Digital Elevation Model Generation Research Articles

Long-term bathymetric changes in the submerged delta of the Turia river since the nineteenth century (Western Mediterranean) and their drivers

Deltas and estuaries are formed through periods of marine transgression and regression, which are the continuity of a river and provide key information about its evolution. However, many of the world's deltas are increasingly exposed to the impacts of human activities. While changes affecting the subaerial parts of deltas have been intensively studied, much less is known of their subaqueous parts, the understanding of which is important in gauging overall potential delta vulnerability. This research evaluates the bathymetric changes in the submerged delta of the Turia river (Western Mediterranean, Spain) before and after the extreme flood event of 1957, after which the riverbed was diverted. Three nautical charts were processed (1878, 1988 and 2022), including georeferencing and Digital Elevation Model (DEM) generation. In order to evaluate changes before and after the event, models for 1878–1988 and 1988–2022 were compared and differences were quantified in order to assess erosion and aggradation trends. The results indicate a more aggradated submerged delta in the surroundings of the old river mouth, favored by the high sediment availability since the end of the Little Ice Age (LIA), and the presence of a smaller delta next to its current river mouth to the south of the harbor of Valencia. Bathymetric reconstructions also made it possible to map some incisions in the inner continental shelf as river channels that migrated eastwards when the sea level was lower during MIS 2. Finally, the comparison of bathymetric models also revealed the scarcity of sediments on the current shelf since 1988, which is attributed to anthropogenic action. The successive extensions of the harbor are increasingly distorting the distribution of sediments along the coast and are thus remodeling seabed sediment distribution. Knowing the sedimentation in deltaic systems means better predicting future alterations due to increased anthropization and of the climate change.

Towards a Guideline for UAV-Based Data Acquisition for Geomorphic Applications

Recent years have seen a rapid rise in the generation of high-resolution topographic data using custom-built or commercial-grade Unmanned Aerial Vehicles (UAVs). Though several studies have demonstrated the application potential of UAV data, significant knowledge gaps still exist in terms of proper documentation of protocols for data acquisition, post-flight data processing, error assessments, and their mitigation. This work documents and provides guidelines for UAV data acquisition and processing from several years of field experience in diverse geomorphic settings across India, including undulating topography (~17 km2), alluvial plains (~142 km2), lowland-river basin (~66 km2), and a highly urbanized area (~5 km2). A total of 37,065 images with 16 and 20 Megapixels and 604 ground control points (GCPs) were captured with multiple UAV systems and processed to generate point clouds for a total area of ~230 km2. The Root Mean Square Error (RMSE) for each GCP for all sites ranged from 6.41 cm to 36.54 cm. This manuscript documents a comprehensive guideline for (a) pre-field flight planning and data acquisition, (b) generation and removal of noise and errors of the point cloud, and (c) generation of orthoimages and digital elevation models. We demonstrate that a well-distributed and not necessarily uniformly distributed GCP placement can significantly reduce doming error and other artifacts. We emphasize the need for using separate camera calibration parameters for each flight and demonstrate that errors in camera calibration can significantly impact the accuracy of the point cloud. Accordingly, we have evaluated the stability of lens calibration parameters between consumer-grade and professional cameras and have suggested measures for noise removal in the point cloud data. We have also identified and analyzed various errors during point cloud processing. These include systematic doming errors, errors during orthoimage and DEM generation, and errors related to water bodies. Mitigation strategies for various errors have also been discussed. Finally, we have assessed the accuracy of our point cloud data for different geomorphic settings. We concluded that the accuracy is influenced by Ground Sampling Distance (GSD), topographic features, and the placement, density, and distribution of GCPs. This guideline presented in this paper can be extremely beneficial to both experienced long-term users and newcomers for planning the UAV-based topographic survey and processing the data acquired.

High-Accuracy Filtering of Forest Scenes Based on Full-Waveform LiDAR Data and Hyperspectral Images

