Geomorphometry and geomorphological mapping are important tools used for understanding landscape processes and dynamics on Earth and other planetary bodies. The recent rapid advances in technology and data collection methods has made available vast quantities of geospatial data for such morphometric analysis and mapping, with the geospatial data offering unprecedented spatio-temporal range, density, and resolution, but it also created new challenges in terms of data processing and analysis.
This inter-disciplinary session on geomorphometry and landform mapping aims to bridge the gap between process-focused research fields and the technical domain where geospatial products and analytical methods are developed. The increasing availability of a wide range of geospatial datasets requires the continued development of new tools and analytical approaches as well as landform/landscape classifications. However, a potential lack of communication across disciplines results in efforts to be mainly focused on problems within individual fields. We aim to foster collaboration and the sharing of ideas across subject-boundaries, between technique developers and users, enabling us as a community to fully exploit the wealth of geospatial data that is now available.
We welcome perspectives on geomorphometry and landform mapping from ANY discipline (e.g. geomorphology, planetary science, natural hazard assessment, computer science, remote sensing). This session aims to showcase both technical and applied studies, and we welcome contributions that present (a) new techniques for collecting or deriving geospatial data products, (b) novel tools for analysing geospatial data and extracting innovative geomorphometric variables, (c) mapping and/or morphometric analysis of specific landforms as well as whole landscapes, and (d) mapping and/or morphometric analysis of newly available geospatial datasets. Contributions that demonstrate multi-method or inter-disciplinary approaches are particularly encouraged. We also actively encourage contributors to present tools/methods that are “in development”.
vPICO presentations: Mon, 26 Apr
Landforms and channel networks have long been analysed through co-variation between topographic slope and drainage area, which is derived from easy-to-implement flow routing algorithms (D8 or Dinf) relying on topographic slopes. The slope-area relationship has been successful to identify morphologic regions in landscapes likely reflecting the erosion and transport processes that shape them. But the implicit assumption for using the slope-area relationship is that channels are narrower than the DEM resolution and that, at this scale, the flow is correctly routed. These assumptions are no more valid for very high-resolution DEM (HRDEM, <2 m) that are now widely available with unprecedented level of vertical accuracy (< 20 cm). In wide rivers, the drainage area algorithm puts the total river discharge in one of the pixel of each channel section and let the others with unrealistically low areas. In other words, D8 or Dinf algorithms are not adapted to resolve the lateral extent of rivers.
In this study, we propose a new topographic analysis relying on realistic hydraulic simulations of surface flow. For this, we use a particle-based hydraulic model, Floodos, which solves the 2D shallow water equations, and we present an analysis of the 1m LiDAR DEM of the Elder creek watershed in California, for which channels are up to ten meters wide. By simulating channel flows with water depth, hydraulic slope, specific discharge and bed shear stress, the hydraulic model reveals landscape patterns that are not described by the slope-area relationship. Additionally, the flow model handles very well the small irregularities of the topography.
We introduce new geomorphic descriptors: the hydraulic slope and the specific drainage area (or specific discharge). The catchment organization is then analysed through a new framework called the hydraulic slope-area diagram. This diagram has several benefits over the classical slope-area diagram. It correctly classifies pixels located in the river for a given discharge in the fluvial domain leading to a sharper transition between the colluvial and fluvial domain. The hillslope-to-valley transition is also insensitive to the DEM resolution. Channel width can also be automatically calculated based on a joint analysis of Dinf and 2D shallow water simulation. Finally, the capability to perform the hydraulic slope-area for various discharges brings a richer description of landscape organization by highlighting discharge-dependent regions such as floodplain areas and fluvial terraces.
How to cite: Bernard, T., Davy, P., and Lague, D.: A hydrodynamically consistent "slope-area" relationship for analysing fluvial landscape with wide rivers., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8423, https://doi.org/10.5194/egusphere-egu21-8423, 2021.
Semantic image classification as practised in Earth Observation is poorly suited to mapping fluvial landforms which are often composed of multiple landcover types such as water, riparian vegetation and exposed sediment. Deep learning methods developed in the field of computer vision for the purpose of image classification (ie the attribution of a single label to an image such as cat/dog/etc) are in fact more suited to such landform mapping tasks. Notably, Convolutional Neural Networks (CNN) have excelled at the task of labelling images. However, CNN are notorious for requiring very large training sets that are laborious and costly to assemble. Similarity learning is a sub-field of deep learning and is better known for one-shot and few-shot learning methods. These approaches aim to reduce the need for large training sets by using CNN architectures to compare a single, or few, known examples of an instance to a new image and determining if the new image is similar to the provided examples. Similarity learning rests on the concept of image embeddings which are condensed higher-dimension vector representations of an image generated by a CNN. Ideally, and if a CNN is suitably trained, image embeddings will form clusters according to image classes, even if some of these classes were never used in the initial CNN training.
In this paper, we use similarity learning for the purpose of fluvial landform mapping from Sentinel-2 imagery. We use the True Color Image product with a spatial resolution of 10 meters and begin by manually extracting tiles of 128x128 pixels for 4 classes: non-river, meandering reaches, anastomosing reaches and braiding reaches. We use the DenseNet121 CNN topped with a densely connected layer of 8 nodes which will produce embeddings as 8-dimension vectors. We then train this network with only 3 classes (non-river, meandering and anastomosing) using a categorical cross-entropy loss function. Our first result is that when applied to our image tiles, the embeddings produced by the trained CNN deliver 4 clusters. Despite not being used in the network training, the braiding river reach tiles have produced embeddings that form a distinct cluster. We then use this CNN to perform few-shot learning with a Siamese triplet architecture that will classify a new tile based on only 3 examples of each class. Here we find that tiles from the non-river, meandering and anastomising class were classified with F1 scores of 72%, 87% and 84%, respectively. The braiding river tiles were classified to an F1 score of 80%. Whilst these performances are lesser than the 90%+ performances expected from conventional CNN, the prediction of a new class of objects (braiding reaches) with only 3 samples to 80% F1 is unprecedented in river remote sensing. We will conclude the paper by extending the method to mapping fluvial landforms on entire Sentinel-2 tiles and we will show how we can use advanced cluster analyses of image embeddings to identify landform classes in an image without making a priori decisions on the classes that are present in the image.
How to cite: Carbonneau, P.: Mapping Fluvial Landforms with Deep Similarity Learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-811, https://doi.org/10.5194/egusphere-egu21-811, 2021.
In recent decades, a huge advance in data collection has favoured the study of many questions related to geomorphic processes and associated landforms (Viles, 2016). This increment in data collection let face new questions and develop new methodologies in Geomorphology (Sofia et al., 2020). In this way, geomorphometry is a challenging discipline with the objective of quantify land-surface analysis and extract as well as detect geomorphological elements (Guyon and Elisseeff, 2008). As result, this discipline complement classis geomorphological maps with others extracted from digital elevation models providing land-surface metrics to investigate the full spectrum of geomorphology (Seijmonsbergen et al., 2011).
In this study, we investigate the extraction of topographical features from fluvial terraces and erosive surfaces by means of mapping procedures applied to a digital elevation model (spatial resolution of 5x5 m) using ArcGIS 10.7. This procedure was focussed on the quantification of elements (altitude, slope angle, length, and curvature, exposure) to characterize morphological elements that may define the presence of fluvial terraces as well as erosive surfaces. A Principal Component Analysis was performed to validate that procedure.
The procedure was conducted in two study areas: detection of fluvial terraces from the watershed of Guadalmedina river as well as of erosive surfaces in Sierras Subbéticas Geopark, both areas located in southern Spain.
How to cite: Martinez-Murillo, J. F. and Carruana-Herrera, D.: Mapping and statistical procedures applied to extract topographical features of fluvial terraces and erosive surfaces. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3443, https://doi.org/10.5194/egusphere-egu21-3443, 2021.
