SRTM Digital Elevation Model Research Articles

Investigation of Lineament Extraction: Analysis and Comparison of Digital Elevation Models in the Ait Semgane Region, Morocco

An automated lineament extraction model was successfully applied in the mountainous region of Ait Semgane, Morocco, enabling the identification of geological and tectonic linear features. Our research implemented several approaches including Topographic Position Index (TPI), shading, and Digital Elevation Models (DEMs), as input for the lineament extraction algorithm and applied to various Dems. We aimed to compare all of these strategies to determine the optimal method and the most favorable input DEM. Results revealed variable performance among methods, emphasizing the importance of choosing the optimum method based on specific objectives. TPI and Hillshade methods showed high sensitivity in detecting lineaments, while ALOS PALSAR and Sentinel 1 InSAR datasets were effective for subtle features. Lineament density exhibited specific orientations for highly-dissected zones, with TPI highlighting NE-SW and E-W orientations. Lineament orientations demonstrated consistency with established geology, confirming pre-existing tectonic knowledge. Cartographic analysis of faults emphasized the success of the SRTM DEM model with the TPI method, highlighting significant faults. Results also revealed a concentration of faults in the NW and southern sectors, corroborating bibliographic references and the well-documented tectonic setting of the study area. This automated methodology facilitated lineament extraction in unmapped areas, reinforcing the validity of the undertaken cartographic analysis.

Multi-Approaches for Flash Flooding Hazard Assessment of Rabigh Area, Makkah Province, Saudi Arabia: Insights from Geospatial Analysis

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning.

Validation of remote sensing derived ice-thickness with in-situ Lake Bathymetry in the recently vacated area of gepang-gath glacier, Lahaul Himalaya, India

ABSTRACT Field observations provide authenticity in remote sensing studies when it comes to glacierized areas where weather conditions and terrain are not accessible. This study is an attempt to validate the relation between the Randolph Glacier Inventory (RGI) ice thickness and DEM through a field survey in the recently vacated area occupied by the moraine-dammed proglacial lake of the rapidly retreating Gepang-Gath glacier in the Lahaul Himalaya. We used Randolph Glacier Inventory (RGI) version-6 data of ice thickness for Gepang-Gath glacier with SRTM digital elevation model (DEM) of the year 2000 and Sentinel-1 Synthetic Aperture Radar (SAR) interferometry-based DEM of the year 2020 to calculate the lake depth. This relation is validated with ground-based investigation and lake bathymetry data collected using a kayak and metallic nob tied with rope and measuring tape in June 2021 where the maximum depth of the lake is 48 m. The SRTM-DEM profile and ice thickness data suggested that the Gepang-Gath glacial lake will expand ~2 km with a maximum depth of ± 10 m measured near the snout. As this lake is potentially vulnerable, this type of study will help measure lake depth using snout height and vice-versa with the help of DEM data to avoid hazards in feeding glaciers retreating.

Morphometric characteristics of the relief and assessment of erosional hazards of the Kalaus River basin, Russia, based on digital modelling

In this work, the main goal was to analyse the erosional hazards within the boundaries of the Kalaus River catchment area.The paper analyses the erosive state of the region's lands, for which industry and cadastral maps, a digital elevation model and satellite images were used. Official data of ministries and departments were used. Geographic information systems – QGIS and SAGA GIS – served as the basis for modelling. The article presents the results of GIS modeling of erosional hazards in the territory of the Kalaus River basin. Based on the (DEM) SRTM digital elevation model, the basic morphometric indicators of the relief were calculated: steepness of the slopes and vertical and horizontal dissection of the relief. Based on morphometric indicators, an integral calculation of the energy of relief and erosion hazard was carried out and a series of relevant thematic maps was created. The values of slope steepness obtained within the boundaries of the basin range from 0° to 51° with average values of 2.5°. The horizontal dissection varies in the range from 0 to 0.84 km/km2, and the erosion bases are characterized by a range of values from – 15.0 to 248.6 m, with an average value of 33.0 m. The formation of the erosional potential of the basin’s relief is facilitated by the location of a significant part of the region on the spurs of the Stavropol Upland, which have widely developed slopes. Based on the calculated raster of the integral energy index of the relief, 3 categories of erosional hazard are identified. Most of the basin of the Kalaus River (58.4 %) is characterised by a low erosion hazard, another 39.0 % of the territory presents an average erosion hazard, and the remaining 2.9 % belongs to territories with a strong erosion hazard. The classification of relief surface shapes based on the Iwahashi and Pike method, which was carried out on the basis of a DEM, shows a much greater prevalence of steep slopes with high convexity in the southern part of the Kalaus River basin.The identified features of the level of erosional hazard of the lands of the Kalaus River basin are recommended to be taken into account in preinvestment, pre‐design and urban project planning of the territories of administrative units located within the boundaries of the basin. Despite the fact that the results of GIS analysis cannot completely replace field erosion surveys, their value as a tool for planning rational land use is obvious. The results of the work can be used to carry out a similar assessment in other regions, primarily in the organisation of agricultural production.

