High-resolution Digital Elevation Models Research Articles

Comprehensive Analysis of Landform and Land Use/ Land Cover Mapping of Waghora Watershed Using Sentinal-2B and SRTM-DEM Data

This paper aims to integrate the use of high spectral resolution a remote sensing data of Sentinel-2B imagery along with the Digital Elevation Model (DEM) derived from the SRTM stereo data to delineate the landforms and analyze the land use and land cover in Waghora watershed, Sausar tehsil, Chhindwara district, situated on basaltic terrain in northern Deccan plateau. A 30 m resolution DEM of the study area was generated, capturing terrain parameters like elevation, slope, aspect, hillshade, and drainage. Sentinel-2B (10 m) images from two seasons were collected FCC (false colour composite) and were prepared by ARCGIS software. combination of Sentinel-2B imagery, SRTM-DEM data, and ground truth verifications to delineate eight major landforms in the studied area. Slope information, extracted from high-resolution DEMs, reveals 4 distinct slope classes. The landforms, including Plateau, Escarpment, Foot Slope, Upper Pediment, Lower Pediment, Valley, Interfluve, and Mound, are characterized based on visual interpretation and geological context. Each landform exhibits unique features, elevation ranges, and dominant land use/cover types, such as single crop cultivation, forest, or degraded land. This detailed terrain analysis aids in land-use planning and environmental management initiatives. The land use/land cover analysis utilizes Sentinel-2B data to identify five classes: degraded forest, double crop, single crop, orchard, and land with and without scrubs. Dense forest dominates 24.2% of the watershed, while double crop and single crop lands cover 36.7% and 24.2% respectively. Orchards occupy a minimal 0.8% of the area, and wastelands account for 5.7%. The relationship between landforms, slope, and land use/cover reveals dominant forestation on steep escarpments, while valleys and interfluves support double cropping due to gentle slopes and depositional processesThe study elucidated connections to examine diverse biophysical phenomena. It highlights the effectiveness of employing SRTM-DEM and Sentinel-2B imagery for analyzing geomorphic characteristics and mapping land use/land cover, offering significant perspectives for land management and planning approaches.

Surface topography modelling and ice flow velocity mapping of Dalk Glacier, East Antarctica: application of UAV remote sensing

Abstract. The Antarctic Ice Sheet is the largest potential contributor to global sea level rise due to global climate warming, making it important to monitor changes in the marginal outlet glacier dynamics and surface topography. The unmanned aerial vehicle (UAV), a new platform, has been proven to be a useful tool in glaciological applications because it can collect data in a variety of ways and has a high spatial and temporal resolution compared to traditional ground-based measurements and remote sensing from space. In this study, we used the combined UAV platform composed of the DJI Mavic 3 Enterprise (M3E) and M300 (L1) versions to perform high-accuracy on-site investigations of Dalk Glacier, a typical outlet glacier near the Zhongshan Station in East Antarctica. During the 39th Chinese Antarctic Expedition (CHINARE) in 2022–2023, UAV surveys with multiple sensors were performed. The M3E optical camera makes it possible to reconstruct high-resolution ortho-mosaic and digital elevation models (DEMs) of Dalk Glacier with high accuracy based on photogrammetric principles, helping us to extract the features of glacier surface topography, while dense point clouds derived from the L1 LiDAR camera are used to validate and improve the three-dimensional accuracy. To further monitor the dynamics and stability of Dalk Glacier, we generate ice flow velocity maps using ortho-mosaics for the years 2019, 2020, and 2023. The spatiotemporal changes in ice flow velocities were further compared with the products derived from satellite remote sensing data.

