GM3.2 | Novel data, methods and applications in Geomorphometry
EDI
Novel data, methods and applications in Geomorphometry
Co-organized by GI4/NP4, co-sponsored by ISG
Convener: Massimiliano Alvioli | Co-conveners: Giulia Sofia, John K. Hillier, Stuart GrieveECSECS, Mihai Niculita
Orals
| Mon, 15 Apr, 10:45–12:30 (CEST)
 
Room G1
Posters on site
| Attendance Mon, 15 Apr, 16:15–18:00 (CEST) | Display Mon, 15 Apr, 14:00–18:00
 
Hall X1
Orals |
Mon, 10:45
Mon, 16:15
Geomorphometry, a science of quantitative land surface analysis, gathers various mathematical, statistical and image processing techniques to quantify morphological, hydrological, ecological and other aspects of a land surface. Geomorphometry and geomorphological mapping are essential tools for understanding landscape processes and dynamics on Earth and other planetary bodies. The rapid growth of available geospatial data available for morphometric analysis and opens up considerable possibilities for morphometric analysis from mapping new landforms to understand the underlying processes. It also presents unique challenges in data processing and analysis.
The typical input to geomorphometric analysis is a square-grid representation of the land surface - a digital elevation model (DEM). Global DEMs and the increasing availability of much finer resolution LiDAR and SFM high-resolution DEMs call for new analytical methods and advanced geo-computation techniques necessary to cope with diverse application contexts. Point clouds have increasing accuracy over complex scenes, characterized by high topographic variation in three (and four) dimensions, generating a shift in geomorphologists’ work.
This session welcomes studies of advanced geo-computation methods, including high-performance and parallel computing implementations. We welcome general, technical and applied studies of geomorphometry applications and landform mapping from any discipline (geomorphology, planetary science, natural hazards, computer science, and Earth observation). Examples are:
- Use of Digital Elevation, Terrain and Surface Models and point clouds
- High-resolution LiDAR, photogrammetry and satellite data
- Automated surface analysis, machine learning, new algorithms
- Earth's and planetary morphometry, surface changes
- Collecting or derivation of geospatial data products
- Tools for extraction and analysis of geomorphometric variables
- Mapping and morphometric analysis of landforms and landscapes
- Modeling natural hazards on the Earth's surface
- Marine Geomorphometry and bathymetry
- Geomorphometry for urban areas and cultural heritage
- Professional and industrial applications of Geomorphometry
Contributions on inter-disciplinary approaches are particularly encouraged. We also welcome professional, commercial and industrial applications of terrain/surface data and geomorphometric techniques, including software packages, to bridge the gap between academic researchers and industry.

Session assets

Orals: Mon, 15 Apr | Room G1

Chairpersons: Stuart Grieve, Massimiliano Alvioli, Mihai Niculita
10:45–10:50
10:50–11:00
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EGU24-6250
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GM3.2
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ECS
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On-site presentation
Viktor Haunsperger, Jörg Robl, Andreas Schröder, and Stefan Hergarten

The negative feedback between relief formation due to valley incision, increasing topographic stress towards a critical stress state dependent on rock strength, and consequently relief-destroying (and stress-reducing) landslides determines the geometry of alpine landscapes. Hence, the computation of topographic stresses for entire mountain massifs is crucial to identify potential landslide hotspots at steep landforms close to rock failure, determining the maximum strength of rocks and rock sequences at the mountain scale, and explaining contrasting geometries of alpine landscapes in dependence on the prevailing rock types. Traditional 2D stress and displacement calculations on valley cross-sections tend to oversimplify the complicated stress pattern, particularly where valleys converge or around ridges and peaks. 3D stress calculations based on standard finite element methods are computationally expensive and not feasible for entire mountain massifs at a reasonable expense.

Our study addresses this limitation by employing a novel three-dimensional approach, utilizing the Marching Volume Polytopes Algorithm for mesh generation and the Finite Cell Method as an alternative to the widely used finite element method. Incorporating an octree-like structure and advancing-front meshing techniques, the Marching Volume Polytopes Algorithm accurately represents given surface data through a tetrahedral mesh. In the Finite Cell Method representing a fictitious domain approach, the difficulty of generating adequate grids for physical domains with complicated geometry is transformed into the problem of specifying an adequate integration scheme for the finite cells and thus saving degrees of freedom. The computational efficiency of our approach is particularly advantageous when dealing with equidistant grids such as digital elevation models for mesh generation.

In a first study, we use our model to compute the 3D topographic stress distribution for the three Austrian UNESCO Global Geoparks known for over-steepened valley flanks and high landslide activity. Initial results show high shear stress maxima occurring predominantly at over-deepened glacial valleys bordered by rock faces, with stress maxima at valley flanks but also at or slightly below the valley floors. Unexpected stress patterns occur in areas with a complicated landscape geometry, where valleys converge, or intersecting ridge lines form pyramid peaks. Lithological contrasts of the investigated mountain massifs are reflected in very different stress patterns, with shear stress maxima showing the highest values in carbonate-dominated units.

In addition to local topographic metrics, the spatial distribution of observed landslides and the rock types that occur, modelled topographic stresses provide a new data set for assessing landslide potential. Beyond that, modeling topographic stresses of entire mountain massifs offers new insights into the evolution of alpine landscapes in the competition between relief-forming and relief-destroying processes.

How to cite: Haunsperger, V., Robl, J., Schröder, A., and Hergarten, S.: Three-Dimensional Stress Analysis of Mountain Ranges: A Novel Approach Using Marching Volume Polytopes Algorithm and Finite Cell Method , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6250, https://doi.org/10.5194/egusphere-egu24-6250, 2024.

11:00–11:10
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EGU24-4207
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GM3.2
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On-site presentation
Xin Yang, Chenghu Zhou, Sijin Li, Junfei Ma, Yang Chen, Xingyu Zhou, Fayuan Li, Liyang Xiong, Guoan Tang, and Michael Meadows

Landform classification and mapping provide fundamental data for Earth science research, natural resource management, environmental monitoring, urban planning, and various other domains. Despite the availability of DEMs with 1-arc second resolution, global-scale studies on landform classification and mapping are inconsistent in terms of general classification systems and methods.

Landforms represent not only assemblages of morphological characteristics but also encompass the human understanding of the Earth, which is constrained by the nature and scale of quantitative analysis. Here, we propose a novel framework for global landform mapping to significantly improve the quantitative evaluation of geomorphological features.

The proposed framework incorporates geomorphological ontology that takes account of their conceptualization to construct classified objects. We propose the accumulated slope (AS) and mountain uplift index (MUI) to emphasize the integrity and continuity of geomorphological units, providing more precise results compared to traditional methods. Aggregating local terrain features into global metrics, AS effectively overcomes the potential negative influence of increased resolution on landform integrity. MUI aligns better with human perception of mountainous morphology and surpasses the limitations of window-based computing.

In presenting the new framework, we have developed and made available a public dataset, Global Basic Landform Unit (GBLU), which incorporates a comprehensive set of objects that constitute the range of landforms on Earth. In emphasizing the integration of classification with quantitative analysis, GBLU highlights the connection between natural objects and human understanding in geomorphology and the Earth sciences. The GBLU outperforms previous datasets (the basic landform classification and global mountain assessment) in expressing landform details. GBLU can be downloaded at https://geomorph.deep-time.org. It serves as a valuable resource in facilitating a deeper understanding of landform spatial distribution and evolution, and supporting research in a diverse range of fields.

How to cite: Yang, X., Zhou, C., Li, S., Ma, J., Chen, Y., Zhou, X., Li, F., Xiong, L., Tang, G., and Meadows, M.: Incorporating ontological characteristics for global landform classification based on 30 meters DEM, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4207, https://doi.org/10.5194/egusphere-egu24-4207, 2024.

