Low-resolution Digital Elevation Model Research Articles

Influence of the Road Model on the Optimal Maneuver of a Racing Motorcycle

Motorcycle motion is largely influenced by the road geometry, which alters the allowable accelerations in longitudinal and lateral directions and influences the vertical wheel loads. Recently, a method for three-dimensional road reconstruction and its incorporation into transient and quasi-steady-state (QSS) minimum lap time simulations (MLTSs) has been proposed. The main purpose of this work is to demonstrate how significantly different results from a minimum lap time optimal control problem can be obtained when using inappropriate elevation data sources in the track reconstruction problem, and how the road model reconstructed using poor input data can lead to misleading conclusions when analyzing real vehicle and driver performances. Two road models derived from high- and low-resolution digital elevation models (DEMs) are compared and their impact on the optimal maneuver of a racing motorcycle is examined. The essentials of track identification are presented, as well as vehicle positioning on the 3D road and the generalized QSS motorcycle model. Obtained 3D and 2D road models are analyzed in detail, on a case example of the Road Atlanta racetrack, and used in minimum lap time simulations, which are validated by the experimental data recorded on the Supersport motorcycle. The comparative analysis showed that great care should be taken when selecting the elevation dataset in the track reconstruction process, and that the 1 m resolution local DEMs seem to be sufficient to obtain MLTS results close to the measured ones. The example of using the 3D free-trajectory QSS minimum lap time problem to localize the track segments where real driver actions can be improved is also presented. The differences between simulation results on different road models of the same racetrack can be large and influence the interpretation of optimal maneuver.

D-SRCAGAN : DEM Super-resolution Generative Adversarial Network

High-resolution digital elevation models (DEMs) are widely used in many fields such as mapping, hydrology, meteorology and geology, where they can improve the accuracy and reliability of many geographic analysis applications as an input. However, due to the high cost and difficulty of acquiring high-resolution DEMs, as well as the problems of edge smoothing, data distortion and fractures in reconstructed ground surfaces with traditional super-resolution DEM reconstruction techniques. Inspired by the excellence of generative adversarial neural networks in super-resolution image analysis, this paper investigates an approach for DEM super-resolution reconstruction with deep residual generative adversarial network. An advanced DEM Super-resolution Generative Adversarial Network (D-SRCAGAN) is proposed in this paper, which can reconstruct a quadruple higher resolution DEM by using low-resolution DEM. Compared with the bicubic and SRGAN methods, the D-SRCAGAN method reconstruction results can retain more topographic features and obtain higher RMSE values.

A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling

Urban floods can cause severe loss of economic and social assets, and remote sensing has been an effective tool for flood mapping during disaster response. Due to the complexity of high-density urban structures, high-resolution (HR) optical images can only extract visible floods in open spaces, and floods in shadows and under the canopy are challenging to map. Accurate digital elevation models (DEMs) are essential for inundation estimation towards urban flood mapping, but HR DEMs are often unavailable due to the high acquisition costs. Through DEM upscaling, HR DEMs could be obtained from existing low-resolution (LR) DEMs using deep learning. To this end, a novel multi-sensory HR urban flood mapping framework is proposed in this research. The framework consists of three components: 1) a new DEM upscaling network to infer HR DEMs from existing LR DEMs with a fusion approach, 2) a rapid flood segmentation network to extract visible flood from very-high-resolution (VHR) optical images with limited human labelling, and 3) an accurate Geographical Information System (GIS)-based tool for floodwater extent and depth estimation from the visible flood information along with HR DEMs. The proposed framework was validated on a fluvial flood that occurred in Calgary, Canada, in 2013, where the proposed DEM upscaling network produced an upscaled HR DEM at 2 m resolution from an existing LR DEM at 18 m resolution. In addition, the proposed flood segmentation network has shown accurate visible flood extraction from VHR RGB aerial imagery with over 80% intersection-over-union (IoU) using 10% of human labelling as training samples. Finally, the floodwater extent and floodwater depth estimation using the proposed GIS tool showed significant improvement over conventional flood mapping methods.