Revealing Subtle Active Tectonic Deformation: Integrating Lidar, Photogrammetry, Field Mapping, and Geophysical Surveys to Assess the Late Quaternary Activity of the Sava Fault (Southern Alps, Slovenia)

We applied an interdisciplinary approach to analyze the late Quaternary activity of the Sava Fault in the Slovenian Southern Alps. The Sava Fault is an active strike-slip fault, and part of the Periadriatic Fault System that accommodated the convergence of Adria and Europe. It is one of the longest faults in the Southern Alps. Using high-resolution digital elevation models from lidar and photogrammetric surveys, we were able to overcome the challenges of assessing fault activity in a region with intense surface processes, dense vegetation, and relatively low fault slip rates. By integrating remote sensing analysis, geomorphological mapping, structural geological investigations, and near-surface geophysics (electrical resistivity tomography and ground penetrating radar), we were able to find subtle geomorphological indicators, detect near-surface deformation, and show distributed surface deformation and a complex fault pattern. Using optically stimulated luminescence dating, we tentatively estimated a slip rate of 1.8 ± 0.4 mm/a for the last 27 ka, which exceeds previous estimates and suggests temporal variability in fault behavior. Our study highlights the importance of modern high-resolution remote sensing techniques and interdisciplinary approaches in detecting tectonic deformation in relatively low-strain rate environments with intense surface processes. We show that slip rates can vary significantly depending on the studied time window. This is a critical piece of information since slip rates are a key input parameter for seismic hazard studies.

UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm × 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

Detection of karst depression in Brazil comparing different semantic and instance segmentations and global digital elevation models

This research assessed the efficacy of deep segmentation in identifying and measuring natural karst depressions in the Bambuí Group's carbonate rocks in Western Bahia, Brazil. The investigation used five Global Digital Elevation Models (DEMs) with 30-m resolution: Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model version 3 (ASTER-GDEM v3), Advanced Land Observing Satellite World 3D – 30 m version 3.2 (AW3D30 v3.2), Copernicus 30 m global DEM (GLO-30), NASA Digital Elevation Model version 1 (NASADEM v1), and Shuttle Radar Topography Mission version 3 (SRTM v3). The karst feature detection analysis compared five semantic segmentation architectures: Feature Pyramid Networks (FPN), LinkNet, UNet, UNet++, DVL3+, using an EfficientNet-B7 backbone, one instance segmentation model (Mask-RCNN), and a semantic-to-instance conversion method. This comparison considers different datasets, including one, two, or eleven variables (DEM, DEM-based sink depth, and nine terrain attributes). Besides, we performed a combinatorial analysis of the DEMs to evaluate the improvement in karst feature detection. The methodology involved the generation of geomorphometric attributes, sample labeling from Sentinel-2 and Operational Land Imager-Landsat 8 datasets, training-validation-testing steps with 128 × 128 samples, reconstruction of large images for semantic and instance segmentation, and accuracy analysis. The findings revealed that GLO-30 and AW3D30 data were the most accurate, while ASTER GDEM performed poorly in both segmentation forms. Among the semantic models, FPN showed the highest accuracy. The 11-variable models preferentially outperformed those with fewer in both types of segmentation. The approach of semantic-to-instance conversion with Geographic Information System tools favored individualizing karst depressions and obtaining efficient quantification, consisting of an easy alternative to achieve instance segmentation. Reconstruction of large remote sensing images through sliding windows considering the specifics of instance and semantic segmentation demonstrated that smaller stride enhances object coverage and reduces the risk of losing crucial information, effectively improving the predictions. The combinatorial analysis for semantic and instance segmentation indicates that models incorporating more DEMs (4- and 5-DEM models) and variables achieve generally higher accuracy. The best result in semantic segmentation was combining the five DEM datasets using 11 variables, reaching an F-score of 85.06 and IoU of 74.00. In instance segmentation, the best result for the bounding box was the model that integrates AW3D30, GLO-30, NASADEM, and SRTM with eleven variables reaching Average Precision (AP) of 45.64, AP50 of 88.40 and AP75 of 39.38, while the best result for the segmentation mask was the model that integrates the five models with eleven variables with AP of 42.61, AP50 of 86.79 and AP75 of 35.43. These results demonstrate that integrating a wide range of DEM data, even the lowest performing ones, improves model generalization and accuracy in segmentation, leveraging strengths and mitigating individual weaknesses. Future work could explore high-resolution DEMs and the integration of various deep-learning methods.