11:10–11:20
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EGU24-16412
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GM3.2
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ECS
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On-site presentation
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Francesco Ioli, Luca Morelli, Livio Pinto, and Fabio Remondino

Geomorphometry and geomorphological mapping are essential tools for understanding landscape changes. The recent availability of 3D imaging sensors and processing techniques, including Artificial Intelligence, is offering interesting solutions for gemorphometric analyses and processes understanding. Photogrammetry stands as a pivotal image-based tool in geomorphology, enabling accurate 3D reconstruction of complex natural environments and effective tackling of multi-temporal monitoring challenges. A key step in photogrammetry is the identification of corresponding points between different images, traditionally achieved through the extraction and matching of local features such as SIFT and ORB. However, these methods face difficulties when using images of complex environments scenarios. Deep Learning (DL) methods have recently emerged as powerful tools to address challenges such as strong radiometric variations and viewpoint changes (Morelli et al., 2022; Ioli et al., 2023). However, their practical application in photogrammetry is hindered by the lack of libraries integrating DL matching into standard SfM pipelines.

The presentation will introduce the recently developed Deep-Image-Matching, an open-source toolbox designed for multi-view image matching using DL approaches, specifically tailored for 3D reconstruction in complex scenarios (https://github.com/3DOM-FBK/deep-image-matching). This tool can be used to achieve a 3D reconstruction with wide camera baselines and strongly varying viewpoints (e.g., with ground-based monitoring cameras), with datasets involving varying illumination or weather conditions typical of multi-temporal monitoring, with historical images, or in low-texture situations (e.g., snow or bare ice).

Deep-Image-Matching provides the flexibility to choose from a variety of local feature extractors and matchers. Supported methods include traditional local feature extractors, such as ORB or SIFT, as well as learning-based methods, such as SuperPoint, ALIKE, ALIKED, DISK, KeyNet + OriNet + HardNet, and DeDoDe. Matcher choices range from traditional nearest neighbor algorithms to state-of-the-art options like SuperGlue and LightGlue. Available semi-dense matching solutions include the detector-free matchers LoFTR and RoMa.

To handle high-resolution images, the tool offers a tiling process. In case of strong image rotations, such as aerial stripes, images are automatically rotated before matching. Image pairs for matching can be selected by exhaustive brute-force matching, sequential matching, low-resolution guided pairs selection, or global descriptor-based image retrieval. Geometric verification is used to discard outliers among matched features. The extracted image correspondences are stored in a COLMAP database for further processing (i.e. bundle adjustment and dense reconstruction) or can be exported in other formats useful for other open-source and commercial software.

The presentation will highlight how image-based geomorphometry and geomorphological mapping could benefit of the realized tool and how complex environmental scenarios (landslides, glaciers, etc.) could be analysed and monitored with the support of deep learning.

References:

Ioli, F., Bruno, E., Calzolari, D., Galbiati, M., Mannocchi, A., Manzoni, P., Martini, M., Bianchi, A., Cina, A., De Michele, C. & Pinto, L. (2023). A Replicable Open-Source Multi-Camera System for Low-Cost 4D Glacier Monitoring. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 48, 137-144

Morelli, L., Bellavia, F., Menna, F., & Remondino, F. (2022). Photogrammetry Now and Then - From Hand-Crafted to Deep Learning Tie Points. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W1-2022, 163–170

How to cite: Ioli, F., Morelli, L., Pinto, L., and Remondino, F.: Deep-Image-Matching: an open-source toolbox for multi-view image matching of complex geomorphological scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16412, https://doi.org/10.5194/egusphere-egu24-16412, 2024.

11:20–11:30
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EGU24-13867
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GM3.2
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ECS
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On-site presentation
Anne Voigtländer, Aljoscha Rheinwalt, and Stefanie Tofelde

Hiking up a steep mountain, in comparison to walking on a flat beach, is unarguably different. But the horizontal distance made, estimated using a Digital Terrain Model (DTM), might be the same. The projection of 3D landscapes onto 2D grids in DTMs leads to a slope-dependent, inhomogeneous sampling of the surfaces, and a first-order error in topographic metrics. Using the slope dependency of this error, we can quantify and revert it. Foremost, correcting the projection error allows for more accurate estimates of area and volume, e.g., to quantify natural hazards; and enables the use of the full slope distribution to define the physical space of surface processes at any scale.

We quantify the projection error using synthetic landscapes for which analytical solutions of slope angles and surface area are known. In applying the correction to DTM data of a real landscapes, we can address geomorphological processes in physically more meaningful ways. The corrected extracted topographic proxies, here exemplary, the erosional response to uplift in the Mendocino Triple Junction (MTJ) area, California, USA, provide two aspects for interpretation of geomorphic processes. First, as all slope angles are now represented equally, the variations in slope distribution by region of uplift rate is more pronounced. Second, the erosional response causes not only a steepening but narrow slope distribution in the regions of high uplift. The transient response is visible in a broadening of the distribution towards the lower slope angles, as deposition becomes more prevalent. In this example, we also find that the surface area ratio, enables determining the effectiveness of Earth surface processes, by increasing or decreasing the differential between the standard-planform and the surface area. Earth surface processes, that involve transport and volume along the surfaces, if not referenced in time, the ratio between the planform and surface area can provide a spatial reference and could be explored further. Correcting topographic metrics also allows addressing additional questions, like, which slope angles characterize which process domains, which processes create steepening, which lowering of slopes, where, and to what extent? Or, which parts of landscapes, maybe not the steepest, correlate to the highest potential to erode?

 

How to cite: Voigtländer, A., Rheinwalt, A., and Tofelde, S.: The effect of correcting the projection error in Digital Terrain Models on Earth surface processes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13867, https://doi.org/10.5194/egusphere-egu24-13867, 2024.

11:30–11:40
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EGU24-20923
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GM3.2
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On-site presentation
Scott D. Peckham

Students of physics typically take a theory course on classical mechanics in which they learn about Hamilton's Principle and how it can be used to derive many well-known physical laws that describe the motion of objects from particles, to light rays, to celestial bodies, including Newton's laws and Snell's Law from geometrical optics.  This powerful principle has also been shown to apply to fields (i.e., continuous systems) such as the electromagnetic and gravitational fields, and it is a foundational concept in quantum physics.  Hamilton's principle states that the dynamics of a physical system will optimize a functional (in our case, an integral over a spatial domain) of the system's Lagrangian, which is typically the difference between its kinetic and potential energies.  Many previous authors have postulated that fluvial landscapes may evolve in such a way that local and/or global kinetic energy dissipation or stream power is minimized, and this is the basis of the optimal channel network (OCN) simulation models that have been widely studied.  However, Hamilton's Principle suggests that these formulations are lacking an important piece, namely the global introduction of potential energy into the fluvial system by rainfall.  The author will show that by introducing this missing piece, Hamilton's Principle and the Euler-Lagrange theorem lead to a partial differential equation (PDE) for idealized, steady-state landforms.  This same PDE can also be derived from conservation of mass and an empirical slope-discharge formula.  These connections therefore point to a new theoretical framework for understanding the interplay between function and form in mature, fluvial landforms;  that is, an explanation for why these landforms take the forms we observe.  The author will also present ideas and algorithms for analyzing digital elevation models (DEMs), in an effort to test for agreement with Hamilton's principle.

How to cite: Peckham, S. D.: Do Mature, Fluvial Landscapes Obey Hamilton's Principle?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20923, https://doi.org/10.5194/egusphere-egu24-20923, 2024.

11:40–11:50
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EGU24-11527
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GM3.2
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ECS
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On-site presentation
Valeria Ruscitto, Michele Delchiaro, Wolfgang Schwanghart, Eleonora Brignone, Daniela Piacentini, and Francesco Troiani

River channel bankfull geometry and discharge are important features providing valuable insights into fluvial monitoring and flood recurrency. The bankfull stage represents the riverbank position that approximates the level at which water overflows onto the floodplain. Bankfull discharge is considered the channel-forming discharge, with a recurrence interval of approximately 1.5 years. Bankfull floods are significant, as they are highly effective in changing channel shape and characteristics. Their recurrence intervals can be used for stream assessment and have implications for infrastructure design and flood mapping. Additionally, gaining insights into the factors influencing floodplain inundation across various time periods is crucial, as the frequency of flood events is predicted to rise with the increase in global temperatures.