Enhancing the Accuracy of Boresight Calibration with Coplanarity Constraint and Relative Height from DEM

As the resolution of airborne hyperspectral imagers (AHIs) continues to improve, the demand for accurate boresight calibration also increases. However, the high cost of ground control points (GCPs) and the low horizontal resolution of open digital elevation model (DEM) datasets limit the accuracy of AHI’s boresight calibration. We propose a method to enhance the accuracy of DEM-based boresight calibration using coplanarity constraints to address this issue. Our approach utilizes the relative accuracy of DEM in low-resolution DEM datasets. To make better use of the DEM, we apply coplanarity constraints to identify image features that display similar displacement in overlapping areas, and extract their corresponding elevation values from the DEM. These features and their relative heights are then incorporated into an optimization problem for boresight calibration. In the case of low-resolution DEM datasets, our method fully utilizes the relative accuracy of the DEM to improve the boresight correction precision. We have proven that the relative accuracy of elevation is more reliable than absolute accuracy in this situation. Our approach has been tested on the dataset from AHI, and the results have shown that the proposed method has better accuracy on low-resolution DEM datasets. In summary, our method provides a novel approach to improving the accuracy of DEM-based boresight calibration for AHIs, which can benefit applications, such as remote sensing and environmental monitoring. This research highlights the importance of utilizing the relative accuracy of low-resolution DEM datasets for improving the accuracy of boresight calibration.

Quantification of global Digital Elevation Model (DEM) – A case study of the newly released NASADEM for a river basin in Central Vietnam

Study regionLai Giang River basin, Central Vietnam Study focusThe Digital Elevation Models (DEM) is essential in hydrological modeling and water cycle understanding. There are several unanswered questions regarding the impact of DEM on the models’ quality of hydrological applications. This study develops a comprehensive framework for evaluating impacts of six global DEM products: MERIT, NASA, SRTM, ASTER GDEM2, AW3D30, and TanDEM-X using the semi-distributed model SWAT for the Lai Giang River basin, Central Vietnam, in which four criteria have been used (i) statistical analysis, (ii) river basin characteristics, (iii) effects of DEM uncertainty on streamflow using hydrological simulations, and (iv) cross-validation’s impacts on hydrological applications. New hydrological insights for the regions(1) NASA and SRTM 30 DEM were ranked as most accurate DEMs among the chosen products; (2) ASTER GDEM2 performed the worst, with more uncertainty in river networks delineation; (3) MERIT, ASTER GDEM2, and SRTM 90 m provided inaccurate basin delineations, particularly for the intersection subbasin; (4) DEM resolution and source are insensitive to most of the hydrological components, except for lateral flow, groundwater flow, water percolation, and sediment yield. Moreover, a proposed cross-validation approach for the DEM performance has been introduced, showing that the calibrating parameters of higher-resolution DEM would positively impact the hydrological simulations of lower-resolution DEMs and the opposite, especially in terms of flood peak observation.

A Generative Adversarial Network for Pixel-Scale Lunar DEM Generation from High-Resolution Monocular Imagery and Low-Resolution DEM

Digital elevation models (DEMs) provide fundamental data for scientific and engineering applications in lunar exploration missions. Lunar DEMs have been mainly generated by laser altimetry and stereophotogrammetry. Complementarity to stereo photogrammetry, reflection-based surface reconstruction methods, such as shape from shading (SFS), have been studied and applied in lunar DEM reconstruction from a single image. However, this method often suffers from solution ambiguity and instability. In this paper, we propose a generative adversarial network (GAN)-based method that is able to generate high-resolution pixel-scale DEMs from a single image aided by a low-resolution DEM. We have evaluated the accuracy of the reconstructed high-resolution DEMs from 25 LROC NAC images of four regions using LROC NAC DEMs (2 m/pixel) as ground truth. The experimental results demonstrate good accuracy and adaptability to changes in illumination conditions. The root mean square error (RMSE) can reach about 2 m in areas where the degree of elevation variation is less than 100 m, and the RMSE value ranges from around 3 m to 10 m without considering the degree of the elevation variation in large-area reconstruction. As high-resolution monocular images and low-resolution DEMs are available for the entire lunar surface, the proposed GAN-based method has great potential in high-resolution lunar DEM reconstruction for lunar mapping applications.

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.

RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks

The digital elevation model (DEM) is known as one kind of the most significant fundamental geographical data models. The theory, method and application of DEM are hot research issues in geography, especially in geomorphology, hydrology, soil and other related fields. In this paper, we improve the efficient sub-pixel convolutional neural networks (ESPCN) and propose recursive sub-pixel convolutional neural networks (RSPCN) to generate higher-resolution DEMs (HRDEMs) from low-resolution DEMs (LRDEMs). Firstly, the structure of RSPCN is described in detail based on recursion theory. This paper explores the effects of different training datasets, with the self-adaptive learning rate Adam algorithm optimizing the model. Furthermore, the adding-“zero” boundary method is introduced into the RSPCN algorithm as a data preprocessing method, which improves the RSPCN method’s accuracy and convergence. Extensive experiments are conducted to train the method till optimality. Finally, comparisons are made with other traditional interpolation methods, such as bicubic, nearest-neighbor and bilinear methods. The results show that our method has obvious improvements in both accuracy and robustness and further illustrate the feasibility of deep learning methods in the DEM data processing area.