Quantification of the spatial distribution of individual mangrove tree species derived from LiDAR point clouds

AbstractMangrove forests have unquestionably high environmental and ecological value. Mangrove trees are believed to have habitat zonation that is controlled mainly by the relative sea level. However, earlier discussions of mangrove habitats have remained limited in terms of their quality and quantity because of a lack of high-resolution spatial information of microtopography and trees. To clarify mangrove habitability over a wide forest area, we compounded mobile laser scanning (MLS) and aerial laser scanning (ALS) LiDAR dataset of the Miyara River mangrove on Ishigaki Island, Okinawa, Japan. The MLS provided sub-canopy data, while the unmanned aerial vehicle ALS data mainly provided a point cloud of the canopy. We corrected point clouds and combined these data. The results indicated that ALS is unable to reconstruct the microtopography of the dense mangrove area well. Moreover, tree species were not identifiable from the ALS data. However, by applying MLS to the mangrove forest, we obtained high-resolution microtopography and tree information inside the forest, although the measurement area was limited to comparison with ALS. By combining ALS and MLS point clouds, 3D point clouds of the forest were well reconstructed. From these point clouds, a high-resolution digital elevation model was created. Subsequently, we segmented trees individually from composite MLS point clouds and identified each tree species. Consequently, the spatial distribution of thousands of mangrove trees was reconstructed at the Miyara River mouth. The spatial distribution of mangrove tree species together with earlier aerial photographs suggests that mangrove species have been segregated in accordance with changes in their elevation and environment over 40 years. Our findings suggest that the distribution of the species changed sensitively along with dynamic variation of the microtopography.

Tracking magma pathways and surface faulting in the Southwest Rift Zone and the Koaʻe fault system (Kīlauea volcano, Hawai ‘i) using photogrammetry and structural observations

Volcanic islands are often subject to flank instability, resulting from a combination of magmatic intrusions along rift zones and gravitational spreading causing extensional faulting at the surface. Here, we study the Koaʻe fault system (KFS), located south of the summit caldera of Kīlauea volcano in Hawaiʻi, one of the most active volcanoes on Earth, prone to active faulting, episodic dike intrusions, and flank instability. Two rift zones and the KFS are major structures controlling volcanic flank instability and magma propagation. Although several magmatic intrusions occurred over the KFS, the link between these faults, two nearby rift zones and the flank instability, is still poorly studied. To better characterize the KFS and its structural linkage with the surrounding fault and rift zones, we performed a detailed structural analysis of the extensional fault system, coupled with a helicopter photogrammetric survey, covering part of the south flank of Kīlauea. We generated a high-resolution DEM (~ 8 cm) and orthomosaic (~ 4 cm) to map the fracture field in detail. We also collected ~ 1000 ground structural measurements of extensional fractures during our three field missions (2019, 2022, and 2023). We observed many small, interconnected grabens, monoclines, rollover structures, and en-echelon fractures that were in part previously undocumented. We estimate the cumulative displacement rate across the KFS during the last 600 ~ 700 years and found a decrease toward the west of the horizontal component from 2 to 6 cm per year, consistent with GNSS data. Integrating morphology observations, fault mapping, and kinematic measurements, we propose a new kinematic model of the upper part of the Kīlauea’s south flank, suggesting a clockwise rotation and a translation of a triangular wedge. This wedge is bordered by the extensional structures (ERZ, SWRZ, and the KFS), largely influenced by gravitational spreading. These findings illustrate a structural linkage between the two rift zones and the KFS, the latter being episodically affected by dike intrusions.