In fluvial geomorphology as well as in freshwater ecology, rivers are commonly seen as nested hierarchical systems functioning over a range of spatial and temporal scales. Thus, for a comprehensive assessment, information on various scales is required. Over the past decade, remote sensing based approaches have become increasingly popular in river science to increase the spatial scale of analysis. However, data-scarce areas have been mostly ignored so far despite the fact that most remaining free flowing – and thus ecologically valuable – rivers worldwide are located in regions characterized by a lack of data sources like LiDAR or even aerial imagery. High resolution satellite data would be able to fill this data gap, but tends to be too costly for large scale applications what limits the ability for comprehensive studies on river systems in such remote areas. This in turn is a limitation for management and conservation of these rivers.
In this contribution, we suggest an approach for river corridor mapping based on open access data only in order to foster large scale geomorphological mapping of river corridors in data-scarce areas. For this aim, we combine advanced terrain analysis with multispectral remote sensing using the SRTM-1 DEM along with Landsat OLI imagery. We take the Naryn River in Kyrgyzstan as an example to demonstrate the potential of these open access data sets to derive a comprehensive set of parameters for characterizing this river corridor. The methods are adapted to the specific characteristics of medium resolution open access data sets and include an innovative, fuzzy logic based approach for riparian zone delineation, longitudinal profile smoothing based on constrained quantile regression and a delineation of the active channel width as needed for specific stream power computation. In addition, an indicator for river dynamics based on Landsat time series is developed. For each derived river corridor parameter, a rigor validation is performed. The results demonstrate, that our open access approach for geomorphological mapping of river corridors is capable to provide results sufficiently accurate to derive reach averaged information. Thus, it is well suited for large scale river characterization in data-scarce regions where otherwise the river corridors would remain largely unexplored from an up-to-date riverscape perspective. Such a characterization might be an entry point for further, more detailed research in selected study reaches and can deliver the required comprehensive background information for a range of topics in river science.
How to cite: Betz, F., Lauermann, M., and Cyffka, B.: Open source riverscapes: Analyzing the river corridor of the Naryn River in Kyrgyzstan based on open access data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4735, https://doi.org/10.5194/egusphere-egu21-4735, 2021.
Fluvial geomorphology provides an integrative space-time framework to target actions for risk mitigation, water resource preservation, and biodiversity and landscape conservation. Geomorphic data provides support critically important knowledge on stream condition, adjustment processes and sensitivity to change. Multiscale frameworks are needed to organize knowledge into useful usable and usable useful information. To move towards a more river rehabilitation or conservation strategy, a planning effort is needed at the regional or network scale, so called upscaled geomorphology, to provide large spatial datasets and new environmental monitoring facilities. This new emphasis on spatial planning resonates also with the concept of “green infrastructures” as a mean to protect fluvial corridors and identify opportunities to restore lateral connectivity and floodplain functionalities, thus providing ecosystem services such as flood expansion zones or better functioning ecological networks.
The development of a new version of the Fluvial Corridor Toolbox (FCT), following the work done by Alber and Piégay (2011) and Roux et al. (2015), started as an effort to implement port the ArcGIS code to the QGis platform for promoting open science and sharing our tools with river practitioners. The initial version of the FCT provided a spatial framework to produce metrics at a fine scale and a disaggregation-aggregation procedure to delineate floodplain functional units along a channel network. The new version of the FCT has been completely rewritten and incorporates ideas from Nardi et al. (2018) and Clubb et al. (2017) for improving the calculation of riverscape feature heights above the water level and delineate floodplain through the river network. We also borrowed the concept of swath profiles from Hergarten et al. (2014) as the basis of a new raster-based approach to characterize floodplain features on cross-sections. These new functionalities are based on high resolution DEM and landcover data to produce different floodplain envelops. Finally, we implemented tiled processing of very large raster datasets after Barnes (2016, 2017). This new version of the FCT also provides a lightweight framework for developing new processing toolchains/workflows. We successfully processed 5 m resolution landcover data over the entire (French) Rhone basin and used these layers to highlight the FCT interest. The new workflows are suitable for working at large network scale and are reproducible.
Further perspectives include an integration of such data and some FCT functionalities in online regional observatories with a visualization interface showing raw data on cross-sections and long profiles and synthetic patterns at the network scale allowing to compare target reaches with regional references.
How to cite: Dunesme, S., Rousson, C., and Piégay, H.: An open platform version of the Fluvial Corridor Toolbox with new functionalities to map nested floodplain envelops at the network scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8771, https://doi.org/10.5194/egusphere-egu21-8771, 2021.
Alluvial staircase terraces are typical Quaternary features of mid- to high latitude rivers. Their formation is linked to i) repeated events of increased sediment production, triggered by Quaternary climatic fluctuations and ii) tectonic uplift. Accordingly, terraces may act as important terrestrial archives of climatic and geodynamic information. Comprehensive stratigraphic and topographic data qualifies the North Alpine Foreland as an ideal study region. Even though it has been subject to extensive investigations for more than a century consistent, basin wide stratigraphic models have not been proposed for more than a century (Penck and Brückner, 1909). Advances in local stratigraphy created major stratigraphic inconsistencies between the related parts of Switzerland, Germany and Austria.
In an aim to resolve these inconsistences we focus on foreland-wide high-resolution topographic data by investigating syn- and postdepositional signals behind the hypsometry and morphology of tributary terraces to the rivers Rhine and Danube.
By utilizing data from digital elevation models, geologic maps as well as outcrop information, morphostratigraphic analyses are provided via a new toolset within the framework of the software R. Semiautomatic projection of terrace data on 2D profiles allow to perform statistical analysis (based on slope, relative heights, concavity) of river long profiles and terrace-tops. We show that extracted parameters are highly suitable to make quantitative statements on fluvio-, glacio- and geodynamic processes controlling Quaternary terrace formation.
Penck, A., & Brückner, E. (1909). Die Alpen im Eiszeitalter. Leipzig: Tauchnitz.
How to cite: Pollhammer, T., Salcher, B., Kober, F., and Deplazes, G.: A statistical framework to analyse the stratigraphy of glaciofluvial terraces from topographic data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5624, https://doi.org/10.5194/egusphere-egu21-5624, 2021.
Grainflow, a fundamental agent moving sediment from the crest to the base of dune surfaces, leaves a temporary geomorphological signature on the slipfaces of aeolian dunes. The grainflow signature reflects the complex morphodynamical interaction between wind-driven sand transport and gravity-driven grainflow on an inclined surface. The purpose of this study is to present a method to objectively and efficiently delineate grainflow boundaries and characterize their morphology features by processing Digital Elevation Models (DEMs) obtained by terrestrial laser scanner in Matlab and ArcGIS. The method allows large numbers of grainflows to be quickly and objectively delineated and extracted from LiDAR data. As an aid tp subsequent analysis, the process avoids the subjective nature of manual measurement, thereby improving the commensurability of different grainflow regimes in both terrestrial and extraterrestrial environments. The results can be compared with the available grainflows morphology characteristics which are manually measured. The method is presented here in the context of analyzing grainflows and related processes on the slipfaces of dunes, but it is applicable over the broader scope of other forms of slope failure and geophysical flows, such as avalanches, snowslides, landslides, and debris flows.
How to cite: Zhang, P.: An object method to characterize grainflow morphology, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5934, https://doi.org/10.5194/egusphere-egu21-5934, 2021.
A comprehensive understanding of river dynamics requires the quantitative knowledge of the grain size distribution of bed sediments and its variation across multiple temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods permit to cover small areas and short time scale, thus the operational measurement of grain size distribution of river bed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images but very limited investigations have been carried out so far on the use of satellite-based sub-pixel mapping of river characteristics relevant to ecohydraulic processes.