In this contribution, we present a novel approach to identify the bankfull geometry through a set of dedicated MATLAB functions. A Digital Elevation Model (DEM) with ground resolution of 1 m/pixel is used as input elevation dataset, obtained with airborne LiDAR (Light Detection and Ranging) survey. The selected river channels are divided in regularly spaced sampling sections, where the bankfull geometry is extracted. Then, the hydraulic depth function that plots the elevation above the river thalweg vs. the ratio between the area and the width is computed for every section. Then, the elevation above river associated to the lowest and the most prominent peaks of the function, corresponding respectively to the bankfull stage or bankfull/floodplain inflection point and to the floodplain, are automatically extracted for each section. Manning’s equation is then applied to the hydraulic geometry corresponding to the lowest peaks elevation to compute the bankfull discharge at every river channel section. The validation process includes the comparison between the results obtained through the automatic bankfull geometry and discharge estimation and discharge data available from river hydrological gauges. Results demonstrate that the developed approach is effective to delineate the bankfull geometry from high-resolution DEMs and complements traditional qualitative field observations. Thus, our approach represents a cost-effective alternative for mapping detailed spatial variations over large spatial extents that are difficult to cover with traditional fieldwork.

How to cite: Ruscitto, V., Delchiaro, M., Schwanghart, W., Brignone, E., Piacentini, D., and Troiani, F.: Identification of river channel bankfull geometry from topographic indicators extracted from high-resolution digital elevation datasets , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11527, https://doi.org/10.5194/egusphere-egu24-11527, 2024.

11:50–12:00
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EGU24-15001
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GM3.2
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Highlight
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On-site presentation
Anette Eltner, Xabier Blanch, Oliver Grothum, Lea Epple, Eliisa Lotsari, Katharina Anders, and Melanie Elias

Cameras that capture images in time-lapse mode of the earth surface enable great opportunities for change detection and thus potential process identification and understanding. The camera systems can range from simple and robust game cameras to complex and synchronised full frame cameras. The main workflow of calculating digital elevation models from overlapping images is similar for the different types of systems; automatically matching the images, performing bundle adjustment considering either calibrated or non-calibrated cameras, geo-referencing the data by automatic ground control point (GCP) measurement, densifying the point cloud and eventually calculating point cloud differences. However, adapted pre-and post-processing steps are needed due to the varying observation conditions considering the camera qualities and the objects of interest. The time-series of point cloud-based change information can be further processed, for example, with time-series clustering approaches to disentangle overlapping processes.

We will introduce three different case studies in the field of fluvial geomorphology, soil erosion research and rockfall assessment. Thereby, different camera systems are utilized. Four low-cost time-lapse cameras are applied in arctic environments to study changes of a river bank at a distance of about 60 m. The high robustness of the cameras encompasses the trade-off of low quality images. In addition, challenging lighting conditions and enduring snow cover complicate the photogrammetric processing. The images are captured with a frequency of two hours, and six permanent GCPs are used to geo-reference the measurements.

Digital SLR cameras are used in moderate climate to measure soil surface changes either due to rainfall simulations or due to natural rainfall events. During the rainfall simulation we use images that are captured by up to ten cameras with a frequency of 10 to 20 seconds and at an object distance between 3 to 4 m. And at the field plot we installed three special camera rigs that encompass five cameras each that are event-controlled by a micro-controller and single board computer solution, which trigger the cameras each time a rain collector bucket is tipping in addition to daily captured images. Challenges for change detection arise from vegetation present at the plots and from runoff water covering the soil surface. Eventually, the derived models of change are used to validate physical based soil erosion models.

The last case study utilizes five full-frame system cameras in the Mediterranean to detect single rockfall events. Images are captured three times a day by an ad-hoc system at a distance of about 100 m. The data is transferred via a locally installed network module. Many areas within the field of view remain stable throughout the measurement period allowing for a time-SIFT approach that matches the images from different points in time. Machine learning algorithms are applied to automatically identify rockfalls in the final 4D dataset. Thereby, we showcase the great potential of time-lapse photogrammetry for different applications of geomorphological change detection.

How to cite: Eltner, A., Blanch, X., Grothum, O., Epple, L., Lotsari, E., Anders, K., and Elias, M.: Applying photogrammetry to time-lapse imagery for geomorphological change detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15001, https://doi.org/10.5194/egusphere-egu24-15001, 2024.

12:00–12:10
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EGU24-15904
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GM3.2
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On-site presentation
Thomas Dewez, Sébastien Linares, Silvain Yart, Florian Masson, Marie Collignon, Lucas Rivera, Caroline Bedeau, and Matthieu Chevillard

Gold is abundant in the greenstone belts of the Guiana shield, in South America, leading to alluvial mining in river sediments and in in-situ rocks. In French Guiana, legal mining takes place under strict environmental regulations and controls, but illegal operations also occur uncontrolled in the vast expanses of the rainforest. Here we describe a successful range of mobile lidar systems, acquisition schemes and processes to map the ground and underground mining operations in a rainforest context. We seek to detect illegal operations, supply and transportation pathways and base camps, using crewed planes and helicopters, uncrewed fixed-wing and multi-copter vehicles (UAV) and handheld lidar systems.

To sense ground elevation below the canopy, airborne lidar systems face three challenges: tree heights (some trees exceed 70 m in height), incised topography (requires performant terrain following capabilities), dark and wet ground surface largely absorbs lidar pulses requiring powerful sources. Tested uncrewed airborne vehicles (UAV) did not yet meet all of the flying autonomy, terrain-following capability, lidar range and on-board decision systems. At present, crewed systems adapt better to conditions and achieve mission objectives.

Over forested areas, observed canopy penetration rates is of the order of 1 ground point for 250 lidar pulses (0.4%). To generate a 1-m/pixel Digital Terrain Model (DTM) with a minimum of occluded pixels, acquisition density should exceed 250 pts/m² at canopy level everywhere. In Dorlin (central French Guiana), a helicopter flew 85-m-above ground-level, 70 % side-lap and 90° cross-lines, using a Riegl VUX-1LR lidar. Targeting 400 pts/m² at canopy-top for 95 % of the 220 ha territory, it reached a canopy-top density of 1400 +/- 750 pts/m² and 43 pts/m² ground density overall. On fully forested areas, ground density dropped to 22.4+/-22.6 pts/m² with 5% of the surface never receiving points at 1 m² level. This enabled interpolation of a 25cm/pixel DTM, which revealed narrow paths, quad tracks, and shaft platforms and head frames under the forest. 2-m kernel high-pass filtering enhanced features better than a standard hill shading. Base camp hut structures, invisible in DTM, are retrievable from native point clouds in a 4 to 5 m-above-ground elevation range. Huts covered in black tarpaulins stand out as rectangular hollow patches due to lidar photon absorption. But even without tarpaulin, hut wooden frames stand out particularly well when point cloud subsets are lit up with the PCV filter of Cloud Compare. Ore-bearing quartz stockpiles however are too small and occluded for a reliable detection and volume computation.

Instead, SLAM-based handheld lidar systems (GeoSLAM Zeb-Revo and Zeb-Horizon) complement the detailed mapping of quartz stockpiles volume, shaft conduit geometry and gallery entrances. Then real-time, SLAM-based quadcopter UAV lidar (Flyability Elios 3) safely penetrates shafts from the surface to explore the undergound gallery network. These new millimetre-scale density point clouds critically reveal spacing, orientation and dimensions of ore-bearing veins, which improves the metallogical understanding of the site and uniquely documents the way artisanal illegal miners operate.

Lidar acquisitions and processing are now being streamlined for systematic use in law enforcement operations and environmental protection actions.

How to cite: Dewez, T., Linares, S., Yart, S., Masson, F., Collignon, M., Rivera, L., Bedeau, C., and Chevillard, M.: Mapping gold mines under the French Guiana rainforest: return of experience with different mobile lidar systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15904, https://doi.org/10.5194/egusphere-egu24-15904, 2024.

12:10–12:20
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EGU24-16317
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GM3.2
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On-site presentation
Edier Vicente Aristizábal Giraldo and Oliver Korup

Fans are cone-shaped depositional landforms composed of a mixture of sediments, mainly derived from debris flow processes at the catchment scale. In mountainous terrains located in humid climates, debris flows are fundamental agents of landscape evolution and a highly destructive natural hazard. In the northern Colombian Andes, fans have been traditionally occupied by human settlements, which has also produced a long history of disasters in many settlements located on fans. For example, a debris flow on November 13, 1985, devastated the city of Armero, killing approximately 22,000 people and causing economic losses totaling over $US 339 million. In 2017, the city of Mocoa was affected by a debris flow where 333 people died, 130 houses were destroyed, and 1461 were partially affected.

