classification confidence. The method presented here uses intuitive morphometrics that can be calculated relatively simply from digital elevation models, including total relief, spatial density of local peaks and basins, and drainage characteristics. A random forest classifier is trained on a selection of manually classified landscapes and the resulting model is used to reclassify the exposed regions of northern North America. Classification confidence is examined using decision tree voting scores, and ‘out of bag’ error values are used to estimate variable importance. The results align broadly with the original 1970s classification scheme, but reveal more local- to regional-scale and intraclass variability than was previously accounted for. Low confidence scores allow identification of landscapes which represent a more complex interplay of different erosional styles, or a transition between classes. The classification also highlights the preservation of a range of non-glacial erosional signatures, even in areas known to have been affected by Quaternary glaciations. Overall, the work demonstrates the value of simple morphometric parameters for condensing information from large elevation datasets and provides a quantitative tool for interpreting landscapes whose glacial history is poorly constrained, such as those hidden beneath modern ice sheets, or on other planets.
How to cite: Lea, E., Paxman, G., Clubb, F., and Ross, N.: Continental-scale machine-learning classification of glacial landscapes using simple morphometric parameters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-342, https://doi.org/10.5194/egusphere-egu25-342, 2025.
Coastal inlets serve important navigation and environmental functions for coastal regions. Managing inlets is a complex task that requires a thorough understanding of several key factors, including sediment transport, areas of erosion and deposition, and feature migration within the inlet system. This knowledge can enhance the maintenance of navigational channels, identify sediment borrow areas for coastal engineering projects, and optimize hydrodynamic and sediment transport models. To improve coastal inlet management in the U.S., the U.S. Army Corps of Engineers (USACE) Coastal Inlets Research Program develops tools to reduce Operations and Maintenance (O&M) costs at federally maintained inlets. One such tool is the U.S. Coastal Inlets Atlas, an online database of information for U.S. inlets (Beck & Arnold, 2019).
This research highlights emerging tools and methodologies to be included in the next generation of the U.S. Tidal Inlet Atlas for inlet geomorphic mapping and analysis that will provide USACE District engineers, scientists, and managers, as well as other public partners, with tools and data to rapidly evaluate O&M alternatives. These tools include workflows to map inlet geomorphic features more accurately and to better track and predict morphologic changes. Emerging methods that were tested in this study include relative relief mapping (Wernette et al., 2016), Geomorphon classification (Jasiewicz & Stepinski, 2013), and chronostratigraphic and conformal mapping analyses (Pearson et al., 2022). These methods make use of publicly available repeat bathymetric data including USACE National Coastal Mapping Program topobathymetric lidar and USACE hydrographic surveys. Workflows to pre-process bathymetry data and conduct the new analyses as well as results from tests cases at New Pass and Merrimack Inlets in the U.S. are presented here. Highlights of the tool and workflow development and testing include: (1) the importance of considering scale when implementing relative relief mapping and geomorphon methods; (2) the importance of choosing the inlet polar grid origin and transect locations for conformal mapping and chronostratigraphic analysis methods. Results of the test study site analyses highlight geomorphic features such as shoals, reveal sediment transport pathways, and provide estimates of shoal and ETD sediment volumes and ages of deposition. The study provides valuable data products and workflows to engineers and scientists interested in applying these approaches to additional inlets. Fully developed workflows and datasets will be included in future iterations of the U.S. Tidal Inlet Atlas for all federal inlets.
Beck, T. M., & Arnold, D. (2019). U.S. Tidal Inlets Atlas: An Update to the CIRP Inlets Database (Coastal and Hydraulics Engineering Technical Note (CHETN) IV–118). USACE, Engineer Research and Development Center.
Jasiewicz, J., & Stepinski, T. F. (2013). Geomorphons—A pattern recognition approach to classification and mapping of landforms. Geomorphology, 182, 147–156.
Pearson, S. G., Elias, E. P. L., Van Prooijen, B. C., Van Der Vegt, H., Van Der Spek, A. J. F., & Wang, Z. B. (2022). A novel approach to mapping ebb-tidal delta morphodynamics and stratigraphy. Geomorphology, 405, 108185.
Wernette, P. A., Houser, C., & Bishop, M. (2016). An automated approach for extracting barrier island morphology from digital elevation models. Geomorphology, 262.
How to cite: McPherran, K., de Assis Bose, M., Shawler, J., Sylvester, C., and Smith, K.: Innovative Tools for Inlet Geomorphic Mapping: Testing Emerging Methods at New Pass Inlet, Florida and Merrimack River Inlet, Massachusetts, USA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3282, https://doi.org/10.5194/egusphere-egu25-3282, 2025.
