GM2.3

Land surface curvature (LSC) is a basic attribute of topography and influences local effects of gravitational energy and surface material transport. However, the calculation of LSCs based on triangulated irregular networks (TINs) has not been fully studied, which restricts further geoscience studies based on TIN digital elevation models (DEMs). The triangular facets and vertices of a TIN are both expressions of the land surface; therefore, based on their adjacency relationship, the LSCs can be calculated. In this study, we propose a mathematical vector framework to enhance LSC system theory. In this framework, LSC can be calculated based on both triangular facets and vertices, and the selection of weighting methods in the framework is flexible. We use the concept of the curvature tensor to interpret and calculate the commonly used LSC, which provides a new perspective in geoscience research. We also investigate the capacity of the TIN-based method to perform LSCs calculations and compare it with grid-based methods. Based on a mathematically simulated surface, we reach the following conclusions. First, the TIN-based method has similar effects on the scale to the grid-based methods of EVANS and ZEVENBERGEN. Second, the TIN-based method is less error sensitive than the grid-based methods by the EVANS and ZEVENBERGEN polynomials for the high error DEMs. Third, the shape of the TIN triangles exerts a great influence on the LSCs calculation, which means that the accuracy of LSCs calculation can be further improved with the optimized TIN but will be discontinuous. Based on three real landforms with different data sources, we discuss the possible applications of the TIN-based method, e.g., the classification of land surface concavity–convexity and hillslope units. We find that the TIN-based method can produce visually better classification results than the grid-based method. This qualitative comparison reflects the potential of using TINs in multiscale geoscience research and the capacity of the proposed TIN-based LSC calculation methods. Our proposed mathematical vector framework for LSCs calculations from TINs is a preliminary approach to mitigate the multiple-scale problem in geoscience. In addition, this research integrates mathematical vector and geographic theories and provides an important reference for geoscience research.

How to cite: Hu, G., Xiong, L., and Tang, G.: Mathematical vector framework for gravity-specific land surface curvatures calculation from triangulated irregular networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13122, https://doi.org/10.5194/egusphere-egu22-13122, 2022.

Gully morphology is an important part of loess geomorphology research. Along with gully development, the variation of its cross section is the most important aspect, and it can intuitively reflect the characteristics of the lateral widening of the gully slope. Therefore, in-depth research of the variation of the cross-sectional morphology of the gully is important to understanding the development process of the loess gully. Based on the data of nine periods of an indoor simulated loess small watershed, this paper deeply studies the evolution model of a complete branch ditch in the watershed from many aspects by using the theory and method of digital terrain analysis. Firstly, we analyse the morphological characteristics of the gully cross section in the simulated small watershed. The test shows that with the development of the gully, the average slope of the slope decreases continuously, and the slope morphology is mostly a concave slope along the slope direction. The degree of downward concave first increases and then gradually tends to be gentle. The gully erosion mode is gradually transformed from downward cutting erosion to lateral erosion. The more mature the gully development, the lower the depth of gully bottom cutting is compared with the width of gully widening. Furthermore, the surface cutting depth tends to be stable and the slope is stable. Then, the transformation law of the slope morphology of the gully cross section with the development of the gully is studied, and the prediction model of the transformation of the slope morphology of the gully cross section is established by using the Markov chain. The Markov model can better reflect the dynamic change of the slope morphology of the gully cross section, which is of considerable importance to revealing the external performance and internal mechanism of the gully morphology.

How to cite: Liu, L., Li, F., Yang, X., and Cheng, J.: Morphological characteristics and evolution model of loess gully cross section, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13131, https://doi.org/10.5194/egusphere-egu22-13131, 2022.

