High-resolution Digital Elevation Model Data Research Articles

Effects of input data accuracy, catchment threshold areas and calibration algorithms on model uncertainty reduction

Effects of input data accuracy, catchment threshold areas and calibration algorithms on model uncertainty reduction

A machine learning approach to the geomorphometric detection of ribbed moraines in Norway

Abstract. Machine learning is a powerful yet underutilised tool in geomorphology, commonly used for image-based pattern recognition. Analysing new high-resolution (1–10 m) elevation datasets, we investigate its usefulness for detecting discrete geomorphological features. This study develops a machine-learning-based method for identifying ribbed moraines in digital elevation data and progresses to test its performance versus time-consuming, manual methods. Ribbed moraines share geomorphometric characteristics with other glacial landforms, hence representing a valuable test of our new methodology in terms of differentiating between similar features, and for detecting landforms with similar characteristics. Furthermore, mapping ribbed moraines may provide valuable indications of their origin, a topic of debate within glacial geomorphology. To automatically detect ribbed moraines, we extract simple morphometrics from high-resolution digital elevation model data and mask regions where ribbed moraines are unlikely to form. We then test several machine learning algorithms before examining the best performer (K-means clustering) for three study areas of 15 km2 in Norway. Our results demonstrate a balanced accuracy of 65 %–75 % when validating versus ground-truthing. The performance depends on the availability of high-resolution elevation data in Norway that are needed to resolve the spatial scale of the target (10–100 m). We find the method effective at detecting both fields of ribbed moraines, as well as individual ribbed moraines. We propose pathways for the future implementation of this method on a large scale and for increasing the detail of information gained about detected landforms. In conclusion, we demonstrate K-means clustering as a promising method for detecting ribbed moraines, with great potential to reduce the time needed to produce landform maps.

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.

Combining remote sensing and social media data for flood mapping: a case study in Linhai, Zhejiang Province, China

The rapid mapping of flood inundation is essential for timely assessment of damage and post-disaster recovery. Remote sensing technology provides clear spatial information for mapping flood inundation, but its real-time (RT) images are often not available due to the severe weather conditions during the disaster events; the combination of RT data can better compensate for the deficiency. In this study, a near RT (NRT) flood inundation probability mapping method based on post-event NRT remote sensing data combined with RT volunteer geographic information (VGI) was proposed for mapping the 2019 flood in Linhai City, Zhejiang Province. First, a probabilistic index distribution (PID) layer was constructed from high-resolution digital elevation model data using an inverse distance-weighted height filter based on each VGI point. Then, a quality evaluation method for non-reference data was introduced to evaluate the validity of the VGI data. The final flood probability map was generated by PID weighting. The results show that, by fusing NRT images and RT data, the proposed model for mapping flood inundation probability is more robust and enhances the spatial characteristics of the flood inundation probability index, allowing it to enable emergency responders to quickly identify areas requiring urgent attention.

Deep learning-based automated terrain classification using high-resolution DEM data

Landforms are a fundamental component of the natural environment, and digital terrain mapping on a large spatial scale is important when studying landforms. In this study, we adopted a semantic segmentation model in computer vision to classify elementary landform types using AW3D30 digital elevation model (DEM) data. We built a semantic segmentation model with an FCN-ResNet architecture that extracts features using a residual network (ResNet) and obtains pixel-level segmentation of the DEM using a fully convolutional network (FCN). A lightweight decoder based on skip connections was adopted to maintain detailed information at different scales. We used the 1:1,000,000 Chinese landform map as the label and tested different combinations of terrain factors. The experiments indicate that increasing the terrain factors has no significant influence on the model, and the semantic information can be learned using only DEM data. The model has strong feature extraction capability and is tolerant to noise and error. The results of landform category prediction confirm that deep learning methods have strong potential for landform classification and will have great application prospects in the field of geomorphological research.