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.

Takhar Province Morphometric Analysis for the Watershed Management of Amu River Basin in Afghanistan by using Remote Sensing & GIS

The drainage characteristics are basic information for watershed management. The morphometric analysis carried the slope contribution and linear, areal, and relief aspects. Takhar province is 390 km distance from Kabul. Located in the North Eastern Region of the country, the capital of Takhar province is Taluqan (Provincial Center). Badakhshan borders Takhar in the North-East, Panjsher in the south, Baghlan in the South-West and Kunduz in the North-West. Takhar is situated at an elevation of 801 meters above sea level and covers a land area of 12328.684 Km2, representing 1.91 percent of the total Afghan territory. The SRTM (90 m) Digital Elevation Model (DEM), in conjunction with SOI toposheets, have been used to delineate the hydrological boundaries of the study area. The finding variation in the elongated shapes of the basins is due to the guiding effect of faulting and thrusting in the basin. The Rc of the basins is fewer than 1. It indicates that the infiltration rate varies throughout the basin. The Bangi, Farkhar, and Warsaj districts have low Fs, which indicates that there is less side flow for a shorter duration and high main flow for a longer duration. High Fs in Khwaja ghar, Taluqan, and Darqad districts with high side flow for a longer duration and low main flow for a shorter duration causing high peak flows in a shorter duration.

A comparative study of satellite altimetry-based and DEM-based methods for estimating lake water volume changes.

This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 > 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope <1.5°) is not satisfactory. The DEM-based method has complete regional applicability (100%) in the Tibetan Plateau (TP) Lake Region, yet its effectiveness drops significantly in the Xinjiang and Eastern China Plain Lake Regions, with applicability rates of 50 and 40%, respectively.

Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations

The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI.

Assessing Soil Erosion Dynamics in the Bekabad district, Uzbekistan: A Remote Sensing Approach Integrating the RUSLE Model and Google Earth Engine

Soil erosion is a critical environmental issue affecting agricultural productivity and sustainability globally. In the Bekabad district of Uzbekistan, soil erosion, primarily driven by wind and water, poses significant threats to the fertility and stability of agricultural lands. This study employs the Revised Universal Soil Loss Equation (RUSLE) model within the Google Earth Engine (GEE) framework to map and evaluate soil erosion dynamics in Bekabad district over a three-year period (2016-2018). By integrating diverse datasets, including CHIRPS precipitation data, OpenLandMap soil properties, SRTM Digital Elevation Model (DEM) data, Sentinel-2 optical imagery, and MODIS land cover data, we conducted a comprehensive spatial and temporal analysis of soil erosion. The results reveal an overall increase in moderate and slight soil erosion classes, underscoring the dynamic nature of soil erosion processes in the district. These findings highlight the necessity for continuous monitoring and the implementation of effective soil conservation measures, such as vegetative cover, terracing, and contour farming, to mitigate erosion impacts and preserve soil resources.

Mapping inundated bathymetry for estimating lake water storage changes from SRTM DEM: A global investigation