In this study, we propose a new approach to retrieve sub-pixel scale grain size classes information from Sentinel 2 imagery building upon a new image-based grain size mapping procedure. Four Italian gravel-bed rivers featuring different morphology were selected to conduct UAV acquisitions and extract ground truth grain size data from the near-ground images, by photo-sieving techniques. We generated grain size maps at the resolution of 2 cm on river bars in each study site by exploiting image texture measurements, and subsequently resampled and co-registered the grain size maps with Sentinel 2 data resolution.
Relationships between the grain sizes measured and the reflectance values in Sentinel 2 imagery - available in 11 bands super resolved at 10 m resolution – were analyzed. Based on these, our first results show statistically significant predictive models (cross validation results: MAE of 3.38 ± 13.4 mm and R2=0.48) by using a machine learning framework (Support Vector Machine) to retrieve grain size classes from reflectance data.
Our proposed approach based on freely available satellite data calibrated by low-cost automated drone technology can provide reasonably accurate estimates of surface grain size for bar sediments in medium to large river channels, over lengths of hundreds of kilometers. Moreover, the proposed methodology is easily replicable to other natural environments where an extensive grain size distribution assessment is crucial to understand geomorphic processes, thus providing a new technique for collecting such precious data and support studies of landscape evolution.
How to cite: Marchetti, G., Bizzi, S., Belletti, B., Lastoria, B., Mariani, S., Casaioli, M., Comiti, F., and Carbonneau, P.: Machine learning-based grain size mapping from satellite images , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14945, https://doi.org/10.5194/egusphere-egu21-14945, 2021.
The Andean foreland of Patagonia features dozens of basaltic plateaus that are spread out from the Argentinean province of Neuquén south to Tierra del Fuego. The plateau margins are undermined by numerous giant slope failures that mostly involved a combination of lateral spreading and rotational sliding, running out up to several kilometres along the plateau margins. However, the overall extent of plateau margins affected by landsliding is still unknown, because manual mapping of such a large area (~500.000 km²) is time-consuming. Therefore, our goal is to test methods that support manual mapping by an automatic and objective detection of giant landslides. All of these landslides share very similar topographic features such as subparallel compression ridges and elongate depressions, distinguishing them in terms of their topographic and optical appearance from surrounding areas (e.g. plains or plateau tops). Using a catalogue of these features, we tested an image classification scheme using convolutional neural networks (CNNs). Our input data consist of Sentinel-2 optical data (20-m resolution) and topographic factors (surface roughness and curvature) acquired from TanDEM-X data (12-m resolution). We applied transfer learning, modifying the pre-existing CNN alexnet to test how well it is able to distinguish different geomorphic features such as unstable terrain from plateau tops or plains. Over 4000 training images were extracted from the Meseta Somuncurá, while the trained algorithm was tested at the Sierra Cuadrada. Both plateaus are part of the Northern Patagonia Massif. Preliminary results show that the modified algorithms performs reasonable and is able to distinguish between giant landslides and other geomorphic features. However, performance strongly depends on the training options of the stochastic gradient descent within the CNN and image quality of the training images, especially the quantity of images and their extracted location with respect to the plateau margin.
How to cite: Schönfeldt, E., Tomáš, P., Diego, W., and Oliver, K.: Using convolutional neural networks to detect giant landslides in the Patagonian Andean foreland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2728, https://doi.org/10.5194/egusphere-egu21-2728, 2021.
Machine learning algorithms are increasingly used in geosciences for the detection of susceptibility modeling of certain landforms or processes. The increased availability of high-resolution data and the increase of available machine learning algorithms opens up the possibility of creating datasets for the training of models for automatic detection of specific landforms. In this study, we tested the usage of LiDAR DEMs for creating a dataset of labeled images representing shallow single event landslides in order to use them for the detection of other events. The R stat implementation of the keras high-level neural networks API was used to build and test the proposed approach. A 5m LiDAR DEM was cut in 25 by 25 pixels tiles, and the tiles that overlayed shallow single event landslides were labeled accordingly, while the tiles that did not contain landslides were randomly selected to be labeled as non-landslides. The binary classification approach was tested with 255 grey levels elevation images and 255 grey levels shading images, the shading approach giving better results. The presented study case shows the possibility of using machine learning in the landslide detection on high-resolution DEMs.
How to cite: Niculita, M.: Landslide detection by machine learning on high-resolution DEMs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13635, https://doi.org/10.5194/egusphere-egu21-13635, 2021.
The reconstruction of scenarios of historical hazardous landslide and erosion processes is a milestone to understand their formation, and perform an appropriate hazard assessment. Here, we focus on the possible conditions that lead to a dramatic cyclone-induced gullying event on La Reunion volcanic island in the Indian Ocean.
In Cirque de Salazie, the Ravine de l’Eglise is a gully of 720 m long and 40 m wide. It formed in just the few days the cyclone Hyacinthe lasted from 15th of January to 27th of January 1980. Hyacinthe drenched Grand-Ilet with world-record-type rainfalls: 5254 mm in 12 days on Grand-Ilet, with a maximum of 1044 mm in one day. This sudden gullying phenomenon, locally called “Déboulé”, poses a substantial threat to local dwellings and inhabitants. Grasping the conditions that lead to such dramatic process is a pre-requisite to mitigating the risks.
The heterogeneous properties of coarse volcanic materials, the complexity of the structural characteristics of the terrain and its hydrogeology make Déboulé a phenomenon that is difficult to understand and anticipate. As this rare, fast and hazardous cyclonic circumstances process cannot be observed in-situ, scenarios combining physically based hydrogeological and slope stability models are explored to describe conditions to form and propagate a Déboulé. The development of such integrated models requires the description of initial conditions that led to the event (rainfall amount, morphology of the terrain and its mechanical and hydrological characteristics) and also a detailed geometry of the gully to validate the simulation output.
In this communication, we present the methods used (1) to document the geometry of the Déboulé of La Ravine de l’Eglise (2) the morphological and hydrological triggering conditions of this event.
The original and final topography where the Déboulé of la Ravine de l’Eglise occurred was reconstructed with ca. 1/27 000 archive aerial photographs taken before (1978) and after (1984) the cyclone above Grand-Ilet. Using Structure-from-Motion processing on these two sets of archived images, we build historical digital surface models and ortho-photographs to retrieve quantitative metrics of the landscape evolution caused by the cyclone. The mass wasted during the Déboulé is ca. 0.6 Mm3 ± 0.1 Mm3.We also access to the morphology of the area before the event allowing to identify conditions favorable to the initiation of such phenomenon such as closed depressions, lineaments and regressive erosion lining up the future gully and steep slope breaks.
The hydrogeological conditions of Grand-Ilet during Hyacinthe that caused the Déboulé, are simulated using GARDENIA, a BRGM application for lumped hydrologic modelling. The historic water table levels, especially that under Hyacinthe rainfall, are hindcast considering the rainfalls since 1978 and water table measured in a piezometer since 2010. The hindcast water reported on the reconstructed topography of 1978, crosses the bottom of the embankment that collapsed during Hyacinthe.
The reconstructed topographies and the hindcast water level are consistent with field evidences. Our results document and propose for the first time a quantification the geometry of a Déboulé and bring insight for the initiation of such process.
How to cite: Rault, C., Thiery, Y., Dewez, T., Reboul, K., and Aunay, B.: How to reconstruct geometrical characteristics and failure conditions of an historic atypical complex flow-like landslide: example of the Déboulé of La Ravine de l’Eglise (La Réunion Island, France). , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4296, https://doi.org/10.5194/egusphere-egu21-4296, 2021.