Risk Assessment of Sea Level Rise for Karasu Coastal Area, Turkey

Sea Level Rise (SLR) due to global warming is becoming a more pressing issue for coastal zones. This paper presents an overall analysis to assess the risk of a low-lying coastal area in Karasu, Turkey. For SLR scenarios of 1 m, 2 m, and 3 m by 2100, inundation levels were visualized using Digital Elevation Model (DEM). The eight-side rule is applied as an algorithm through Geographic Information System (GIS) using ArcMap software with high-resolution DEM data generated by eleven 1:5000 scale topographic maps. The outcomes of GIS-based inundation maps indicated 1.40%, 6.02%, and 29.27% of the total land area by 1 m, 2 m, and 3 m SLR scenarios, respectively. Risk maps have shown that water bodies, low-lying urban areas, arable land, and beach areas have a higher risk at 1 m. In a 2 m scenario, along with the risk of the 1 m scenario, forests become at risk as well. For the 3 m scenario, almost all the territorial features of the Karasu coast are found to be inundated. The effect of SLR scenarios based on population and Gross Domestic Product (GDP) is also analyzed. It is found that the 2 and 3 m scenarios lead to a much higher risk compared to the 1 m scenario. The combined hazard–vulnerability data shows that estuarine areas on the west and east of the Karasu region have a medium vulnerability. These results provide primary assessment data for the Karasu region for the decision-makers to enhance land use policies and coastal management plans.

HORAYZON v1.2: an efficient and flexible ray-tracing algorithm to compute horizon and sky view factor

Abstract. Terrain parameters like topographic horizon and sky view factor (SVF) are used in numerous fields and applications. In atmospheric and climate modelling, such parameters are utilised to parameterise the effect of terrain geometry on radiation exchanges between the surface and the atmosphere. Ideally, these parameters are derived from a high-resolution digital elevation model (DEM) because inferring them from coarser elevation data induces a smoothing effect. Computing topographic horizon with conventional algorithms, however, is slow because large amounts of non-local terrain data have to be processed. We propose a new and more efficient method, which is based on a high-performance ray-tracing library. The new algorithm can speed up horizon calculation by 2 orders of magnitude relative to a conventional approach. By applying terrain simplification to remote topography, the ray-tracing-based algorithm can also be applied with very high-resolution (<5 m) DEM data, which would otherwise induce an excessive memory footprint. The topographic horizon algorithm is accompanied by an SVF algorithm, which was verified to work accurately for all terrain – even very steep and complex terrain. We compare the computational performance and accuracy of the new horizon algorithm with two reference methods from the literature and illustrate its benefits. Finally, we illustrate how sub-grid SVF values can be efficiently computed with the newly derived horizon algorithm for a wide range of target grid resolutions (1–25 km).

Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks.

High-resolution digital elevation models (DEMs) play a critical role in geospatial databases, which can be applied to many terrain-related studies such as facility siting, hydrological analysis, and urban design. However, due to the limitation of precision of equipment, there are big gaps to collect high-resolution DEM data. A practical idea is to recover high-resolution DEMs from easily obtained low-resolution DEMs, and this process is termed DEM super-resolution (SR). However, traditional DEM SR methods (e.g., bicubic interpolation) tend to over-smooth high-frequency regions on account of the operation of averaging local variations. With the recent development of machine learning, image SR methods have made great progress. Nevertheless, due to the complexity of terrain characters (e.g., peak and valley) and the huge difference between elevation field and image RGB (Red, Green, and Blue) value field, there are few works that apply image SR methods to the task of DEM SR. Therefore, this paper investigates the question of whether the state-of-the-art image SR methods are appropriate for DEM SR. More specifically, the traditional interpolation method and three excellent SR methods based on neural networks are chosen for comparison. Experimental results suggest that SRGAN (Super-Resolution with Generative Adversarial Network) presents the best performance on accuracy evaluation over a series of DEM SR experiments.

A Vehicle-Based Laser System for High-Resolution DEM Development – Performance Evaluation of System Components.