Over the past decades advanced technology has become available, revolutionizing the assessment of surface topography. At smaller scales (up to a few km²) structure from motion (SfM) algorithms applied to uncrewed aerial vehicle (UAV) imagery now allow sub-meter resolution. On the other hand, spaceborne digital elevation models (DEMs) are becoming increasingly accurate and are available at a global scale. Two recent spaceborne developments are the 12 m TanDEM-X and 30 m Copernicus DEMs. While sub-meter resolution UAV-SfM DEMs generally serve as a reference, their acquisition remains time-consuming and spatially constrained. However, some applications in geomorphology, such as the estimation of regional or national erosion quantities of specific landforms, require data over large areas. TanDEM-X and Copernicus data can be applied at such scales, but this raises the question of how much accuracy is lost because of the lower spatial resolution.

Here, we evaluate the performance of the 12 m TanDEM-X DEM and the 30 m Copernicus DEM to i) estimate gully volumes, ii) establish an area-volume relationship, and iii) determine sediment mobilization rates, through comparison with a higher resolution (0.2 m) UAV-SfM DEM. We did this for six study areas in central Madagascar where lavaka (large gullies) are omnipresent and surface area changes over the period 1949-2010s are available. Copernicus derived lavaka volume estimates were systematically too low, indicating that the Copernicus DEM is not suitable to estimate erosion volumes for geomorphic features at the lavaka scale (100 – 10⁵ m²). The relatively coarser resolution of the DEM prevents to accurately capture complex topography and smaller geomorphic features. Lavaka volumes obtained from the TanDEM-X DEM were similar to UAV-SfM volumes for the largest features, while smaller features were generally underestimated. To deal with this bias we introduce a breakpoint analysis to eliminate volume reconstructions that suffered from processing errors as evidenced by significant fractions of negative volumes. This elimination allowed the establishment of an area-volume relationship for the TanDEM-X data with fitted coefficients within the 95% confidence interval of the UAV-SfM relationship. Combined with surface area changes over the period 1949-2010s, our calibrated area-volume relationship enabled us to obtain lavaka mobilization rates ranging between 18 ± 3 and 311 ± 82 t ha^-1 yr^-1 for the six study areas, with an average of 108 ± 26 t ha^-1 yr^-1. This does not only show that the Malagasy highlands are currently rapidly eroding by lavaka, but also that lavaka erosion is spatially variable, requiring the assessment of a large area in order to obtain a meaningful estimate of the average erosion rate.

With this study we demonstrate that medium-resolution global DEMs can be used to accurately estimate the volumes of gullies exceeding 800 m² in size, where the proposed breakpoint-method can be applied without requiring the availability of a higher resolution DEM. This might aid geomorphologists to quantify sediment mobilisation rates by highly variable processes such as gully erosion or landsliding at the regional scale, as illustrated by our first assessment of regional lavaka mobilization rates in the central highlands of Madagascar.

How to cite: Brosens, L., Campforts, B., Govers, G., Aldana-Jague, E., Razanamahandry, V. F., Razafimbelo, T., Rafolisy, T., and Jacobs, L.: Comparative analysis of the Copernicus (30 m), TanDEM-X (12 m) and UAV-SfM (0.2 m) DEM to estimate gully volumes and mobilization rates in central Madagascar, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5587, https://doi.org/10.5194/egusphere-egu22-5587, 2022.

Digital Elevation Models (DEMs) depict the elevation of the Earth’s surface and are fundamental to many applications, particularly in the geosciences. To date, global DEMs contain building and forest artifacts that limit its functionality for applications that require precise measurement of terrain elevation, such as flood inundation modeling. Using machine learning techniques, we remove both building and tree height bias from the recently published Copernicus GLO-30 DEM to create a new dataset called FABDEM (Forest And Buildings removed Copernicus DEM). This new dataset is available at 1 arc second grid spacing (~30m) between 60°S-80°N, and is the first global DEM to remove both buildings and trees.