Lake water budgets are sensitive to climate change and human activities and have been undergoing rapid changes worldwide over the past few decades. Satellite altimeters greatly promote the large-scale monitoring of lake level and storage variations. However, limited spatiotemporal coverage of the existing individual altimeters prevents long-term and spatially seamless observations on global lakes. These limitations are partially surmounted by the gridded digital elevation model (DEM) data, which can facilitate spatially contiguous measurements of lake bathymetry exposed at the low-level period. The high-resolution SRTM DEM has been widely used for estimating lake level variations due to its early collection time and reliable data quality, while how many lakes worldwide and to what extent of the newly inundated areas can be monitored based on SRTM DEM remains unexplored. Here, we conduct a quasi-global investigation of SRTM DEM regarding its capability of mapping inundated-area bathymetry and estimating lake storage variations. After generating a global-scale lake inventory (122,444 lakes below 60°N, >1 km2) representing the maximum water inundation extents between 1984 and 2020, the feasibility of mapping lake inundated-area bathymetry was evaluated by comparing the maximum inundated area with the waterbody extent derived from SRTM DEM and analyzing the characteristics of interannual and seasonal variability of each lake. Results show that the maximum exposed lake bathymetries from the SRTM DEM data cover an area of 372,986 km2, accounting for 15.95% of the maximum water area of our investigated lakes. Climate-driven lake expansions or human-induced reservoir impoundments after 2000 (the SRTM mission period) are the main contributors to the higher availability of SRTM DEM-based bathymetry mapping on the Tibetan Plateau, India, eastern China, and Amazon Plains. As the newly launched Surface Water and Ocean Topography (SWOT) mission is anticipated to monitor lake water budget variation at an unprecedented resolution, this study provides a base map for supplementing inundated bathymetry information. The joint use of SRTM and SWOT-based observations will extend the time and space extents of lake-level measurements, thereby enhancing our understanding of lake dynamics at a longer timescale.

Evaluation of vertical accuracy of TanDEM-X Digital Elevation Model in Egypt

The study conducted aimed to examine the accuracy of Digital Elevation Models (DEMs) in Egypt, specifically the TanDEM-X mission's 30 m and 12 m resolution DEMs and the SRTM DEM with a 30 m resolution. The accuracy of DEMs is essential for various civil engineering and surveying applications, especially in geoscience applications. To ensure the comparison's accuracy, the study used ellipsoidal heights instead of orthometric heights, preventing errors caused by global geopotential models in the conversion process. The evaluation of the three DEMs was carried out using 352 GNSS points. The findings indicate that both TanDEM-X DEMs with 30 m and 12 m resolutions outperform the SRTM 30 m in terms of vertical accuracy, making them ideal for geomatic applications that require higher-resolution DEMs. The TanDEM-X 30 m generates a standard deviation (STD) and a Root Mean Square Error (RMSE) of approximately 3.03 m and 3.45 m, respectively. On the other hand, the TanDEM-X 12 m generates an STD and RMSE of approximately 2.86 m and 3.18 m, respectively. Comparatively, the SRTM 30 m produces an STD and RMSE of approximately 4.67 m and 5.35 m, respectively.

Vertical accuracy assessment for open-source digital elevation models using GPS control points and watershed basins delineation using GIS

Digital elevation models (DEMs) are crucial for numerous assessments of the Earth's surface, so this research aim to evaluate the quality of several open source DEMs like advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTM DEM), SENTINEL-1, and TerraSAR-X for digital elevation measurement (TanDEM-X). TanDEM-X is one of the newest DEM models; however, ASTER and SRTM are still the most used. To evaluate the qualities of these DEMs, the vertical accuracies of ASTER, STRM (all 30 m), SRTM, TanDEM-X (90 m), and SENTINEL 1 (range from 5 to 12.5 m) were tested using GPS reference points and The RMSE gave the values of ± 3.56 m ± 57.68 m, ±5.89 m, ±10.21 m and ± 20.25 mfor used SRTM1, SENTINEL-1, SRTM3, TanDEM-X, and ASTER DEMs respectively. As is well known, obtaining a high-quality DEM is exceedingly challenging since it is time-consuming, expensive, and often constrained, therefore this study examined how to enhance SRTM1 as it gave the best result and assessed the effects of increasing the number of points on digital elevation model accuracy by using raster calculator and merge model in ArcGIS. The results showed that the merge model method gave the best result and, by increasing the number of reference points, the accuracy increased. The DEM produced by the enhanced SRTM (iSRTM) demonstrated noticeably better outcomes than the baseline SRTM DEM, with around 3.56 m RMSE.In this research, STRM-1 DEM is used to determine watershed basin delineation. The topographic map showed significant variations in the level of the Earth's surface, as the heights of the area ranged between 20 m and 120 m. The number of watersheds extracted in the Benban is 132 with different areas, the large area is about 72.14 km2, while the small area is about 1.13 km2, and between those areas, there are various areas. The largest recorded watershed circumference reached 48.94 km in area (72.14 km2), while the lowest recorded perimeter is 5.40 km for the area (1.13 km2).

