GM2.8

How to cite: Abad, L., Hölbling, D., Dabiri, Z., and Robson, B.: An open-source Python package for DEM generation and landslide volume estimation based on Sentinel-1 imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-693, https://doi.org/10.5194/egusphere-egu22-693, 2022.

Spaceborne digital elevation models (DEMs) are fundamental data for mapping and analyzing geomorphic features at regional and continental scale, but are limited by both their spatial resolution and accuracy. Typically, accuracy is measured using point- or profile-based geodetic measurements (e.g., sparse GNSS). We develop new methods to quantify the vertical uncertainty in spaceborne DEMs relevant to geomorphic analysis, focusing on the pixel-to-pixel variability internal to a given DEM, which we term the inter-pixel consistency. Importantly, the methods we develop are not based on external, geodetic measurements. Our codes are published open-source (https://github.com/UP-RS-ESP/DEM-Consistency-Metrics), and we particularly highlight a novel sun-angle rotation and hillshade-filtering approach that is based on the visual, qualitative assessment of DEM hillshades. Since our study area is in the arid Central Andes and contains diverse steep (volcano) and flat (salar) features, the environment is ideal for vegetation-free assessments of DEM quality across a range of topographic settings. We compare global 1 arcsec (~30 m) resolution DEMs (SRTM, ASTER, ALOS, TanDEM-X, Copernicus), and find high quality (high inter-pixel consistency) of the newest Copernicus DEM. At higher spatial resolution, we also seek to improve the stereo-processing of 3 m SPOT6 optical DEMs using the open-source AMES Stereo-Pipeline. This includes optimizing key parameters and processing steps, as well as developing metrics for DEM uncertainty masks based on the underlying image texture of the optical satellite scenes used to triangulate elevations. Although higher resolution spaceborne DEMs like SPOT6 are only available for limited spatial areas (depending on funds and processing power), the improvement in geomorphic feature identification and quantification at the hillslope scale is significant compared to 30 m datasets. Improved DEM quality metrics provide useful constraints on hazard assessment and geomorphic analysis for the Earth and other planetary bodies.

How to cite: Purinton, B. and Bookhagen, B.: DEM quality assessment and improvement in noise quantification for geomorphic application in steep mountainous terrain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1191, https://doi.org/10.5194/egusphere-egu22-1191, 2022.

The eroding rocky coasts export sediment to the ocean, the amount of which is poorly known. At the global scale it could amounts 0.15-0.4 Gt/a (1). Recent evaluations of large retreat rates on monitored sections of sea cliffs indicate it can be comparable to the sediment input from medium to large rivers. We quantify rocky coast input to the ocean sediment budget at the European scale, the continent characterized by the best dataset.

The sediment budget from European rocky coasts has been computed from cliff lengths, heights and retreat rates. For that, we first compiled a large number of well-documented retreat rates; the analysis of whom showed that the retreat rates are at first order explained by cliff lithology (GlobR2C2, 2). Median erosion rates are 2.9 cm/a for hard rocks, 10 cm/a for medium rocks and 23 cm/a for weak rocks. These retreat rates were then applied to the European coast classification (EMODnet), giving the relative coast length for cliffs of various lithology types. Finally the cliff height comes from the EU-DEM (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/elevation).

Due to data availability, we only worked on ~70% of the whole Europe, corresponding to a 127,000 km-long coastline (65,000 km of rocky coast). We calculated it originates 111±65 Mt/a, corresponding to 0.38 times the sediment input from rivers from the equivalent area (3.56 10⁶ km²), calculated after Milliman and Farnsworth (3)’s database (290 Gt/a). A crude extrapolation to the 1.5 10⁶ km-long Earth’s coastline reaches an amount of 0.6-2.4 Gt/a, an order of magnitude less that the sediment discharge from rivers (11-21 Gt/a, e.g., 3).

This up-to-now overlooked sedimentary source must further be explored for: (i) its effects on the geochemical ocean budget; (ii) the rising sea level control on the cliff retreat rates; and (iii) the characteristics and location of sediment deposition on ocean margins.