In the context of climate change, ice sheets are strongly influenced by the reorganization of the subglacial hydrological system and the dynamics of ice flow. Interactions between meltwater, ice flow and subglacial sediments give rise to a unique assemblage of periodic subglacial landforms composed of sediments known as bedforms. These subglacial bedforms therefore provide a large-scale observational window into the subglacial environment, which is difficult to analyze beneath current ice masses.
Mapping subglacial bedforms is traditionally performed using digital elevation models (DEMs) and/or aerial or satellite imagery through manual digitization in GIS software. This method is time-consuming and introduces operator subjectivity, heavily dependent on the expertise level of the operator. This manual approach is also a significant barrier to the use of new datasets with increasingly higher resolution (e.g. ArcticDEM, RGE ALTI®, HiRISE) and coverage of ever larger areas. Addressing these limitations is essential to efficiently analyze the distribution and morphometry of subglacial bedforms over large territories.
To overcome these challenges, we designed an automated tool to delineate and analyze the shape of subglacial bedforms using a recently defined land surface parameter, the Volumetric Obscurance. This parameter highlights convex and concave surfaces while minimizing the impact of noise from the topographic signal, making it particularly suited for detecting and mapping subglacial morphologies. The automated tool is based on the assumption that the diversity of subglacial bedform shapes reflects a continuum: therefore, unlike traditional methods, no pre- or post-mapping classification of bedforms is performed.
Our method uses DEMs and optical satellite images, including ArcticDEM and Sentinel-2 data, to generate regional morphological maps (bedform outlines and crestlines) and regional morphometric maps (spatialized statistical analysis of bedform morphometrics). It employs a multi-threshold segmentation approach to extract bedform features and calculate both dimensional morphometric parameters (e.g., volumes, areas) and dimensionless parameters (e.g., sinuosity, circularity, elongation). These provide synthetic and spatialized information on the distribution of morphological parameters across entire bedform fields.
We tested the tool on ArcticDEM data over a portion of the former Laurentide Ice Sheet bed, specifically the Keewatin Dome region in northern Canada, which displays a wide diversity of bedform shapes. The produced morphological maps demonstrated strong consistency, with approximately 75% correspondence between individual bedform outlines generated automatically and reference maps manually digitized by two distinct glacial geomorphologists. Despite a 25% difference in individual bedform outlines, the derived morphometric maps were highly comparable and provide reliable insights into subglacial deformation and hydrology.
By reducing subjectivity and significantly accelerating the mapping process, this tool enables the analysis of larger areas with greater precision compared to manual methods. The derived datasets allow for reconsideration and refinement of ice-sheet scale reconstructions of ice flow and meltwater dynamics. The tool is developed in Python and is freely accessible to the research community.
How to cite: Hesni, S., Bessin, P., Ravier, E., Bourgeois, O., Vérité, J., and Buoncristiani, J.-F.: Automated delineation and morphometry of unclassified subglacial bedforms., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8508, https://doi.org/10.5194/egusphere-egu25-8508, 2025.
Most of the global actions for sustainability focus on the biosphere, overlooking the role of abiotic components, embedded in and supported by the geosphere and its ecosystem services [1]. As a consequence, proxy for geodiversity evaluation can act as indicator of areas with a strong propensity for naturalness, enhancing their protection and promoting conservation strategies. Geodiversity assessment depends on the size of the study area, the nature and resolution of available data, and the choice of an empiric or a quantitative method to define a categorical geomorphodiversity index [2]. Geomorphodiversity, a simpler but meaningful quantity, describes the diversity of landforms in an area, resulting from the surface processes modelling the landscape.
We recently defined a geomorphodiversity index (GmI) considering a few morphological features derived from digital elevation models: slope angle, drainage density, and landforms (through the geomorphons model [3]), as an approximation to field-observed features, and lithological information, as descriptors of the geological constraints and geomorphological processes of the landscape. Compared to previous approaches [4-6], we introduced a scale–independent method that considers contributions of the partial diversities of the four descriptors, calculated through focal statistics with a range of radii. The partial maps were classed, combined, and classified again into a raster with five final GmI classes. We dropped the dependence from the window radius by combining the set of radius–dependent GmI maps into a single map, selecting for each cell the most common value across the set of maps [7]. This approach has the advantage or removing the parameter dependence and embedding information from different scales in each grid cell of the GmI, which makes it suitable for and suitable accuracy for national, regional and urban scale analysis [8].