Surface microtopography is quantified by the deviations in the direction of the normal vector of the surface. Soil microtopography is a major factor influencing soil erosion by water and wind. Surface microtopography can be accurately described using the Digital Elevation Model (DEM). In this study, a vehicle-based laser system was developed to generate high-resolution DEM data. The system consisted of five major components: a laser line scanner, a gyroscope sensor, a real-time kinematic GPS, a frame-rail mechanism, and a data acquisition and control unit. A series of experiments were conducted in the laboratory to evaluate the performance of the components. The result of distance measurements indicated that the system gave the most consistent distance measurement on a white paper. Static gyroscope sensor accuracy tests showed that angle measurement errors observed in combined pitch/roll rotations were larger than in single rotations. Within ±30º of single rotations, the measurement errors for pitch and roll angles were within 0.8º and 0.4º, respectively. The tests of the angular displacement on the linear rail showed that it slightly tilted towards the laser line scanner when it moved along the rail.

Sensitivity Assessment of Spatial Resolution Difference in DEM for Soil Erosion Estimation Based on UAV Observations: An Experiment on Agriculture Terraces in the Middle Hill of Nepal

Soil erosion in the agricultural area of a hill slope is a fundamental issue for crop productivity and environmental sustainability. Building terrace is a very popular way to control soil erosion, and accurate assessment of the soil erosion rate is important for sustainable agriculture and environmental management. Currently, many soil erosion estimations are mainly based on the freely available medium or coarse resolution digital elevation model (DEM) data that neglect micro topographic modification of the agriculture terraces. The development of unmanned aerial vehicle (UAV) technology enables the development of high-resolution (centimeter level) DEM to present accurate topographic features. To demonstrate the sensitivity of soil erosion estimates to DEM resolution at this high-resolution level, this study tries to evaluate soil erosion estimation in the Middle Hill agriculture terraces in Nepal based on UAV derived high-resolution (5 × 5 cm) DEM data and make a comparative study for the estimates by using the DEM data aggregated into different spatial resolutions (5 × 5 cm to 10 × 10 m). Firstly, slope gradient, slope length, and topographic factors were calculated at different resolutions. Then, the revised universal soil loss estimation (RUSLE) model was applied to estimate soil erosion rates with the derived LS factor at different resolutions. The results indicated that there was higher change rate in slope gradient, slope length, LS factor, and soil erosion rate when using DEM data with resolution from 5 × 5 cm to 2 × 2 m than using coarser DEM data. A power trend line was effectively used to present the relationship between soil erosion rate and DEM resolution. The findings indicated that soil erosion estimates are highly sensitive to DEM resolution (from 5 × 5 cm to 2 × 2 m), and the changes become relatively stable from 2 × 2 m. The use of DEM data with pixel size larger than 2 × 2 m cannot detect the micro topography. With the insights about the influencing mechanism of DEM resolution on soil erosion estimates, this study provides important suggestions for appropriate DEM data selection that should be investigated first for accurate soil erosion estimation.

How much spatial resolution do we need to model a local flood event? Benchmark testing based on UAV data from Biga River (Turkey)

Digital elevation models (DEMs) are important in flood modelling as they present surface information and obstacles giving the direction of water during a flood event. All hydraulic modelling software needs both channel and floodplain elevation data to simulate the water flow from upstream to downstream. Based on the accuracy and representative capacity of the surface, modelling results can be evaluated in terms of hazard and risk analysis. Developments in remote sensing and GIS technology provide many sources to freely obtain DEM data of different resolution and accuracy. However, access to and the processing of high-resolution data associated with large areas is difficult and time-consuming. This study, therefore, focuses on determining the availability of different resolutions and DEM sources in flood modelling by testing unmanned aerial vehicle (UAV) data. For this, freely available DEMs such as ALOS DEMs (12.5 and 30 m), EU-DEM (25 m), ASTER GDEM (30 m), SRTM (30 and 90 m), MERIT DEM (90 m) and TANDEM-X (90 m) were chosen to investigate part of Biga River (Turkey) which features a natural channel. Then, 1D flood modelling was examined using different DEM resolutions, Manning’s values and flood data of various frequencies (10, 100 and 1000 years). Results were tested using high-resolution DEM data (5 cm) gathered from a UAV. It was concluded that EU-DEM is the most suitable dataset in terms of flood extent, depth and velocity. However, depending on the boundary characteristics of the data, ALOS 12.5 DEM and MERIT DEM (90 m) data are the most suitable in their extent, depth and velocity for high-resolution and low-resolution flood modelling worldwide extent.