Application of different building representation techniques in HEC-RAS 2-D for urban flood modeling using the Toce River experimental case.

This paper presents the impact of the choice of building representation techniques and hydrodynamic models on urban flood simulations using HEC-RAS 2-D for the Toce River physical model. To this end, eight numerical models based on previous laboratory experiments were prepared to simulate unsteady urban flooding on each side of building units. Two simplified building layouts (aligned and staggered) were examined, where models were prepared for two different building representation techniques: Building Block (BB) and Building Resistance (BR). Water depth variation computations using the BR and BB techniques were compared to the laboratory measurements and previous studies in the literature. A statistical analysis was performed using both the Root Mean Square Error (RMSE) and the Pearson Product-Moment Correlation Coefficient (PPMCC) in order to evaluate the performance of the models. A sensitivity analysis showed that the proper mesh resolution and model parameter values were obtained. As far as the BR technique is concerned, it is well-suited for representing building units in numerical simulations using high Manning coefficients. Furthermore, this study confirms the importance of the BR technique, which should help researchers in using low-resolution Digital Elevation Models (DEMs) along with open-source programs. Moreover, the study aims to produce a deeper comprehension of numerical modeling and urban flooding.

Improved Accuracy of Riparian Zone Mapping Using Near Ground Unmanned Aerial Vehicle and Photogrammetry Method

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.

High-resolution Alos Palsar for the Characterization of Water Storage at the Fountaine Des Gazelles Dam in Biskra, Eastern Algeria

This study estimates water volume variations in the Fontaine des Gazelles Dam in the province of Biskra using high-, medium-, and low-resolution digital elevation models (DEMs). The water surface was extracted using NDWI, and the volume of storage was obtained using the water elevation and spatial analysis. The results obtained in comparison with Peng equation show that there exists a significant change in the water surface. This represents a decrease of more than 29 hm3 between 2005 and 2020, on which precipitations were found to be the main factor of these changes. For validation purposes, NRMSE and MAPE were used. Respectively, with values of 1.30% and 1.50% for Alos Palsar, 8.10% and 8.70% for SRTM1, and 8.20% and 10.10% for SRTM3, it is clear that Alos Palsar performs much better results. This leads us to conclude that Alos Palsar is the most reliable to be adopted in studies related to water volume variations.

Mars3DNet: CNN-Based High-Resolution 3D Reconstruction of the Martian Surface from Single Images

Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo images of Mars are rare. An alternative is the convolutional neural network (CNN) technique, which implicitly learns features by assigning corresponding inputs and outputs. In recent years, CNNs have exhibited promising performance in the 3D reconstruction of close-range scenes. In this paper, we present a CNN-based algorithm that is capable of generating DEMs from single images; the DEMs have the same resolutions as the input images. An existing low-resolution DEM is used to provide global information. Synthetic and real data, including context camera (CTX) images and DEMs from stereo High-Resolution Imaging Science Experiment (HiRISE) images, are used as training data. The performance of the proposed method is evaluated using single CTX images of representative landforms on Mars, and the generated DEMs are compared with those obtained from stereo HiRISE images. The experimental results show promising performance of the proposed method. The topographic details are well reconstructed, and the geometric accuracies achieve root-mean-square error (RMSE) values ranging from 2.1 m to 12.2 m (approximately 0.5 to 2 pixels in the image space). The experimental results show that the proposed CNN-based method has great potential for 3D surface reconstruction in planetary applications.

D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network algorithms.This study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution.

