ICG2022-4

Geomorphic systems face significant changes of climate, land uses, vegetation cover and socio-demographic characteristics. In this context, the accurate monitoring of geomorphological features may be a relevant tool to understand the consequences and the risks of these changes. At the same time, monitoring some geomorphic systems (for instance a coastal cliff) may be challenging, difficult and dangerous. The direct georeferencing techniques, which do not require acquiring Ground Control Points (GCP), recently integrated into new platforms (mainly Unmanned Aerial Systems or UAS) and sensors (photographic or laser) facilitate this task. Here we test the accuracy of direct georeferencing approaches for two UAS: a DJI Phantom 4RTK (P4) and a MD4-1000 LIDAR. Two flights were carried out to capture images with the P4 over a beach and a coastal cliff in Cantabria (N Spain): a) in Real Time Kinematic (RTK) mode receiving NTRIP corrections and b) without real time corrections to later process the data using a Post Processing Kinematic (PPK) approach with simultaneously registered data at permanent GNSS stations. The PPK approach for the P4 dataset was carried out using Redcatch software and three permanent stations located at different distances of the study area to analyse the influence of the baseline. The P4 datasets were processed by the Pix4Dmapper Pro photogrammetric software to produce 3D models. The same study area was surveyed by the MD4-1000 LIDAR UAS that performs in PPK mode and produces a 3D model. The MD4-1000 LIDAR dataset was processed using PosPac UAV and mdLIDAR processing software packages. Two datasets were used to test the accuracy of the resulting 3D models: a set of 18 check control points established in the study area and surveyed using a GNSS instrument in RTK mode and a 3D model surveyed by a Terrestrial Laser Scanner placed at 5 locations (23.4·10⁶ points with a registration error of 7 mm).

The P4 RTK approach showed a Root Mean Square Error (RMSE) for the Z coordinate of 0.12 m against 0.02 m obtained for the PPK approach. The MD4-1000 LIDAR showed a RMSE for the XY coordinates of 0.03 m and 0.06 for the Z coordinate. These figures were corroborated by the 3D distances between each resulting model and the TLS 3D model. In terms of point density and coverage the P4 resulting point clouds outperformed that obtained by the MD4-1000 LIDAR system, except in vegetated surfaces, where the photogrammetric technique completely failed to reconstruct the surface (i.e. to calibrate the images). These results and figures may be useful for geomorphologists and surveyors interested on monitoring features without the need for GCPs.

How to cite: Gómez-Gutiérrez, Á., Sánchez-Fernández, M., de Sanjosé-Blasco, J. J., and Lavado-Contador, F.: Accuracy of direct georeferencing strategies to monitor geomorphological features, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-91, https://doi.org/10.5194/icg2022-91, 2022.

River restoration projects have long relied on the use of modelling in both 1D and 2D to simulate the changes to flow hydraulics and flood extent that alterations to a river and floodplain will have caused. These methods have grown more accurate with increasing computing power and more complex software. However, despite the developments in modelling, a significant gap remains in the usage and accuracy of sediment modelling to identify arguably one of the most important metrics of river restoration: Geomorphic Change. Therefore, this paper attempts to address this by investigating the accuracy of high-resolution sediment modelling to identify geomorphic change on Blaze Beck in Cumbria, a high energy wandering river system that has recently been restored. Comparisons have been made between the impacts of a simulated flood event created using HEC-RAS 6.1 modelling software and a real-life flood event on October the 27^th 2021, to compare changes to geomorphology and identify the degree of accuracy of the 2D change modelling compared to change measured using drone-based photogrammetry. The results appear generally accurate, predicting locations of head cutting and more general low-level erosion and deposition. Bar formation, splay deposition and general bed raising are all predicted. The model is, however, very sensitive to the gradation of sediment it has been trained with and this can skew the ratio and pattern of deposition and erosion depending on the sample data used to simulate real life conditions. The model, once refined and iterated to best simulate potential change, is functional even in a high energy hydraulically diverse environment like Blaze Beck, and will have significant value in predicting the degree to which geomorphic change will occur because of restorative or other changes to a river reach.

How to cite: Abid-Waheed, Z., Entwistle, N., and Heritage, G.: Using 2D Sediment Modelling to Simulate Geomorphic Change for River Restoration Initiatives, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-63, https://doi.org/10.5194/icg2022-63, 2022.

On a global scale, inventories and databases are gaining value and there is a demand for openly accessible and reusable data. Here presented GIS database of glacial geomorphological evidence is a compilation of all available glacial and other relevant data in the South-Eastern Alps and the Northern Dinaric Mountains in Slovenia, Italy and Croatia. The database contains the following elements: glacial landforms (e.g., moraines, cirques, erratic fields, kame terraces), non-glacial landforms relevant for the interpretation of the glacial environment (e.g., alluvial fans, alluvial terraces, rock glaciers, protalus ramparts, talus cones), outcrop locations, geochronological data, and data on geophysical exploration. The accompanying attribute tables contains some basic information for each feature, such as type and description of a landform, assumed age, reference, etc. In the first step of database creation, the data were digitised, georeferenced (if not already done by the authors themselves) and cited accordingly. In the second step, the input data were revised in the field and topographically adjusted using a high-resolution lidar-based digital elevation model. The GIS database of glacial geomorphological data was used to develop the web map viewer, which also displays empirically reconstructed ice limits for different time spans during the last glacial cycle (130-0 ka). The data stored in the GIS database is available as ESRI shapefile format. Both the GIS database and the map viewer are open access. Disseminating the results of past and ongoing studies on glacial geomorphological evidence in the Alps-Dinarides junction will improve the availability and accessibility of data, build on previous findings and potentially prevent unnecessary repetition of work already done. Improved accessibility of data also offers greater potential for further research in large-scale studies in the area.