Our correction algorithm is trained on a comprehensive and unique set of reference elevation data from 12 countries that covers a wide range of climate zones and urban types. This results in a wider applicability compared to previous DEM correction studies trained on data from a single country. As a result, we reduce mean absolute vertical error from 5.15m to 2.88m in forested areas, and from 1.61m to 1.12m in built-up areas, compared to Copernicus GLO-30 DEM. Further statistical and visual comparisons to other global DEMs suggests FABDEM is the most accurate global DEM with median errors ranging from -0.11m to 0.45m for the different landcover types assessed. The biggest improvements were found in areas of dense canopy coverage (>50%), with FABDEM having a median error of 0.45m compared to 2.95m in MERIT DEM and 12.95m for Copernicus GLO-30 DEM.

FABDEM has notable improvements over existing global DEMs, resulting from the use of Copernicus GLO-30 and a powerful machine learning correction of building and tree bias. As such, there will be beneifts in using FABDEM for purposes where depiction of the bare-earth terrain is required, such as in applications in geomorphology, glaciology and hydrology.

How to cite: Uhe, P., Hawker, L., Paulo, L., Sosa, J., Sampson, C., and Neal, J.: FABDEM - A 30m global map of elevation with forests and buildings removed, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8994, https://doi.org/10.5194/egusphere-egu22-8994, 2022.

Digital elevation models (DEMs) contain some of the most important data for providing terrain information and supporting environmental analyses. However, the applications of DEMs are significantly limited by data voids, which are commonly found in regions with rugged terrain. We propose a novel deep learning-based strategy called a topographic knowledge-constrained conditional generative adversarial network (TKCGAN) to fill data voids in DEMs. Shuttle Radar Topography Mission (SRTM) data with spatial resolutions of 3 and 1 arc-seconds are used in experiments to demonstrate the applicability of the TKCGAN. Qualitative topographic knowledge of valleys and ridges is transformed into new loss functions that can be applied in deep learning-based algorithms and constrain the training process. The results show that the TKCGAN outperforms other common methods in filling voids and improves the elevation and surface slope accuracy of the reconstruction results. The performance of TKCGAN is stable in the test areas and reduces the error in the regions with medium and high surface slopes. Furthermore, the analysis of profiles indicates that the TKCGAN achieves better performance according to a visual inspection and quantitative comparison. In addition, the proposed strategy can be applied to DEMs with different resolutions. This work is an endeavour to transform perceptive topographic knowledge into computer-processable rules and benefits future research related to terrain reconstruction and modelling.

How to cite: Li, S., Xiong, L., and Tang, G.: Integrating topographic knowledge into deep learning for the void-filling of digital elevation models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13124, https://doi.org/10.5194/egusphere-egu22-13124, 2022.

The delineation of geomorphometrical objects that can be translated to geomorphological features is one of the most practical aspects of geomorphometry. The concave (closed depressions) or convex features (mounds) are often important to be delineated from multiple points of view: theoretical approaches, planning for practical purposes, or various other aspects. In this work, I have approached sinkholes and burial mounds as representative cases of concave and convex features represented on high-resolution DEMs. Based on manual delineations, several algorithms of object-based delineation were tested for accuracy. The interest was in delineating as much as accurate possible the targeted features. Further, the segments were fed to a multilayer perceptron for the classification of the delineated segments. The results show promising accuracy in regard to both types of features.

How to cite: Niculiță, M.: Machine learning and geomorphometrical objects for convex and concave geomorphological features detection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1853, https://doi.org/10.5194/egusphere-egu22-1853, 2022.

