Advances in geomorphometry and landform mapping: possibilities, challenges and perspectives
Geomorphometry and geomorphological mapping are important tools used for understanding landscape processes and dynamics on Earth and other planetary bodies. Recent rapid growth of technology and advances in 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. This explosion in the availability of geospatial data opens up considerable possibilities for morphometric analysis and mapping (e.g. for recognising new landforms and processes), but it also presents 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”.
Science progresses when we are afforded with a ‘better look’ at nature. For geomorphology, the production over the last 100 years of an ever-increasingly resolved view of landscapes with topographic maps, DEMs, remote-sensing images, etc. has always been accompanied by new geomorphologic discoveries. LiDAR images of formerly glaciated landscapes reveal glacial landforms in extraordinary detail, showing previously mapped landforms in exquisite new detail (for example, end moraines, drumlins, eskers, ice-walled-lake plains etc.). Particularly important are a range of ‘mesoscale’ landforms that are better seen with LiDAR: De Geer moraines, crag-and-tail ridges, low-relief lineations, post-glacial faults, glacial hummocks, and raised shorelines to name a few. A spate of research has come out recently on such features. LiDAR images also have the potential of revealing landforms new to glacier science, of which ‘murtoos’ are an example. But LiDAR also raises the challenge of geomorphic classification. For example, glacial hummocks and glacial hummocky topography are a mesoscale landform that is known for its high variability. This variability, made more dramatic with new LiDAR images, along with the polygenetic origin of landforms called ‘hummocks’ reveals a weakness in our terminology that needs to be acknowledged and dealt with.
How to cite:
Johnson, M.: The LiDAR revolution in glacial geomorphology: its gifts and challenges, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7953, https://doi.org/10.5194/egusphere-egu2020-7953, 2020.
Vincent Godard, Jean-Claude Hippolyte, Edward Cushing, Nicolas Espurt, Jules Fleury, Olivier Bellier, Vincent Ollivier, and Aster Team
The spatial distribution of tectonic uplift is often investigated using river profiles, as fluvial gradient is predicted to be strongly dependent on rock uplift. A similar response is expected from hillslope morphology which is also dependent on the relative base-level lowering rate. However, the reduced sensitivity of near-threshold hillslopes and the limited availability of high resolution topographic data has often been a major limitation for their use to investigate the distribution of tectonic activity.
Here we combined high-resolution analysis of hillslope morphology and cosmogenic nuclide-derived denudation rates to constrain the distribution of rock uplift across a thrust system at the Southwestern Alpine front in France. Our study is located in the Valensole Mio-Pliocene basin, where a series of folds and thrusts has deformed a plateau surface. Using a 1-m LiDAR Digital Terrain Model, we analyzed the morphology of hillslopes and extracted proxies for the relative spatial variations in denudation such as hilltop curvature (CHT) and non-dimensional erosion rates (E*). We observed systematic variation of these metrics coincident with the location of a major underlying thrust system identified by seismic surveys. Using a simple deformation model, the inversion of the E* pattern allows us to constrain the geometry of a blind thrust, which is consistent with available geological and geophysical data.
We also sampled clasts from eroding conglomerate at several hilltop locations for 10Be and 26Al concentration measurements. Calculated hilltop denudation rates range from 40 to 120 mm/ka. These denudation rates appear to be correlated with E* and CHT extracted from the morphological analysis, and are used to derive absolute estimates for the fault slip rate. This high resolution hillslope analysis allows us to resolve short wavelength variations in rock uplift that would not be possible to unravel using commonly used channel profile-based methods.
How to cite:
Godard, V., Hippolyte, J.-C., Cushing, E., Espurt, N., Fleury, J., Bellier, O., Ollivier, V., and Team, A.: Tectonics from topography : insights from high-resolution hillslope morphology analysis , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2985, https://doi.org/10.5194/egusphere-egu2020-2985, 2020.