This work was funded by the Past climate change and glaciation at the Alps-Dinarides junction project (J1-2479) supported by the Slovenian Research Agency.

How to cite: Gostinčar, P., Žebre, M., Jamšek Rupnik, P., Mencin Gale, E., Jež, J., Stepišnik, U., Atanackov, J., and Bavec, M.: Towards a comprehensive overview of glaciation at the Alps-Dinarides junction: compilation of a GIS database and creation of a web map viewer, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-227, https://doi.org/10.5194/icg2022-227, 2022.

Satellite earth observation data has been frequently applied to monitor shoreline change at very large geographic scales. Techniques focus on the extraction of the instantaneous waterline boundary, a shoreline proxy that can be extracted from publicly available multispectral satellite data with sub-pixel accuracy. Interpreting coastal change through this proxy can be uncertain as the position of the waterline is dynamic, a function of beach gradient and constantly fluctuating marine processes, and might not capture information of the full spectrum of drivers influencing change for a given section of coast.

Satellite data is acquired in raster format and there is useful information stored in each pixel across the entire coastal zone. Applying per-pixel change detection techniques could offer further insights to the role of drivers of coastal change beyond a vectorised land-water boundary. This paper describes a new method for monitoring coastal change at large geographic scales with public satellite remote sensing data through pixel-based change detection. The first step is the classification of pixels into specific coastal landcover classes. This is challenging at large scales owing to complex and diverse physical environmental characteristics. A methodology was developed and applied to New Zealand’s coastline, identifying 9 landcover types including sedimentary coast. A combination of Sentinel multispectral and synthetic-aperture-radar data were used to derive composite imagery for 2019 using Google Earth Engine cloud computing platform. This was classified using a set of hierarchal rules and machine learning in a Python programming environment. This was validated nationally against high-resolution aerial photography and commercial satellite imagery. This produced a coastal specific landcover classification from which per-pixel change detection techniques can be applied. Overall accuracy was 86.38% and exceeded 90% when normalised by class area.

The outputs and code are available, and the framework has been designed to work with a range of earth observation datasets and can be applied to other regions around the world. Ongoing work includes implementing a framework to assess long-term change, at the coast in New Zealand. By investigating how specific coastal landcover types are changing, useful information can be acquired to better interpret drivers of coastal change and the impacts on coastal geomorphology at large geographic scales.

Keywords: Satellite Remote Sensing, Multispectral, Synthetic Aperture Radar, Landcover classification, Change detection, Coastal change.

How to cite: Collings, B., Ford, M., and Dickson, M.: Pixel-based coastal change detection, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-382, https://doi.org/10.5194/icg2022-382, 2022.

The first aim of this work is to introduce a new geostatistical-based algorithm that permits to detect specific aspects of short-range surface roughness (or of image texture) which does not require user defined choices, except for the radius of the search window, and provides a high interpretability of the results.  In particular, differently from usual geostatistical approaches, this algorithm does not require the derivation of a residual digital elevation model. The new proposed algorithm, despite its simplicity, permits to detect relevant aspects of surface texture, including anisotropy. Moreover, adopting approaches based of digital elevation model smoothing it can also be applied in the context of multiscale analysis. A second aim, functional to the introduction of the new algorithm, is to furnish a general overview of the key aspects of the geostatistical methodologies, highlighting analogies and differences with other approaches. In presenting the algorithm, a comparison with the roughness computed by means of dispersion of vectors normal to surface is performed.

ATKINSON, P.M. and LEWIS, P., 2000. Geostatistical classification for remote sensing: An introduction. Computers and Geosciences, 26(4), pp. 361-371.

BALAGUER, A., RUIZ, L.A., HERMOSILLA, T. and RECIO, J.A., 2010. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Computers and Geosciences, 36(2), pp. 231-240.

GUTH, P.L., 2001. Quantifying terrain fabric in digital elevation models. GSA Reviews in Engineering Geology, 14, pp. 13-25.

HERZFELD, U.C. and HIGGINSON, C.A., 1996. Automated geostatistical seafloor classification - Principles, parameters, feature vectors, and discrimination criteria. Computers and Geosciences, 22(1), pp. 35-41.

TREVISANI, S., CAVALLI, M. and MARCHI, L., 2009. Variogram maps from LiDAR data as fingerprints of surface morphology on scree slopes. Natural Hazards and Earth System Science, 9(1), pp. 129-133.

TREVISANI, S., CAVALLI, M. and MARCHI, L., 2012. Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin. Geomorphology, 161-162, pp. 26-39.