Topography and geology are considered the primary factors influencing groundwater flow and accumulation. To evaluate their potential in identifying groundwater potential, an integrated approach was provided and used in this work to delineate groundwater potential zones in Sahel-Doukkala, Morocco, by combining geomorphometric variables and a Multi-Criteria Evaluation (MCE) technique. Aside from lithology, all variables used in this approach were derived from a 10 m Digital Elevation Model (DEM) generated from ALOS-PRISM stereo-images using photogrammetric techniques. The chosen variables were considered to be very closely associated with groundwater circulation and accumulation, namely lithology, topographic wetness index (TWI), convergence index (CI), lineament density, lineament intersection density, and drainage network. These variables were given weights based on their respective importance in the occurrence of groundwater, by using a cumulative effect matrix. This process has shown that lineament density had the most effects on other variables, with the biggest weight (24%), followed by lineament intersection density (20%). TWI and CI succeeded 16% while lithology and drainage network density had the least weight (12%). Later, in a GIS system, an MCE based weight sum method was used for generating the groundwater potential zones map.

The obtained map was classified into three zones, viz. “poor”, “moderate” and “high”. These zones delineate areas where the subsurface has varying degrees of potential to store water and also indicate the availability of groundwater. It was found that the zone with “high” potential covered an area of approximately 714 km² (44 % of the study area), and it identified areas that are suitable for groundwater storage. These zones showed a high association with low drainage density, low TWI values, and a high density of lineaments and lineament intersections. The groundwater potential zones map produced by the proposed approach was verified using the location and groundwater level depth of 325 existing wells that were categorized as successful, and the result was found satisfactory, with 91% of the successful exiting wells were located at zones that fall in the “moderate” and “high” areas. In addition, the validity of the proposed approach was tested according to the groundwater level depth, which indicates the actual groundwater potential. It was found that places with "high" potential have an average groundwater level depth of approximately 27 m, whereas areas with “moderate” and “poor” potential showed an average of 31 m and 37 m, respectively. The validation results show a good agreement between existing groundwater wells and the obtained groundwater potential zones map and were considered to be reasonable. Therefore, the produced map can be of great help to hydrogeologists to detect, with time and cost-effectively, new zones that may carry a high groundwater potential.

Because DEM data is one of the most widely and easily accessible data, the proposed method is well suited for areas where data is scarce. As result, it can be widely used to develop conceptual models based on geomorphometric variables as primary inputs for similar arid and semi-arid regions suffering from data scarcity.

How to cite: Habib, A., El Arabi, A., and Labbassi, K.: Evaluating Geomorphometric Variables to Identify Groundwater Potential Zones in Sahel-Doukkala, Morocco, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13325, https://doi.org/10.5194/egusphere-egu22-13325, 2022.

Sediment connectivity, defined as the degree to which a system facilitates the transfer of sediment through itself by means of coupling relationships between its components, has recently emerged as a paramount property of geomorphic systems. The growing interest of the earth sciences community in connectivity led this property to become a key concept concerning sediment transfer processes analysis and one of the building blocks of modern geomorphology. The increasing availability of high-resolution Digital Elevation Models (DEMs) from different sources as LiDAR and Structure from Motion (SfM) paved the way to quantitative and semi-quantitative approaches for assessing sediment connectivity. A geomorphometric index of sediment connectivity, based on DEM derivatives as drainage area, slope, flow length and surface roughness, has been developed along with related freeware software tool (SedInConnect). The index aims at depicting spatial connectivity patterns at the catchment scale to support the assessment of the contribution of a given part of the catchment as sediment source and define sediment transfer paths. The increasing interest in the quantitative characterization of the linkages between landscape units and the straightforward applicability of this index resulted in numerous applications in different contexts. This work presents and discusses the main applications of the sediment connectivity index along with a recent application in the frame of the Interreg ITAT3032 SedInOut Project (2019-2022). Being a topography-based index, it is focused on structural aspects of connectivity, and quality and resolution of DEMs may have a significant impact on the results. Future development should consider process-based connectivity and incorporate temporal variability directly into the index. Moreover, this work demonstrates that, when carefully applied considering the intrinsic limitations of the topographic-based approach, the index can rapidly provide a spatial characterization of sediment dynamics, thus improving the understanding of geomorphic system behavior and, consequently, hazard and risk assessment.