References

(1) Mahowald NM, Baker AR, Bergametti G, Brooks N, Duce RA, Jickells TD, Kubilay N, Prospero JM, Tegen I (2005). Atmospheric global dust cycle and iron inputs to the ocean: ATMOSPHERIC IRON DEPOSITION. Global Biogeochemical Cycles 19. DOI: 10.1029/2004GB002402

(2) Prémaillon M, Regard V, Dewez TJB, Auda Y (2018). How to explain variations in sea cliff erosion rates? Insights from a literature synthesis. Earth Surface Dynamics Discussions:1–29. DOI: https://doi.org/10.5194/esurf-2018-12

(3) Milliman J, Farnsworth K (2011). River Discharge to the Coastal Ocean: A Global Synthesis. Cambridge University Press

How to cite: Regard, V., Prémaillon, M., Dewez, T., Carretier, S., Jeandel, C., Godderis, Y., Bonnet, S., Schott, J., Pedoja, K., Martinod, J., Viers, J., and Fabre, S.: Coastal erosion: an overlooked source of sediments to the ocean. Europe as an example, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2877, https://doi.org/10.5194/egusphere-egu22-2877, 2022.

The long-term (ka to ma) degradation of a volcanic edifice is controlled by both regional (e.g., climate, tectonics) and local factors (e.g., original morphology, lithology), resulting in both long-lasting weathering and river incision and short-term hazardous events, such as flank collapses and lahars. Trends among the morphometry of stratovolcanoes, their drainage network, denudation, and regional factors were recently characterised for composite volcanoes along the Indonesian arc. Denudation was shown to be negatively correlated with drainage density; the across-arc variations expose a tectonic control on the level of denudation and volcanoes’ irregularity. This study applies the same method on age-constrained volcanoes in Japan to find coherent trends between arcs despite the different local and regional factors. We aim to better understand the factors that control erosion rates and patterns, and the evolutionary phases of volcano degradation.       

We first compile a dataset of 35 singular, non-complex composite volcanoes with known eruption ages and spatially spread throughout the Japanese Island arc system. Using 30m TanDEM-X Digital Elevation Models, morphologies, and drainage metrics (e.g., volume, height, slopes, irregularity index, Hack’s Law exponent, and drainage density) are extracted for each volcano, using the MORVOLC algorithm adapted in MATLAB as well as the newly developed DrainageVolc algorithm. Correlations between the morphometric parameters and potential controlling factors (e.g., age, climate, lithology, and tectonics) are analysed to determine quantitative relationships of edifice degradation throughout the arc. Finally, we compare relationships and correlation values of the Japanese Arc system to those from the Indonesian Arc.   

The analysis shows that volcano age is positively correlated with irregularity and negatively correlated with height and volume. From the drainage parameters, we find that basins become wider and merge, resulting in lower drainage densities. The variation in erosion rates along the Japanese arc provides evidence for the degree of climatic control on the volcano degradation. The between-arc comparison shows which trends are susceptible to arc-scale variations and highlights consistent trends that have the potential to be extrapolated to other volcanic arcs and be used as a relative age determination tool for composite volcanoes.

How to cite: van Wees, R. M. J., O'Hara, D., Grosse, P., Kereszturi, G., Lahitte, P., and Kervyn, M.: Quantifying the morphometry and drainage patterns of composite volcanoes: A comparison of the Japanese and Indonesian volcanic arcs  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6152, https://doi.org/10.5194/egusphere-egu22-6152, 2022.

Despite the long record of applications and the well-known theoretical framework, geostatistical based image/surface texture tools have still not gained a wide diffusion in the context of geomorphometric analysis, even for the evaluation of surface roughness. Many geomorphometric studies dealing with various aspect of surface roughness use well-known approaches based on vector dispersion of normals to surface or even the popular Topographic Ruggedness Index. In many comparative studies on roughness metrics, geostatistical approaches are cited but not tested; in other studies, geostatistical approaches are tested using algorithms not adapted to the analysis of morphometric data. In remote sensing, geostatistical approaches are more popular, even if there is not a consensus on which are the most suited metrics for computing image texture indices. In metrology of manufactured surfaces, equipped by various industrial standards for surface texture measurements, approaches based on autocorrelation are widely adopted.  However, “natural” surfaces and related morphogenetic factors are much more complex than manufactured surfaces and ad-hoc concepts and algorithms should be devised. This presentation is mainly focused on topographic surface analysis, but the considerations and results are applicable also in the context of image analysis. This presentation aims to clarify some aspects of the geostatistical methodologies, highlighting the effectiveness and flexibility in the context of multiscale and directional evaluation of surface texture. In doing this, the connections with other methodologies and concepts related to spatial data analysis are highlighted. Finally, it is introduced a simplified algorithm for computing surface roughness indices, which does not require the preliminary detrending of the input DEM.