During the last decade a significant progress in methods and techniques of elevation data acquisition has been achieved. With lidar-derived digital elevation models it is now possible to investigate landforms with precision and detail which was never possible before. The resolution of global and continental elevation models is approaching first meters, which enables detailed geomorphometric analysis and mapping in wide spatial extents. At the same time, Earth scientists are interested not only in learning the properties of small landforms, but also in investigating the large regional terrain features, as well as hierarchical properties of terrain structure. For this, small details must be omitted from digital elevation model, and the analyzed dataset is expected to have coarser resolution. Currently available coarse-resolution global digital elevation models such as GMTED2010, GEBCO_2019 and ETOPO1 are characterized by spatial resolution ranging from 7.5” to 1’, which is approximately equal to cell size of 250-2000 m on the equator. Such resolution fits well into the small-scale mapping and analysis context. However, these models have excessive level of detail in relation to their resolution, which is a consequence of the method of their derivation — mainly aggregation and resampling of more detailed data. As a result, terrain maps created using these models, are characterized by lack of generalization, which prevents realistic portray of large terrain forms. To solve the problem, the new high-quality mutiresolution digital elevation model HYPSO has been developed. HYPSO is derived based on GEBCO_2019 model (15” resolution) using the structural generalization, during which the less detailed terrain surface is reconstructed from characteristic stream and watershed lines. HYPSO includes eight levels of detail (LoDs) with resolutions 30”, 1’, 2’, 4’, 8’, 16’, 32’ and 64’ which are suitable for mapping any region on the Earth including the seabed at scales 1:1 000 000 and smaller. The sequence of LoDs is characterized by sequential decrease in detail, which enables production of multiscale maps. Additionally, HYPSO is spatially conflated with river/lake centerlines in popular Natural Earth cartographic database and can be used as a background terrain layer in production of general geographic (base) maps. While the primary purpose of HYPSO is hypsometric mapping, it is also suitable as a data source for performing the geomorphometric analysis aimed at investigating the properties of large terrain landforms, which is demonstrated on several examples.
The study was supported by the Russian Science Foundation grant No. 19-77-10071
How to cite: Samsonov, T.: HYPSO: a new multiresolution global raster digital elevation model for small-scale terrain mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14468, https://doi.org/10.5194/egusphere-egu21-14468, 2021.
Overwhelming amounts of geological and geomorphological data have accumulated over the last ca. 160 years for the Netherlands. Also, the amounts and diversity of digital map products summarizing all this data also have grown overwhelmingly. Combining, updating and synchronizing the various information sources while keeping matters user-friendly is a challenge. We present the current status of our GIS solutions for managing landform age information and performing palaeogeographical analysis utilizing past landscape visualizations (i.e. query-generated map time series).
Our mapping uses so-called base maps connected to landform catalogue database to store information, which are published as open data. Base maps and catalogues are to be kept up-to-date with new actual data through iterative manual revision, and are ‘living’ datasets. For palaeogeographical analysis we query the base maps and recombine subselections using scripts. This generates derived map series in which the information is arranged for the analysis, which independently gets open data status. To allow communal maintenance of the information, we designed interfaces to the landform catalogue databases of our base maps to make them editable in wiki-style (i.e.: ‘non-static open data’).
Attitudes like this are needed to get the most out of accumulating data and overcome integration, actuality and divergence challenges felt by users working with different maps claimed to be based on the same shared body of geodata generated in densely populated lowland countries.
How to cite: Cohen, K., Pierik, H.-J., Woolderink, H., Moree, J., and Cox, H.: Semi-automated past landscape visualizations for the Netherlands: solutions to keep national overviews actual and in-sync, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4976, https://doi.org/10.5194/egusphere-egu21-4976, 2021.
Many dryland regions of the world are at high risk of desertification from combined human land use and anthropogenic climate change. One symptom of desertification is the reactivation of previously stable dunefields. Since morphologies of stable dunes are thought to reflect wind regimes at the time of their formation, the degree to which dune orientation reflects modern winds may be one way to assess changes in wind regimes and the progression of desertification in a region.
Here we investigate the relationship between wind dynamics and desert dune orientation in one region at risk of desertification, southeast Kazakhstan in Central Asia, on the basis of open-source software and open-access datasets. Using Google Earth Engine, we map dunes or interdune spaces within six palaeo-dunefields in the Ili-Balkhash area, by performing a multi-layer object-based image analysis (OBIA) on satellite remote sensing data (Sentinel-2 optical imagery and SRTM digital elevation models). A semi-automated GIS approach is used to undertake data cleansing and the quantification of dominant palaeo-dunefield orientations. The resulting orientation trends are concurrent with the region’s topography: The dunefields within the Ili valley show a narrow, mostly E-W oriented trend concurrent with the course of the valley while the orientation ranges become broader towards the open pre-Balkhash area.
We then predict modern dune orientations by applying the maximum gross bedform-normal transport rule on reanalysed wind data for 2008-2018. This approach by Rubin and Hunter (1987) allows the deduction of sand transport and resulting bedform trends from wind direction frequencies. The predicted modern orientation trends for the dunefields in the Ili-Balkhash area yield only partial consensus with observed palaeo-bedform trends. We therefore propose that modern wind regimes are not exclusively responsible for existing dune morphologies in the region, and that dune orientation may be inherited from earlier wind regimes.
How to cite: Nowatzki, M., Fitzsimmons, K., Harder, H., and Rosner, H.-J.: Dunes as palaeo-wind vanes: Investigating palaeowind regimes using semi-automated remote sensing approaches to dunefield mapping and orientation quantification , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6067, https://doi.org/10.5194/egusphere-egu21-6067, 2021.
Regional-scale glacial geomorphological maps provide important empirical data for reconstructions of former ice sheets, which may serve as analogues for the behaviour of modern ice sheets under climate warming. In particular, the extensive LiDAR-derived record of former ice sheet beds, provides an outstanding archive from which to infer former ice sheet behaviour. The stacking together and analysis of, tens of thousands of individual landforms, based on their spatial coherency, provides a powerful tool to reconstruct ice flow dynamics, temporally evolving ice divide positions and the “unzipping” of ice sheets into separate masses during deglaciation. In this study, we develop a glacial geomorphological dataset focussing on the mountain-piedmont region of Jämtland in west-central Sweden. We focus on this region because it is where the last (Weichselian) ice sheet is believed to have unzipped into separate domes and was inundated by vast ice dammed lakes. Jämtland also records a complex temporal evolution of subglacial processes and was formerly mapped without the benefit of a LiDAR-based elevation model. The dataset was created by mapping in GIS and covers an area of 50 000 km2 and almost 88 000 landforms, including glacial lineations, crag-and-tails, ice marginal moraines, lateral meltwater channels, eskers, and glacial lake shorelines. We use this unique dataset–in terms of spatial density and resolution–and quantitatively analyse cross-cutting relationships to establish a relative ice flow chronology. Our key findings include 1) a previously unmapped landform system, formed by the Early-to-Middle Weichselian westward expansion of a mountain centred ice sheet, and 2) a complex early Holocene deglaciation sequence with ice sheet unzipping occurring in southern and east-central Jämtland. The ice sheet split into a larger sheet retreating northward and a smaller ice sheet remaining southeast of the mountain piedmont. Our results provide new insights into the late deglaciation of the Scandinavian Ice Sheet.
How to cite: Blomdin, R., Peterson Becher, G., Smith, C., Regnéll, C., Öhrling, C., and Goodfellow, B.: Ice sheet unzipping in the mountain-piedmont region of west-central Sweden: complex late-deglaciation of the Scandinavian ice sheet, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6517, https://doi.org/10.5194/egusphere-egu21-6517, 2021.
The data from the Rosetta mission enabled the reconstruction of the shape of comet 67P/Churyumov-Gerasimenko (hereafter 67P) and the identification of the terrains and features forming its surface. The highly irregular shape of the comet poses a challenge for the depiction of these geological features on two-dimensional maps. Standard global map projections cannot display the complete surface of 67P because different points on the surface can have the same longitude and latitude. As a consequence, the geological maps published to date are created on top of comet images, making them dependent on the viewing angle and image coverage and resolution.