How to cite: Cavalli, M., Crema, S., Cucchiaro, S., Macchi, G., Trevisani, S., and Marchi, L.: Sediment connectivity assessment through a geomorphometric approach: a review of recent applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8456, https://doi.org/10.5194/egusphere-egu22-8456, 2022.

Deserts have obvious textural features. In detail, different types of sand dunes have significant differences in their morphological texture features. Existing studies on desert texture have mainly focused on extracting dune ridges or sand ripples using remote sensing images. However, comprehensive understanding of desert texture at multiple scales and quantitative representation of texture features are lacking. Our study area is in the Badain Jaran Desert. Four typical sand dunes in this desert are selected, namely, starlike chain megadune, barchans chain, compound chain dune, and schuppen chain megadune. Based on Sentinel-2 and ASTER 30m DEM data, the macroscopic and microscopic texture features of the desert are extracted using positive and negative topography, edge detection and local binary pattern (LBP) methods, respectively. Eight texture indexes based on gray level co-occurrence matrix(GLCM) are calculated for the original data and the abstract texture data respectivelyThen these texture parameters are clustered based on the result of Spearman correlation. Finally, the coefficient of variation is used to determine representative indicators for each cluster in order to construct a geomorphological texture information spectrum library of typical dune types. The results show that the macroscopic and microscopic texture features of the same type of sand dunes have high similarity. And geomorphological texture information spectrum can well distinguish different types of sand dunes by curve features.

How to cite: Ma, J., Li, F., Liu, L., Cheng, J., and Tang, G.: Research on texture features for typical sand dunes using multi-source data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13129, https://doi.org/10.5194/egusphere-egu22-13129, 2022.

The World Urbanization Prospects (ONU) estimates that within 2050 about 70% of the world's population will live in urban areas. The use of GIS and spatial analysis are essential tools for proper land use planning, which takes into account the geomorphological characteristics of the territory, as the starting point for the safeguard of urban ecosystems.

Several geological and environmental approaches have been proposed, albeit they usually lack a new objective, quantitative and scale independent model. At variance with common approaches, recently a new geomorphodiversity index was proposed which aims at an objective classification of joint geological, hydrological, biotic and ... features, in Italy.

In this work, we show results of a study performed in urban areas in Italy, where we apply systematic spatial analysis for the identification of the geomorphodiversity index. The approach proposed a quantitative assessment of topographic features (i.e., slope and landforms classification) is a spatial analysis in GRASS GIS through the use of geomorphon method and additional morphometric quantities. We aim at the definition of a new scale-independent approach, analyzing all of the morphometric quantities calculated at different scales (i.e., within moving windows of different sizes). We shown that scale- and model-independent selection of such features is possible for most of the considered quantities.

We argue that our work is relevant for the objective selection of quantities to define a geomorphodiversity index, and its calculation in  areas of arbitrary size and geomorphological properties, provided the same input data is available.

How to cite: Melelli, L., Burnelli, M., and Alvioli, M.: A scale-independent model for the analysis of geomorphodiversity index, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13343, https://doi.org/10.5194/egusphere-egu22-13343, 2022.

The concept of terrain visibility is vast and hard to summarise in a single definition. It can be generically said that it is a property that measures how observable a territory is from a single or multiple points of view. 

The estimation or calculation of visibility indices has been used in multiple fields, including architecture, archaeology, communications, tourism, land planning, and military applications. Recently (Meinhardt et al., 2015, Bornaetxea et al., 2018, Knevels et al., 2020, ) the concept of viewshed, i.e. the geographical area that is visible from one or more points of view, has been called into play for applications involving geomorphology.  In particular, it has been used to identify the portions of territory in which existing landslide inventories, carried out through field surveys, can be considered valuable for the calculation of landslide susceptibility. The aim is to delineate the Effective Surveyed Area, i.e. the area that has actually been observed by the operators in the field. 