Multiscale Integration of High-Resolution Spaceborne and Drone-Based Imagery for a High-Accuracy Digital Elevation Model Over Tristan da Cunha

Very high-resolution (VHR) optical Earth observation (EO) satellites as well as low-altitude and easy-to-use unmanned aerial systems (UAS/drones) provide ever-improving data sources for the generation of detailed 3-dimensional (3D) data using digital photogrammetric methods with dense image matching. Today both data sources represent cost-effective alternatives to dedicated airborne sensors, especially for remote regions. The latest generation of EO satellites can collect VHR imagery up to 0.30 m ground sample distance (GSD) of even the most remote location from different viewing angles many times per year. Consequently, well-chosen scenes from growing image archives enable the generation of high-resolution digital elevation models (DEMs). Furthermore, low-cost and easy to use drones can be quickly deployed in remote regions to capture blocks of images of local areas. Dense point clouds derived from these methods provide an invaluable data source to fill the gap between globally available low-resolution DEMs and highly accurate terrestrial surveys. Here we investigate the use of archived VHR satellite imagery with approx. 0.5 m GSD as well as low-altitude drone-based imagery with average GSD of better than 0.03 m to generate high-quality DEMs using photogrammetric tools over Tristan da Cunha, a remote island in the South Atlantic Ocean which lies beyond the reach of current commercial manned airborne mapping platforms. This study explores the potentials and limitations to combine this heterogeneous data sources to generate improved DEMs in terms of accuracy and resolution. A cross-validation between low-altitude airborne and spaceborne data sets describes the fit between both optical data sets. No co-registration error, scale difference or distortions were detected, and a quantitative cloud-to-cloud comparison showed an average distance of 0.26 m between both data sets. Both point clouds were merged applying a conventional georeferenced approach. The merged DEM preserves the rich detail from the drone-based survey and provides an accurate 3D representation of the entire study area. It provides the most detailed model of the island so far, suitable to support practical and scientific applications. This study demonstrates that combination archived VHR satellite and low-altitude drone-based imagery provide inexpensive alternatives to generate high-quality DEMs.

A Relief Dependent Evaluation of Digital Elevation Models on Different Scales for Northern Chile

Many geoscientific computations are directly influenced by the resolution and accuracy of digital elevation models (DEMs). Therefore, knowledge about the accuracy of DEMs is essential to avoid misleading results. In this study, a comprehensive evaluation of the vertical accuracy of globally available DEMs from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM), Advanced Land Observing Satellite (ALOS) World 3D and TanDEM-X WorldDEM™ was conducted for a large region in Northern Chile. Additionally, several very high-resolution DEM datasets were derived from Satellite Pour l’Observation de la Terre (SPOT) 6/7 and Pléiades stereo satellite imagery for smaller areas. All datasets were evaluated with three reference datasets, namely elevation points from both Ice, Cloud, and land Elevation (ICESat) satellites, as well as very accurate high-resolution elevation data derived by unmanned aerial vehicle (UAV)-based photogrammetry and terrestrial laser scanning (TLS). The accuracy was also evaluated with regard to the existing relief by relating the accuracy results to slope, terrain ruggedness index (TRI) and topographic position index (TPI). For all datasets with global availability, the highest overall accuracies are reached by TanDEM-X WorldDEM™ and the lowest by ASTER Global DEM (GDEM). On the local scale, Pléiades DEMs showed a slightly higher accuracy as SPOT imagery. Generally, accuracy highly depends on topography and the error is rising up to four times for high resolution DEMs and up to eight times for low-resolution DEMs in steeply sloped terrain compared to flat landscapes.

Accuracy of the TanDEM-X Digital Elevation Model for Coastal Geomorphological Studies in Patagonia (South Argentina)

The aim of this paper is to evaluate the usefulness of TanDEM-X DEM (digital elevation model) for remote geomorphological analysis in Argentinian Patagonia. The use of a DEM with appropriate resolution and coverage might be very helpful and advantageous in vast and hardly accessible areas. TanDEM-X DEM could represent an unprecedented opportunity to identify geomorphological features because of its global coverage, ~12 m spatial resolution and low cost. In this regard, we assessed the vertical accuracy of TanDEM-X DEM through comparison with Differential Global Positioning System (DGPS) datasets collected in two areas of the Patagonia Region during a field survey; we then investigated different types of landforms by creating the elevation profiles. The comparison indicates a high agreement between TanDEM-X DEM and reference values, with a mean absolute vertical error (MAE) of 0.53 m, and a root mean squared error (RMSE) of 0.73 m. The results of landform analysis show an appropriate spatial resolution to detect different features such as beach ridges, which are impossible to delineate with other lower resolution DEMs. For these reasons, TanDEM-X DEM constitutes a useful tool for detailed geomorphological analyses in Argentinian Patagonia.