Geological features of landslides caused by the 2018 Hokkaido Eastern Iburi Earthquake in Japan

Abstract Before dawn on 6 September 2018, a powerful earthquake with a Japan Meteorological Agency magnitude ( M j ) of 6.7 hit central Hokkaido, causing more than 6000 landslides. As human damage, 36 of the 44 fatalities from the earthquake were from earthslides in Atsuma Town. Most slope movements were shallow earthslides of mantle-bedded tephra and soil layers, but some were deep rockslides involving basement rocks such as shale and mudstone of Miocene. Although the shallow earthslides were easily distinguishable in photos from satellites or airplane, the rockslides were more difficult to identify owing to their vegetation. Based on the quick interpretation of a high-resolution (0.5 m horizontal resolution) shaded relief map made from digital elevation model data by airborne laser survey and supplemental field surveys, we effectively identified 259 rockslides (196 certain ones and 63 possible ones) in Atsuma and Mukawa towns under newly invented identification criteria based on the scarp depth and positional relation between scarp and ridge topography. It was revealed that most rockslides were distributed within 10 km from the epicentre, while earthslides were distributed until 20 km north of the epicentre, and they seemed to be controlled by the thickness of mantle-bedded tephra and the soil layer. We also identified many traces of past rockslides and earthslides. The results show the possibilities for effective measurement of slope by clarifying the landslide distribution both this earthquake and past ones using high-resolution digital elevation model data.

EVALUATION OF DIGITAL ELEVATION MODELS FOR GEOMORPHOMETRIC ANALYSES ON DIFFERENT SCALES FOR NORTHERN CHILE

Abstract. The resolution and accuracy of digital elevation models (DEMs) have direct influence on further geoscientific computations like landform classifications and hydrologic modelling results. Thus, it is crucial to analyse the accuracy of DEMs to select the most suitable elevation model regarding aim, accuracy and scale of the study. Nowadays several worldwide DEMs are available, as well as DEMs covering regional or local extents. In this study a variety of globally available elevation models were evaluated for an area of about 190,000 km2. Data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30 m and 90 m, Advanced Land Observing Satellite (ALOS) World 3D 30 m and TanDEM-X WorldDEM™ – 12 m and 90 m resolution were obtained. Additionally, several very high resolution DEM data were derived from stereo satellite imagery from SPOT 6/7 and Pléiades for smaller areas of about 100–400 km2 for each dataset. All datasets were evaluated with height points of the Geoscience Laser Altimeter System (GLAS) instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite on a regional scale and with nine very high resolution elevation models from UAV-based photogrammetry on a very large scale. For all datasets the root mean square error (RMSE) and normalized median absolute deviation (NMAD) was calculated. Furthermore, the association of errors to specific terrain was conducted by assigning these errors to landforms from the topographic position index (TPI), topographic roughness index (TRI) and slope. For all datasets with a global availability the results show the highest overall accuracies for the TanDEM-X 12 m (RMSE: 2.3 m, NMAD: 0.8 m). The lowest accuracies were detected for the 30 m ASTER GDEM v3 (RMSE: 8.9 m, NMAD: 7.1 m). Depending on the landscape the accuracies are higher for all DEMs in flat landscapes and the errors rise significantly in rougher terrain. Local scale DEMs derived from stereo satellite imagery show a varying overall accuracy, mainly depending on the topography covered by the scene.