TREVISANI, S. and ROCCA, M., 2015. MAD: Robust image texture analysis for applications in high resolution geomorphometry. Computers and Geosciences, 81, pp. 78-92.

TREVISANI, S. and CAVALLI, M., 2016. Topography-based flow-directional roughness: Potential and challenges. Earth Surface Dynamics, 4(2), pp. 343-358.

How to cite: Trevisani, S.: Introducing a new and simplified geostatistical-based roughness algorithm, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-384, https://doi.org/10.5194/icg2022-384, 2022.

Effective geomorphological education for young students such as university undergraduates and high school students is crucial for fostering future geomorphologists and the long-term development of Geomorphology. As an outreach activity, geomorphological education for common people is also meaningful. Especially, education related to geomorphological hazards for citizens will lead to disaster risk reduction. We have been developing materials and curricula for geomorphological education using GIS, Internet technology, close-range remote sensing, and virtual reality, and have applied them to practical courses in high school classes and social events in Japan and China. The developed materials include: 1) online resources for learning GIS operations including geomorphometric analysis, 2) Web-based online GIS for a better understanding of flood hazard maps in relation to landforms, 3) explanatory materials of typical landforms in Japan based on photographs and topographic data obtained by Unmanned Aerial Vehicles (UAVs), and 4) visual contents for virtual tours of geomorphological sites such as a coastal cliff and an underground cave. This presentation introduces the main points of our educational activities and discusses their implications to provide future perspectives.

How to cite: Oguchi, T., Yamauchi, H., Song, J., Ogura, T., Hayakawa, Y., Tsuruoka, K., and Iizuka, K.: Applications of GIS, Internet technology, close-range remote sensing, and virtual reality to develop geomorphological education, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-389, https://doi.org/10.5194/icg2022-389, 2022.

Thanks to the modern development of technology, for the morphometric analysis of landforms, we can use accurate data from, for example, laser scanning or UAVs. However, these methods require access to quite expensive equipment, and the available public domain data does not always meet our accuracy requirements. The aim is to present how to obtain high-resolution DTM of small landforms using the photogrammetric Structure from Motion (SfM) technique and open source software available on the web.

In obtaining data, the first stage of fieldwork can be distinguished, where the necessary simple tools are, e.g. stakes and a tape measure/ rangefinder, used to scale or build local coordinate systems of landforms and analyse height and distance errors. However, the essential tool is a digital camera, which, based on the rules of acquiring stereoscopic models (appropriate coverage of adjacent photos), will allow filming the object/ landform from all sides. This principle and image recognition techniques are the basis of the SfM method. The next stages of the work use open-source software to extract the right frames from the movie (e.g. one frame per second), adjust them by combining identical control points, generate a point cloud, rectify it and generate digital elevation models. The last stage is the creation of high-resolution DTM using appropriate interpolation methods in a geoinformation environment.

The above operation scheme allows obtaining DEM with very dense coverage of about 1 point per 1 cm², having one film frame per second. The resolution of 0.05–0.1 m of the created DTM is a good and optimal value. The accuracy of the model can be increased by increasing the sampling of film frames. The results also show that the values of basic morphometric parameters on models with a resolution of, e.g. 2 m are significantly overestimated. It is possible to examine the minimum size of the landforms for which models with a resolution of 1–2 m will be optimal for the needs of morphometric analyses.

The described method is a cheap, simple and attractive alternative to commercial techniques and software and can be used directly in geomorphological studies, e.g. in the case of documentation of rapid changes in the topography under the influence of extreme events caused by climate change. The method can also be widely applied in many other fields, e.g., archaeology, palaeontology, architecture, and forensics.

How to cite: Szafraniec, J. E.: Structure from Motion technique and open-source software in obtaining high-resolution DTM, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-403, https://doi.org/10.5194/icg2022-403, 2022.

Shallow coral reefs are biodiverse ecosystems currently severely at risk from anthropogenic disturbance and climate change. To ensure their survival through the Anthropocene, ecologically informative maps of these areas, that can guide ecosystem-based management, are necessary. In recent years, Unmanned Aerial Vehicles (UAVs) have emerged as a new tool to obtain spatially explicit data of the benthic community in very shallow habitats (<3.5 m deep), where acoustic survey techniques used in deeper water become challenging or unfeasible. Structure-from-Motion (SfM) processing allows the conversion of separate images into high-resolution orthomosaics and Digital Elevation Models (DEM), spanning several hundred metres. The orthomosaic is commonly used to create a habitat map. Object-Based Image Analysis (OBIA), in which an image is first segmented into homogeneous segments before classification, provides an effective method to work with such high-resolution data. In this study we investigated the possibilities to enhance habitat classification by also using the DEM in the OBIA. Therefore, we surveyed three shallow reef areas on the central Saudi-Arabian coast with a consumer-grade UAV and processed the imagery with SfM. We then separately assessed the impact of adding the DEM’s three-dimensional information on the image segmentation and automatic classification steps in the OBIA. Integrating both the 3D model and spectral information into the segmentation greatly reduced the amount of oversegmentation. The addition of DEM-derived variables into a Random Forest classifier increased overall classification accuracy up to 11%. Thus, this study demonstrates the previously untapped potential of a drone’s DEM to improve the accuracy of semi-automated OBIA for habitat mapping in shallow tropical marine ecosystems.