References

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.: Returning to geostatistical-based analysis of image/surface texture: from generalization to a basic one-click short-range surface roughness algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6924, https://doi.org/10.5194/egusphere-egu22-6924, 2022.

High-resolution Light Detection and Ranging (LiDAR) data provide unique opportunities for landscape-scale mapping of hydrological features. LiDAR-derived digital elevation models are particularly valuable for identifying channel networks in densely forested landscapes, where satellite imagery-based mapping approaches are challenged by forest canopies. Artificial drainage practices have caused widespread alteration of northern landscapes of Europe and North America which likely have had significant impacts on hydrological connectivity and ecosystem functioning. However, these artificial channels are rarely considered in ecosystem management and poorly represented in existing geomorphological datasets. In this study, we conducted a landscape-scale analysis across 11 selected study regions in Sweden using LiDAR data for the virtual reconstruction of artificial drainage ditches to understand the extent of their ecological impacts.

We utilized a 0.5 m resolution digital elevation model for mapping natural channel heads and artificial ditches across the study regions. We also implemented a unique approach by back-filling ditches in the current digital elevation model to recreate the prehistoric landscape. This enabled us to map and model the channel networks of prehistoric (natural) and current (drained) landscapes. We found that 58% of the prehistoric natural channels had been converted to ditches. Moreover, the average channel density increased from 1.33 km km^‑2 in the prehistoric landscape to 4.66 km km^-2 in the current landscape, indicating substantial ditching activities in the study regions.

Our study highlights the need for accurate delineation of natural and artificial channel networks in northern landscapes for effective ecosystem restoration and management. We presented an innovative technique for comparing the channel networks between the prehistoric natural landscape and current modified landscape by integrating advanced LiDAR data, extensive manual digitization, and modeling; a highly suitable combination for channel network mapping in dense forest landscapes. The developed methodology can be implemented in any landscape for understanding the extent of human modification of natural channel networks to guide future environmental management activities and policy formulation.

How to cite: Paul, S. S., Hasselquist, E. M., Lidberg, W., and Ågren, A. M.: Mapping of natural and artificial channel networks in forested landscapes using LiDAR data to guide effective ecosystem management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8728, https://doi.org/10.5194/egusphere-egu22-8728, 2022.

Ecodiversity stems from the interaction between the biosphere and the geosphere, and it is one of the necessary conditions for achieving a sustainable planet. Thus, the relationship between geodiversity and biodiversity should be clearly defined. The  relationship between climate and topography in roughened mountain areas at low-latitudes, as constrains for the high values of biodiversity, has already been established. As a consequence, topography is the first and most important input parameter for investigating the connections between abiotic and biotic variety. Spatial analysis in a GIS framework is the key approach to better understand the role of topographic and hydrographic variables in evaluating geodiversity (geomorphodiversity) .

In this paper we focused on analyzing urban areas, where in 2030 60% of the world's population is expected to live. A science of cities is the future challenge for Earth Sciences: urban geomorphology could be the key to have a complete overview on the abiotic and biotic parameters in sustainable cities. To achieve this aim, the conservation of urban biodiversity is fundamental. Analysing the correlation between substantial geodiversity and biodiversity may be a guideline for science of cities and for designing and managing sustainable urban areas.

These ideas, if transposed in an urban context, should go beyond morphometric analysis of topography and take into account anthropogenic features and natural landforms modified by humans in time.  To this end, geomorphological mapping is fundamental to calibrate the quantitative models in a truly multidisciplinary approach to a science of cities and urban biodiversity. We consider our contribution as a new model for the analysis of geodiversity in urban areas.

How to cite: Burnelli, M., Alvioli, M., Melelli, L., and Pica, A.: Geodiversity as a key component for the evaluation of urban biodiversity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9650, https://doi.org/10.5194/egusphere-egu22-9650, 2022.