Inselbergs shaped by collapse: considerations on the structural control on granitic scarps

Inselbergs characterised by fracturing features correspond to the geomorphological manifestation of break-up of the rock mass and collapse. The steep slopes partially covered by angular boulders scattered chaotically reveal, in evolutionary terms, the predominance of the morphostructural controls, given by the presence of veins/dikes, sheeting joints, and vertical fractures. These structural planes in turn create anisotropies guiding fracturing directions and rockfall. In this work, we propose a model of geomorphological evolution of granitic inselbergs subjected to a structural propensity to fracturing. For this purpose, we analysed the Pedra da Galinha Choca inselberg (PGC) and Pedra Faladeira (Quixadá, NE of Brazil) inselbergs, whose morphologies attest to mechanical disintegration processes. Data extraction and interpretation were based on fieldwork and UAV photogrammetry, which provided products such as high-resolution models, orthomosaic, and DEM. Networks of multidirectional fractures were observed to promote an arrangement of interlocking blocks, allowing the maintenance of steep slopes (>60°), in which solutional features tend to be incipient due to the structural instability promoting collapse. Thus, the main features on the hillslopes are cavities like collapse tafone (CT) and large collapsed boulders (LCB). As a result of this structural setting, the inselberg displays sharp outlines, steep slopes, and wide talu deposits.

Qualitative and quantitative comparisons of high resolution DEMs from two different VHR satellite sensors

Qualitative and quantitative comparisons of high resolution DEMs from two different VHR satellite sensors

An On-Site InSAR Terrain Imaging Method with Unmanned Aerial Vehicles.

An on-site InSAR imaging method carried out with unmanned aerial vehicles (UAVs) is proposed to monitor terrain changes with high spatial resolution, short revisit time, and high flexibility. To survey and explore a specific area of interest in real time, a combination of a least-square phase unwrapping technique and a mean filter for removing speckles is effective in reconstructing the terrain profile. The proposed method is validated by simulations on three scenarios scaled down from the high-resolution digital elevation models of the US geological survey (USGS) 3D elevation program (3DEP) datasets. The efficacy of the proposed method and the efficiency in CPU time are validated by comparing with several state-of-the-art techniques.

Accuracy assessment of global DEMs for the mapping of coastal flooding on a low-lying sandy environment: Cassino Beach, Brazil

Projected sea level rise in the 21st century, driven by climate change, threatens low-lying coastal areas with the risk of persistent flooding. Accurately estimating flooding extent and depth is crucial to implementing effective coastal management, with digital elevation models (DEMs) playing a pivotal role. Intrinsic inaccuracies of DEMs might lead to uncertainty in the flooding projections due to SLR scenarios, which are problematic for decision-makers. This work assessed the accuracy of five global digital elevation models for coastal flooding mapping on a low-lying sandy coastal environment in the south of Brazil (Cassino Beach). Five global DEMs were assessed, namely ALOS-AW3D30, ALOS-PALSAR, ASTER, SRTM, and TanDEM-X, and a high-resolution digital elevation model (created using local aerial vehicle (UAV) imagery) was used as a benchmark. A morphometric comparison was conducted between the five global DEMs and the high-resolution local DEMs. Several statistical metrics were calculated, including the root mean squared error, correlation coefficient, and bias.Additionally, flood depths and extent were computed for different scenarios of sea level rise, and comparisons between the different DEMs were quantified. Results show that among the DEMs evaluated, TanDEM-X DEM achieved the highest correlation with the UAV DEM elevation in the beach and dune region, presenting R² equal to 0.56 and RMSE equal to 0.88 m. On the other hand, the ALOS-AW3D30, ALOS-PALSAR, ASTER, and SRTM DEMs presented low correlation and RMSE values above 2.49 m. The analysis of coastal flooding extension and depths confirms that the TanDEM-X DEM exhibited the highest similarity with the UAV DEM across various sea level scenarios, and it was the only global DEM analyzed capable of capturing dune depressions and roads, which serve as primary pathways for flooding in the type of coastal region under consideration.

Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain

This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management.