How to cite: Nieuwenhuis, B. O., Marchese, F., and Benzoni, F.: Integrating a drone’s DEM and orthomosaic enhances performance of Object-Based Image Analysis for habitat mapping of shallow coral reefs, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-439, https://doi.org/10.5194/icg2022-439, 2022.

Remote sensing studies based on satellite and aerial imagery have generated a good understanding of the distribution of several shallow reefs found along the Arabian coast (Bruckner et al., 2011, Rowlands et al., 2012, Rowlands et al., 2016). However, data concerning the deeper benthic assemblages' spatial distribution, type, and conditions are missing, especially in the central Red Sea. Using high-resolution MBES and video collection, we aim to map, describe and classify the reefs found in Thuwal's coastal area, filling the information gap by producing the first benthic habitat map of this area.

High-resolution bathymetric data were collected using the Norbit iWBMS Multibeam Echosounder, which allowed us to classify the reefs in 12 morphotypes. From those morphotypes, 28 areas were selected for ground-truthing examination using a BlueROV2 equipped with a 4K camera and USBL positioning system to characterize the benthic community.

With the information obtained from the bathymetry data and the ROV video transects, a benthic habitat map for Thuwal's reefs was created, where 23 benthic habitat types were observed.

This research uncovered previously poorly studied reef morphologies in the Red Sea and their benthic composition, demonstrating how different reef communities express a different three-dimensional morphological structure. Moreover, this work will help us improve the understanding of the spatial distribution and condition of benthic communities located on Thuwal's coastline, giving a baseline with the potential to provide fundamental information that can be used for management, conservation, and future research in Saudi Arabia.

How to cite: Ezeta Watts, M. A., Marchese, F., and Benzoni, F.: Reef morphotype characterization as an effective tool for benthic habitat description: a case study from the Red Sea., 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-516, https://doi.org/10.5194/icg2022-516, 2022.

Present terrestrial landforms result from hydrogeomorphological processes during geological times that could be deciphered with DEM with specific algorithms. DEM could be considered as a "footprint" with two folds: the "downprint" referring to a tessellation of catchment basins organized according to lowest points and the "upprint" as a topographical splitting in relation to highest points.
Specific algorithms allow the extraction of both "prints" that must be analyzed with ad hoc geostatistical methods. For the downprint, various algorithms have been been developed since the 1980ties to extract of catchments according to the steepest descent downstream lines (I.e. D8 method or variant of it); for the upprint, the previous algorithms must be modified to fit with the specific topological properties of steepest ascent morphological lines with stepwise iterations to delineate humps, hills and mountain ranges of embedded (or enshrined?) magnitudes.
This geomorphometrical approach is applied to DEM SRTM 1 arc second (About 30 meters resolution) on the Sao Miguel and the Pico volcanic islands for comparison purposes. The results pinpoint the valuable contribution of the combined geostatistical analysis of both downprint and upprint to unravel the intricate patterns of hydrological and lithological entities so to say Steepest descent hydrological units (SDHU) and Steepest ascent morphological units (SAMU).
The SDHU delineated from DEM correspond to catchment basins and specific algorithms are implemented in many GIS for hydrological and other types of modeling. This is not the case for SAMU which need to be explained; each unit will be called "massif" and refers to all steepest ascent lines converging to one and only one summit for elementary SAMU (order 0) and the limits between them are related to thalwegs and saddle points; at higher orders, the progressive merging of the elementary massifs creates hierarchical sets (orders 1 to n) of larger massifs with several summits. The result delineates morphological units such as major volcanic mountains and also smaller adventives volcanoes in the case of both islands of San Miguel and Pico. SAMU are then mostly related to lithological and tectonic features and are complementary to SDHU which are hydrological functional units.
The comparative geostatistical analysis (area-number Korcak method) of SDHU and SAMU of the two islands suggests that they have different downprints and upprints despite that there are in the same morphotectonic and climatologic contexts. The discrepancies between the two islands may be because the volcanic geology of San Miguel is older than the one of the island of Pico.

References: 

Depraetere C., Riazanoff S. (2004). « The new Digital Elevation Model data set from the Shuttle Radar Topography Mission : Hydrogeomorphological applications in the Ohrid region (Albania, Greece and Macedonia) ». Conference on Water Observation and Information System for the Balkans Countries BALWOIS, Ohrid, Northern Macedonia, 25-29 May 2004.

Depraetere C., Riazanoff S. (2021). « A Stepwise Massif Partitioning Algorithm Based On Steepest Ascent Method From DEM: Application To Potential Prospection of new archaeological sites ». Extended abstract, 17th International Conference of Computational Methods in Sciences and Engineering ICCMSE 2021 conference, Heraklio, Greece, 4-7 September 2021.

How to cite: Depraetere, C. and Riazanoff, S.: Downprint and upprint of landforms from DEM: the case of the volcanic Acores islands., 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-631, https://doi.org/10.5194/icg2022-631, 2022.