One of the main environmental threats to sustainability and crop productivity in the agricultural sector is soil erosion. For the mitigation of this problem in agricultural fields, no-till management is considered a key approach. The measurement of soil erosion is particularly challenging, especially when surficial morphological changes are relatively small. Conventional experiments are commonly time-consuming and labour-intensive in terms of both field surveys and laboratory methods. However, the Structure from Motion (SfM) photogrammetry technique has enhanced the experimental activities by enabling the temporal evolution of soil erosion to be assessed through detailed micro-topography. This work presents a multitemporal quantification of soil erosion, using SfM through Uncrewed Aerial Vehicles (UAV) survey for understanding the evolution of no-till (NT) and conventional tillage (CT) in experimental plots. Considering that plot-scale soil surface (mm grid size) by several orders of magnitude, it was necessary to minimise SfM errors (e.g., co-registration and interpolation) in volumetric estimates to reduce noise as much as possible. Therefore, a methodological workflow was developed to analyse and identify the effectiveness of multi-temporal SfM-derived products, e.g. the conventional Difference of Digital Terrain Models (DoDs) and the less used Differences of Meshes (DoMs), for soil volume computations. To recognise the most suitable estimation method, the research validated the erosion volumetric changes calculated from the SfM outputs with the amount of soil directly collected through conventional runoff and sediment measurements in the field. This study presents a novel approach for using DoMs instead of DoDs to accurately describe the micro-topography changes and sediment dynamics. Another key and innovative aspect of this research, often overlooked in soil erosion studies, was to identify the contributing sediment surface, by delineating the channels potentially routing runoff directly to water collectors. The sediment paths and connected areas inside the plots were identified using a multi-temporal analysis of the sediment connectivity index for achieving the volumetric estimates. The DoM volume estimates showed better results with respect to DoDs and a mild overestimation compared to in-situ measurements. This difference was attributable to other factors (e.g., the soil compaction processes) or variables rather than to photogrammetric or geometric ones. The developed workflow enabled a very detailed quantification of soil erosion dynamics for assessing the mitigation effects of no-till management that can also be extended in the future to different scales with low-costs, based on SfM and UAV technologies.

How to cite: Cucchiaro, S., Carretta, L., Nasta, P., Cazorzi, F., Masin, R., Romano, N., and Tarolli, P.: Assessment of soil erosion induced by different tillage practices through multi-temporal geomorphometric analyses, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2000, https://doi.org/10.5194/egusphere-egu22-2000, 2022.

State-of-the-art applications in various earth science domains shows that different classification methods are playing an increasingly important role in mapping due to their improving accuracy. However, in the field of geological mapping, the exclusive use of morphometric and spectral indices in classification models are still often considered as subsidiary mapping tools. This is particularly true in areas where the surface is covered by vegetation and the soil layer is relatively thick, since in such places geological structures can only be observed at first hand at rock outcrops. The aim of our research is to investigate the automatic mapping of rock outcrops in the Dorog Basin in Hungary, where outdated geological maps are currently being updated. In this research, we applied the random forest classification combined with a wider range of input data including satellite imagery and ecosystem information.

The Dorog Basin, located in northern central Hungary, has a medium-density settlement network, with built-up and cultivated areas alternating with areas of wooded or scrub-covered terrain with rugged topography. The region is tectonically fragmented, where former fluvial erosion is of great importance. In several cases the Mesozoic carbonates, Paleogene limestones or limnic coal sequences outcrop the Quaternary sediments resulting a diverse, although a well identifiable surface. In the 86.86 km² study area, the input of the model included 14 morphometrical raster layers derived from SRTM-1, six raster layers with mineral indices derived from Sentinel II, and one ecosystem layer [1], all set to a uniform ~25m resolution. To test the performance of random forest classification in modelling pre-Quaternary formations, we applied two different approaches. In the first one, we used conventional training areas to model pre-Quaternary outcrops, as well as we modelled the physical characteristics of the surface formations. Whereas in the second one, we modelled the pre-Quaternary outcrops and physical characteristics of the surface formations by using randomly selected zones on the study area with around 6000-10000 random training polygons. The randomly generated training polygons were circles of about 1-2 pixels in size around points.  The training areas were derived from the former geological map of the Dorog Basin [2]. The importance of input parameters were also observed for further use. A six-fold cross-validation of the selected training areas showed that the two methods were equally accurate, but the automatic processing of randomly selected training areas was faster.