Boris Gailleton, Simon Mudd, Fiona Clubb, Martin Hurst, and Stuart Grieve
The analysis of river profiles is a fundamental tool in modern quantitative geomorphology. Since the 1960's, workers have demonstrated a systematic power-law relationship between river gradient and discharge, or its proxy drainage area, predicting a steepening of rivers towards the headwaters. This relationship provides means of quantitatively describing river profiles by extracting a concavity index (θ), the rate at which slope decreases as a function of drainage area, and steepness index (ks), the steepness of river reaches independent of changes in drainage area. Recent developments have provided an alternative representation of the slope-area relationship, aiming to circumvent its high sensitivity to topographic noise and to the branching nature of fluvial networks by directly integrating drainage area normalised to a concavity index into a transformed coordinate (χ). These parameters can be easily extracted from digital elevation models, resulting in their widespread application to detect tectonic, climatic, and autogenic signals from landscape morphology, such as active faulting, stream piracy, drainage divide migration or sea-level changes.
River profile concavity, or θ, is an essential metric to constrain, as it is necessary to fix a reference value θref in order to compare χ or ks values between different drainage basins. This exposes a key problem with the slope-area relationship: the watersheds within a study area do not necessarily all have the same optimal θ, potentially leading to incorrect interpretations of the relative distribution of χ and ks within a landscape. This problem is enhanced over large spatial scales, such as over the width of an orogen, where the probability of θ heterogeneity increases drastically. However, the distortion of χ and ks linked to a θref being different than the local best-fit has been poorly explored: we currently do not know how much these concavity variations influence channel steepness interpretations.
In this contribution, we explore the extent of the effect of varying concavity on channel steepness using analytical and numerical methods both on landscape evolution models and real landscapes. We show that (i) relative values of χ and ks, i.e location of local maxima, minima and variations, can be significantly and non-linearly impacted as a function of their Δθ from optimal θ and drainage area; (ii) we identify cases where asymmetries in θ can cause incorrect interpretations of changes in channel steepness (iii) present tools to quantify the extent and therefore the risk of misinterpretation.
How to cite:
Gailleton, B., Mudd, S., Clubb, F., Hurst, M., and Grieve, S.: Importance of concavity for interpreting rates and patterns of landscape evolution from river profiles, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-503, https://doi.org/10.5194/egusphere-egu2020-503, 2019.
Lukas Graf, Mariano Moreno-de-las-Heras, and Joan Estrany
Digital elevation models (DEM) are mathematical representations of the Earth's bare surface in computer-readable format. The underlying measurements are often obtained by remote sensing and photogrammetry methods and processed into continuous raster data. Each of these data sources, however, provides imperfect information, and further processing steps often increase the degree of imperfection. Consequently, the process of DEM generation cumulates in uncertainty, which affects subsequent hydro- and geomorphological analyses and modelling (e.g., stream network delineation, flowpath distribution, erosion modelling).
In many DEM-based studies, however, the aspect of uncertainty related to the DEM data source has been neglected. Therefore, we propose a new approach for quantifying the effects of DEM uncertainty on hydro-geomorphological modelling based on Gaussian white noise, a concept widely used in signal processing to map noise in signals and extract the actual message context. The basic idea is to add noise to the original DEM values by means of a Gaussian distribution whose parameters are determined from the mean value of the elevation values in a moving window and the device-specific properties (precision and accuracy).
We postulate that such an approach can be used to determine uncertainties and their effect on subsequent analysis steps of hydro-geomorphological modelling. It is conceivable to create DEM ensembles depending on known parameters such as the accuracy and precision of the measuring instrument, as is used operationally in weather forecasting. Using such ensembles, probability ranges for terrain and catchment hydro-geomorphological properties can be determined and uncertainty ranges can be specified. Thus, the currently mostly deterministic approach of digital terrain modelling will be replaced by a more probabilistic understanding. Overall, our approach will help decision-makers and scientists to better assess the results of digital terrain analysis. Furthermore, it will also facilitate determining whether a result of DEM-based hydro-geomorphological analysis is sufficiently certain to answer specific research questions.