Debris-flow risk is likely to increase as a consequence of the increasing magnitude and frequency of extreme weather and rapid population growth over the past few decades. Hence, identifying fan spatial distribution and debris flow occurrences is important for land use planning. In this study, we implemented geomorphometric analyses in the northern Colombian Andes to understand debris flow occurrence in terms of landscape evolution. Using digital elevation models, fan inventory, morphometric parameters, and geomorphic indices associated with the drainage network at the catchment scale, the close interconnection between debris-flow hazards and landscape evolution is explained.

The results show a clear spatial pattern of fans location and debris-flow-prone basins with knickpoint upstream migration and transient-state catchments, those characterized by high values of Ksn, hypsometric index and constraint values of 𝛘. Those findings suggest that landscape evolution indexes could improve debris flow susceptibility assessment at regional scale.

How to cite: Aristizábal Giraldo, E. V. and Korup, O.: Debris flow catchments and landscape evolution in the northern Colombian Andes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16317, https://doi.org/10.5194/egusphere-egu24-16317, 2024.

12:20–12:30
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EGU24-22282
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GM3.2
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ECS
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On-site presentation
Meaghan Dinney and Tracy Brennand

Eskers are ubiquitous features on previously glaciated landscapes, recording the configuration and dynamics of the channelized meltwater system. Studies of esker composition and form have resulted in a variety of genetic interpretations surrounding the ice, water, and sediment characteristics under which they may develop. However, issues of apparent equifinality currently limit the usefulness of eskers for reconstructing broad-scale glacial hydrology. Although some authors have attempted to asses esker morphogenesis, previous studies are limited by their small sample size and/or use of qualitative morphometric indices.

This project aims to explore whether eskers have a distinct morphogenetic signature using data science techniques. Published research has been mined for empirical studies of esker composition and structure. These data were compiled into a database summarizing the genetic interpretations commonly invoked for eskers (e.g., depositional environment, meltwater flow regime) as well as the supporting evidence for such inferences (e.g., sedimentary logs). Semi-automated methods will be tested to map eskers from high resolution (1-2 metres) LiDAR digital terrain models and to extract their morphometry. A range of planform- and profile-scale morphometric indices will be employed and new indices that can more precisely quantify esker morphometry will be developed.

The resulting highly-dimensional dataset can be analyzed using machine learning techniques in order to assess the relationships between sedimentologic, morphometric, and genetic variables. Preliminary results from database development and analysis will be presented and methodological concerns will be discussed.

How to cite: Dinney, M. and Brennand, T.: A data-driven approach to understanding esker morphogenesis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22282, https://doi.org/10.5194/egusphere-egu24-22282, 2024.

Posters on site: Mon, 15 Apr, 16:15–18:00 | Hall X1

Display time: Mon, 15 Apr 14:00–Mon, 15 Apr 18:00
Chairpersons: Stuart Grieve, Massimiliano Alvioli, John K. Hillier
X1.131
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EGU24-7859
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GM3.2
Michael Fuchs, Lars Tiepolt, Karsten Schütze, and Jewgenij Torizin

Airborne Light Detecting and Ranging (LiDAR) surveys became essential in tracking the evolving coastal landscapes of Mecklenburg-Vorpommern on the Baltic Sea for more than one decade, producing a data series of Digital Terrain Models (DTMs) crucial for estimating coastal erosion along the exposed cliffs. Although change detection based on differences between these DTMs is supposed to represent erosion and deposition accurately, a detailed analysis indicates that the initial and final DTMs in the data series sometimes fail to capture the full extent of changes due to various factors. So, natural phenomena, such as the movement of cliff materials (rolling, sliding, creeping), human activities aimed at coastal protection, and errors in DTM processing may disturb clear trends, introducing uncertainties and, in particular, making the data series appear alternating.

To address these issues, we proposed to apply the robust Mann-Kendall test, a non-parametric statistical method used to identify trends in a data series without assuming any particular data distribution. It focuses on determining the direction and consistency of trends (ascending or descending), rather than the change’s magnitude. By implementing this approach, we can pinpoint areas that exhibit clear trends, thereby significantly improving the accuracy of coastal retreat estimations. In regions where trends are not readily apparent, it becomes crucial to investigate potential contributing factors thoroughly by exploring natural environmental dynamics, assessing the impact of human activities, and scrutinizing any errors in data processing. Such a comprehensive analysis ensures a more holistic understanding of the factors influencing these zones.

We employed the proposed approach across four distinguished shore areas characterized by the distinct geological composition of the cliffs, delving into the trends of coastal retreat over the past ten years. As expected for areas with clear trends, the estimation of the dimensions of the recent coastal retreat was in good agreement with historically recorded data. Additionally, in areas exhibiting no discernible trends, we were able to identify the underlying reasons, shedding light on the intricacies of coastal dynamics.

How to cite: Fuchs, M., Tiepolt, L., Schütze, K., and Torizin, J.: The Performance of the Man-Kendall Test in the Analysis of Coastal Changes along Cliff Sections on the Baltic Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7859, https://doi.org/10.5194/egusphere-egu24-7859, 2024.

X1.132
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EGU24-7114
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GM3.2
Yuhui Liu, Yu Zhou, and Xiaoqiang Yang

Mapping benthic reefs at high resolution and accuracy is vital for the management and conservation of coral habitats. Optical remote sensing data has emerged as a valuable tool for large-scale reef mapping in the past decades, with numerous data sets and methods being utilised and developed. In this study, we present a comprehensive comparison of optical remote sensing based bathymetry and benthic mapping methods. We use different optical data including WorldView-2 stereo and Sentinel-2 imagery to map the water depths of coral reef areas in the Xisha region of the South China Sea. Bathymetry data derived from photogrammetric and linear regression methods are compared to the reprocessed Ice, Cloud and land Elevation Satellite-2 (ICESat-2) data. We find that the linear regression method (root-mean-square-error, RMSE=0.60 m) outperforms photogrammetry (RMSE=1.02 m), and the higher resolution WorldView-2 data yields less systematic biases than Sentinel-2 data. Considering that water depths reflect changes in temperature and light, which are critical factors influencing coral reef distribution, we propose to use satellite-derived bathymetry as a feature for coral reef classification. We demonstrate that combining topography and spectral information can improve the overall mapping accuracy, particularly for compositions characterised by sharp boundaries.

How to cite: Liu, Y., Zhou, Y., and Yang, X.: Bathymetry derivation and slope-assisted benthic mapping using optical satellite imagery in combination with ICESat-2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7114, https://doi.org/10.5194/egusphere-egu24-7114, 2024.

X1.133
|
EGU24-196
|
GM3.2
|
ECS
|
|
Andrea Pezzotta, Alessia Marinoni, Mohammed Al Kindi, Michele Zucali, and Andrea Zerboni

Riverscapes in arid and semi-arid environments serve as crucial archives, enabling us to understand the landscape evolution and the active and fossil geomorphological processes that shape the Earth's surface. Such environmental contexts are generally wide, and these settings are routinely investigated with remote sensing tools. We selected two distinct study areas from the south-eastern margin of the Arabian Peninsula (Sultanate of Oman) to detect climate and tectonic imprints over landform development: 1) Jebel Akhdar (JAK), and its surrounding areas, located in the Al-Hajar Mountains (to the North), is a wide anticline formed by the Late Cretaceous obduction of the Semail Ophiolite and the associated time-equivalent tectonics, followed by the Cenozoic tectonic events; and 2) Jebel Qara (JQA), situated in the Dhofar Mountains (to the South), is placed along the Gulf of Aden transform margin, featuring transtensional faults giving rise to stepped escarpments and grabens. The extant landscapes of both regions are characterized by a network of narrow and deep canyons that incised limestone massifs, while the surrounding plain areas show the development of important alluvial fan systems.