We implemented the method in a simple and versatile GRASS GIS procedure, suitable for implementation in a general-purpose raster module. The software accepts a variable number of raster or vector layers, and for each layer it calculates partial diversities with the desired working resolution and a user-defined number of different radii for focal statistics. The software combines intermediate layers with different weights, defined by the user based on the available geomorphological information, and reclassified into a final geomorphodiversity index.
Results show that such GmI proxy can reproduce the essentials of the observed distribution of landforms, using a small number of widely available datasets. We consider the proposed GmI map as a discrete measure of richness and variability of abiotic components, providing an intuitive information, readily available for subsequent applications in different locations and at different resolutions.
References
[1] Schrodt et al., PNAS (2019). https://doi.org/10.1073/pnas.1911799116
[2] Zwoliński et al., Geoheritage (2018). https://doi.org/10.1016/B978-0-12-809531-7.00002-2
[3] Jasiewicz & Stepinski, Geomorphology (2013). https://doi.org/10.1016/j.geomorph.2012.11.005
[4] Benito-Calvo et al., Earth Surf. Proc. Land. (2009). https://doi.org/10.1002/esp.1840
[5] Melelli et al., Sci. Tot. Env. (2017). https://doi.org/10.1016/j.scitotenv.2017.01.101
[6] Burnelli et al., Earth Surf. Proc. Land. (2023). https://doi.org/10.1002/esp.5679
[7] Burnelli et al., Geomorphology (2024). https://doi.org/10.1016/j.geomorph.2024.109532
[8] Burnelli et al., Geomorphology (2024). https://doi.org/10.1016/j.geomorph.2024.109582
How to cite: Burnelli, M., Melelli, L., and Alvioli, M.: A multiscale approach for geomorphodiversity indices on large areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17089, https://doi.org/10.5194/egusphere-egu25-17089, 2025.
Many landscapes are characterized by marked differences in rock erodibility. Cuesta and escarpment landscapes, for example, develop their characteristic form due to contrasts of physical and chemical properties of individual rock layers in lithostratigraphic sequences. Over long time scales, 3D-variable rock properties and resistances to erosion imply that landscapes do not attain a steady state and exhibit autogenically migrating drainage divides. One may thus argue that landscapes with layered rocks are much more dynamic than landscapes characterized by homogenous rocks. Here, we present the development and implementation of TTLEM3D, an enhancement to TTLEM based on TopoToolbox which relies on the detachment-limited stream-power incision model and the eikonal equation to simulate eroding landscapes characterized by spatial variations in rock erodibility and threshold slopes. We show that 3D variations in rock properties strongly affect how landscapes respond to changes in uplift rates and uplift patterns. While simulated elevations compare well with the topography of actual landscapes, we note several discrepancies that reflect theoretical shortcomings of geomorphic transport laws and their application in landscapes with layered rocks.
How to cite: Schwanghart, W., Kearney, W. S., Rau, M., and Scherler, D.: TTLEM3D - Simulating erosion of uplifting landscapes with layered rocks , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20069, https://doi.org/10.5194/egusphere-egu25-20069, 2025.
To assess the grain-scale morphology and size distribution of sediments which are important factors controlling the erosion efficiency, sediment transport and the aquatic ecosystem quality, we developed the G3Point algorithm to extract grain information from 3D point clouds. Field measurements can be very tedious and G3Point is a semi-automatic and non-destructive method developed to tackle the determination of the grain-scale shape of sediments in geomorphology. Initially developed with Matlab, G3Point i) segments a given 3D point cloud with sufficient resolution into individual sub-clouds, ii) fits ellipsoids to the sub-clouds and iii) computes metrics not only on the size and shape of grains but also on their orientation and organization. Unfortunately, the ergonomics of the Matlab code are not optimal to process and visualize 3D points clouds. The interaction with the user is based on scripts, Matlab is limited in terms of point clouds visualization functions, and it is not possible to edit point clouds directly in the graphical interface.
CloudCompare is a powerful free software for 3D point clouds and triangular meshes processing. Thanks to its plugin mechanism, we have fully integrated G3Point into the main application. It is available easily through the graphical user interface making the processing very productive and practical. The first step of G3Point, based on a trial-and-error approach, is much simpler with the plugin. Furthermore, even with a satisfying set of parameters, the segmentation needs often to be refined and CloudCompare allows to edit directly the point cloud. As CloudCompare includes many advanced tools for registration, resampling, color/normal/scalar fields handling, statistics computation and display capabilities, it is now possible to prepare data, precisely visualize them, apply G3Point and post process them with a single tool.