Here, we make use of the recently published Quincuncial Adaptive Closed Kohonen (QuACK) map. It projects the complete surface of 67P unambiguously onto a square. The QuACK map is topologically equivalent to the Peirce quincuncial projection of the world, which makes it possible to define generalized longitudes and latitudes. These can be used within any global map projection in order to obtain an unambiguous QuACK version.
The mapping of geological features is carried out in three dimensions employing the Small Body Mapping Tool (SBMT). We use images from the OSIRIS Narrow Angle Camera aboard Rosetta which have been projected onto the shape model of the SBMT. The three-dimensional coordinates are then projected onto two-dimensional maps, either in the QuACK map projection or in the QuACK version of the equidistant cylindrical projection. We present individual maps for 17 of the 26 regions of 67P, mostly located in the northern hemisphere. The new maps combine features published in previous studies with newly identified features.
We discuss the distribution of geological features and the characteristics of the regions. In order to align region boundaries with geological features, we propose two modifications of region definitions.
How to cite: Grieger, B., Leon-Dasi, M., Besse, S., and Küppers, M.: Mapping a Duck: Geological Features and Region Definitions on Comet 67P/Churyumov-Gerasimenko, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5966, https://doi.org/10.5194/egusphere-egu21-5966, 2021.
Fault scarps are steps in the landscape created by surface-rupturing faults. Study of their morphology can supply paleoseismic information, such as the timing and size of past events, but the form of a single fault scarp can show great variability along the length of the scarp, complicating its interpretation. We developed a methodology to quantify this variability: scarp-normal profiles are extracted from point clouds, their shape is automatically classified using a supervised learning algorithm, and these classification results are used to calculate the morphologic variability metric, a measure of the frequency and degree of change in profile form along strike.
Using point clouds derived from structure-from-motion photogrammetry, we computed the morphologic variability along thirteen jointed-bedrock fault scarps from four field sites, located in southwestern Iceland, northern and central California, and southeastern Hawai’i. Quantifiable characteristics such as climate, vegetation, lithology, fault throw, and fracture spacing change either internally along a single scarp or between these four sites.
In an individual scarp, we make pairwise comparisons between measurements of a characteristic and the morphologic variability: a strong correlation between the two indicates that this characteristic is an important driver of scarp form. For example, in the young Icelandic scarps, scarp throw is correlated with morphologic variability, suggesting that the initial slip distribution along a fault contributes to the variability in the profile forms of younger scarps. We also compute the fracture intensity and orientation along the scarps and hypothesize that increased fracture spacing leads to decreased morphologic variability.
To understand variation between sites, we make pairwise comparisons between the average values of morphologic variability of the scarps and site-specific characteristics. For example, maximum scarp throw is negatively correlated with average morphologic variability, suggesting that scarp profile form evolves towards a common morphology as scarps mature.
We show that the morphologic variability metric is a useful tool to understand the agents responsible for changes in scarp form, an essential step in accurately interpreting any paleoseismic information that might be present. Morphologic variability is a metric that can be determined for any other type of linear landform (e.g. coastal bluffs or cross-channel profiles) and our approach of linking morphologic variability to process is applicable to a wide array of geomorphic questions.
How to cite: Brigham, C. and Crider, J.: How morphologic variability, a novel metric derived from supervised landform classification, can offer insights into the processes governing fault scarp morphology in jointed rock , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14031, https://doi.org/10.5194/egusphere-egu21-14031, 2021.
In nadir view, normal to topography, landscapes with gentle slopes and those with steep surfaces look similar. This is due to the projection of a 3D structure onto a 2D plane. In orthophotos and digital elevation models (DEMs) topography is represented in this nadir or bird’s eye perspective. Elevation models of the Earth or planetary surfaces are often represented by gridded cells, each cell assigned with a mean elevation. In geomorphic studies, DEMs are widely used to calculate hillslope angles and surface area. Due to the projection, hillslopes at a steeper angle appear shorter and thus are represented by a smaller fraction of grid cells. Consequently, mean or median hillslope angles are distorted towards gentle slopes. This bias becomes even more obvious when comparing the projected 2D surface area versus the 3D surface area. The ratio by which a 3D surface area is underrepresented in 2D is by the cosine of the slope of the plane. Hence, the degree of area under-representation increases towards steeper slopes. At an angle of 60°, theoretically only half of the 3D surface area would be accounted for in a gridded DEM. And a hillslope at 90° is a no-show in the DEM. But already gentle slopes of 20° to 30° would be under-represented by about 10%. In addition to the under-representation of steep slopes due to the projection, DEM’s spatial resolution amplifies this bias where increasing grid size decreases the representation of steep slopes.
In essence, due to the bird’s eye view, measures of hillslope angle distribution and surface area have a bias disadvantaging steep slopes and skewing our perception towards a (flat) world of gentle slopes. Here we will discuss if and by how much this bias due to the bird’s eye view matters. First, we investigate artificial DEMs of Gaussian hills. We compare slope and surface area values using standard methods of gridded-data analysis to analytical solutions. Second, we investigate the impact of under-representation on a range of natural landscapes. This potential bias favouring gentle landscape elements has several implications for geomorphological interpretation of DEMs, including for example analyses of average erosion rates, landslide distribution or hydrological processes.
How to cite: Voigtländer, A. and Tofelde, S.: Bird’s eye view on steep topography, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11959, https://doi.org/10.5194/egusphere-egu21-11959, 2021.
Mapping bedrock outcrops is useful across disciplines, but is challenging in environments where ground surface visibility is obscured. The presence of soil or bedrock affects sediment production and transport, local ecology, and runoff generation. The distribution of bedrock outcrops in an area reflects the interplay between regolith production and sediment removal. Outcrop classification methods from Terrestrial-lidar produce millimeter or centimeter resolution DEMs that are highly successful because lidar penetrates through vegetation to the ground surface. However, data availability at such high resolution is limited, and the associated computational complexity required for identifying outcrop, or other surface features, is often impractical for landscape-scale analysis. Aerial lidar datasets at ~1-m resolution (e.g., moderate resolution) are more widely available and less computationally expensive than higher resolution datasets. With increasing accessibility of moderate resolution surface data, there is a need to develop outcrop classification methods and understand the efficacy of these methods across diverse environments. Our objectives are to present a simplified technique that builds on existing methods, and to examine the success of current outcrop identification methods in a variety of landscapes.
At moderate resolution, the two most cited metrics to differentiate bedrock from soil-mantled surfaces are based on gradient (e.g., DiBiase et al., 2012) or on surface roughness (e.g., Milodowski et al., 2015). We developed a method that simplifies and combines both metrics, and that improves overall accuracy. We applied all three methods to six landscapes in the USA. For each site, we delineated ground truth from high-resolution orthoimagery for 7-10 test patches with visible ground surface, that evenly spanned 0-100% exposed outcrop. Overall accuracy, true positive rate, and false positive rate for each patch were calculated by comparing the ground truth grids to each lidar-derived outcrop grids on a cell-by-cell basis. Metric success was evaluated for each landscape by assessing the mean and distribution of performance measures across patches. Our combined metric had the highest overall accuracy in an arid, horst and graben landscape (Canyonlands National Park, Utah). It also performed well in a vegetated, high sediment load, active volcano (Mount Rainier, Washington), a canyon carved by channel incision (Boulder Canyon, Colorado), and a chaparral mixed bedrock canyon environment (Mission Trails, San Diego, California). All three methods systematically failed for portions of the landscape in glacially carved canyons (Southern Wind River Range, Wyoming) and on terraced sea cliffs (Santa Cruz County, California). These environments have significant outcrop that is both smooth and low gradient, and therefore cannot be identified using a slope or roughness-based algorithm.