How to cite:
Graf, L., Moreno-de-las-Heras, M., and Estrany, J.: Assessing the Impact of Uncertainties of Digital Elevation Models on Hydro-Geomorphological Analysis Using Gaussian White Noise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1347, https://doi.org/10.5194/egusphere-egu2020-1347, 2019.
Loess gully is the most active and changeable landform unit in the Loess Plateau of China, whose morphology has been shaped under various formation processes. During the evolution process, gullies in the Loess Plateau interacted with each other and formed an intricate network system, which was the channel for material transportation and energy transmission in this area. From the perspective of the gully evolution process, the development of gully network is dynamic because such a network gradually tends to equilibrium through continuous reorganization. During the evolutionary process, stream capture occurs when a stream or watershed is diverted from its own bed, and flows instead down the bed of a neighboring stream. The stronger and more powerful streams (in terms of channel gradient, stream velocity, discharge and kinetic energy) capture the upstream of weak streams. In the process of dynamic reorganization, the loess gullies formed different shapes and gradually evolved into a stable network structure. In this paper, several gully areas in the Loess Plateau were selected. Based on the geological background, 5 m horizontal-resolution DEM data were used to analyze these areas. The χ index was used to describe the dynamic characteristics of the gully network, which could characterize the evolution trend of the gully. Finally, the author reveals the evolution and reorganization process of the loess gully networks by comparing the χ index diagrams of different areas in different developmental stages. The results show that the area with the stable geological background is closer to the equilibrium state than the area with the complicated geological structure. In other regions, networks composed of gullies in the middle development stage are more stable than networks in the early development stage. More importantly, for two adjacent mature gully networks, the developmental trends at different locations on their watershed boundaries may be different. The results provide for an understanding of gully network evolution and reorganization process in the Loess Plateau, which also contribute to the development of a process-based gully evolution model.
How to cite:
Chen, S. and Xiong, L.: Interaction and Reorganization of Loess Gully Network Evolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2079, https://doi.org/10.5194/egusphere-egu2020-2079, 2020.
Longitudinal river profiles have been a central if not even the most important subject in tectonic geomorphology since the 1950s. During the last decades, considerable progress has been made in unraveling the tectonic history from river profiles. Going along with the rapidly increasing availability of DEMs, however, scientists try to derive more and more information from the topography. So the quality of the DEM is still a limiting factor in many studies. In particular, local channel slopes are strongly affected by the DEM. Several approaches have been proposed in order to reduce the errors and to distinguish specific features such as knickpoints from noise of the DEM.
In this study we use DEMs with a mesh width of 1 m obtained from airborne laser scans and reduce their resolution artificially in order to analyze the effect of the mesh width on the accuracy of river profiles systematically. Based on the results, we present an idea how the errors in channel slope could be reduced with focus on narrow valleys. Going beyond the majority of the previously published approaches, our idea does not only take into account the elevation along the river profile, but also the curvature of the topography in direction normal to the valley floor.
How to cite:
Hergarten, S. and Robl, J.: River profiles from digital elevation models – limitations and new ideas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3015, https://doi.org/10.5194/egusphere-egu2020-3015, 2020.