Gully erosion affects land and water resources, resulting in serious environmental and socio-economic consequences. To aid mitigation and rehabilitation efforts, gully susceptibility mapping of broader gully-prone regions should be augmented by the rapid detection of existing gully features. Numerous works have been published on (semi-)automated approaches to detect gully erosion, most recently incorporating machine learning. However, upscaling and transferability capabilities of these approaches are rarely investigated. Establishing algorithms that are scalable and transferrable will constrain uncertainties when conducting quantitative analysis, allowing comparable results at different landscape scales and/or geo-environmental settings. Here, we aim to develop and apply a semi-automated approach based on Object-Based Image Analysis (OBIA) with low data needs, at different scales and geo-environmental regions. The segmentation process is underpinned by two gully morphological properties: 1) Height Above Nearest Drainage (HAND) and normalised slope, calculated from a Digital Elevation Model (DEM) with a spatial resolution of 2 m, with 93% coverage of South Africa’s 1.22 million km² expanse. HAND is a terrain model that normalises topography according to local relative heights above a drainage channel (herein, a gully channel). While this has been implemented in flood mapping studies for river systems, it remains unused in gully detection algorithms. Slope, which is often used as a gully predictor variable, is used to confine HAND and implemented here as a normalised slope input, calculated by subtracting a convolved mean slope value with a designated filter size from the DEM-derived slope. Detected gully features are refined using expert knowledge, merging, and pixel-based growing and shrinking. Preliminary development at a local gully scale suggests good performance, with an overall accuracy of 82.3% (includes a user accuracy of 65.5% of gully and 99.0% for non-gullied areas, and a producer accuracy of 98.5% for gully and 74.2% for non-gullied areas) and a kappa index of 0.65. We also discuss the broader performance of our approach when upscaling and implemented in other geo-environmental settings covered by the 2 m-DEM. 

How to cite: Olivier, G., Van De Wiel, M., and De Clercq, W.: Giving gully detection a HAND, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-102, https://doi.org/10.5194/icg2022-102, 2022.

Landslides are major hydro-geological and anthropogenic hazards that affect not only the mountains areas but also gullies, mining areas, plateau terrain, river banks, coastal areas, and offshore as undersea slides. Slides occur as a result of ground movement, rock falls, and failure of unstable slopes; sand and debris flow on slopes which can cause lots of damage with direct and indirect impacts on human settlements and physical infrastructures. The study used secondary data that consist of other literature from which the likely triggering factors – slope angle, land use land cover change (LULCC), aspect, soil texture and type, curvature, drainage density, elevation, lineament density, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), geology, topographic wetness index (TWI), geomorphology, rainfall, temperature, wind speed, wind pressure, settlements, rivers and roads construction were extracted, and satellite imageries (SRTM and Landsat 8 OLI-TIRS) data obtained from USGS Earth Explorer, processed and mapped based on the triggering factors using ArcGIS v10.4 and validation visit was made to confirm results. Microsoft excel 2007 was used to compute and assigned weights to the factors while weighted overlay methods in spatial analyst tool of ArcGIS v10.4 were applied in mapping the landslide vulnerable areas in the study area. The study recommended the use of laws to secure and regulate land use activities in the vulnerable areas, educating inhabitants of such areas about the dangers associated with certain land use activities and the need to avoid them, providing alternative means of livelihood that will discourage mining, deforestation, forest fire, and overgrazing, and encourage sustainable resource use and management that will not expose the areas to the triggering factors of landslide.

Keywords: GIS-Remote Sensing, Assessment, Mapping, Landslide Vulnerability Areas, South East

How to cite: Ayadiuno, R. U., Ndulue, D. C., and Mozie, A. T.: GIS-Remote Sensing Integrated Based Assessment and Mapping of Landslide Vulnerability Areas in South East, Nigeria, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-399, https://doi.org/10.5194/icg2022-399, 2022.

The Istrian Peninsula, in the western part of Croatia, belongs to an undeformed part of the Adriatic Plate. The central part of Istria is the area of the Eocene flysch basin, i.e. “Gray Istria“, which is prone to weathering and active geomorphological processes. The siliciclastic rocks of the basin are homogeneous hemipelagic marls (Globigerina marls with a maximal thickness of 150 m) overlain by gravity-flow deposits or flysch-type rocks. The high erodibility of the Istrian marls led to the formation of steep barren slopes and badlands exceptionally susceptible to slope movements. This research presents the application of Light Detection and Ranging (LiDAR) data for the landslide and erosion inventory mapping at a large scale. Airborne laser scanning (ALS) was taken in March 2020 for the pilot area in the City of Buzet. Based on the characteristics of the acquired LiDAR Point Cloud, a bare-earth digital elevation model (DEM) with 30 cm resolution was created. Different topographic derivative datasets such as slope, hillshade, contour lines, roughness, curvature, flow accumulation and stream power index maps were created to interpret the LiDAR data. Visual identification and mapping of landform features were done on the study area (20 km²) at a large scale (1:500) to produce detailed landslide and erosion maps for implementation in the spatial planning system. After preliminary visual interpretation of LiDAR DTM and field verifications, it was concluded that three types of landforms could be mapped, i.e. badlands, unstable slopes and landslides. Badlands were first identified on the digital orthophoto maps 1:5.000 as areas with sparse or no vegetation, and then the boundary of the entire affected slope was mapped in detail on the LiDAR DTM morphometric derivatives. Additional field checking showed that badland boundaries based on digital orthophoto images and LiDAR DTM have significant deviations. Badlands dominantly appears in the form of plane forms on steep slopes and less often as linear forms such as erosion gullies or torrent beds. Unstable slopes were categorised as areas with dense sliding where the exact landslide boundaries could not be mapped due to the coalescence of landslides or poor contrast between affected and unaffected areas due to the intense erosion. Landslide identification on the LIDAR DTM morphometric derivatives is based on recognising landslide features (e.g., concave main scarps, hummocky landslide bodies and convex landslide toes). A digital orthophoto map from 2020 was used during landslide identification to check the morphological forms along roads and buildings, such as artificial fills and cuts, which can have a similar appearance to landslides on DTM derivatives. Sliding is most often along contacts between weathered and fresh flysch-type rock, especially at places with concentrated surface runoff (e.g., near roads), which typically cause small and shallow landslides. The final result of the visual interpretation of LiDAR DTM derivatives is multi-hazard inventory map which can be implemented in the spatial planning system of the city of Buzet.