Our work highlights the importance of tailoring DEM-based bedrock mapping algorithms to its geomorphic context, and of the need for ground truth. Such data provides the basis for developing more robust methods for error evaluation. In addition, new methods are needed to identify bedrock outcrop from surface DEMs in smooth and low gradient, yet rocky landscapes.
How to cite: Selander, B., Anderson, S., and Rossi, M.: Evaluating bedrock outcrop mapping algorithms across diverse landscapes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6620, https://doi.org/10.5194/egusphere-egu21-6620, 2021.
The use of airborne remote sensing techniques for geological mapping offers many benefits as it allows coverage of large areas in a very efficient way. While hyperspectral imaging from airborne/spaceborne platforms is now a well-established method applied to resolve many geological problems, it has mostly been developed only in the Visible-Near Infrared (VNIR, 0.4–1.0 mm) and Shortwave Infrared (SWIR, 1.0–2.5 mm) regions of the electromagnetic spectrum. However, the reflectance spectral features measured in the VNIR and SWIR spectral ranges are generally overtones and combination bands from fundamental absorption bands at longer wavelengths, such as in the Longwave Infrared (LWIR, 8–12 mm). The single absorption bands in the VNIR and SWIR spectral ranges are often very closely spaced so that the reflectance features measured by common spectrometers in this spectral region are typically broad and/or suffer from strong overlapping, which raises selectivity issues for mineral identification in some cases.
The inherent self-emission associated with LWIR under ambient conditions allows airborne mineral mapping in various weather (cloudy, partly cloudy or clear sky) and illumination (day or night) conditions. For this reason, LWIR often refers to the thermal infrared (TIR) spectral range. Solid targets such as minerals not only emit but also reflect TIR radiation. Since the two phenomena occur simultaneously, they end-up mixed in the radiance measured at the sensor level. The spectral features observed in a TIR spectrum of the sky and the atmosphere mostly correspond to ozone, water vapor, carbon dioxide, methane and nitrous oxide with pretty sharp and narrow features compared with the infrared signature of solid materials such as minerals. The sharp spectral features of atmospheric gases are mixed up with broad minerals features in the collected geological mapping data, to unveil the spectral features associated with minerals from TIR measurements, the respective contributions of self-emission and reflection in the measurement must be «unmixed» and the atmospheric contributions must be compensated. This procedure refers to temperature-emissivity separation (TES). Therefore, to achieve an efficient TES and atmospheric compensation, the collection time and conditions of LWIR airborne hyperspectral data is of importance. Data of a flight mission in Southern Spain collected systematically at different times of the day (morning, mid-day and night) and in different altitudes using the Telops Hyper-Cam airborne system, a passive TIR hyperspectral sensor based on Fourier transform spectroscopy, were analyzed. TES was carried out on the hyperspectral data using two different approaches: a) Telops Reveal FLAASH IR software and b) DIMAP In-scene atmospheric compensation algorithm in order to retrieve thermodynamic temperature map and spectral emissivity data. Spectral analysis of the emissivity data with different mineral mapping methods based on commercial spectral libraries was used to compare results obtained during the different flight times and altitudes using the two post-processing methodologies. The results are discussed in the light of optimizing LWIR-based airborne operations in time and altitude to achieve best results for routine field mineral mapping applications such as in mining, soil science or archaeology, where the spatial analysis of mineral and chemical distribution is essential
How to cite: Boubanga Tombet, S., Gagnon, J.-P., Eichstaedt, H., and Ho, J.: Influence of the daylight illumination a weather conditions on Airborne Thermal Infrared Hyperspectral geological mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7494, https://doi.org/10.5194/egusphere-egu21-7494, 2021.
Structure-from-Motion – Multi-View Stereo (SfM-MVS) has become a widely used approach in the study of Earth surface processes to reconstruct high-resolution topography (HRT) models. Starting in the early 2010s, it has become a cheap, flexible and user-friendly alternative to aerial/terrestrial laser scanning in geosciences and in change detection analyses in particular. In this context, previous work has dealt with the spatial distribution of error and with appropriately accounting for uncertainty estimates of such models in change detection results. However, error distribution and propagation are still not widely accounted for in standard analyses: Various sources of error result in complex distribution of model precision and accuracy. This poses challenges on study effort and complexity.
In this study, we developed a novel approach for obtaining spatially distributed estimates of precision for SfM-MVS derived digital elevation models (DEM). We applied block resampling to simulate repeatedly surveyed flights. This approach allows us to create multiple independently-resampled image sets that capture the general geometry of the original survey for SfM-MVS reconstruction. In a case study of observing erosion and deposition patterns of a highly active badass gully (Mangatu fluvio–mass movement gully complex, East Coast, NZ) we simulated 20 repeated flights (i.e. images sets) for images acquired from UAVs in 2018 and 2019. The subsequent precisions were used for deriving confidence intervals for sediment budgets. Overall, the precision estimates in open-terrain matched well with previous studies based on repeated surveys (~ <5cm). Weaker precisions were observed in areas of vegetation or where viewing angles could be obstructed by surrounding vegetation. The simulated DEMs, which were based on the mean value for each grid cell across the simulations, were in good agreement with the original reconstructed scene: differences were mainly less than 2 cm for most of the exposed erosion and deposition areas.
We estimated volumetric net change to be within [– 113.07;–101.48]×1000m³ with 95% confidence between April 2018 and April 2019. Gross sediment erosion was [–123.07;–111.73]×1000m³; gross deposition was [8.9;11.7]×1000m³ in the same time frame. This is well within findings of previous studies. However, compared to these, we could substantially improve the precision of uncertainty estimates. While computationally intensive, our method is able to reduce field work compared to similar studies. It additionally has the advantage of computing precisions that account for uncertainties in both SfM and MVS reconstruction algorithms. This means that SfM-MVS precisions can be computed on past surveys given the images were taken with sufficient overlap, as we demonstrated in our case study.
How to cite: Strohmaier, F., Goetz, J., and McColl, S.: Dealing with uncertainties in assessing geomorphic change. Spatially estimating structure-from-motion precisions using a block-resampling approach., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7846, https://doi.org/10.5194/egusphere-egu21-7846, 2021.
Rainfall is one of the primary geomorphic drivers on Earth’s surface. How a surface responds to rainfall directly impacts erosional, geomorphic, and natural hazard processes. In the absence of vegetation, whether a land surface retains rainfall as soil moisture or whether rainfall is quickly infiltrated or run off is largely a function of geomorphologic and geologic conditions. In this study, we combine a time series of synthetic aperture radar (SAR) backscatter with daily precipitation to analyze the response of arid and semi-arid land surfaces to rainfall from the event to seasonal scale. The study focuses on northwestern Argentina, where we have extensive field knowledge of local geomorphic features, and is implemented using the cloud computing capacities of Google Earth Engine (GEE).
Th Sentinel-1 satellites provide high spatial (10 m) and temporal resolution images of Earth’s surface, irrespective of cloud cover. We created a 3 year time series from 2018 through 2020 of Sentinel-1 sigma-naught (σ0) backscatter from Ground Range Detected (GRD) products available on GEE. Combining the ascending and descending orbits of the Sentinel-1A and -1B satellites into a single time series provides 3 to 6 day temporal resolution in our area of interest. The Global Precipitation Measurement Mission (GPM) was aggregated to daily and monthly precipitation measurements to identify single rainfall events and the seasonal rainfall signal.
The response and recovery of SAR backscatter to individual rainfall events across different land surfaces was calculated over 4 to 6 week periods centered on and following a specific rainfall date, respectively. The temporal trend of the backscatter data in these time windows is calculated for every pixel in the backscatter stack to create a map how the surface responds to a large rainfall event. The location of standing water, increased soil moisture, and high infiltration surfaces are detectable in the response maps. The recovery maps provide a proxy for the rate of drying following the rainfall event.