Vulnerability and risk assessment mapping of Bhitarkanika national park, Odisha, India using machine-based embedded decision support system

The vulnerability and flood risk assessment of Bhitarkanika National Park in Odisha, India, was conducted using a data-driven approach and a machine-based embedded decision support system. The park, located in the estuaries of the Brahmani, Baitarani, Dharma, and Mahanadi river systems, is home to India’s second-largest mangrove environment and the world’s most active and diverse saline wetland. To evaluate its vulnerability and risk, various threats were considered, with a focus on floods. Satellite imageries, such as Landsat 8 OLI, SRTM digital elevation model, open street map, Google pro image, reference map, field survey, and other ancillary data, were utilized to develop vulnerability and risk indicators. These indicators were then reclassified into ‘Cost’ and ‘Benefit’ categories for better understanding. The factors were standardized using the max-min standardization method before being fed into the vulnerability and risk model. Initially, an analytical hierarchy approach was used to develop the model, which was later compared with machine learning algorithms (e.g., SVM) and uncertainty analysis indices (e.g., overall accuracy, kappa, map quality, etc.). The results showed that the SVM-RBF machine learning algorithm outperformed the traditional geostatistical model (AHP), with an overall accuracy of 99.54% for flood risk mapping compared to AHP’s 91.12%. The final output reveals that a large area of Bhitarkanika National park falls under high flood risk zone. The Eastern coastal regions of Govindapur, Kanhupur, Chinchri, Gobardhanpur and Barunei fall under high risk zone of tidal floods, The Northern and western regions of Ramachandrapur, Jaganathpur, Kamalpur, Subarnapur, Paramanandapur, etc., Fall under high risk region of riverine floods. The study also revealed that the areas covered with mangroves have a higher elevation and hence are repellent to any kind of flood. In the event of a flood high priority conservation measures should be taken along all high flood risk areas. This study is helpful for decision-making and carrying out programs for the conservation of natural resources and flood management in the national park and reserve forest for ecological sustainability to support sustainable development goals (e.g., SDGs-14, 15).

F2BFE: development of feature-based building footprint extraction by remote sensing data and GEE

ABSTRACT Monitoring the spatiotemporal dynamics of building footprints (BF) is necessary for understanding urbanization growth. It is a difficult task to extract residential sites, mainly BF, because of the complexity of their makeup and spectral variety. Additionally, conventional methods for building mapping typically rely on abundant training data and expertise from human operates. This study presents a new unsupervised Feature-Based Building Footprint Extraction (F2BFE) strategy using Sentinel-1&2 satellite images and the SRTM Digital Elevation Model (DEM). The newly developed radar index (NRI) from Sentinel-1 images was utilized to extract the Primary Building Footprints (PBF) through histogram analysis and thresholding techniques, based on the mean of annual Sentinel-1 VV and VH Backscatter channels in the Ascending orbit. In this research, the integration of the Otsu and Unimodal thresholding technique was developed as an optimal thresholding method for feature extraction. Furthermore, Sentinel-2 images were applied to extract spectral indices related to vegetation (NDVI, GNDVI, RDVI indices), water (NDWI index), and residential/built-up (NDBI, BuEI). The qualitative and quantitative validation results indicate that the NRI-based BF map achieved higher Overall Accuracy (OA) values of 98.14%, 90%, and 91% in Region of Interest-1 (ROI-1), ROI-2, and ROI-3, respectively. Additionally, the Kappa Coefficients (KC) for these regions were 0.96, 0.97, and 0.85, respectively. The NRI index provides an excellent OA result when vegetation, water, and slope features are carefully eliminated. Finally, it can be inferred that the simultaneous use of the sentinel-1&2 and slope data in feature space leads to increased BF accuracy.

Assessment of influencing level of rainfall and physical factors on groundwater level for a semi-arid flat terrain watershed using grid-based geospatial analysis: a case study from Lower Palar Basin, Tamil Nadu, India.