How to cite: Bernat Gazibara, S., Sinčić, M., Krkač, M., Jagodnik, P., Arbanas, Ž., and Mihalić Arbanas, S.: Landslide and erosion inventory mapping based on LiDAR data: A case study from Istria (Croatia), 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-408, https://doi.org/10.5194/icg2022-408, 2022.

Wall collapses, sometimes accompanied by mass movements, is one of the main degradation processes in long-abandoned agricultural terraces and critically affects the hydrological and geomorphological behaviour of terraced hillslopes (e.g., development of new sediment sources, increasing soil erosion, increasing hydrological connectivity, re-establishment of natural drainage pathways). An efficient monitoring of the spatial and temporal dynamics of wall collapses is fundamental to understand all these processes. However, the identification of terrace wall collapses in the field is a time-consuming task with spatial limitations. In this study, Object Based Image Analysis (OBIA) applied on High-Resolution Topography was used (and evaluated) for detecting wall collapses in terraced hillslopes. The approach, which includes image segmentation and Support Vector Machine classification, was implemented in the Cidacos Valley (Iberian Range, Spain), extensively affected by the abandonment and degradation of agricultural terraces. Point clouds extracted from three different remote data sources (airborne LiDAR, terrestrial LiDAR and airborne photogrammetry) were used to obtain three different digital terrain models (DTM). The low spatial resolution of the DTM derived from airborne LiDAR (1 m pixel size) was not sufficient to detect any terrace wall collapse, which had a median size of 10 m². The results showed that slope, Sky-View factor, topographic openness and curvature DTM-derivatives produced the best segmentations. Field inventories of wall collapses were used to train the classification algorithm (Support Vector Machine) and validate the results of the OBIA approach. Terrace wall collapses were identified with an Overall Accuracy of 0.80 and a Producer Accuracy of 0.50. The results of the approach and further improvements are discussed. Although promising, the detection of wall collapses in terraced hillslopes using OBIA is challenging, especially when it is compared to the detection of larger scale hillslope geomorphological processes (e.g.: landslides).

How to cite: Fernández-Olloqui, G., Lorenzo-Lacruz, J., Lana-Renault, N., and Pérez-Cabello, F.: Identification of wall collapses in abandoned agricultural terraces using Object Based Image Analysis and High-Resolution Topography, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-536, https://doi.org/10.5194/icg2022-536, 2022.

In the last two decades several studies have applied automatic and semiautomatic methods to produce landslide inventories using high resolution Earth Observation products such as satellite imagery and Digital Elevation Models (DEMs). In Brazil, inventory maps for large areas are not systematic and the application of automated and semiautomated methods for landslide detection is unusual. In January 2014 intense rainstorms (200mm/2h) occurred in the Ribeira de Iguape river Valley (São Paulo State, Southeast Brazil), triggering landslides and a debris flows that caused 25 deaths and damage to urban, transportation and energy infrastructures. In the following years, a few event-based inventories were produced for small subbasins affected in 2014, by the means of empirical methods and freely available images. To the best of our knowledge there are no large-scale event-based inventories for the whole area affected by landslides in the 2014 event. In this study, we tested a combination of unsupervised classification of high-resolution orbital images, visual interpretation and DEM analysis to map landslides triggered in 2014 event. The landslide inventory was produced for an area of 110 km². The ISODATA algorithm from SAGA GIS was used to clusterize a NDVI difference layer derived from RapidEye (5m) pre-event and post-event orhorectified mosaics. The resulting clusters were checked and corrected through visual interpretation. Finally, we used a TanDEM-X DEM (12m) to identify valley bottoms and separate the inventory in two classes: landslide scars and landslide deposits. Our results shows that an area of 5.48 km² (approximately 5% of the total area) was affected by landslides in 2014, with 4.04 km² classified as landslide scars and 1.44 km² as landslide deposits. A total of 2089 landslide scars were detected, with mean area of 1.926 m², minimum of 25 m², maximum of 91.100m² and median of 800 m². Given the scarcity of detailed geographical information about the landslides in the area, the inventory presented here may improve future susceptibility and hazard studies, especially regional scale statistically based models. This inventory can also be used as labeled sample for the application of Machine Learning and Deep Learning algorithms aiming to detect landslide scars and the timing of ruptures using satellite imagery time series, as well to launch a systematic mapping of such phenomena in Brazil.

How to cite: Bonini, J. E., Vieira, B. C., and Martins, T. D.: Semiautomatic inventory of the landslides triggered in the 2014 event in Ribeira Valley using Remote Sensing Data, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-664, https://doi.org/10.5194/icg2022-664, 2022.

In mountainous regions such as mountainous Croatia, natural and semi-natural landscapes are most common and are changing due to climate change and more intense economic activities. Due to these changes, there is a need of inventarisation and valorisation of mountainous landscapes in order to manage and preserve them. In Croatia, there is no inventory that would provide us with a zero status of mountain landscapes. On the other hand, the mountainous environment and the size of the area ​present an obstacle for landscape mapping. The use of Sentinel-2a imagery and a digital relief model allow ​a fast, inexpensive, and accurate mapping of landscapes in large areas such as mountainous Croatia. Orthophoto images were used to increase the accuracy of sampling and thus the accuracy of the final classification. Remote sensing techniques, GIS analysis and field validation were used to create an inventory of the landscapes types. This type of landscape mapping, which includes geological, geomorphological and land cover data, provides a better understanding of the composition and configuration of mountain landscapes, leading to knowledge-based management and landscape-level protection.

How to cite: Butorac, V., Krtalić, A., and Buzjak, N.: Sentinel 2a in mountain landscape mapping – region of mountainous Croatia, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-207, https://doi.org/10.5194/icg2022-207, 2022.

Geomorphometry is an interdisciplinary science that aims to quantify the earth's surface landforms. This science techniques have supported the development of several knowledge fields, including geomorphology, in which the relationship between landforms and processes can be better understood and used in practical applications. In order to evaluate the use of geomorphometric techniques for structural best management practices (BMPs) placement in basin scale and what spatial resolution can provide better results, a morphological mapping methodology has been developed and 3 different digital elevation models (DEM) have been evaluated as data sources (SRTM – 30 m; ALOS/PALSAR – 12.5 m; GEOBASES – 2 m). The methodology has been applied in a small watershed in Espírito Santo state (Brazil) using 3 DEMs with different spatial resolutions. In such basin, slope segments previously defined as priorities for the BMP placement has been mapped using the Qgis software and the SAGA tools, for the 3 DEMs. The landforms mapped have been compared to the landforms observed in a field work. The results show the differences between the DEMs related to the mapping of relief units (summit, slope, valley) as well as the curvature of the slope segments (concave, straight, convex). Different results in terms of mapping priority areas for BMP placement have been noticed. The SRTM and Alos/Palsar DEMs are proven to be great options for carrying out morphological mapping for BMP placement purpose. Both DEMs have produced very similar results compared to the field work, while the 2-meters spatial resolution DEM was not able to map the features satisfactorily due misrepresentation of landforms and slope segments.

How to cite: Macao Campos, F. L. and Nascentes Coelho, A. L.: Relief mapping to best management practices placement purpose: an evaluation of different Digital Elevation Models, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-264, https://doi.org/10.5194/icg2022-264, 2022.

The present study is a geomorphological overview of Greece on a scale of 1:1,000,000. It is the first attempt of a cartographic synthesis that aims to bring together and interpret the geological and geomorphological factors that contributed to the landscape formation and evolution of Greece on a national scale. The production of the geomorphological map was based on a literature review of previous geomorphological studies of the greek terrestrial and coastal area at different scales as well as on the application of semi-automated, GIS techniques. High resolution topography obtained from the Hellenic Cadastre as well as the geological maps of Greece at a scale of 1:50,000 obtained from the Institute of Geology and Mineral Exploration of Greece were used as initial inputs in a spatial geodatabase for the production of a series of secondary layers. These layers, which included a hillshade map, a slope-aspect map, and a red relief image map, were combined with Google Earth Imagery to delineate small- and large scale landform across the Greek territory. The final map incorporates landforms of both endogenic and exogenic origin. It was organized genetically including structural landforms, landforms due to fluvial erosional and depositional processes, gravity induced landforms as well as coastal, karst, volcanic, glacial and periglacial landforms and anthropogenic facilities. A series of accompanying maps and tables were also produced, providing topographic parameters as well as information about the geotectonic setting and the climatic regime of Greece. The results show that the greek territory comprises a landscape with heterogeneous geomorphological environments, the formation and evolution of which, is the result of primarily active tectonics, and exogenic processes. The map reflects the extent of recent developments of geomorphology of Greece and can be used as a management tool for stakeholders, as well as a reference for further interdisciplinary research.

How to cite: Karymbalis, E., Tsanakas, K., Griva, D., Batzakis, D.-V., and Valkanou, K.: Geomorphological map of Greece, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-367, https://doi.org/10.5194/icg2022-367, 2022.

Ephemeral rivers (IRES – Intermittent Rivers and Ephemeral Streams) differ from perennial rivers in that they do not have a base flow, therefore, when direct flow stop, they dry up. This condition is spatially accentuated in the ephemeral streams of arid and semi-arid environments (SAES – Semi-arid Ephemeral Streams). Much of the East of the Iberian Peninsula has a climate and lithological conditions that favour the presence of SAES.

The hydrogeomorphological dynamics of these rivers are controlled by flash-floods, with marked rises inflow, short delay times, and on numerous occasions, a significant sediment load. These characteristics that define them, added to the urban development of recent decades, mean that SAES is not usually part of restoration plans. In addition to the technical-administrative “forgetting”, there is scarce appreciation and lack of social sensitivity towards the SAES.

In this project (CCAMICEM Project from Spanish Research National Plan) we focus on the cartographic development of ephemeral rivers with the aim of knowing the geomorphological evolution of several reaches, and between different dates, as a geoindicator of global change using historical and UAVs images. A diachronic geomorphological mapping has been carried out in six reaches distributed throughout the Ebro River Basin (Tudela, Reajo, Alpartir, Cariñena, Sosa and Seco). The timeframe covers 65 years, from 1956-57 (American Flight B) to 2021 through images taken with an unmanned aerial vehicle (UAV). As intermediate years, images were taken from the mid-1980s, and the latest official orthoimage available (2017). The official images belong to the National Geographic Institute (IGN). An altimetric correction has been made in the first two images.

The categories identified have been active channel, main channel and secondary channel, sediment bars (which can be vegetated, scant vegetated and non-active paleo-bars), the deposits coming from bank failures or tributaries, rocky areas, exhumed old sediment areas, and consolidated or unconsolidated granular bed. The categories were mapped at different scales according to the quality of the image, that is, from a scale ≤1/300 of the UAV to another scale ≤1/1000 of the American Flight B. The results achieved are allowing geomorphological changes and basin processes to be related to global change.

How to cite: Ibisate, A., García, J. H., Ollero, A., Ortiz, J., Gómez-Gutierrez, A., and Sáenz de Olazagoitia, A.: Ephemeral rivers, geomorfphological evolution and mapping. A case study in NE Iberian Peninsula, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-483, https://doi.org/10.5194/icg2022-483, 2022.

Salt marshes are highly valued coastal environments for different services: coastline protection, biodiversity, and carbon storage (blue carbon). However, they are vulnerable to climate changes, particularly sea-level rise (SLR). For this reason, it is essential to project the evolution of marsh areas until the end of the century. This work presents the results of applying a reduced complexity two-dimensional rule-based model developed by the authors: SMRM (Simplified Marsh Response Model). The model requires four parameters: a high-resolution digital terrain model (DTM) (LiDAR survey from 2011), local tidal levels (modeled from Setúbal-Tróia tide gauge), at least one SLR scenario (IPCC RCP4.5, FCUL MOD.FC_2b and NOAA Extreme were used) and accretion rates (determined through the analysis of ²¹⁰Pb and ¹³⁷Cs isotopic activity from a core in one of the studied marshes). Furthermore, additional parameters such as the error of the DTM (RMSE) or the acceleration of SLR and accretion rates can be added. The process is done through a MATLAB script and the output is a Geotiff file. The presented results are for the estuarine margin of the Tróia sandspit salt marshes (Sado estuary, Portugal – 40 km south of Lisbon), where six marsh patches(Caldeira de Tróia (CT-N (North) and CT-S (South)), Malha da Costa (MC-N and MC-S) and Comporta (Cmp-N and Cmp-S)) are present, totalizing an area of 109 ha.

Projections indicate that a significant reduction in the marsh area is expected until 2100, that will be transformed in tidal flats/subtidal areas. Depending on the chosen SLR scenario, the losses will be between 6 (IPCC RCP4.5) and 84 % (NOAA Extreme) in area. If sea-level rises around 1 m until the end of the century (FCUL MOD.FC_2b), a loss of 36 % in global area is expected. The evolution will not be similar along the sandspit. The most mature marshes (MC-S, TC-N and TC-S) will be more resilient to SLR. These projections consider that the surrounding areas will not be occupied until the end of the century, reducing the probability of these areas disappearing in the future. In fact, limited saltmarsh displacement to inland will occur (between 4 and 35 ha – from IPCC RCP4.5 to NOAA Extreme) to adjacent areas with low slopes.

The influence of each parameter was also evaluated in this work through a sensitivity analysis. The results indicate that SLR is the most significant parameter to consider, followed by accretion rates and the error of the DTM. The main conclusion is that the studied salt marshes could be resilient to conservative SLR scenarios but not to more severe projections, even if they have space landwards to expand.

This research was funded by Portuguese Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 + PTDC/CTA-GEO/28412/2017 and Ph.D. Grants PD/BD/142781/2018 + PD/BD/106074/2015.

How to cite: Inácio, M., Cunha, A., Freitas, M. D. C., Lopes, V., Leira, M., and Andrade, C.: Tróia sandspit (Sado estuary, Portugal) salt marshes' evolution until the end of the 21st century, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-436, https://doi.org/10.5194/icg2022-436, 2022.