In the monsoon-dominated region of northwestern Argentina, both precipitation and SAR backscatter show a clear, periodic seasonal signal over our three-year time series. By aggregating all data to monthly resolution, we can calculate pixel-wise linear regressions and correlation coefficients between precipitation and SAR backscatter. Regressions and correlation analysis are done at the resolution of the Sentinel-1 data and are used to identify whether a surface retains soil moisture, has high infiltration, or experiences seasonal standing water or snow cover. Areas dominated by highly weathered granites and sandstones that can retain soil moisture, for example, have strong positive correlation between rainfall and backscatter due to the increased dielectric constant of wet sediment. In contrast, gravel terraces where rainfall can easily infiltrate the surface show little correlation between backscatter and precipitation. The result is a high resolution map characterizing the propensity for soil moisture retention, high infiltration, and standing water and snow cover. Future work will focus on using these relationships to classify geomorphic surfaces across the arid and semi-arid central Andes.
How to cite: Olen, S. and Bookhagen, B.: Sentinel-1 application to rainfall response and geomorphic mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8228, https://doi.org/10.5194/egusphere-egu21-8228, 2021.
Sinkholes linked to cover evaporite karst in urban environments still represent a challenge in terms of clear identification and mapping considering the anthropic rehash and the presence of man-made structures.
We propose and tested a methodology to identify the subsiding features in an urban area within a cover evaporite karst environment, through an integrated and non-invasive multi-scale approach combining seismic reflection, DInSAR, leveling and full 3D GPR.
The analysis was conducted in a small village in the Tagliamento valley (Friuli Venezia Giulia region, NE Italy) named Quinis, where sinkholes are reported since a long time as well as the hazard linked to their presence: within the years, several houses have been demolished and at present many of them are damaged.
First we applied each methodology independently and after we compared, combined and integrated them to obtain more coherent and cross-validates results. Seismic reflection imagined the covered karst bedrock identifying three depocenters; DInSAR investigation allowed to identify an area with higher vertical velocities; leveling data presented a downward displacement comparable with DInSAR results; 3D GPR, applied here for the first time in the study and characterization of sinkholes, clearly defined shallow sinking features imaging also under a shallow dense pipe network. Combining all the obtained results with accurate field observations we identified and map the highest vulnerable zones.
The final result is the combining of the geophysical, DInSAR and leveling information, while also locating the damaged buildings, the local asphalt pavement breaks or renovation and the buildings which are nowadays demolished, by using vintage photographs and historical maps. The data are consistent, being the most relevant present damages and the demolished building within the zones with higher sinking velocity on the base of both leveling and DInSAR. Geophysically imaged depocenters lie within the most critical area and perfectly correlate with the local pavement damages.
In a complex geological and hydrological framework, as in the study area, a multidisciplinary and multi-scale approach is mandatory to identify and map the zone most affected by sinking phenomena. While punctual data such as borehole stratigraphy, local groundwater level variations with time, extensometers measurements and geotechnical parameters are useful to highlight local hazard due to occurring deformation, the proposed integrated methodology addresses a complete and quantitative assessment of the vulnerability of the area. It’s fundamental, especially in anthropized environments, using different integrated techniques, without forgetting the role of the fieldwork of the geologists who can detect the precursors or already occurred, even elusive, signs of the ongoing or incipient sinking.
How to cite: Busetti, A., Calligaris, C., Forte, E., Areggi, G., Mocnik, A., and Zini, L.: Integrated reflection seismics, 3D-GPR, leveling, DInSAR to detect and characterize high-risk sinkholes in urban cover evaporite karst. A NE Italian case study., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10837, https://doi.org/10.5194/egusphere-egu21-10837, 2021.
Peatlands have long been recognized as providing a wide range of ecosystem services valuable to humans. In recent decades their role in the global climate and particularly their importance in long-term carbon sequestration has come into focus. Peatlands and peat basins are an important carbon store globally, and are estimated to cover nearly 25% of the Scottish landscape: they constitute a significant carbon stock, but being able to accurately estimate the volume of peat stored in coastal basins, both locally and regionally, remains a time-consuming process. Traditional methods of investigating peat depth and volume involved the measurement of peat to depth of contact with a mineral horizon, such as sand. This process is conducted with a peat depth probe or corer, with the spatial density of measurements varying significantly with basin size. Volumetric assessments based on such measurements therefore require interpolation between control points, leading to unquantifiable errors particularly if the base of peat has significant and unrecorded topography. Geophysical methods, in particular the 3D application of ground-penetrating (GPR), offer a promising solution to improve the accuracy in basin volumetrics.
In this paper, a 3D dataset of 100 MHz GPR data was acquired with a Mala Geosciences Rough Terrain system over a buried Holocene coastal environment near Arisaig, northwest Scotland. 3D surveying involves the acquisition of a suite of parallel GPR profiles, with a small profile separation to capture the full variability of subsurface structure. For this site, a profile was acquired every 0.5 m, over an area of 62 x 32 m. The site is also sampled by 39 boreholes, which record the base of peat between 1-3.2 m depth and indicate a peat volume of 3720 m3. By revealing the true topography of the base of the basin, the GPR data suggest that the borehole-derived volume is overestimated by almost 50%, and instead predict a basin volume of 2529 ± 200 m3. Of this, 2064 ± 200 m3 is classified as organic peat (81.6%) and the remaining 465 ± 200 m3 is marine clay (18.4%). The principal source of error in this estimate is in the constraint of the GPR velocity, required to convert the time-axis of the GPR dataset to depth. This was measured at 0.034 m/ns ± 8%.
The acquisition of 3D GPR data is nonetheless time-consuming and requires precise positional control to locate the GPR antennas and avoid misinterpretation. Nonetheless, sufficient topographic information is captured even if the acquisition had recorded only every 5th GPR profile: for this downsampled dataset, the estimated basin volume is 2490 m3 ± 200 m3 (a difference of only 2.5% from the full 3D dataset). 3D survey methods, therefore, give confidence to a volumetric estimate, but the need for full-resolution 3D sampling can likely be relaxed. However, GPR surveys reveal subsurface variability that would be difficult to reconstruct from a sparse set of borehole observations. Nonetheless, some amount of borehole control is invaluable for validating the GPR data and providing ground-truth control of subsurface structure.
How to cite: Rees-Hughes, L., Barlow, N., Booth, A., West, J., Grossey, T., and Tuckwell, G.: Value added? Comparative estimates of peat basin volumetrics using borehole methods and 3D ground-penetrating radar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9898, https://doi.org/10.5194/egusphere-egu21-9898, 2021.
Volcanic eruptions may constitute a severe threat for local communities and their infrastructure. Important information as to the prediction of future eruption sites and the likelihood of activity can be obtained by analysis of spatio-temporal eruption pattern in an area of interest. The fact that monogenetic volcanoes, unlike polygenetic ones, erupt only once (within a geologically short period) at a certain spot and then volcanic activity jumps to another spot, renders a quantitative, probabilistic assessment of eruptive cycles challenging. In other words, the purely temporal risk assessment relevant for polygenetic volcanism has to be supplemented by a spatial dimension in case of monogenetic volcanic fields to allow for a combined spatio-temporal forecast.
While the eruption history of many stratovolcanoes along the Cameroon Line (CL) in Central Africa is comparatively well studied, only fragmentary data exists on the distribution and timing of monogenetic volcanism (mainly scoria cones and maars), presumably associated with Quaternary timescales. Here, we undertake an initial step in closing this gap and present for the first time a map of monogenetic volcanic features for most parts of the CL. Scoria cones and maars were identified by their characteristic morphologies using a combination of field knowledge, digital elevation models and satellite imagery. More than ~1300 scoria cones and 41 maars were detected and divided into eight monogenetic volcanic fields (MVF), as defined by the convex hull of the outermost vents: Bioko, Mt. Cameroon, Kumba, Tombel Graben (including Mt. Manengouba), Noun, Oku, Adamawa, and Biu (Nigeria). However, due to the rugged topography in the Oku volcanic field and the difficulty of identifying volcanic features remotely, the number of mapped scoria cones appears rather incomplete.