The application of remote sensing is essential for investigating the development of fluvial systems at a regional scale, combined with field survey to validate specific sites of interest, thereby understanding the geomorphological evolution at various scales. Specifically, remote sensing techniques include the processing of satellite imagery and the comparison with the available historical imagery and maps to detect changes in geomorphic processes. Remote sensing and field survey allow the recognizing of different geomorphological features; the dominant ones are represented by elements and landforms related to structural setting, fluvial activity, and karst processes. The associations of the abovementioned landforms make it possible to assess the structural influence on drainage and karstic network development. Data collected from remote sensing implements the geomorphometric quantification of geomorphological processes, mostly considering changes in topography and river network analyses. The most meaningful morphometric indices applied (such as drainage divide stability, normalized steepness index, knickpoint detection, and swath profiles…) suggest their values strongly vary along faults in JAK, highlighted even with the alignment of knickpoints; while, in JQA, values show little changes in correspondence of faults and knickpoints are controlled both by karst and structural settings. In this way, the combination of remote sensing and morphometrical analyses permits to quantify the central role of litho-structural influence on the development of riverscapes in the south-eastern Arabian Peninsula. This approach facilitates the identification of the primary geomorphological processes that have shaped the landscape in arid and semi-arid contexts of the Sultanate of Oman, making it a versatile method that can be applied to understand the riverscapes evolution processes in analogous regions.

How to cite: Pezzotta, A., Marinoni, A., Al Kindi, M., Zucali, M., and Zerboni, A.: Remote sensing and geomorphometry application in riverscapes evolution in the south-eastern Arabian Peninsula (Sultanate of Oman), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-196, https://doi.org/10.5194/egusphere-egu24-196, 2024.

X1.134
|
EGU24-1613
|
GM3.2
|
ECS
Ronald Tabernig, Vivien Zahs, Hannah Weiser, and Bernhard Höfle

Terrestrial Laser Scanning (TLS) systems have been refined to automatically and continuously scan defined areas with high temporal resolution (sub-hourly), leading to the development of Permanent Laser Scanning (PLS). This temporal resolution requires the development of new methods for efficient extraction of change information. The creation of labeled 4D point clouds (3D+time), classified by surface change type, remains time-consuming. This hinders the evaluation of change detection methods and the training of machine learning (ML) and deep learning (DL) models.

This study explores how synthetic 4D point clouds can be effectively utilized for detecting and classifying spatiotemporal changes. We combine simplified process path simulations, simulated PLS, and change detection methods (e.g. M3C2) [1]. This combination is used to automatically evaluate calculated distances compared to a pre-defined reference. It also generates labeled 4D training datasets for ML/DL approaches.

We adapted the Gravitational Process Path model (GPP) [2] to create gravity-influenced process paths for our PLS simulations. Utilizing these paths, we simulate two different scenarios, 1) including a forest situated on top of a large landslide and 2) an outcrop with rockfall activity. For the forest scenario, a constant velocity is applied to each tree to simulate slope movement. The velocity of the objects in the rockfall scene is determined by the GPP model. Dynamic 3D scenes are generated from these scenarios and used as input for Virtual Laser Scanning (VLS). Realistic simulation of LiDAR surveys (of these virtual scenes) is achieved by using the open-source simulator HELIOS++ [3]. This workflow allows for the determination of the accurate position of each object at any given time. It provides reference data that is usually unavailable in real data acquisitions. In the rockfall scenario, M3C2 distances are calculated, and areas of similar change are clustered. For the forest located on the landslide, 2D and 3D displacement vectors are derived from the displacement of the tree trunks. These changes are then compared to the actual change occurring between epochs. Furthermore, the time steps between each epoch can be chosen arbitrarily, enabling the exploration of various scenarios and processes using labeled point clouds at any temporal resolution.

Preliminary results suggest that this workflow can assist in determining the scan resolution required to detect changes of a specific size and magnitude. We establish a simulation-based error margin for each method used by comparing the results to the reference data. This enables direct evaluation of method performance during implementation.

We demonstrate the potential of combining process simulation and laser scanning simulation for resource efficient planning of TLS and PLS campaigns, geographically sound generation of dynamic point clouds, the evaluation of change detection and quantification methods, and generating labeled point clouds as training data for 4D ML/DL methods. 

References:
[1] py4dgeo: https://github.com/3dgeo-heidelberg/py4dgeo
[2] Wichmann, V. (2017): https://doi.org/10.5194/gmd-10-3309-2017.
[3] Winiwarter, L. et al. (2022): https://doi.org/10.1016/j.rse.2021.112772. 

How to cite: Tabernig, R., Zahs, V., Weiser, H., and Höfle, B.: Simulating 4D scenes of rockfall and landslide activity for improved 3D point cloud-based change detection using machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1613, https://doi.org/10.5194/egusphere-egu24-1613, 2024.

X1.135
|
EGU24-5190
|
GM3.2
|
ECS
Marco Loche and Gianvito Scaringi

The influence of temperature as a key factor in slope stability, particularly in temperate regions, remains insufficiently explored. This study investigates the thermo-hydro-mechanical (THM) response of expansive soils, focusing on the thermally-induced activity in clay landslides.

Establishing a representative thermal variable for broad-scale assessments poses challenges due to material heterogeneities and the intricate nature of THM processes. Our research employs landslide spatial modelling in Italy, concentrating on clay-rich areas with shallow landslides on gentle slopes. Utilizing geo-lithological and geological maps and the Italian National Inventory (IFFI), we apply a Generalized Additive Model (GAM) based on slope units to capture nonlinearities in the temperature-shear strength relationship. A decade-long dataset of Land Surface Temperature (LST) from MODIS, accessible in Google Earth Engine, serves as a key input.

The study produces spatial probability maps for clay deposits across Italy, revealing a positive correlation between landslide occurrence and LST on warmer, gentle slopes, especially in Southern Italy. This aligns with the observation that higher temperatures reduce soil and water viscosity, amplifying shear creep rates in clay-rich materials. By elucidating the temperature-slope stability relationship, this study contributes to understanding landslide dynamics in temperate climates, facilitating the development of effective risk recognition strategies.

How to cite: Loche, M. and Scaringi, G.: Exploring Temperature-Shear Strength Dynamics: A Spatial Modelling Approach for Clay Landslide Susceptibility in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5190, https://doi.org/10.5194/egusphere-egu24-5190, 2024.

X1.136
|
EGU24-8222
|
GM3.2
|
ECS
|
David Mair, Guillaume Witz, Ariel Henrique Do Prado, Philippos Garefalakis, and Fritz Schlunegger

The size and shape of sediment particles record crucial information on erosion, transport, and deposition mechanisms during sedimentary processes. Therefore, data on grain morphometry is a critical component in understanding sediment production and transport dynamics in various environments, such as fluvial or hillslope settings. However, traditional field methods are labor-intensive, and results may suffer from a limited number of observations. At the same time, remote measurements in images or point clouds still need improvements to counter low accuracy or the need for time-consuming manual corrections (e.g., Steer et al., 2022). These persisting challenges impede the capability of routinely obtaining size and shape information.

Here, we present a new and automated approach (Mair et al., 2023) for obtaining morphometric information on coarse sediment particles from segmented images. To do so, we tap into the capability for transfer learning of deep neural networks. In particular, we use state-of-the-art deep learning, developed to find cells in biomedical images, to segment individual grains in pictures of various sediments and image types. Our method validation includes assessing segmentation performance against ground truth from annotated images and evaluating the measurement quality by comparing results to independent measurements in the field and in images. This approach facilitates precise and rapid grain segmentation and outperforms existing methods. In addition, we observe that higher segmentation quality directly leads to improved precision and accuracy for grain size and shape data. Furthermore, any model of the used architecture can easily be re-trained for new image conditions, which we successfully did for several different settings. This highlights the potential for easy adapting to different environments and scales with comparatively small datasets.

References

Mair, D., Witz, G., Do Prado, A. H., Garefalakis, P., and Schlunegger, F.: Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning, Earth Surf. Process. Landforms, 1–18, https://doi.org/10.1002/esp.5755, 2023.

Steer, P., Guerit, L., Lague, D., Crave, A., and Gourdon, A.: Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds, Earth Surf. Dyn., 10, 1211–1232, https://doi.org/10.5194/esurf-10-1211-2022, 2022.

How to cite: Mair, D., Witz, G., Do Prado, A. H., Garefalakis, P., and Schlunegger, F.: Automated and flexible measuring of grain size and shape in images of sediment with deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8222, https://doi.org/10.5194/egusphere-egu24-8222, 2024.

X1.137
|
EGU24-12288
|
GM3.2
Wolfgang Schwanghart, William Kearney, Anna-Lena Lamprecht, and Dirk Scherler

The Earth’s surface results from the interplay of tectonic and erosive forces, and the action of organisms and humans. To gain a deeper understanding of these interactions, accurate monitoring and analysis of topography is essential. Digital elevation models (DEMs) are powerful tools for achieving this goal and are available at ever increasing spatial resolution. TopoToolbox is a research software that provides a “laboratory” for the analysis of DEMs, enabling customized, automated analysis, prototyping and creative method development. Its high computational efficiency, ease-of-use and extensive documentation have attracted a worldwide user base across multiple research disciplines.

Over the last ten years, TopoToolbox, now in version 2, has undergone numerous changes and additions. The development of version 3 of TopoToolbox seeks to build on those past successes and take the software to the next level. Specifically, our goals are (1) to improve usability and accessibility, (2) to enhance quality assurance in the software’s development process, and (3) to increase community involvement in the ongoing development of TopoToolbox. We strive to achieve these goals in a recently funded 2-year project, in which community involvement is a key aspect. In this presentation, we aim to interact with other researchers interested in terrain analysis to discuss avenues for future developments and activities that improve TopoToolbox's usability, expand its usage, and increase its impact in a new version 3.

How to cite: Schwanghart, W., Kearney, W., Lamprecht, A.-L., and Scherler, D.: TopoToolbox 3 – avenues for the future development of a software for terrain analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12288, https://doi.org/10.5194/egusphere-egu24-12288, 2024.

X1.138
|
EGU24-13034
|
GM3.2
Mihai Niculita

After successfully applying segmentation and machine learning for landform identification and delineation for concave, convex, and generic landforms (landslides, floodplains), the used approach is generalized as a framework. The approach can be implemented in any GIS software that allows scripting and is based on four steps: (i) object-based segmentation based on a specific geomorphometric variable, (ii) contextual merging if the landform is composed of multiple shapes, (iii) selection of the training data segments, (iv) statistical classification by machine learning. The framework refers to creating a set of rules for various scenarios of landform types to allow the implementation of the approach for various landforms and areas around the globe. One of the main requirements regarding the DEM is that its feature resolution be high enough to allow at least a segment to cover the target landform spatially. This requires either LiDAR or RADAR DEMs, with medium or high resolution. We tested COPDEM in areas where there is no vegetation cover and the results show that landslides, floodplains, gullies, sinkholes, and closed depressions can be depicted by the approach.

How to cite: Niculita, M.: A generic framework for the identification and delineation of landforms from high-for DEMs using segmentation, contextual merging, and machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13034, https://doi.org/10.5194/egusphere-egu24-13034, 2024.

X1.139
|
EGU24-13135
|
GM3.2
Anna Maria Dichiarante, Tim Redfield, Espen Torgersen, Anne Kathrine Svendby, and Volker Oye

Spectral analysis (SA) is a technique commonly used in signal and image processing that makes use of the Fast Fourier Transform to compute the 2D power spectrum, which is a representation of the magnitude of each frequency component of the signal or image. SA can be similarly performed on a topographic map, and the orientation, frequency and magnitude (or power) of general topographic trends can be automatically retrieved and displayed in the 2D power spectrum. Recent studies have shown that spectral analysis can be successfully used to characterize repetitive and spatially homogeneous features or landforms, such as ridge and valley or glacial lineations. However, although these repetitive features dominate the 2D power spectrum, all the topographic information of the map is still present. Therefore, SA can be used on heterogenous and complex topographic map as a proxy for lineament analysis.

Lineament analysis is broadly used in a wide number of applications which include tectonic studies, exploration for groundwater, hazard evaluation for tunnel excavation, rockfalls or waste repository etc. Here, we propose a new methodology for lineament analysis based on spectral analysis and we demonstrate that this is a fast and effective way to derive lineament spatial distribution from images that can be visualized as rose diagrams. To validate our methodology, we stochastically generated 1000 synthetic lineament networks and numerically compared the rose diagrams derived from the power spectra to known lineament distribution. The comparison held a similarity of 94%.

The methodology was also applied to the Oslo region and compared to automatically extracted lineaments from OttoDetect software (developed by the Geological Survey of Norway). Results on three pre-selected areas characterized by different topographic patterns showed similarity of 97%, 95%, and 90%, respectively.

One of the pitfalls of spectral analysis is the lack of positioning on the original map of the signatures in the power spectrum. To locate the main signature on the map, we used the orientation of the main signatures from the power spectrum and used cross-correlation and clustering methods on topographic profiles.

How to cite: Dichiarante, A. M., Redfield, T., Torgersen, E., Svendby, A. K., and Oye, V.: Spectral analysis as proxy for lineament spatial distribution: validation and case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13135, https://doi.org/10.5194/egusphere-egu24-13135, 2024.

X1.140
|
EGU24-14080
|
GM3.2
Mary Krapac

The integration of point-cloud data in geo- and environmental sciences has become increasingly pivotal, with applications ranging from UAVs, spaceborne and airborne lidars to ground-based lidars and stereo-photogrammetric techniques. This session seeks contributions that delve into challenges related to classification, segmentation, and noise removal in the context of point-cloud data, crucial for facilitating change detection studies. Our study focuses on the Navigational Branch of the ERDC Coastal Hydraulics Laboratory tasked with developing a Digital Twin model for a Dam, exemplifying the complexities involved in creating CAD models of terrain and structures.

To address the intricacies of point-cloud data processing, we employed both open-source and proprietary software solutions—Cloud Compare and Autodesk ReCAP— for noise reduction, ensuring the prepared data is seamlessly integrated into CAD modeling software, specifically Inventor. Surface modeling involved the strategic application of planes on cloud points to generate a foundation for sketching and subsequent solid surface extrusion.

Classification of data points was initiated through the implementation of regions in the noise removal software, facilitating the depiction of various areas on the model. Further, color and material assignment in the CAD software enhanced the identification of distinct part areas. Microstation TopoDOT played a pivotal role in creating a detailed terrain model, complete with physical landmarks and water bodies specific to the Dalles dam site.

The resulting models were exported in the desired file format, ensuring compatibility with sponsor requirements. This case study not only showcases the practical challenges encountered in working with point-cloud data but also highlights effective strategies for noise reduction, classification, and model exportation. The presented methodologies contribute to the broader spectrum of geo- and environmental sciences, emphasizing the significance of accurate point-cloud processing for comprehensive modeling endeavors.

How to cite: Krapac, M.: Advancements in Point-Cloud Processing for Geo-Environmental Modeling: A Case Study of The Dalles Dam Digital Twin Creation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14080, https://doi.org/10.5194/egusphere-egu24-14080, 2024.

X1.141
|
EGU24-14955
|
GM3.2
Marta Guinau, Celeste Fernández-Jiménez, Anna Barra, Marc Viaplana-Muzas, Ariadna Flores, Maria Ortuño, Marta González, Jordi Pinyol, and Clàudia Abancó

The interaction between slope instability processes and river dynamics often triggers a cascade effect. Sediment influx from slopes can obstruct rivers, leading to upstream flooding and potential catastrophic flash floods downstream upon dam breakage. In addition, the incision of the drainage network steepens the valley hillslopes, further exacerbating slope instability processes, modifying the geomorphology and the sedimentary fluxes and increasing the occurrence of landslide-derived hazards.

In this regard, a comprehensive and updated landslide inventory, especially focusing on the interconnection between landslides and drainage networks, is crucial for effective hazard assessment considering these cascading effects induced by slope and fluvial processes.