A spatio-contextual probabilistic model for extracting linear features in hilly terrains from high-resolution DEM data

ABSTRACTThis article introduces our research in developing a probabilistic model to extract linear terrain features from high resolution Digital Elevation Models (DEMs). The proposed model takes full advantage of spatio-contextual information to characterize terrain changes. It first derives a quantifiable measure of spatio-contextual patterns of linear terrain features, such as ridgelines, valley lines and crater boundaries, and then adopts multiple neighborhood analysis and a probability model to address data uncertainty in terrain surface modeling. Different from traditional approaches, the proposed model has the ability to achieve near-automated processing. It also supports effective extraction of terrain features in both smooth and rough surfaces. Through a series of experiments, we demonstrate that the proposed approach outperforms existing techniques, including thresholding, stream/drainage network analysis, visual descriptor detection, object-based image analysis and edge detection. This work contributes to both the geospatial data science and geomorphology communities with a new way of utilizing high-resolution imagery in terrain analysis.

Assessing Surface Flowpath Interception by Vegetative Buffers Using ArcGIS Hydrologic Modeling and Geospatial Analysis for Rock Creek Watershed in Central Iowa

Abstract. Nonpoint-source (NPS) pollution is a major cause of surface water quality degradation due to the transport of chemicals, nutrients, and sediments into lakes and streams. Vegetative buffers comprise several effective landscape best management practices (BMPs) that include vegetative filter strips (VFS) and grassed waterways. However, some BMPs are less effective due to concentrated surface flow, improper cropland-to-VFS area ratios, and surface flowpaths that partially or completely bypass vegetative buffers. The overall objective of this study was to quantify the accuracy of simulated flowpaths relative to observed and global positioning system (GPS)-assisted ground-truthed surface flowpaths for improved placement of VFS and other vegetative buffers to effectively intercept surface runoff. This study was conducted on three research sites in Rock Creek watershed in central Iowa. Geographic information system (GIS) software was used for flowpath hydrologic modeling and geospatial map comparison analysis. Digital elevation model (DEM) datasets were used for flowpath simulation and included internet-available USGS 30 m × 30 m grid (typically used to design and site VFS buffers) and light detection and ranging (LiDAR) 5 m × 5 m grid DEMs. Results from this study indicate that the LiDAR 5 m × 5 m DEM generated significantly more accurate simulated flowpaths than the USGS 30 m × 30 m DEM. These results quantitatively underscore the efficacy of using high-resolution LiDAR DEM data to more accurately determine how well surface flowpaths are intercepted by VFS and other vegetative buffers. These results also demonstrate the benefits of coupling high-resolution aerial imagery with quantitative geospatial map comparison data to improve visualization and comparison of field-scale and watershed-scale hydrologic and terrestrial attributes. Ultimately, the results and procedures from this study will be applied to the development of a novel cloud-based, user-interactive, virtual-reality decision support (DS) tool that can be used to remotely assess hydrologic landscape conditions, prescribe improvements to existing BMPs, and determine new sites for enhanced BMP placement and functionality within a high-resolution 3-D imagery environment. Keywords: ArcGIS, Best management practices (BMPs), Decision support (DS) tool, Digital elevation model (DEM), Geospatial analysis, Light detection and ranging (LiDAR), Nonpoint-source (NPS) pollution, Surface runoff, Vegetative filter strip (VFS), Watershed hydrol

The Preliminary Investigation on the Uncertainties Associated With Surface Solar Radiation Estimation in Mountainous Areas

In mountainous areas, surface solar radiation (SSR) exhibits high spatiotemporal variation at different slopes and aspects due to its great topographic relief. To get mountain SSR spatial distribution, remote sensing-based methods have been popularly used, which separate SSR into direct solar radiation, diffuse sky radiation, and adjacent terrain radiation. However, the methods are highly depended on the atmospheric and angular information derived from different data sources. To clearly address the uncertainties associated with the estimation, this letter conducted a preliminary comparison study by using different atmospheric transmittance models and digital elevation model (DEM) data to retrieve SSR in the Mt. Gongga region. The comparison results indicated that the uncertainty of the atmospheric constituent data greatly limited the performance of the physical atmospheric transmittance models. The resolution of DEM data also played an important role in SSR determination because of the determination of surface angular information. High-resolution (30-m) DEM data showed better performance than low one (90 m). In addition, the systematic underestimation of SSR estimation with the empirical model was significantly improved by using the averaging method with nearby pixel values. It indicated that the geometric errors of satellite image and DEM data should be considered in the estimation.