Capabılıtıes of usıng UAVs to determıne forest road excavatıon volumes ın mountaınous areas

Decision support systems based on spatial data are widely used in some countries for planning production activitiesin forestry and designing forest roads. In recent years, Unmanned Aerial Vehicles (UAVs) have been playingan active role in the production of high resolution and accurate digital elevation models (DEMs) of forested areas.In the preparation of progress payments for forest road construction works, the workload is high, while the feethat has to be paid to the contractor company after the completion of the road construction is determined accordingto the final cost in the progress payment schedules. In this study, the applicability of UAV technology for determiningforest road cut volume and land classification was investigated. A 640-meter section of the forest roadcoded 001 of Taşlıyayla Forest Management Sub-Directorate (Bolu/Türkiye) was examined in the study. Flightswith an UAV (drone) were carried out in autonomous mode before and after the construction of the road examinedin the study. In addition, in order to determine the amount of cut, measurements of the geometric characteristicsof the road were made in the field after the road construction. As a result of the study, 2189 m³ cut volumewas found with the UAV data with an average error of 3 cm. According to the field measurement results, theamount of cut was determined as 2080 m³. A difference of +5% was found between the UAV data and the fieldmeasurement results. In addition, using the JavaScript coding language on the Google Earth Engine platform, soil(46%) and loose soil (54%) land classification was identified from the orthophoto images. The obtained resultsreveal the usability of UAVs in determining forest road volumes.

Automated Estimation of Sub-Canopy Topography Combined with Single-Baseline Single-Polarization TanDEM-X InSAR and ICESat-2 Data

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data.

Topographical dynamics based on global and UAV-SfM derived DEM products: a case study of transboundary Teesta River, Bangladesh

ABSTRACT Topographical changes in riverine floodplains need to be measured for assessing geomorphological dynamics and protecting floodplain areas, although topographic evaluation in disaster-prone floodplain in South Asia has been limited due to the lack of multitemporal, high-definition digital elevation models (DEMs) derived from modern techniques including airborne laser scanning, structure-from-motion (SfM) photogrammetry accompanied with Unmanned Aerial Vehicle (UAV, often referred as drone), and field-based mapping approaches. Here we conducted a preliminary study at two locations of Teesta River (Brahmaputra’s tributary) in Bangladesh using the UAV-SfM techniques and generated high-resolution DEMs. The selected locations represent dynamic changes of sediment and water on the floodplain over the years, and the UAV-SfM approach can be an effective method for monitoring those changes, but an archive of the past data has been unavailable. Here we evaluated the topographic changes by comparing the UAV-SfM-derived DEMs of 2022 with global DEM products (NASADEM of 1999), which are often the only available choice of DEMs in this river floodplain. The elevation differences of these two sets of DEMs were in the range of −5.23 to −84.66 m, and volumetric changes of −4.11 ± 0.15 to −86.25 ± 0.20 million m3, likely dominated by erosional processes towards the left side bank where the elevation errors are supposed to be several meters for UAV-DEM and ca. 5–12 m for NASADEM. Although it is not easy to accurately evaluate the absolute values of the changes, these changes may be associated with the upper basin’s morphodynamics.

Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

Allocation of reservoirs sites for runoff management towards sustainable water resources: Case study of Harirud River Basin, Afghanistan

The intensification of drought conditions in arid and semi-arid regions coupled with the growing population increased water demands (domestic, and irrigation). It signifies the importance of addressing the pressing need for sustainable water resource management. Seventeen criteria spanning topographical, hydrological, geological, environmental, and socio-economic categories, were used for dam site suitability analysis for Harirud River Basin, Afghanistan. Weighted Linear Combination (WLC) model and Geographic Information System (GIS) were employed to perform multi-criteria analysis (MCA) method. The results revealed that the final suitability map exposed 12.31 % highly suitable, 21.0 % moderately suitable, and 66.68 % non-suitable sites, totaling 125.8 km, 214.6 km, and 681.4 km, respectively. Segmentation accuracy assessment was utilized to evaluate the existing dam’s accuracies. The study revealed that accuracies of 84.65 % for the Salma Dam and 88.08 % for the Pashdan Dam were achieved, respectively. Six new dams, along with the existing two, formed the series of eight dams. The dam series were optimized for the total annual runoff of (1,600,000,000 m3), and the final dam heights were determined as follows: 107.5 m, 42 m, 100 m, 23.9 m, 30 m, 18 m, 23.44 m, and 22.65 m for dams (1 to 8), respectively. The study underscores the methodology's superiority in site suitability analysis and runoff optimization, while recommending future focus on observational data and higher-resolution digital elevation model (DEM) files for enhanced precision, environmental impact assessment, climate change considerations, social and economic implications, improved data resolution, and transboundary cooperation.