Kasper Johansen, Yu-Hsuan Tu, Matteo Ziliani, Bruno Aragon, Yoseline Angel, Bonny Stutsel, Samir Al-Mashharawi, Oliver Lopez, and Matthew McCabe
Detailed information on rock formations and assessment of their geological structures, such as joints, faults, shears and bedding planes, are required for evaluation of rock integrity and stability. Our research demonstrates a comprehensive approach for producing high spatial resolution 3D information of rock formations from unmanned aerial vehicle (UAV) imagery for rock joint identification, and presents an innovative technique for using Terrestrial Laser Scanning (TLS) data to derive ground control points (GCPs) for geo-referencing of UAV imagery of vertical rock walls. UAV imagery was collected from a freestanding 90 m tall rock formation with a 3.5 km perimeter, via oblique rock façade scans and also at-nadir, covering a ground and rock facade area of approximately 0.32 and 0.25 km2, respectively. Seventy-two GCPs were distributed around the rock for geo-referencing of the UAV imagery. As GCPs could not be deployed on vertical rock walls, a TLS system was used for identification of 93 distinct natural features as pseudo-GCPs on the rock walls. Forty scans were collected and geo-referenced from triplets of GCPs placed on the ground near the TLS system for each scan. A Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) survey was performed on the GCPs, using a base station and a rover. Continuously Operating Reference Stations (CORS) data were used to fix the position of the base station and improve the absolute geometric position to an average and lowest accuracy of 3.3 and 18.6 mm for 177 GCPs. A total of 44 façade scans, as well as 14 low and 5 high altitude nadir-viewing UAV flights, were undertaken with a Zenmuse X3 RGB camera mounted to a DJI Matrice 100 platform, resulting in a collection of nearly 17,000 photos. The five high altitude flights were designed to include a larger area around the rock to incorporate additional GCPs, while the low altitude flights were to increase the spatial resolution of the imaged rock. Flight planning was undertaken with the Universal Ground Control Station Client application. Façade scans were flown horizontally and parallel to the rock walls at a distance of 20-30 m and at heights between 10-80 m with sidelaps >70% between horizontal flight lines and >80% forward overlap along flight lines. Façade scans were collected with a 4° viewing angle to ensure the base of the rock was included. The Agisoft MetaShape software was initially used to generate a sparse point cloud using all façade scans and nadir-viewing imagery. Geo-referencing of the UAV imagery was based on 136 GCPs, which produced an accuracy of 0.1558 m, with an addition 100 control points kept aside for independent evaluation, yielding an accuracy of 0.2018 m. Subsequent image processing was split into 14 evenly sized “chunks” to enable more efficient processing of a dense point cloud (4.33 billion points) and 3D model (mesh with 129.5 million faces and texture layer) for the entire area. The produced 3D model was found suitable for identification of rock joints larger than 1 m in length.
How to cite:
Johansen, K., Tu, Y.-H., Ziliani, M., Aragon, B., Angel, Y., Stutsel, B., Al-Mashharawi, S., Lopez, O., and McCabe, M.: 3D Mapping of Rock Formations from Oblique and Nadir Viewing UAV Imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4068, https://doi.org/10.5194/egusphere-egu2020-4068, 2020.
Mauro Bonasera, Alessandro Petroccia, Fabiola Caso, Sara Nerone, and Michele Morelli
The landscape evolution of the U-shaped Maira Valley was mainly led by glacial dynamics during Pleistocene. The Holocene linear fluvial erosion creates higher steepness slopes in a narrow valley in which gravitational phenomena involves buildings and facilities of Acceglio municipality (Piedmont, Italy). A geomorphological survey in an unmapped area of about 12 km2 has been carried out and a new map at scale 1:10000 has been realised. In order to improve the accuracy of fieldwork data, several multidisciplinary techniques have been investigated. The landforms and slope evolution were analysed by using a 5-meters resolution ARPA Digital Elevation Model (DEM) in GIS environment. Discontinuities and geomorphological features were recognized and mapped observing aerial-photos provided by Regione Piemonte. Multi-temporal dataset of orthophotos were useful to examine the river pattern behaviour coupled with interdigitating polygenic fan deposition. The stratigraphic sequence knowledge was achieved using boreholes, inclinometers and piezometers evaluating eventual detrital cover thickness. Detailed field investigations allowed to understand the relationship between structural geology and landslide evolution, in particular concerning several detachment zones characterising the slope overlooking Acceglio town. In the uppermost range of that slope, the fracturation is intense and influences the rock-falls and rock avalanches trigger, whilst debris flows were identified throughout the detected area associated with a homogeneous presence of weathered cover. Widespread accumulation bodies suggest how avalanche and debris flow occurrences have affected Acceglio human activities, testified by historical archives documents as well. In the past, several trial to mitigate these risks were performed through engineering activities which could be refined and implemented with further local analysis on landslide susceptibility. Research on this issue, in addition to having a great scientific interest, can provide essential tools for upper Maira Valley Administrations, being the main available support for an appropriate urban planning.
How to cite:
Bonasera, M., Petroccia, A., Caso, F., Nerone, S., and Morelli, M.: Multidisciplinary approach to assess landslide hazards in alpine environment: the geomorphological map of the upper Maira Valley (Western Alps, Italy)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-656, https://doi.org/10.5194/egusphere-egu2020-656, 2019.
Aydogan Avcioglu, Tolga Görüm, Omer Yetemen, and Mariano Moreno de las Heras
Badlands are unique landscapes that are extensively developed on unconsolidated sediments or poorly consolidated bedrocks that are covered by little or no vegetation. They are widely observed landscapes in Turkey similar to arid and semi-arid regions of the world. Turkish badlands are commonly formed on Miocene and Plio-Quaternary deposits, especially in the inner parts of Anatolia. Additionally, these erosional landscapes are also characteristic in the volcanic provinces of Central Anatolia and Eastern Anatolia. Unlike the cognatic badland landscapes in the different arid and semi-arid sections of the world, we have very limited information about the geomorphological characteristics of Turkish badlands.
In this study, we present results from a quantitative analysis of a new inventory of badland areas (~756 km2) at six major badland landscapes in Turkey. Previously partly known but not documented badland geomorphological units were expanded by mapping badland forms from aerial photos and high-resolution multispectral image interpretations focused on the Western and Central Anatolia. The geomorphometric data on badland units, associated structures, and catchment characteristics were extracted from a 5-m Digital Elevation Model (DEM) and compiled in a GIS environment. In total 53 badland geomorphologic units, having a size from 0.15 to 89.2 km2, were analyzed by comparing their topographic dissection, roughness, texture, channel density, slope height and curvature, and lithological variations to characterize their morphology further.
The regional comparison results display statistically significant topographic differences concerning their proportions of morphometric classes. The geomorphometric regional comparisons indicate that the standard deviation and modal values of slope height, topographic dissection, roughness, and curvature vary with a significant fraction in badlands formed in the terrigenous clastic and volcano-sedimentary lithological units. Moreover, the geomorphometric comparison results demonstrate that the skewness of the standard deviation of elevation and hillslope steepness varies in badland landscapes across the semi-arid Western and arid Central Anatolia, and further point out the significance of climatic conditions (i.e., amount of rainfall and evaporation) on geomorphic diversities.
How to cite:
Avcioglu, A., Görüm, T., Yetemen, O., and Moreno de las Heras, M.: Geomorphometric characteristics of major badland landscapes of Turkey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5272, https://doi.org/10.5194/egusphere-egu2020-5272, 2020.
Victoria Milichenkova, Tatiana Trifonova, and Artem Lebedev
In recent years it has become evident that techniques implemented while studying landscape structure of a large geographical area should be comprehensive and consider both endogenous and exogenous factors affecting its formation.
Owing to a specific location in the active zone at the junction between three tectonic plates — the African, the Arabian and the Eurasian - Southwest Asia region, which is subdivided into the Anatolian plateau, the Armenian plateau and the Iranian plateau, was chosen as a target territory. To conduct a further landscape analysis three above-mentioned geographical subregions were segmented into 9 marine basins, with the largest - the Persian Gulf basin, the Caspian sea basin, the Mediterranean sea basin, and the Black sea basin - occupying areas of 1710000, 250000, 330000, 710000 sq.km respectively. In our view, these basins are stated to be isolated macro-geosystems with directed substance and energy flow, where rivers and streams play an essential role in their functioning.
Thus, according to data obtained from segmentation we can also claim that the target territory is located at the intersection of large watersheds, which is reflected in the basin landscape structure.
Bearing in mind these separate geographical units and taking into account the classification we built up, dominant folded, volcanic and depressive morphostructures were distinguished within each basin. As a result, the Caspian sea basin was represented by all the morphostructures present in the classification with folded and depressive occupying virtually equal areas, whereas the Black sea, the Mediterranean sea and the Persian gulf basins were dominated by folded morphostructures.
Depressive morphostructures in foregoing units appeared in large river valleys and intermontane areas, while volcanic morphostructures were not significant, but they expanded impressively on the Armenian plateau.
Moreover, each morphostructure type represents characteristic landscape patterns according to natural areas and altitudinal zonality concepts, the latter applied to mountain ranges. So, using remote sensing datasets and ArcGIS software general sequence of landscapes in the main mountain ranges, such as Pontic ridge in the Black sea basin, Taurus mountains in the Meditteranean sea basin, Elburz in the Caspian sea basin and Zagros in the Persian Gulf basin were visually interpreted. Then, a comparative analysis of the interrelation between landscapes in watersheds was conducted.
Eventually, the obtained data could be applied to further studying and mapping of Southwest Asia region landscape structure formation and, subsequently, refreshing soil cover maps using remote sensing data.
How to cite:
Milichenkova, V., Trifonova, T., and Lebedev, A.: Comparative analysis of watershed basins landscape structure in Southwest Asia region based on remote sensing data., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-580, https://doi.org/10.5194/egusphere-egu2020-580, 2019.
Enok Cheon, Seung Rae Lee, Deuk Hwan Lee, Hwan Hui Lim, and Seung Min Lee
In order to mitigate the potential damage from debris-flow hazards, the debris-flow hazard assessment is conducted to quantify the risk of landslide occurrence and assign potential levels of damage to surrounding buildings. DAN3D (Three-Dimensional Dynamic Analysis), a 3D numerical model for debris-flow simulation, has been widely used to conduct the debris-flow hazard assessment by computing the magnitudes of debris-flow characteristics, such as the velocity, depth, and volume. DAN3D software presents the results on a map plot that shows the magnitude of debris-flow characteristics over the inundated areas at a particular simulation time. These plots neither provide the exact values of debris-flow characteristics at a specific location nor compute the area and the width of debris-flow. Furthermore, a static image can be inconvenient for visualizing the changes in the debris-flow characteristics through time. Therefore, the present study created a program to interprets the DAN3D simulation results and to generate interactive plots using web programming. The interactive plots represent the position of a debris-flow cluster with a centroid location and show debris-flow characteristics, including the area and width of debris-flow, with a color scale. Additionally, the generated plots provide a graphical-user-interface to extract more details or change the plot. The accessibility and customization provided by the generated plots can be very useful for the design of the protection measures and evaluation of the effectiveness of them. A case study of the debris-flow at Sindonga watersheds, Mt. Umyeon, Korea, in 2011, was used to generate several interactive plots and their usefulness in designing barriers as mitigation against debris-flow.
How to cite:
Cheon, E., Lee, S. R., Lee, D. H., Lim, H. H., and Lee, S. M.: Combination of Web Programming and DAN3D to Generate Interactive Plots for Debris Flow Hazard Assessment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6498, https://doi.org/10.5194/egusphere-egu2020-6498, 2020.
In 2020 we celebrate the 40th anniversary of the seminal works of Wood (1980a,b) who was one of the first researchers who considered the shapes of volcanoes in a global point of view. These four decades have seen a number of new approaches that were made possible by the ever increasing computer power and the improvements in Digital Terrain Model (DTM) production. The improving resolution and accuracy of the DTMs of various volcanic fields (VF) opened the way of wide variations of volcanic geomorphometric considerations. However, the differences in approach and, even more importantly, the differences in DTM production technology and resolution make the comparative studies and especially global considerations very difficult.