This study presents advancements in landslide mapping by integrating data from Multi-Temporal Synthetic Aperture Radar (MT-InSAR) and landscape evolution analysis through geomorphological indices such as Chi, Normalized Channel Steepness Index (Ksn) and Stream Length-Gradient Index (SL). Identification of anomalies along rivers using Ksn and SL (knickpoints or knickzones) aided in pinpointing abnormal slopes due to sediment influx from landslides. Additionally, active areas were delineated using the ADAfinder tool, extracting data from MT-InSAR provided by the European Ground Motion Service (EGMS). This multi-technique analysis highlighted the slopes of interest. Landslides identified with these techniques were delimited and characterized in terms of type assignment, using 2x2 m DTM hillshades derived from airborne LiDAR data and field observations.

The upper catchments of the Garona and Noguera Pallaresa rivers (central Pyrenees-NE Spain) were selected as study cases. The study highlights the disequilibrium in the watershed divide between Noguera Pallaresa and Garona basins, suggesting a transition toward equilibrium favouring a main divide migration towards the Noguera Pallaresa due to hillslope processes. The assessment of the equilibrium profile geometry of the Noguera Pallaresa river at a regional scale suggests at least two main knickpoints. The river sections downstream of the knickpoints are associated with landslides triggered by post-glacial dynamics and incision wave effects. Combining SL and Ksn curves with Active Deformation Areas (ADA) underscores areas with potentially reactivating deep-seated landslides, signifying potential high damages in case of low-probability but catastrophic reactivations.

In conclusion, the integration of diverse methodologies shed light on the spatial relationship between transient features in the landscape (knickpoints) and landslide occurrence, emphasizing the need for a comprehensive approach to mitigate landslide and fluvial risks in the Noguera Pallaresa and Garona river basins.

How to cite: Guinau, M., Fernández-Jiménez, C., Barra, A., Viaplana-Muzas, M., Flores, A., Ortuño, M., González, M., Pinyol, J., and Abancó, C.: Multi-Technique Analysis and Landscape Evolution: Implications for Landslide-Fluvial Cascading Hazards Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14955, https://doi.org/10.5194/egusphere-egu24-14955, 2024.

X1.142
|
EGU24-15418
|
GM3.2
|
ECS
|
Highlight
|
Martina Burnelli, Laura Melelli, Francesco Bucci, Michele Santangelo, Federica Fiorucci, and Massimiliano Alvioli

Geodiversity is “the variety of abiotic features and processes of the land surface and subsurface” [1,2]. Consensus is growing that geodiversity is the geosphere counterpart of what biodiversity represents within the biosphere, atmosphere, and hydrosphere [2]. Thus, it is potentially relevant to ecosystem functions and services [2]. Since the introduction of geodiversity, several scholars studied it from the theoretical and practical points of view, with different approaches, assumptions and purposes. Methods to define diversity of the geosphere are quantitative, qualitative, or a combination of the twos, with the occasional addition of heuristics [3].

Here, we describe a quantitative derivation of a subset of geodiversity, namely, geomorphodiversity. The effort stems from the need of an objective method, apt to providing easy to understand results, readily available for subsequent applications. To that end, requirements are in order about the data included in the analysis: they should be widely available, to allow reproduction of the analysis in most geographical locations, and they should contain enough information to approximate real-world geodiversity.

Geomorphodiversity is one implementation fulfilling the requirements, obtained in the literature by different groups, for different locations [4,5], using simple geomorphometry. Data for the method implemented in Italy [6] are a digital elevation model (EUDEM, 25 m resolution), and a lithological map at 1:100,000 scale [7]. DEM provides derived quantities such as slope, drainage network, landforms [8] and slope units [9], all of which contribute in different ways to produce partial diversity maps. We eventually combine partials into an overall geomorphodiversity raster index, GmI, distinguishing five classes of land surface diversity.

The inherent parameter dependence in the existing implementations of GmI, partially resolved in [6], is one issue to overcome. Free parameters are embedded in the size of neighborhoods (moving windows, or focal statistics) used to calculate the variety, the arbitrary output resolution, and procedures to polish the final raster diversity map from artifacts. We suggest a multiple assessment of the variety of partial abiotic parameters with a full range of different neighborhood sizes, and a-posteriori statistical selection of local values of diversity. This results in a parameter-free approach to GmI, also allowing a custom resolution of the output, with the lower bound of DEM resolution.

We consider a parameter-free geomorphodiversity as a measure of the potential of morphological evolution of the landscape, useful to investigate natural and human-induced diversity in urban areas [10], in combination with accurate, local mapping of geomorphological landforms [11].

 

References

[1] Gray, (2004) Geodiversity: valuing and conserving abiotic nature. ISBN 978–0–470-74215-0

[2] Schrodt et al., PNAS (2019) https://doi.org/10.1073/pnas.1911799116

[3] Zwoliński et al., Geoheritage (2018) https://doi.org/10.1016/B978-0-12-809531-7.00002-2

[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] Bucci et al., Earth System Science Data (2022) https://doi.org/10.5194/essd-14-4129-2022

[8] Jasiewicz et al., Geomorphology (2013) https://doi.org/10.1016/j.geomorph.2012.11.005

[9] Alvioli et al., Geomorphology (2020) https://doi.org/10.1016/j.geomorph.2020.107124

[10] Alvioli, Landscape and Urban Planning (2020) https://doi.org/10.1016/j.landurbplan.2020.103906

[11] Del Monte et al., Journal of Maps (2016) https://doi.org/10.1080/17445647.2016.1187977

How to cite: Burnelli, M., Melelli, L., Bucci, F., Santangelo, M., Fiorucci, F., and Alvioli, M.: A definition of land surface geomorphodiversity across different scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15418, https://doi.org/10.5194/egusphere-egu24-15418, 2024.

X1.143
|
EGU24-18657
|
GM3.2
|
ECS
|
Rossana Napolitano, Michele Delchiaro, Leonardo Maria Giannini, Claudia Masciulli, Giandomenico Mastrantoni, Marta Zocchi, Massimiliano Alvioli, Paolo Mazzanti, and Carlo Esposito

The Latium region (Central Italy) is currently updating the institutional hydro-geological plan, one of the main planning tools to prevent geo-hydrological hazard at regional scale. The plan focuses on landslides, erosion and hydraulic hazard assessment using both conventional and innovative approaches. This analysis required different scales of study, according to the different processes acting on slopes, and their broader physiographic context. In this multiscale approach, slope units represent the most suitable territorial units of analysis and mapping, considering their morpho-hydrological representativeness and scalability.

Slope units are a particular type of terrain units, characterized by internal homogeneity and external heterogeneity, delineated from a digital elevation model considering the natural setting of the territory. A widely used tool for slope unit delineation is the software ‘’r.slopeunits’’ [1,2]. The parameters controlling the delineation are both morphological and hydrological, derived from a digital elevation model. The software implements an iterative and adaptive process, depending on the aforementioned parameters, resulting in slope unit sets optimized for the local morphology. The accurate selection of input parameters requires careful consideration, but it also allows extra flexibility in defining the proper scale of the output slope unit map.

Here, we aim at obtaining a new way to select the values of the software’s input parameters, considering their relations with the different processes, to single out the proper scale of analysis. Specifically, we provide additional terrain analysis methods to find “good” parameter ranges, implemented in simple computer scripts that make use of r.slopeunits. The workflow is organized as follow. First, the geomorphological domains (i.e. hillslope, unchanneled, and fluvial domain) are discriminated by the implementation of the slope – area function, with the area weighed by the runoff values available from the GIS-based model BIGBANG [3]. Next, the flow paths related to the hillslope and unchanneled domains and related basins are hierarchized using Strahler ordering. Then, delineation of basins and half-basins for every path order is computed. Finally, implementation of zonal statistics functions on the half-basins of every path order and calculation of the parameters ranges that for slope unit delineation is performed.

Implementation of a multi – scale derivation of slope units with a range of input parameters, customized according to the type of natural phenomena (landslide, flooding, erosion etc.), allows an adaptive multi – scale approach, specific for each process, for a comprehensive multi-hazard evaluation. One of the future applications of the research is the application of this approach for the definition of ‘’buffer zones’’ covered by natural or semi-natural vegetation, capable of counteracting slope instabilities. In the context of the hazard and risk mitigation management, these outcomes could represent an efficient aid for regulating urban development in a proper and secure manner.