A Comparison of Multiple DEMs and Satellite Altimetric Data in Lake Volume Monitoring

Lake volume variation is closely related to climate change and human activities, which can be monitored by multi-source remote-sensing data from space. Although there are usually two routine ways to construct the lake volume by the digital elevation model (DEM) or satellite altimetric data combined with the lake area, rarely has a comparison been made between the two methods. Therefore, we conducted a comparison between the two methods in Texas for 14 lakes with abundant validation data. First, we constructed the lake hypsometric curve by five commonly applied DEMs (SRTM, ASTER, ALOS, GMTED2010, and NED) or satellite altimetric products combined with the gauge lake area. Second, the lake volume was estimated by combining the hypsometric curve with the gauge lake area time series. Finally, the estimation error has been quantitatively calculated. The results show that the relative lake volume estimation error (rVSD) of the altimetric data (4%) is only 10–18% of that of the DEMs (22–41%), and the DEM with the highest resolution (NED) has the least rVSD with an average of 22%. Therefore, for large-scale lake monitoring, we suggest the application of satellite altimetric data with the lake area to estimate the lake volume of large lakes, and the application of high-resolution DEM with the lake area to calculate the lake volume of small lakes that are gapped by satellite altimetric data.

Towards the deep learning recognition of cultivated terraces based on Lidar data: The case of Slovenia

Abstract Cultivated terraces are phenomena that have been protected in some areas for both their cultural heritage and food production purposes. Some terraced areas are disappearing but could be revitalised. To this end, recognition techniques need to be developed and terrace registers need to be established. The goal of this study was to recognise terraces using deep learning based on Lidar DEM. Lidar data is a valuable resource in countries with overgrown terraces. The U-net model training was conducted using data from the Slovenian terraces register for southwestern Slovenia and was subsequently applied to the entire country. We then analysed the agreement between the terraces register and the terraces recognised by deep learning. The overall accuracy of the model was 85%; however, the kappa index was only 0.22. The success rate was higher in some regions. Our results achieved lower accuracy compared to studies from China, where similar techniques were used but which incorporated satellite imagery, DEM, as well as land use data. This study was the first attempt at deep learning terrace recognition based solely on high-resolution DEM, highlighting examples of false terrace recognition that may be related to natural or other artificial terrace-like features.

Numerical assessment of the risk of GERD break on the downstream countries

The potential effects of Grand Ethiopian Renaissance Dam (GERD) failure on the downstream countries were numerically assessed. Flow discharge reaching Lake Nasser and the corresponding water levels were determined, and the inundated areas were defined as a result of GERD failure. High-resolution digital elevation model was developed with 2670-km length starting from GERD till Aswan High Dam. GERD’s reservoir, urban, and rural lands downstream of the GERD were presented using a numerical model. The modeled areas included parts of Ethiopia, Sudan, and Egypt, not to mention Toshka Spillway. The model areas were not divided into parts because of calculating the water levels at the existence of intermediate dams as model outputs. In case possible failure of GERD, Roseires and Sennar dams could collapse. Meanwhile, Merowe dam can sustain with the operation of its emergency spillway. Wave height reaches 100-m downstream of the GERD and 64-m and 31-m downstream of the Roseires and Merowe dams, respectively.