We have envisioned a global geomorphometric analytical methodology to analyse cinder cone morphometry in terms of shape versus age: The aim is to establish a relationship between the age of scoria cones age and their morphometry. This is knowingly a rather difficult undertaking and we have made only the first steps yet, but our methodological advancements are always developed with this demand in mind.
For the sake of diversity, in the current study four volcanic areas were considered with different age ranges, four different resolution DEMs and different number of cones: San Francisco Volcanic Field, Arizona, USA (SFVF, 30 m horizontal resolution, 313 pcs), the Chaîne des Puys, France (CdP, 0.5 m, 26 pcs), the central-eastern part of the Sierra Chichinautzin, Mexico (SCVF, 5 m, 152 pcs) and Kula Volcanic Field, Turkey (KVF, 12.5 m, 64 pcs). As age data we had either age ranges or measured ages of the individual cones.
A great number of derivatives (mostly related to slope angles) have been calculated for the individual cones. Their most important statistics and their distribution were computed. Irregularities and, especially, cone degradation modify the original statistical distribution; these distributions can be compared in statistical way. A quantitative distance (metric) has been introduced to study the similarity or dissimilarity of the cones.
For the comparison, we have grouped the cones in several ways – they have been observed individually, by areas and by age groups (based on previous researches). For every cone boxplot diagrams, histograms and cumulative histograms were made to detect differences together with average and median values. These age groups were subjects of the Mann – Whitney statistical test to discriminate statistically independent or dependent samples in the populations. The test showed some clear relations between erosion (shape) and age.
We created a cinder cone viewer for visualization purposes. This tool can display the aforementioned distributions and helps in picking pairs or groups of cinder cones to compare. As expected, the intra-VF comparisons are typically more successful as inter-VF comparisons. However, promising new morphometric derivatives (e.g., sectorial distributions) are under development.
Wood, C. A.: Morphometric evolution of cinder cones, J. Volcanol. Geoth. Res., 7, 387–413, 1980a.
Wood, C. A.: Morphometric analysis of cinder cone degradation, J. Volcanol. Geoth. Res., 8, 137–160, 1980b.
How to cite:
Székely, B. and Vörös, F.: Studying the distributions of DTM derivatives of cinder cones: a statistical approach in volcanic morphometry, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10465, https://doi.org/10.5194/egusphere-egu2020-10465, 2020.
Valérie Baumann, Marc-Henri Derron, Jean-Luc Epard, and Michel Jaboyedoff
The main goal of this project is to harmonise the different geological maps (scale 1:25.000) and to improve the Quaternary mapping of the region of “canton de Vaud” in Switzerland using a high resolution LiDAR digital elevation model, and geophysical or boreholes data. We present here the results for the geologic mapping of two test areas: one in the Prealps and the second in the Molasse Plateau.
Detailed geological maps (scale 1:25.000) have been produced during the XX century for the whole region. During the last Late Glacial Maximum (LGM) the canton de Vaud area was covered by ice sheets, then soils and loose rock deposits were formed toward the end of ice age, however the Quaternary formations are sometimes not represented especially when their thickness is only of a few meters and the interpretation of geomorphologic features with aerial photographs was difficult in areas covered by forest.
In recent years, the high-resolution digital elevation model derived from high resolution LiDAR data with the possibility to remove the trees in the forested areas offers the possibility to detect and interpret new morphologies.
In this study, different LIDAR-derived hillshade maps have been used to improve the delimitation of bedrock and Quaternary formation through morphological feature analyse. Borehole data gave us fundamental data about geology and stratigraphy and field surveys were performed for selected places. Additionally, a terrain classification system first developed in Canada (Cruden and Thomson, 1987) was used to add information for each polygon like genetic material, surface expression, modifying processes and stratigraphic data. All the mapping was performed in a GIS (Geographic system information) environment.
Detailed bedrock and Quaternary mapping will provide very good information for the management of the resources, land planning and geo-hazards. The additional information (terrain classification) for each polygon allow us to create different thematic maps starting from the geological map.
Cruden, D. M., and S. Thomson. Exercises in terrain analysis. Pica Pica Press, 1987.
How to cite:
Baumann, V., Derron, M.-H., Epard, J.-L., and Jaboyedoff, M.: Geology mapping with an emphasis on the Quaternary in the Swiss Prealps and Molasse Plateau, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11161, https://doi.org/10.5194/egusphere-egu2020-11161, 2020.
Homogeneous topography pattern - can be an indicator of similar Earth's surfaces genesis and age. It is difficult to automatically formally describe these features and map the terrain. To describe the Earth's surface periodicity, we developing spectral terrain characteristics (STC). Their calculation consists of the following: a sliding window of different sizes decomposes the DEM into a Fourier row from which it is extracted: 1) amplitude of the main harmonic wave; 2) its length; 3) dispersion of heights given by 5% of the most important waves in relation to the general dispersion of heights; 4) general direction of oscillations of the height field; 5) unidirectionality / expression of this direction, etc. Areas with similar values of these parameters have visually homogeneous topographic pattern. We have calculated the above mentioned and some more complicated parameters for the whole territory of the Russian Arctic on a shallow scale: according to GMTED 2010 30" (1000 m per cell) on moving windows with sizes from 40 to 100 km and with the step of 10 km. Fifty-six raster models of SRC were obtained - 8 parameters at 7 scales each. Using them, a map of topographic dissection types in the Russian part of the Arctic was created with the help of self-organizing Kohonen neural networks and subsequent hierarchical clustering of individual neurons. 10 clusters have been identified related to geostructural, geological and geomorphological differences.
This study was funded by the Russian Science Foundation, project no. 19-77-10036.
How to cite:
Kharchenko, S.: Small-scale clustering of the Russian part of Arctic by periodicity type of the topographic patterns, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19509, https://doi.org/10.5194/egusphere-egu2020-19509, 2020.
Sijin Li, Liyang Xiong, Guoan Tang, and Josef Strobl
Landform classification is one of the most important aspects in geomorphological research, dividing the Earth’s surface into diverse geomorphological types. Thus, an accurate classification of landforms is a key procedure in describing the topographic characteristics of a given area and understanding their inner geomorphological formation processes. However, landform types are not always independent of one another due to the complexity and dynamics of interior and external forces. Furthermore, transitional landforms with gradually changing surface morphologies are widely distributed on the Earth’s surface. With this situation, classifying these complex and transitional landforms with traditional landform classification methods is hard. In this study, a deep learning (DL) algorithm was introduced, aiming at automatically classifying complex and transitional landforms. This algorithm was trained to learn and extract landform features from integrated data sources. These integrated data sources contain different combinations of imagery, digital elevation models (DEMs), and terrain derivatives. The Loess Plateau in China, which contains complex and transitional loess landforms, was selected as the study area for data training. In addition, two sample areas in the Loess Plateau with complex and transitional loess hill and ridge landforms were used to validate the classified landform types by using the proposed DL method. Meanwhile, a comparative analysis between the proposed DL and random forest (RF) methods was also conducted to investigate their capabilities in landform classification. The proposed DL approach can achieve the highest landform classification accuracy of 87% in the transitional area with data combination of DEMs and images. In addition, the proposed DL method can achieve a higher accuracy of landform classification with better defined landform boundaries compared to the RF method. The classified loess landforms indicate the different landform development stages in this area. Finally, the proposed DL method can be extended to other landform areas for classifying their complex and transitional landforms.
How to cite:
Li, S., Xiong, L., Tang, G., and Strobl, J.: Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22334, https://doi.org/10.5194/egusphere-egu2020-22334, 2020.