Understanding natural phenomena with the help of modern scientific approaches helps to reach sustainable solutions for current and future water-related problems. In this context, present study aims to assess relative influencing level of physical factors in controlling groundwater level, using a novel grid-based delineation technique, in Lower Palar River Basin, in Kanchipuram and Chengalpattu districts of South India. The influencing factors viz-a-viz: rainfall, soil texture, land use/land cover, terrain slope, geomorphology, lithology, and drainage characteristics were considered for the study. Archived data (2011 to 2020) of monthly rainfall at four rain gauge stations and monthly groundwater level of 22 locations, soil texture, lithology, and geomorphology data were considered for the study. SRTM digital elevation model with 30-m resolution was used for analyzing drainage characteristics and terrain slope. Thematic maps for considered factors were prepared, using common grid delineation method in GIS platform that divided study area into 52 grids, to inter-relate the discrete and continuous parameters with groundwater level. Results indicate that level of influence increases in the order of precipitation followed by lithology, land use/land cover, terrain slope, geomorphology, infiltration number, and soil texture. The study shows groundwater resilience is highly influenced by soil texture and infiltration number compared to other factors considered. It can be concluded that grid-based delineation successfully identifies grids with significant influence of individual factors by comparing with groundwater resilience. Common grid-based delineation method proves to be more effective in assessing groundwater resilience and can be used more efficiently in groundwater studies.

High-temporal-resolution monitoring of reservoir water storage of the Lancang-Mekong River

The Lancang-Mekong River (LMR) is an important transboundary river in Southeast Asia shared by China, Myanmar, Laos, Thailand, Cambodia, and Vietnam from upstream to downstream. Construction and operation of dams in the LMR basin has profoundly affected its natural streamflow regime. It is therefore important to monitor changes in reservoir water storage and to quantify the impact of reservoir operation on the redistribution of surface water resources over this basin. Given the difficulty in obtaining in-situ measurements of reservoirs on the LMR, we integrated multisource remote sensing, including satellite altimetry and optical and synthetic aperture radar (SAR) images, to generate weekly water levels and water storages of nine largest reservoirs on the main stem of the LMR from 2017 to 2021. Specifically, partial surface water extent (SWE) of reservoirs was extracted from Sentinel-1 SAR images and digital elevation models (DEMs), using Random Forest algorithms trained by partial SWE derived from Sentinel-2 optical images, showing an overall accuracy higher than 95%. Based on the partial SWE and water level estimates from ICESat-2 and Global Ecosystem Dynamics Investigation (GEDI, International Space Station-based) data, the relationships between water levels and partial SWE were derived to convert partial SWE into water level time series. Furthermore, water storage time series of the nine reservoirs were obtained from water level time series and hypsometric functions derived from SRTM DEMs that were corrected by ICESat-2 data to remove systematic errors. For the Xiaowan Reservoir on the Lancang River, there is close agreement between remote sensing-derived water levels and in-situ water levels in terms of a normalized RMSE lower than 5%. Results indicate that multisource remote sensing has large potential for high-temporal-resolution monitoring of reservoir water levels and water storage. This could more precisely evaluate impacts of cascade reservoirs on the streamflow of the LMR and facilitate drought and flood mitigation for riverine countries.

Spatial variability in melting on Himalayan debris-covered glaciers from 2000 to 2013

Glaciers covered by debris are widespread in the Himalayas. Debris layer makes the melting process of glaciers complicated, and the correlation between the spatial melt differences of glacier and its surface features is still not clear. The aim of this study was to identify possible causes of thickness changes for Himalayan debris-covered glaciers. Firstly, three high resolution Digital Elevation Model (DEM) datasets including SRTM DEM, AW3D and TanDEM-X DEM were used to estimate glacier thickness changes from 2000 to 2013. By checking and removing geometric displacements, systematic deviations and elevation-related bias, as well as correction of radar wave penetration depth in the clean glacier areas, reliable thickness change information in this complex glaciated region were provided. Then debris thicknesses on the glaciers were estimated. The surface lowering caused by ice cliffs or supraglacial ponds significantly reduces debris thickness. Finally, some surface features including debris thicknesses, spatial patterns of supraglacial ponds and ice cliffs extracted from high-resolution satellite images in different times, were comprehensively used to evaluate the main drivers of glacier melting differences. Results demonstrate that spatial variability of glacier melting in Himalayas is caused by the combined effects of debris thicknesses, ice cliffs and supraglacial lakes, with different dominant factors for different regions. The thickened glacier region, which mostly appears above 5300 m a.s.l., with minimal debris cover, displayed large patches of thickening and partial serious thinning in the debris-free areas. Although the thick debris layer (> 0.20 m) would slow down the glacier melting at the low-elevations, it leads to the heavy glacier thinning for the regions located 5000–5300 m a.s.l., where there are dense ice cliffs. In some areas with >1 m of average debris thickness, glaciers have thinned slowly because of erosion from the ice cliffs or large supraglacial ponds. Area and number of cliffs have proven to be key factors in controlling regional ablation for debris-covered glaciers, and supraglacial lakes serve as assistance.

Morphometric analysis of the large en- closed depression of the Southern East European plain

The results of morphometric analysis based on the SRTM digital elevation model of large enclosed depressions (LEDs) of controversial origin, commonly found on the loess interfluves in the Northern Black Sea region, around the Sea of Azov, at the Western flanks of Caucus Mountains and in the Lower Don basin, are presented in the paper. We have registered 312 LEDs landforms. The morphometric characteristics of landforms vary from 0.4 to 216 km2 for area, from 1 to 21 m for depth, from 0.5 to 13.3 km for width, from 0.7 to 27.5 km for length, from 1 to 4 for elongation coefficient, and from 3.3 to 103.3 m a.s.l for height. The most common depressions have the following parameters: area 24 km2; depth 23 m; width 1.01.5 km; length 2.53.0 km; elongation coefficient 1.21.4; height 1520 m a.s.l. There is a correlation between the area and depth of the depressions. The depressions' shape is mostly elongated, e. g. teardrop-shaped, eggshaped, elliptical, triangular, and rarely round. The sharp ends of the egg-shaped depressions always tend to point to the north, and the blunt ones to the south. We grouped the depressions into seven restricted sites where the differences in size and other morphological features of the LEDs are very small. Within all sites, there is a high consistency of orientation of the long axes of the depressions. The largest depressions around the Sea of Azov and the Western flanks of Caucus Mountains are characterized by longitudinal ridges confined to western side of LEDs. Comparison analysis of sites demonstrated a fan-shaped pattern in changing of the long axes orientation from the NW in the Northern Black Sea region; to the East in the Azov Sea region; to the N in the Western flanks of Caucus Mountains; and the NE in the Central flanks of Caucus Mountains. A radial-centripetal pattern of the erosion network is observed across the territories where LEDs are distributed. Small erosive forms flowing into the center of depressions are represented by very flat and wide gullies and hollows with intermitted channel flow. Such morphological characteristics suggest the relict origin of the erosional forms and, as a result, indicates the pre-Holocene age of the depressions themselves. Morphological and geological data suggests that wind erosion was probably the main factor in the formation of LEDs.

DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics

China’s first optical stereo mapping satellite with a sub-meter resolution, GaoFen-7 (GF-7), launched in November 2019, shows significant potential for providing high-resolution topographic and geomorphic data for quantitative research on active tectonics. However, no studies have evaluated the capability of the GF-7-generated digital elevation model (DEM) for quantitatively studying active tectonics. This study aimed to validate the accuracy of the DEMs extracted from GF-7 stereo imagery, with or without ground control points (GCPs), and evaluated the potential of applying GF-7 DEMs to active tectonics. First, GF-7 stereo images were processed to obtain DEMs with a spatial resolution of 2 m, utilizing three different methods, including block adjustment without GCPs, block adjustment with the aid of Google Earth images and SRTM DEM, and block adjustment with GCPs derived from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data. These three generated DEMs were called GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3, respectively, and were verified by the airborne LiDAR data in the Hasishan section of the Haiyuan fault. Second, the capability of the GF-7 DEMs for identifying active faults, fault scarps, and horizontal offsets was evaluated. Finally, 8 vertical and 13 horizontal offsets were measured based on three different GF-7 DEMs, and airborne LiDAR data were used to verify the measurements’ accuracies. The results indicated that the accuracy of GF-7 DEMMethod1 was the worst and that of GF-7 DEMMethod3 was superior to that of GF-7 DEMMethod2. The GF-7 DEMs could effectively identify the apparent fault scarps and horizontal offsets. The RMSE values of the vertical offsets measured based on GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3 were 0.55 m, 0.55 m, and 0.41 m, respectively. The horizontal offsets yielded RMSE values of 3.98 m, 2.52 m, and 1.37 m, respectively. These findings demonstrated that vertical and horizontal offsets could be accurately measured using the DEMs generated from GF-7 stereo images. Meanwhile, our study indicated that the GCPs derived from ICESat-2 data could be utilized to improve the accuracies of the GF-7 DEM, and the measurements of vertical and horizontal offsets.

Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)

Abstract Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.