DEM Fusion of Elevation REST API Data in Support of Rapid Flood Modelling

Digital elevation models (DEM) are an integral part of flood modelling. High resolution DEM data are not always available or affordable for communities, thus other elevation data sources are explored. While the accuracy of some of these sources has been rigorously tested (e.g., SRTM, ASTER), others, such as Natural Resources Canada’s Canadian Digital Elevation Model (CDEM) and Google and Bings’ Elevation REST APIs, have not yet been properly evaluated. Details pertaining to acquisition source and accuracy are often unreported for APIs. To include these data in geospatial applications and test and reduce uncertainty, data fusion is explored. Thus, this paper introduces a new method of elevation data fusion. The novel method incorporates clustering and inverse distance weighting (IDW) concepts in the computation of a new fusion elevation surface. The results of the individual DEMs and fusion DEMs are compared to high-resolution Light Detection and Ranging (LiDAR) surface and flood inundation maps for two study areas in New Brunswick. Comparison of individual surfaces to LiDAR find that the results meet their posted accuracy specifications, with the Bing data computing the smallest mean bias and the CDEM the smallest RMSE. Fusion of all three surfaces via the proposed method increases the correlation and minimizes both RMSE and mean bias when compared to LiDAR, independent of the terrain, thus producing a more accurate DEM.

A New Rapid Simplified Model for Urban Rainstorm Inundation with Low Data Requirements

This paper proposes a new rapid simplified inundation model (NRSIM) for flood inundation caused by rainstorms in an urban setting that can simulate the urban rainstorm inundation extent and depth in a data-scarce area. Drainage basins delineated from a floodplain map according to the distribution of the inundation sources serve as the calculation cells of NRSIM. To reduce data requirements and computational costs of the model, the internal topography of each calculation cell is simplified to a circular cone, and a mass conservation equation based on a volume spreading algorithm is established to simulate the interior water filling process. Moreover, an improved D8 algorithm is outlined for the simulation of water spilling between different cells. The performance of NRSIM is evaluated by comparing the simulated results with those from a traditional rapid flood spreading model (TRFSM) for various resolutions of digital elevation model (DEM) data. The results are as follows: (1) given high-resolution DEM data input, the TRFSM model has better performance in terms of precision than NRSIM; (2) the results from TRFSM are seriously affected by the decrease in DEM data resolution, whereas those from NRSIM are not; and (3) NRSIM always requires less computational time than TRFSM. Apparently, compared with the complex hydrodynamic or traditional rapid flood spreading model, NRSIM has much better applicability and cost-efficiency in real-time urban inundation forecasting for data-sparse areas.

Integration of 2-D hydraulic model and high-resolution lidar-derived DEM for floodplain flow modeling

Abstract. The rapid progress of lidar technology has made the acquirement and application of high-resolution digital elevation model (DEM) data increasingly popular, especially in regards to the study of floodplain flow. However, high-resolution DEM data pose several disadvantages for floodplain modeling studies; e.g., the data sets contain many redundant interpolation points, large numbers of calculations are required to work with data, and the data do not match the size of the computational mesh. Two-dimensional (2-D) hydraulic modeling, which is a popular method for analyzing floodplain flow, offers highly precise elevation parameterization for computational mesh while ignoring much of the micro-topographic information of the DEM data itself. We offer a flood simulation method that integrates 2-D hydraulic model results and high-resolution DEM data, thus enabling the calculation of flood water levels in DEM grid cells through local inverse distance-weighted interpolation. To get rid of the false inundation areas during interpolation, it employs the run-length encoding method to mark the inundated DEM grid cells and determine the real inundation areas through the run-length boundary tracing technique, which solves the complicated problem of connectivity between DEM grid cells. We constructed a 2-D hydraulic model for the Gongshuangcha detention basin, which is a flood storage area of Dongting Lake in China, by using our integrated method to simulate the floodplain flow. The results demonstrate that this method can solve DEM associated problems efficiently and simulate flooding processes with greater accuracy than simulations only with DEM.

Fusion of multi-scale DEMs using a regularized super-resolution method

The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases.