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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.