However, this purely geometric approach cannot guarantee that objects are actually visible just because they are in a direct line-of-sight relationship with the observer. Due to their size and/or orientation in space, they may be (i) poorly or not at all detectable and/or (ii) observable from only a few viewpoints.    

For this reason we have developed r.survey (Bornaetxea & Marchesini, 2021), a plugin (Python script) for GRASS GIS, which allows to simulate (i) from how many observation points each point of the territory is visible, (ii) from which point of observation each point of the territory is most effectively visible, (iii) whether an object of a specific size can be detected. Concerning, in particular, the last element, r.survey calculates the solid angle subtended by a circle of equivalent dimensions to those of the object to be surveyed and assumed to be lying on the territory, oriented according to the slope and aspect derived from a digital terrain model. The solid angle provides a continuous measure of the visibility of the object sought, which can be compared with typical values of a human visual acuity. What happens then is that the concept of 'Effective Surveyed Area' can be reworked into the more accurate 'Size-specific Effective Surveyed Area' (SsESA). The new concept makes it possible to identify those portions of territory in which, during fieldwork, it is possible to observe objects of equal or greater size than those of interest, also considering their orientation in space with respect to the observer. 

The code of r.survey, which is based on the libraries and modules of GRASS GIS and was written to exploit multi-core processing, is open source and available for downloading (https://doi.org/10.5281/zenodo.3993140) together with a manual and some example data.

How to cite: Marchesini, I. and Bornaetxea, T.: r.survey: a tool to assess whether elements of specific sizes can be visually detected during field surveys, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5715, https://doi.org/10.5194/egusphere-egu22-5715, 2022.

Gullies are some of the areas with the most frequent material exchanges in loess landforms. By studying the influence of the spatial structure of gully networks on material transport and describing the difficulty of material transport from sources to sinks, it is of great significance to understand the development and evolution of loess landforms. This study is based on graph theory and digital terrain analysis and describes the relationship between gully networks and terrain feature elements via a gully network graph model. The adjacency matrix of the gully network graph model is constructed to quantify the connectivity. Taking six typical small watershed sample areas of the Loess Plateau as the research objects, the changes in the gully network connectivity characteristics in different loess geomorphic areas are analyzed from the aspects of overall network connectivity and node connectivity. The results show that (1) From Shenmu to Chunhua (the sample areas from north to south), the average values of the gully network edge weights first decrease and then increase. The maximum value is 0.253 in the Shenmu sample area, and the minimum value is 0.093 in the Yanchuan sample area. These values show that as the gully development increases, the greater the capacity of the gully network to transport materials is, and the less resistance the material receives during the transfer process. (2) The average node strength reaches the minimum in the Yanchuan sample area, and from Yanchuan to the north and south sides, it gradually increases. It can be concluded that the overall connectivity of the gully network shows a gradually weakening trend from the Yanchuan sample area to the north and south sides. (3) The potential flow (Fi) and network structural connectivity index (NSC) show similar characteristic changes; from north to south, the connectivity of nodes from the Shenmu to Yanchuan sample areas gradually increases, and from the Yanchuan to Chunhua sample areas, it gradually weakens. The accessibility from source to sink (Shi) shows the opposite trend. At the same time, the connectivity index values of the gully network nodes in the six typical areas all show clustered spatial distribution characteristics. (4) By comparing the results of the connectivity indicators calculated by the Euclidian distance used in the previous study and the sediment transport capacity index used in this study and by comparing the variation in the gully network quantitative indicators and the gully network connectivity indicators, this comparison result indicates the rationality of connectivity indicators in this paper. The connectivity of the gully network contains abundant and important information on the development and evolution of loess gullies. Research on the connectivity of the gully network will help deepen the understanding of the evolution process and mechanism of loess gullies.

How to cite: Cheng, J., Luo, L., Li, F., and Liu, L.: Regional differences in gully network connectivity based on graph theory: a case study on the Loess Plateau, China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13130, https://doi.org/10.5194/egusphere-egu22-13130, 2022.