Deep gradient prior network for DEM super-resolution: Transfer learning from image to DEM

Digital elevation model (DEM) super-resolution (SR) aims to increase the spatial resolution of a DEM through data processing, rather than using sensors with higher accuracy. Inspired by the success of convolutional neural networks (CNNs) in image SR, this study introduces the CNN into DEM SR. However, directly training a robust network for DEM SR has remained a challenge as it has been difficult to obtain sufficient high-resolution DEM samples. Therefore, we proposed a novel method that includes two measures to address this issue. The first is to design a deep CNN for acquiring gradient prior knowledge. Based on this prior, high-resolution gradient maps of the studied DEM can be estimated. The second measure is to introduce transfer learning, which applies the knowledge learned from the natural image to the DEM SR problem. Therefore, a CNN is pretrained by the gradient of numerous high-resolution natural images and then fine-tuned with the gradient maps of some DEM training data. Finally, the high-resolution DEM is reconstructed under the constraints of the estimated gradient maps and the original low-resolution DEM. Overall, the experiments indicate that the proposed framework is superior to current state-of-the-art methods.

Modeling the Natural Drainage Network of the Grand River in Southern Ontario: Agriculture May Increase Total Channel Length of Low-Order Streams

The Grand River watershed is an important agricultural area in southern Ontario, with several large and growing municipalities. Based on digital elevation models (DEMs), the natural drainage network was modelled to predict flow paths. Channel lengths and locations of the predicted network were compared with a ground-truthed channel network to determine efficacy of the models. Approximately 5% of predicted channels lay >40 m from actual channel locations. This amounted to 388 km of channel that had no corresponding channels in reality. The model was unable to predict, based on topography, 2535 km of actual channel present in the watershed. Channels not anticipated by topography were mostly first-order, with low sinuosity, were most common in areas with high agricultural land use, and are likely excavated extensions to headwater streams to facilitate drainage. In addition, this study showed that Soil and Water Assessment Tool (SWAT) models produced using different DEM resolutions did not predict significantly different stream flows, even when resolution was as low as 200 m. However, these low resolution DEMs did result in under-prediction of sediment export entering Lake Erie, most likely because the low resolution maps failed to account for small localized areas of high slope that would have relatively higher rates of erosion.

Flood map boundary sensitivity due to combined effects of DEM resolution and roughness in relation to model performance

In comprehending flood model results, we performed sensitivity analyses and evaluated how different combinations of digital elevation model (DEM) resolution and Manning’s roughness affect flood maps produced from a 2D hydraulic model. Moreover, we analysed how the estimation of accuracy can further be influenced by the performance measure and the area’s topography. Various combinations of DEM and Manning’s produced different results, in terms of quantified performance in relation to actual flood extent and the generated flood boundaries. High-resolution DEMs performed better with higher Manning’s while lower values were better for lower resolution DEMs. Furthermore, although lower resolution DEMs (25 and 50 m) received higher quantified performances, there are more discrepancies in the flood maps and water surface elevations (WSE) produced by them. The current statistical estimators of model performance do not necessarily provide an accurate estimate of which combination of DEM resolution and roughness are more suitable for application to modelling. Different statistical estimates have different assumptions, which can affect the model selection. Therefore, a more holistic approach towards model selection should be adopted that gives equal importance to statistical estimators, as well as the quality of flood inundation extents.

Influence of digital elevation model resolution on gravimetric terrain correction over a study-area of Croatia

High-resolution digital elevation models (DEMs) have become available in the last decade. They are used in geodesy and geophysics as the main data for modeling of topographic mass effects included in gravimetric and gradiometric measurements. In modeling process, gravimeric terrain correction is the central quantity which accounts for the variations of topographic masses around measured stations. This study deals with one segment of terrain correction computation: the impact of the resolution of digital elevation models. Computations are performed on study area of Republic of Croatia. Newly created DEM/DBM for the study area is created from global digital surface model ASTER for continental area, and digital bathymetric model GEBCO for the sea area. DEMs with lower resolution were created by resampling of the created ASTER/GEBCO DEM/DBM in 1″ resolution. Terrain correction map is computed and published for the first time for the Republic of Croatia. The differences between terrain correction solutions obtained by using lower resolution DEMs compared to the solution obtained by using DEM with 1″ are indicating average influence of DEM resolution on terrain correction from 0,5·10–5 to 3·10–5 ms–2, for DEMs with lower resolution than 5″. The results also reveal that rugged and mountainous areas are particularly problematic in such computations.