Based on the modelling results, the pre-Quaternary rock outcrops of the area can be determined with at least 80% confidence using random forest classification. These results will be used in future field mapping, which will also provide a field validation of the method.

From the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.

[1] Ecosystem Map of Hungary. DOI: 10.34811/osz.alapterkep

[2] Gidai, L., Nagy, G., & Sipass, S. (1981). Geological map of the Dorog Basin 1: 25 000. [in Hungarian] Geological Institute of Hungary, Budapest.

How to cite: Pogácsás, R. and Albert, G.: Automatic detection of rock outcrops on vegetated and moderately cultivated areas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10469, https://doi.org/10.5194/egusphere-egu22-10469, 2022.

Remotely piloted aircrafts (UAVs) and Structure-from-Motion photogrammetry (SfM) have become a widely used approach for producing high-resolution topographical measurements of river systems. This approach has the benefit of capturing data over large spatial scales while requiring little time in the field. In small, forested rivers, the dense canopy has hindered the use of remote sensing techniques, limiting topographic data collection to more time-consuming and lower-resolution methods. This complicates monitoring the response of these systems to individual floods, as in many situations there is not enough time to complete more time-consuming surveys between events.

In this study, we pilot the use of sub-canopy UAV surveys (flown at 1-3 m altitude) to monitor the response of a small mountain stream (1-3 m wide) in British Columbia to a sediment pulse generated by the removal of an upstream culvert. Using eleven surveys flown over a three-year period, we track the downstream propagation of the pulse and the subsequent responses in bed topography and roughness along the 240 m reach. We observe a “build-and-carve” response of the channel, where some channel segments aggrade during the first floods after pulse generation, whereas others undergo little morphologic activity. In subsequent floods, these aggradational segments rework through the carving of well-defined channels that release this aggraded sediment downstream. These “build-and-carve” segments serve as temporary storage reservoirs that caused the pulse to fragment as it progressed downstream. The locations of these storage reservoirs were set by the initial channel morphology and the movement of in-stream wood and debris. This study highlights the importance of temporary sediment storage reservoirs for fluvial morphodynamics and provides some insights and suggestions for the future monitoring of forested river systems using sub-canopy drone surveys.

How to cite: McDowell, C., Carina, H., David A., R., and Marwan, H.: Response of a small mountain river to a sediment pulse tracked using sub-canopy UAV surveys, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10675, https://doi.org/10.5194/egusphere-egu22-10675, 2022.

1.    INTRODUCTION

Monitoring land use and land cover change (LULCC) is one of the best methods to understand the interactive changes of agriculture, climate change, and ecological dynamics. In eastern Asia, Taiwan is characterized by high population density, rich biodiversity, and complex terrain. However, recent climate change has impacted the people and ecosystems in Taiwan.  Therefore, we applied landscape metrics and the deep learning U-net semantic segmentation model to enhance the remote sensing images based LULCC monitoring efficiency and take a case study in suburban areas of central Taiwan, a place that plays an important economic role in Taiwan occupied with intensive agricultural activities.

2.    METHOD

This study focuses on six townships in Nantou County in Central Taiwan, where the major agricultural products are rice, tea, and fruit. We obtained four dates of Sentinel-2 images in February for 2018 and 2021 and classified the landscape into five classes: agricultural, forest, built-up, free water bodies, and bare land. The spectral bands information (Blue, Green, Red, NIR), the normalized difference vegetation index (NDVI), and soil-adjusted vegetation index (SAVI) were obtained for establishing the deep learning U-net semantic segmentation model. The accuracy and the loss function of the training model results are 0.89 and 0.02, respectively. In addition, the ground truth data was consulted with the official land-use classification information and the high spatial resolution imagery in Google Earth Pro. Finally, we analysed the classified images' results to detail the study area's changing trajectory to explore the complex spatiotemporal landscape patterns.