How to cite: Leroy, P., Steer, P., Guérit, L., and Lague, D.: Leveraging CloudCompare to measure grain geometries from 3D point clouds: a plugin for the G3Point algorithm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15463, https://doi.org/10.5194/egusphere-egu25-15463, 2025.
Quantifying riverscape topography is challenging because riverscapes comprise of both wet and dry surfaces. Over the last decade, considerable advances have been made in demonstrating the capability of mounting topo-bathymetric LiDAR sensors on crewed, occupied aircraft to quantify riverscape topography. However, only recently has miniaturisation of electronic components enabled topo-bathymetric LiDAR to be mounted on consumer-grade Unoccupied Aerial Vehicles (UAVs). Here we evaluate the capability of YellowScan Navigator topo-bathymetric LiDAR sensor, mounted on a DJI Matrice 600 UAV, to survey a 1 km long braided riverscape. This sensor uses full waveform returns, to ensure continuity between underwater points and the surrounding terrain, and has a 44° field of view. In August 2024, a point cloud was collected across a 1 km long, 200 m wide reach of the braided River Feshie, Scotland. Ground-truth data were collected across wet areas using a Sontek M9 Acoustic Doppler Current Profiler (ADCP) as an echo-sounder, and also survey-grade RTK-GNSS (Global Navigation Satellite System) in both wet and dry areas. The processed LiDAR point cloud featured over 10 million points, with a density of approximately 62 points / m2. These points were compared to the ground truth data to assess the vertical accuracy of the survey. Ground-truth mean errors (and standard deviation) across dry gravel bars surveyed with RTK-GNSS were 0.06 ± 0.04 m (n=237). Mean errors along the bed of the shallow channels surveyed with RTK-GNSS were -0.04 ± 0.23 m (n=562). Additionally, mean errors for deeper channels measured with the ADCP’s echo sounder were -0.08m ± 0.23 m (n = 2673). Overall, this case study demonstrates the potential of using a new generation of topo-bathymetric LiDAR sensors that can be mounted on UAVs. This has the potential to further enhance field surveys of wet-dry environments by reducing logistical workloads and increasing efficiency of these surveys. Compared to the various techniques for correcting bathymetric data from Structure-from-Motion imagery or point clouds this direct measurement needs less processing to achieve to continuous topo-bathymetric surface.
How to cite: MacDonell, C., Williams, R., White, J., and Roberts, K.: Seamless quantification of wet and dry riverscape topography using UAV topo-bathymetric LiDAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4410, https://doi.org/10.5194/egusphere-egu25-4410, 2025.
The development of low-cost sensing systems for 3D data collection has revolutionised geomorphic research. High-resolution 3D data can now be collected, processed on-site, and analysed with ease, thanks to advancements in devices such as iPhone/iPad LiDAR sensors and the processing of Structure-from-Motion (SfM) data on a mobile phone. Despite these advances, systematic comparisons between low-cost methods and industry-standard techniques, such as Terrestrial Laser Scanning (TLS), remain limited, particularly in fluvial environments.
This study evaluates the efficacy of multiple low-cost 3D monitoring methods, including iPad LiDAR, mobile phone SfM, and digital SLR SfM, against TLS in a fluvial context. Six leaky wooden dams, widely used as natural flood management interventions across the UK, were selected as monitoring subjects. These dams significantly impact river hydrology, sediment transport, and geomorphic evolution, yet the lack of repeat monitoring limits our empirical understanding of their effects.
Our results quantify the spatial errors associated with each low-cost technique, offering critical insights into their applicability for geomorphic data collection. Additionally, this work establishes the first accessible, spatially distributed database of high-resolution surveys of leaky wooden dams. This database provides a valuable foundation for future research and enables academics, industry, the third sector, and the public to contribute to a global record of geomorphic change. By demonstrating the untapped potential of low-cost sensing technologies, this study promotes more widespread, cost-effective monitoring of geomorphic processes.
How to cite: Wolstenholme, J. and Houseago, R.: Quantifying the spatial error of low-cost 3D monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18866, https://doi.org/10.5194/egusphere-egu25-18866, 2025.
Bathymetry is a critical component in many geomorphological and ecological studies of coastal and fluvial environments. Bathymetric lidar remote sensing enables the acquisition of high-resolution, 3D data on shallow sea- and riverbeds, thus providing precise modeling of their topography. However, in certain contexts, the optical interactions between light and water make extracting bathymetry from lidar signals particularly challenging, resulting in incomplete bed coverage. This is the case in very shallow waters at the transition between land and water – where the signals of the water surface, column, and bottom become entangled – and in deep or turbid waters, where signal extinction hinders the detection of the sea- or riverbed.
In this work, we explore new approaches for bathymetry extraction from lidar waveforms across diverse environments. With temporal convolutional neural networks, we show improvements in detecting the position of the sea- or riverbed using bathymetric lidar waveforms. Our experiments, conducted on simulated data representing a wide range of environmental conditions – varying turbidity, depth, and reflectance – yield promising results. They highlight the potential to extract the position of the sea- or riverbed even in simulated waveforms with low signal-to-noise ratios or highly overlapping signals – cases that have posed challenges for existing processing methods. Given the increasing popularity of bathymetric lidar, particularly with the advent of UAV-mounted sensors, enhancing waveform processing methods could help advance the surveying of submerged areas.
How to cite: Letard, M., Corpetti, T., and Lague, D.: Extracting bathymetric information from LiDAR waveforms with 1D neural networks., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20513, https://doi.org/10.5194/egusphere-egu25-20513, 2025.
Geomorphological maps are essential tools for understanding landscape evolution and natural hazards in mountain environments. However, their creation requires substantial time investment and expert knowledge, limiting the availability of up-to-date mapping products. While automated mapping approaches have been developed for selected individual landforms, no spatially exhaustive method exists for the complex terrain of high alpine environments. To overcome this, we evaluated different CNN models, achieving state-of-the-art results for semantic segmentation, for automated geomorphological mapping. For the training we created homogenized maps of three alpine valleys covering more than 170 km² and comprising 20 landform classes. As input layer we tested various three-band raster composites, consisting of various combinations of digital elevation model (DEM) derivatives like topographic openness or terrain wetness index.
Preliminary results show that several models achieve F1 scores exceeding 0.8 for the most relevant geomorphological features, including ground moraine, rock glaciers, lateral moraines, and fluvial terraces. Lower performance was observed for narrow and shallow landforms and anthropogenic features like streets and buildings. However, anthropogenic features are often underrepresented in high alpine valleys explaining their worse performance. Hence, our results indicate that additional manual correction is necessary to use these automatically derived maps in downstream tasks. However, the time required to create geomorphological maps of consistent quality, on the basis of these automatically derived maps, can significantly be reduced. This enables rapid geomorphological mapping in previously unmapped high alpine catchments and facilitates the creation of multitemporal maps within single study areas. The latter application opens new possibilities for quantifying structural changes in alpine geomorphic systems over time, contributing to our understanding of landscape evolution and response to climate change.
How to cite: Himmelstoss, T., Mikolka-Flöry, S., Haas, F., Becht, M., Pfeifer, N., and Heckmann, T.: Evaluation of convolutional neural networks (CNNs) for the automatic generation of geomorphological maps in high alpine environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8600, https://doi.org/10.5194/egusphere-egu25-8600, 2025.
Agricultural terraces are among the most significant anthropogenic land modifications in the Mediterranean. They are constructed to reduce local slope gradients and facilitate farming by artificially increasing soil thickness. Terraces also reduce soil erosion and enable irrigation practices. Yet, if not maintained or abandoned, they become prone to piping, gully erosion, and landsliding. Nevertheless, incorporating these effects into large-scale hydrological, geomorphological, and agronomic research remains challenging due to limited information on terrace locations and characteristics.
We aim to address this gap by presenting a new predominantly automatic approach for detecting and classifying terraced units on a large scale. This scalable tool utilizes freely available data: Google optical satellite imagery (≈2.1 m spatial resolution), ALOS Global DSM (30 m spatial resolution), and ESA WorldCover (10 m spatial resolution). Our study site, Cyprus, with an area of 9,250 km² has a long history of terraced agriculture driven by its rugged terrain. The island features terraces ranging from old, abandoned ones to newly constructed terraces using heavy machinery.
The approach employs Object-Based Image Analysis (OBIA). First, images are segmented using SLIC (Simple Linear Iterative Clustering). These segments are populated with information from 22 derivative layers generated from the initial data. For each segment, 36 statistical parameters are calculated. The derived layers include slope, curvature, Gray-Level Co-Occurrence Matrix (GLCM) features, Canny edge detection results.
To ensure robust classification, the data was split into tiles, with some used for training and others for validation to minimize spatial autocorrelation. The model was trained using AutoGluon, focusing on CatBoost and Neural Networks. The binary classification achieved a ROC-AUC value of 0.87 and a Matthews Correlation Coefficient (MCC) of 0.44. Subsequently, detected terraces were classified into three morpho-functional classes. Broad agricultural terraces were identified with high accuracy (0.84). Narrow agricultural terraces on steeper slopes with stone walls showed moderate performance (accuracy = 0.72). However, distinguishing narrow terraces built for reforestation from agricultural terraces in similar conditions proved challenging (accuracy = 0.46).
Our results demonstrate the potential for developing detailed, large-scale terrace datasets. This, in turn, opens promising perspectives to better assess soil erosion and other geohydrological processes at such scale.
How to cite: Kedich, A., Vandam, R., Verstraeten, G., Vervust, S., Devos, Y., and Vanmaercke, M.: Automatic mapping of terrace systems at large scales: a case study of Cyprus, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17335, https://doi.org/10.5194/egusphere-egu25-17335, 2025.
Mountain relief results from a delicate balance between erosion by rivers and glaciers, which increases the relief and thus topographic stresses, and mass wasting processes, which counteract them. These processes involve complex interactions of mechanical failure, stress redistribution, and material transport, which collectively govern the evolution of alpine landscapes. Understanding these interactions is crucial when it comes to assessing geological hazards but also to gain deeper insights into landscape evolution, especially for landscapes that are transitioning from a glacial to a fluvial state.
This study uses an advanced computational framework to investigate how landslides influence the state of stress in mountain massifs undergoing topographic decay. We use a probabilistic landslide simulation model focused on material detachment (without tracking deposition), combined with the Finite Cell Method (FCM) for stress modeling, to analyze variations in the stress state of a mountain massif at successive topographic snapshots following rockfall events. The FCM enables precise, three-dimensional stress analyses across entire mountain ranges by leveraging the flexibility of fictitious domain approaches. Traditional methods, such as finite element techniques, rely on boundary-conforming meshes tailored to complex topography. These meshes are computationally intensive to generate and refine, especially for large-scale models. In contrast, the FCM operates on simpler, regular grids, enabling scalable and efficient analysis of large and complex terrains. Its adaptive integration schemes ensure high accuracy without the need for computationally expensive mesh refinement tailored to irregular geometries.
We applied this novel approach to mountain massifs located at the three Austrian UNESCO Global Geoparks featuring iconic alpine landscapes characterized by steep slopes and active landslide processes. Our results show significant reductions in peak shear stresses following rockfall events, with stress maxima strongly correlating to steep valley flanks, highlighting areas of potential failure. Stress redistribution following landslides reduces localized stress concentrations, leading to a more homogeneous stress state and resulting in stabilization of the remaining rock mass. This finding supports the hypothesis that mass-wasting processes regulate topographic relief by limiting hillslope steepness. In contrast to traditional topographic metrics, which focus solely on surface features, our framework enables the determination of subsurface stresses and gradients, providing valuable insights into slope failure mechanics. This is critical for advancing predictive models of geological hazards and enhancing landscape stability assessments. By incorporating three-dimensional stress analysis, this framework offers novel insights into landscape evolution and a more refined understanding of the equilibrium between relief-forming and relief-reducing processes.
How to cite: Haunsperger, V., Robl, J., Argentin, A.-L., Hergarten, S., and Schröder, A.: Computing the state of stress in mountain massifs undergoing topographic decay , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1292, https://doi.org/10.5194/egusphere-egu25-1292, 2025.
For 15 years, TopoToolbox has provided a platform for quantitative geomorphology. Its extensibility, user-friendly interface and comprehensive documentation have enabled users across the geosciences and around the world to build custom data analysis and modeling workflows in MATLAB. The third version of TopoToolbox builds on this legacy by making it accessible from other programming and data analysis environments and implementing sustainable research software engineering practices to ensure that TopoToolbox will continue to provide a stable foundation upon which to build geoscientific data analysis tools.
The new software architecture factors the fundamental TopoToolbox algorithms into a separate library that can be accessed from other programming languages such as Python and R while maintaining the existing MATLAB toolbox. Interoperability with the existing geospatial software ecosystems in these languages is encouraged by exposing a minimal interface to a few key data structures and composing transformations between these data structures. This architecture has already allowed us to integrate new tools such as the GraphFlood hydrodynamic model (Gailleton et al. 2024) with TopoToolbox.
Accompanying these software changes has been a reorganization of the software development workflow. Extensive automated testing ensures consistent behavior across languages and helps prevent the introduction of bugs throughout the refactoring process. Modern testing methodologies like property-based testing make it possible to test TopoToolbox even when the correct outputs of our algorithms are unknown. TopoToolbox is developed publicly on GitHub (https://github.com/TopoToolbox), and we encourage contributions from members of the community.
How to cite: Kearney, W., Schwanghart, W., Lamprecht, A.-L., Scherler, D., Bringezu, T., Bartha, D., and Gailleton, B.: TopoToolbox 3 -- a laboratory for topographic analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8678, https://doi.org/10.5194/egusphere-egu25-8678, 2025.
In this study, we present a comprehensive analysis of landforms derived from a 10-meter resolution digital terrain model (DTM) using unsupervised classification with different combination of morphometric variables and established algorithms, including the Topographic Position Index (TPI; Weiss, 2001) and Geomorphons (Jasiewicz & Stepinski, 2013). These methodologies allowed us to delineate distinct landforms, which were subsequently subjected to detailed spatial and statistical analyses to evaluate their geomorphological characteristics and interrelationships. Specifically, we aim to compare how different landform classifications, derived from these approaches, correlate with geothematic variables such as lithology, engineering geological characteristics, and the distribution of shallow landslides. To statistically assess the congruence between landform classifications and geothematic variables, we applied statistical tests such as chi-square tests for independence (for categorical variables) which is used to determine whether there is a significant relationship between landform classes and categorical geothematic variables. Moreover, the strength and direction of these relationships are further evaluated using Cramér’s V. These tests provided insights into the relative effectiveness of each different landform classification in describing the variability of geothematic variables. The study was conducted in northern Tuscany, a region characterized by a complex interplay of geological, morphological, and climatic factors that make it particularly susceptible to shallow landslides and debris flows. These phenomena are frequently triggered by intense rainfall events, which highlight the importance of understanding the distribution of predisposing factors for slope instability in such areas. In conclusion, this study explores different methods to perform the landform classification and establishes a framework to evaluate how they are related to independent geothematic variables, which may be used to assess landslide susceptibility and hazard.
How to cite: Pippi, M., D'Addario, E., Masoni, G., Marques e Silva Rocha de Oliveira, E., and Disperati, L.: The performance of different landform classification methods as assessed by their relationship with geotemathic variables, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19353, https://doi.org/10.5194/egusphere-egu25-19353, 2025.
As critical zones in fluvial geomorphology shaped by hydrological processes, valley floors play an essential role in material exchange and circulation between upland and groundwater bodies. Their accurate delineation is crucial for understanding river morphology, analyzing the distribution of valley floor sediments, and maintaining the riverine landscape ecosystem. However, current methods for delineating valley floors are highly artificial, region-specific and require subjective parameter selection. To address these limitations, we develop a multi-scenario adaptive framework for delineating valley floors. This framework designs several indicators for automatically detecting topographical cross-sectional and longitudinal features, providing a basis for parameter determination in valley floor extraction and achieving geomorphologically adaptive automatic extraction. The framework includes the following components: (1) The initial drainage network was extracted by setting drainage thresholds based on geomorphological texture features obtained using the gray-level co-occurrence matrix (GLCM); (2) The drainage network generated in the previous step was filtered by calculating the average river gradient and setting adaptive parameters, removing drainage networks located in steep valleys; (3) The valley floor extent was adaptively extracted by proposing terrain factors such as slope accumulation and its variation. The experimental results demonstrate that this method applies to the extraction of valley floors in various geomorphological types, exhibiting high precision. This study also explored the correlation between river valleys, geological sedimentation, and surface hydrological processes, finding a significant consistency between sediment distribution and valley floor extent. These findings provide a new perspective for research on geological mapping, and the evolutionary patterns of valley floor morphology.
How to cite: Sun, W., Chen, Y., Zhou, X., Yang, X., Ma, J., Li, S., and Tang, G.: Understanding the hydrological valley landscape: a multi-scenario adaptive framework for delineating valley floors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5028, https://doi.org/10.5194/egusphere-egu25-5028, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 2
EGU25-17381 | Posters virtual | VPS25
3D Mapping of Submerged Landscapes: A Cost-Effective Approach to Shallow-Water Bathymetry Using Underwater PhotogrammetryMon, 28 Apr, 14:00–15:45 (CEST) | vP2.6
The exploration of seabed topography is of paramount importance for a wide range of scientific and environmental applications. In deep water, sonar or multibeam technology among others are commonly used to map details of the sea floor, but applying these techniques in shallow waters is challenging due to the complex nature of the submerged terrain. Moreover, these techniques are costly and not accessible for small-scale projects. In recent years, underwater photogrammetry emerged as an effective solution for shallow water bathymetric mapping, bridging the gap between land topography and deep-water bathymetric measurements. Photogrammetry also enables a 3D or 4D visual representation of the submerged terrain, habitats, and objects.
Our research proposes a novel approach applying underwater photogrammetry to generate a 3D model of submerged terrain in shallow-waters over rocky coastline. Using underwater photographs and advanced land surveying techniques, we successfully generated a high-resolution, georeferenced 3D model with detailed geospatial maps covering 162 m2 at depths ranging from 1 to 5 meters below sea surface of a submerged upper subtidal zone of a rugged, rocky-coast landscape.
The proposed method offers a practical and affordable tool for shallow water bathymetric mapping over subtidal zones in rocky coasts, providing scientists with geospatial maps, measurements and visual representations for applications in marine research, coastal management, habitat monitoring, or underwater archeology.
How to cite: Elias, A. R. and Khalil, A.: 3D Mapping of Submerged Landscapes: A Cost-Effective Approach to Shallow-Water Bathymetry Using Underwater Photogrammetry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17381, https://doi.org/10.5194/egusphere-egu25-17381, 2025.
EGU25-20437 | Posters virtual | VPS25
Criteria to Map Areas of High Risk of Soil Hydric Erosion in Portugal using USLEMon, 28 Apr, 14:00–15:45 (CEST) | vP2.12
The Portuguese spatial planning legislation includes legal restrictions to land use in order to preserve the ecosystems. These restrictions are framed by the legal structure called National Ecological Reserve (NER), and have associated a cartographic representation. Among the land use protection areas included in the NER are the Areas of High Risk of Soil Hydric Erosion (AHRSHE). Our goal is to improve the models and derived cartography and to use the enhanced maps as a basis to test and apply new and more advanced technologies, data and methods.
Currently, AHRSHE are determined based on USLE. The computation of the LS factor in this equation has been a challenging issue and, since this action is a legal responsibility of the municipalities, we could face a situation where different municipalities use different methodologies and, eventually, the results being not comparable. Thus, efforts have being made in order to produce a common methodology to standardise and enhance the cartographic representation of the LS, namely, by improving its accuracy and precision and by harmonizing and making it compatible with the other USLE factors. For this purpose, several methods of LS computation have been tested to evaluate soil loss risk in different geomorphic contexts. Based on the test results USLE's second revision, RUSLE2 (USDA, 2008), was selected together with imposing a maximum value to unorganised runoff length (L).
The results of using RUSLE2 might be affected by the lack of information on detailed soil properties caused by different geomorphological contexts and the lack of resolution of the Digital Terrain Model (DTM) to accurately identify the AHRSHE. The lack of DTM resolution affects the slope values (S), the shape of the hydrographic network and, above all, the delimitation of the disorganized flow domain, where AHRSHE are mapped.
In order to reach an acceptable solution, tests were made with varying maximum unorganized runoff length (L) and using different formulas to determine S, according hillslope values and rainfall regime. The test results show that the more accurate LS is obtained when L is limited to 305 m and S is calculated according to slope thresholds: below and above 9% (Panagos, et al., 2015) or above 18% (Liu, 1994; 2002), and excluding areas where the USLE is not applicable, like plane surfaces, water, or surfaces with high slopes.
Another conclusion was that small resolution DTM are inappropriate which lead us to use in the tests a 10m pixel DTM. Even so, and in order to prevent unjustified land use restrictions, we suggest the need to validate the results (by sampling), at least in specific geomorphologic contexts. Otherwise, the likelihood to get biased results, with adverse practical effects, will be high.
The shape and accuracy of AHRSHE depend on the methodologies and georeferenced data used. Thus, we intend to use, in the near future, a very-high resolution DTM derived from aerial LiDAR and to work on the identification of differentiated geomorphological contexts in each municipality in order to further improve the AHRSHE mapping, which have substantial impacts in the NER.
How to cite: Silva, A. and Reis, R.: Criteria to Map Areas of High Risk of Soil Hydric Erosion in Portugal using USLE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20437, https://doi.org/10.5194/egusphere-egu25-20437, 2025.