 

References

[1] Alvioli et al. (2016). Geosci Mod Dev, https://doi.org/10.5194/gmd-9-3975-2016

[2] Alvioli et al. (2020). Geomorphology, https://doi.org/10.1016/j.geomorph.2020.107124

[3] BIGBANG model, https://www.isprambiente.gov.it/pre_meteo/idro/BIGBANG_ISPRA.html

 

How to cite: Napolitano, R., Delchiaro, M., Giannini, L. M., Masciulli, C., Mastrantoni, G., Zocchi, M., Alvioli, M., Mazzanti, P., and Esposito, C.: Tailoring slope units delineation according to different natural phenomena for institutional land use planning at the regional scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18657, https://doi.org/10.5194/egusphere-egu24-18657, 2024.

X1.144
|
EGU24-18943
|
GM3.2
|
ECS
|
Alessia Sorrentino, Gaia Mattei, and Pietro Patrizio Ciro Aucelli

This research aims to obtain coastal paleo-environmental reconstructions through the analysis of direct and indirect paleo sea-level markers (SLMs, i.e., SLIPs, TLPs, MLPs) by GIS-aided geostatistics.  

In this work, we used classical SLMs combined with a caves inventory in the Cilento area in the Campania Region (Southern Italy). In this area, mainly characterized by carbonatic rocks, numerous emerged and submerged caves are present along active and fossil cliffs as evidenced in the papers of Antonioli et al., 1994 and Esposito et al., 2002.  

As reported in Ferranti, 1998 and Florea et al., 2007, coastal caves can be considered positively correlated to the glacial-hydro-eustatic sea-level oscillations, especially on the carbonatic substratum.  

Therefore, caves cannot be classified as sea-level markers (SLMs) strictu sensu, anyway, they can be considered as a mark of ancient sea-level position, especially when the occurrence of floor elevation is well-distributed all along the coast (in the case of areas characterised by homogeneous tectonic behaviour). In detail, in this work, the floor elevation of the cave entrances was correlated with tidal notches, wave-cut platforms, Lithophaga burrows, and marine deposits deriving both from previous knowledge and new direct and indirect surveys carried out through classic geomorphological investigations and using robotic technologies and remote sensing.  

All collected data were used to produce a specific geodatabase “PALEOScape (PALEO SeasCAPE)” (Sorrentino et al., 2023) structured based on international standards for sea-level studies. Caves information was obtained from an existing caves’ Inventory (Federazione Speleologica Campana; Russo et al., 2005) integrated by field surveys. Thanks to the well-documented tectonic stability of the area, it was possible to ascribe at the same age SLMs having the equal altimetric position.

These records were analysed by a geostatistical approach by correlating the cave entrances to known sea-level stands increasing the information available on paleo sea-level stands along the examined coast.

By integrating this approach with a new method for semi-automatic landform recognition and classification, it was possible to reconstruct ancient coastal landscapes related to known sea level stands, but also to some new altimetric positions not previously reported in the area.

REFERENCES

Antonioli, F., Cinque, A., Ferranti, L., & Romano, P. 1994. Emerged and Submerged Quaternary Marine Terraces of Palinuro Cape (Southern Italy). Memorie Descrittive Carta Geologica d’Italia, 52, 237–260.

Federazione Speleologica Campana https://www.fscampania.it/catasto-2/catasto/  

Ferranti, L. 1998. Underwater cave systems in carbonate rocks as semi-proxy indicators of paleo-sea levels. Il Quaternario-Italian Journal of Quaternary Sciences, 11(1), 41-52.

Florea, L. J., Vacher, H. L., Donahue, B., Naar, D. 2007. Quaternary cave levels in peninsular Florida. Quaternary Science Reviews, 26(9-10), 1344-1361.

Russo, N., Del Prete, S., Giulivo, I., Santo, A. 2005. Grotte e speleologia della Campania : atlante delle cavità naturali. Elio Sellino Editore.

Sorrentino, A., Maratea, A., Mattei, G., Pappone, G., Tursi, M. F., Aucelli, P. P. 2023. A GIS-based geostatistical approach for palaeo-environmental reconstructions of coastal areas: the case of the Cilento promontory (southern Italy). In 2023 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea) (pp. 488-493). IEEE.

 

How to cite: Sorrentino, A., Mattei, G., and Aucelli, P. P. C.: Reconstructing ancient coastal landscapes and sea-level stands in Southern Italy (Cilento coast): a geostatistical approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18943, https://doi.org/10.5194/egusphere-egu24-18943, 2024.

X1.145
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EGU24-20335
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GM3.2
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ECS
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Neslihan Dal and Tolga Görüm

Türkiye, 61% of which consists of mountains, has an extremely rugged topography. Anatolia, which is located in the collision zone of plates with different characteristics, exhibits a morphological character with different stages of mountain formation due to the Paleotectonic and Neotectonic movements it has been exposed to during geological times. In Anatolia, where the main physiographic character is mountains, the proportion and boundaries of mountains and mountainous areas have not been quantitatively defined and there has not been a geomorphometric approach to this until now. In this study, the mountain definition obtained from the pixel-based and multi-scale basic data matrix was subjected to various analyzes with the modeling created in geographic information systems. In addition, how the mountain definition and classification change at varying scales and thresholds is revealed.

The characterization has two main purposes: To determine the framework of the methodology in the definition of macro landforms and to determine the most optimum model that quantitatively defines mountain and mountainous area. According to the model, mountains cover 61% of Türkiye. In this context, in addition to developing a model to geomorphometrically define mountain and mountainous area characterization, the thesis approaches mountains, which are a macro morphological landform, from an ontological perspective and approaches the questions we asked at the beginning in terms of geographical epistomology. In this respect, the thesis is a contribution to traditional geomorphology.  A bivariate map of 16 classes to visualize the relationships between morphological variables and a combination of mean elevation and topographic relief classifies mountains. The classification shows a transition from low rugged and low mountains, to moderate rugged and moderate height mountains, to high rugged and high mountains, to very high rugged and very high mountains. Within the framework of the classification, according to four different ruggedness ratios in Türkiye, low rugged mountains occupy 37%, moderate rugged mountains 33%, high rugged mountains 20% and very high rugged mountains 9%.

How to cite: Dal, N. and Görüm, T.:  Bivariate mountain definition: a case study for the turkish mountain system , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20335, https://doi.org/10.5194/egusphere-egu24-20335, 2024.

X1.146
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EGU24-10314
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GM3.2
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ECS
Yue Zhu, Paolo Burlando, Pauy Yok Tan, Christian Geiß, and Simone Fatichi

High-resolution Digital Elevation Model (DEM) data provides essential information for pluvial flood simulation. Although the increased accessibility and quality of publicly available DEM datasets can facilitate geospatial analysis at various scales, existing DEM datasets with global coverage mostly lack sufficient spatial resolution for pluvial flood simulations, which require detailed topographic information to be included in the simulation. Simulating flood scenarios with low-resolution DEMs (>30m) can result in substantial deviations from real cases. This issue becomes even more severe for flood-prone areas in data-scarce developing countries.

Image super-resolution is a technique for reconstructing low-resolution information into high-resolution data. Various deep-learning models have been employed for this task, primarily focusing on generating high-resolution natural-colour images. However, the effects of these deep learning models on enhancing the resolution of DEM data have not been extensively investigated. One of the state-of-the-art super-resolution models, the Residual Channel Attention Network (RCAN), has gained popularity due to its accuracy and efficiency. Leveraging publicly available low-resolution global DEM data and high-resolution regional DEM data, this study assesses the performance of RCAN models in a DEM super-resolution task. The experimental results suggest that, compared to conventional interpolation methods, the tested RCAN model exhibits superior performance in constructing high-resolution DEM data. The generated super-resolution DEM data were then tested in pluvial flood simulations and achieved substantially higher realism in modelling floodwater distribution. The proposed method for constructing super-resolution DEMs opens up the possibility of simulating flooding at hyper-resolution globally.

How to cite: Zhu, Y., Burlando, P., Tan, P. Y., Geiß, C., and Fatichi, S.: A deep learning-based super-resolution DEM model for pluvial flood simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10314, https://doi.org/10.5194/egusphere-egu24-10314, 2024.