While the delineation of individual MVF bears an inherent subjective moment, statistical analyses of the primary dataset clearly shows that the mean nearest neighbour distance increases from <1 km to ~2 km from the oceanic sector (Bioko, Mt. Cameroon) in the southwest towards the continental part in the northeast (Adamawa, Biu). Correspondingly, the areal density of monogenetic features decreases along this gradient by about one order of magnitude from >0.2 km-2 (southwest) to 0.02 km-2 (northeast). This finding is in general agreement with prior geochronological results, indicating increased Quaternary activity towards the central and oceanic part of the CL (e.g., Njome and de Wit, 2014). Tests for the spatial organization of monogenetic volcanoes using the Geological Image Analysis Software (GIAS, v2; Beggan and Hamilton, 2010) revealed that the vents in all MVF are clustered (98% credible interval), thus allowing inferences to be drawn on the tectonic control of (future) eruption locations.
Beggan, C., Hamilton, C.W., 2010. New image processing software for analyzing object size-frequency distributions, geometry, orientation, and spatial distribution. Computers & Geosciences 36, 539-549.
Njome, M.S., de Wit, M.J., 2014. The Cameroon Line: Analysis of an intraplate magmatic province transecting both oceanic and continental lithospheres: Constraints, controversies and models. Earth-Science Reviews 139, 168-194.
How to cite: Schmidt, C., Laag, C., and Profe, J.: Distribution of monogenetic volcanism along the Cameroon Line, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1755, https://doi.org/10.5194/egusphere-egu21-1755, 2021.
The global solid flux from continent to ocean is usually reduced to the input of sediments from rivers, and is estimated at approximately 20 Gt/year. Another input of sediments to ocean is coastal erosion, but this flux is difficult to estimate on a global scale and it is often neglected, perhaps wrongly according to regional studies [1,2]. Most studies attempting to quantify coastal erosion have focused on the coasts of developed countries and are limited to the timescale of decades or less . The difficulty in quantifying long-term coastal erosion is that there are still many uncertainties about the factors controlling coastal erosion on this time scale, and it would be necessary to know the initial geometry of coastlines to calculate an eroded volume.
Volcanic islands, as geomorphological objects, seem to be very good objects of study to remedy these limitations. Indeed, many young volcanic islands are made of only one central edifice with a strong radial symmetry despite its degradation by erosion [4,5]. By knowing the age of an island and by comparing reconstructed shape with current shape, we can calculate a total eroded volume and an integrated average coastal erosion rate on the age of the island. Moreover, due to their geographical, petrological and tectonic diversity, volcanic islands allow to compare the influence of different factors on long-term coastal erosion, such as climate, wave direction and height, rock resistance or vertical movements. Thus, we will be able to prioritize them to propose coastal erosion laws that would applicable to all rocky coasts.
Here we built on previous works that have used aerial geospatial databases to reconstruct the initial shape of these islands [6,7] but we improve this approach by using offshore topographic data to determine the maximum and initial extension of their coasts. From both onshore and offshore topographies, we determine a long-term mean coastal erosion rate and we quantify precisely its uncertainty. Using the example of Corvo Island, in the Azores archipelago, we show how our approach allows us to obtain first estimates of long-term coastal erosion rate around this island.
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 Prémaillon M. (2018). Ph.D. thesis, University of Toulouse.
 Karátson D., Favalli M., Tarquini S., Fornaciai A., Wörner G. (2010). Journal of Volcanology and Geothermal Research, 193, 171-181.
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How to cite: Bossis, R., Regard, V., and Carretier, S.: Reconstructing the initial shape of volcanic islands to quantify long-term coastal erosion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8763, https://doi.org/10.5194/egusphere-egu21-8763, 2021.
This work emphasizes the efficient use of geomorphic parameters to form a unified index ~ Relative Index of Active Tectonics (RIAT), which has seldom been tested in areas with broader variability in the rate of deformation. This study aims to verify whether the geomorphic parameters can be used efficiently for RIAT to assess the spatial variability in deformation along the fault. The Himalayan Frontal Thrust has been chosen for morphotectonic evaluation owing to its active interplate thrust fault setting. For this purpose, we select vertical uplift sensitive geomorphic parameters viz., Mountain front sinuosity (Smf), Valley floor width-height ratio (Vf), and Steepness index (Ksn), as a primary tool to test the RIAT.
The result of RIAT shows the along-strike variation in response to the varying degree of deformation along the HFT. This is in fine agreement with the available long-term uplift/shortening rates and geodetic rates. Overall examination reveals RIAT being an excellent tool to assess the spatial variability in uplift rates in large tectonically active regions. However, the detailed scrutiny of individual geomorphic parameters reveals that only Vf, and the Ksn index are more responsive and go hand-in-hand with the RIAT variation. Whereas, Smf shows no spatial variation and function as least sensitive to such an investigation. The sensitivity of these individual parameters has implications for studies with similar settings elsewhere when quantitative rates are absent.
How to cite: Tandon, A. and Prizomwala, S.: Examining the suitability of the geomorphic parameters in generating the Relative Index of Active Tectonics: an example from Himalayan Frontal Thrust, India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2342, https://doi.org/10.5194/egusphere-egu21-2342, 2021.
The Tokaj Mountain is a part of the large Carpathian Volcanic Arc which was active in the Middle-Upper Miocene in this area. It is built up of mainly andesitic and dacitic volcanic-sedimentary sequence which filled up the 1.5-3 km deep tectonic basin between two strike-slip faults: the Hernád Fault (W) and the Bodrog Fault (SE). Though the most tectonically active phase was during the Miocene, minor recent tectonism is observed in the area in the form of rare 2 to 3 M earthquakes. Due to its relatively low activity, the complexity and the thickness of the volcano-sediments, the tectonism and its effect on the recent shape of the mountain was not accurately mapped.
The present study aims to reveal the possible connections between the morphology and the present day stress field of the area. In this regard, several stress field model has been generated with the web based application “Geonuleus” accompanied with morphotectonic statistical analyses of the DEM from the region.
The method is based on the categorization of neotectonic lineaments (faults) by their geodynamic properties using the TPO method (Type-Property-Orientation) naming system for eventually generate the active stress field that reigns the region (Albert et al. 2016). The ALOS Palsar DEM (12.5 m resolution) has been used for the morphotectonic study. During the process, multiple filter have been utilized to eliminate the noise and to highlight lineaments (e.g. directional perpendicular filters). A statistical analysis was done from the lineaments orientation in order to define the general trend and to compare it with the main neotectonic directions and the stress field that have produced them.
The study area concentrated on the western part of the mountain since the detailed geophysical data is available only from that area (Bodor, 2011). The area was subdivided into three region based on the difference of the lithology, the tectonic regime and the topography (northern area, the western side of the Hernád stream and the eastern side of the Hernád stream). The result of the modeling shows a confluence between the two methods (SFM and the morphometry) especially in the western part with a regional N-S stress orientation. In the eastern side the flexure is clearly highlighted by stress trend movement. It is important to report that the model could be enhanced with further detailed data.
From the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.
Albert, G., Barancsuk, Á., & Szentpéteri, K. 2016. Stress field modelling from digital geological map data. Geophysical Research Abstracts, v. 18, EGU2016-14565.
Bodor B. 2011: A Hernád-árok szerkezetföldtani vizsgálata. MSc thesis, Eötvös University, Dept. Regional Geology, 99 p
How to cite: Ammar, S. and Albert, G.: Morphotectonic assessment in the Tokaj Mountain region (Hungary), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15843, https://doi.org/10.5194/egusphere-egu21-15843, 2021.
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