3.    RESULTS AND DISCUSSIONS

According to the result, the forest area on the eastern side accounts for more than 70% of the study area. The construction area and the agricultural area have an upward trend during the research period (16% and 5%); in addition, except for the number of patches in free water bodies decreased, all other categories had an upward trend, especially the construction and agricultural area are the largest. The Shannon's Evenness Index reflects that all patches are evenly distributed in space and the area-weighted average fractal dimension index decreases reflecting possible influences of anthropogenic activities. Thus, the results indicate an increasing level of fragmentation, supported by the decrease of the area-weighted average fractal dimension index. In conclusion, using satellite imagery with the deep learning U-net semantic segmentation model can sufficiently discern a detailed LULCC. Furthermore, with the combination of landscape matrix information, the interactions between humans and the environment can be understood better quantitatively.

References

Huete, A. R., Hua, G., Qi, J., Chehbouni, A., & Van Leeuwen, W. J. D., 1992: Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment, 41(2-3), 143-154.

Ronneberger, O., Fischer, P., & Brox, T., 2015: U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D., Deering, W. 1973: Monitoring vegetation systems in the Great Plains with ERTS, ERTS Third Symposium, NASA SP-351 I, pp. 309-317.

How to cite: Zhuang, Z.-H. and Tsai, H. P.: Application of Deep Learning Model to LULCC Monitoring using Remote Sensing Images-A case study in suburban areas of central Taiwan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11764, https://doi.org/10.5194/egusphere-egu22-11764, 2022.

To enhance the understanding of aeolian landforms and their processes, the assessment of origin, migration and evolution of newly-born sand dunes is vital. In this regard, Lake Urmia, in NW Iran, was considered as a representative case study, given that it has lost approximately two-thirds of its water volume in the past two decades and, consequently, the newly-born sand ridges and sand dunes on its western shores were formed. The emerging sand dunes are located close to the villages, adjacent to the agricultural and farmlands, international transit road, and industrial zone, encompassing the whole area. The present study aims to assess the sand dunes’ origin and their migration both in speed and direction in the past decade.

To understand the questions above, remote sensing techniques and in-field studies were coupled. Therefore, wind data from the closest meteorological station were employed to calculate the wind rose, drift potential (DP), the resultant drift potential (RDP), and the resultant drift direction (RDD) across the region. Change detection techniques using high-resolution satellite images were chosen to detect the migration rate and morpho-dynamic changes of Lake Urmia sand dunes. To classify the geomorphological features and land uses in the region, a hybrid supervised classification approach including a customised decision tree classifier was used to distinguish sand dune units from other signatures. Using the minimum bounding geometry method, feature classes were created. These feature classes represent the length, width, and orientation of sand dunes, retrieved after the image classification process. Also, fieldwork surveying was carried out on the sixteen sand dunes in different periods to measure the morphological and evolutionary changes.

 As the wind results show, the trend of DP parameters between the years 2006-2009 and the years 2015-2020, the percentage of wind speeds above the threshold velocity (V>Vt%) to DP has significant gaps, suggestive of weaker winds in those periods. However, between the years 2009-2015, the V>Vt% and DP values are corresponding and coequal. This indicates that the most erosive and shifting winds are between 2009-2015, with the weakest wind power in tails. Moreover, the annual variability of DPt is well correlated with Lake Urmia water level changes; but there is no correlation between the DPt and precipitation amount. The evaluation of image processing results depicted that after 2003, the area of sand dunes had dramatically increased. On average, the smallest area belongs to 2010 (287.3 m2), and the largest area is for years 2019 (775.96 m2), 2018 (739.08 m2), and 2017 (739.74 m2). In addition, between the years 2010 and 2014, a significant increase in area of the sand dunes from 287.25 to 662.8 m2 was observed. The migration rate is the highest between 2010 and 2015, with the lowest values before 2010 and after 2015.

The results of this study have broad implications in the context of sustainable development and climate-related challenges, ecosystem management and policy-making for regions with sand dune challenges, hence crucial insights can be gained by coupling remote sensing techniques and in-situ studies.

How to cite: Ahmady-Birgani, H., Ravan, P., Yao, Z., and Afrasinei, G. M.: Newly-Born Sand Dunes of Lake Urmia: Assessing Migration Rate and Morphodynamic Changes Using Remote Sensing Techniques and Field Studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12041, https://doi.org/10.5194/egusphere-egu22-12041, 2022.