Airborne light detection and ranging (LiDAR) technology has been widely utilized for collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation of high-accuracy digital elevation models (DEMs) for the efficient investigation and management of forest resources. Point cloud filtering serves as the crucial initial step in DEM generation, directly influencing the accuracy of the resulting DEM. However, forest filtering presents challenges in dealing with sparse point clouds and selecting appropriate initial ground points. The introduction of full-waveform LiDAR data offers a potential solution to the problem of sparse point clouds. Additionally, advancements in multi-source data integration and machine learning algorithms have created new avenues that can address the issue of initial ground point selection. To tackle these challenges, this paper proposes a novel filtering method for forest scenes utilizing full-waveform LiDAR data and hyperspectral image data. The proposed method consists of two main steps. Firstly, we employ the improved dynamic graph convolutional neural network (IDGCNN) to extract initial ground points. In this step, we utilize three types of low-correlation features: LiDAR features, waveform features, and spectral features. To enhance its accuracy and adaptability, a self-attention module was incorporated into the DGCNN algorithm. Comparative experiments were conducted to evaluate the effectiveness of the algorithm, demonstrating that the IDGCNN algorithm achieves the highest classification accuracy with an overall accuracy (OA) value of 99.38% and a kappa coefficient of 95.95%. The second-best performer was the RandLA-net algorithm, achieving an OA value of 98.73% and a kappa coefficient of 91.68%. The second step involves refining the initial ground points using the cloth simulation filter (CSF) algorithm. By employing the CSF algorithm, non-ground points present in the initial ground points are effectively filtered out. To validate the efficacy of the proposed filtering method, we generated a DEM with a resolution of 0.5 using the ground points extracted in the first step, the refined ground points obtained with the combination of the first and second steps, and the ground points obtained directly using the CSF algorithm. A comparative analysis with 23 reference control points revealed the effectiveness of our proposed method, as evidenced by the median error of 0.41 m, maximum error of 0.75 m, and average error of 0.33 m.

Assessment of Using UAV Photogrammetry Based DEM and Ground-Measurement Based DEM in Computer-Assisted Forest Road Design

Computer-assisted forest road design mainly relies on a high-resolution digital elevation model (DEM), which provides terrain data for supporting the analysis of road design features. The resolution and accuracy of the DEM in representing the terrain structures vary depending on the preferred dataset, which then reflects some of the essential road features such as alignment, road slope, and earthwork. In this study, three forest road sections were designed by using high-resolution DEMs generated from UAV photogrammetry data, GNSS-GPS data and Total Station data. NetCAD 7.6 software, developed in Turkey and mostly used in road design applications, was used to perform the road design while calculating horizontal profiles, vertical profiles, curves, cross sections, and earthwork. The DEM generation capabilities for three datasets were compared based on spatial resolution, data collection and data processing stage. Then, the differences between three road sections were evaluated by considering specified road features such as alignment properties, road slope, and earthwork. The results indicated that the UAV (Unmanned Aerial Vehicles) based DEM generation method provided the highest resolution (10 cm), followed by the Total Station (56 cm) and GNSS-GPS (61 cm) based methods. When comparing the time for data collection procedure, it took 14 minutes, 70 minutes, and 110 minutes for UAV data, GNSS-GPS data, and Total Station data, respectively. On the other hand, UAV based method falls into a disadvantageous situation in data processing stage, due to high data processing time (3 hours). However, GNSS-GPS and Total Station based methods work only with spatial point data, so they require less processing time of 15 minutes and 25 minutes, respectively. The results indicated that road lengths were 294.8, 272.4 and 282.1 m and the average road slopes were 3.41%, 3.39%, and 3.31% for the road sections designed by using UAV, GNSS-GPS, and Total Station based DEMs, respectively. The excavation and landfill volumes were 369.16 m3 and 166.98 m3, 285.86 m3 and 201.83 m3, and 433.17 m3 and 183.95 m3, respectively. The results indicated that UAV photogrammetry data generates high-resolution DEMs that can be effectively used to design forest roads.

Baseline Calibration of L-Band Spaceborne Bistatic SAR TwinSAR-L for DEM Generation

The Terrain Wide-swath Interferometric L-band Synthetic Aperture Radar (TwinSAR-L) mission is a spaceborne bistatic synthetic aperture radar (SAR) mission to derive a high-quality global digital elevation model (DEM). The prerequisite of the high-accuracy DEM is knowing the interferometric baseline with high precision. The challenging problem is that the baseline of the bistatic system is highly dynamic due to the fast relative motion between the two satellites. In this paper, a pixel-related baseline model based on the geometrical shift is proposed to accurately reflect the position change of satellites. The baseline error is then calibrated using height gradient information and a small number of point targets with a slight incidence angle difference, eliminating the need for low-frequency corner reflectors and avoiding the difficulty of selecting a calibration site. The proposed method has been successfully exploited during the initial Commissioning Phase of TwinSAR-L, demonstrating its effectiveness in evaluating the precise baseline and supporting the generation of high-precision DEM.

Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m.

Comparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions

Digital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimating the height values of the grid cells. The use of machine learning methods, such as artificial neural networks for spatial interpolation, contributes to spatial interpolation with more accuracy. In this study, the performances of FBNN interpolation based on different parameters such as the number of hidden layers and neurons, epoch number, processing time, and training functions (gradient optimization algorithms) were compared, and the differences were evaluated statistically using an analysis of variance (ANOVA) test. This research offers significant insights into the optimization of neural network gradients, with a particular focus on spatial interpolation. The accuracy of the Levenberg–Marquardt training function was the best, whereas the most significantly different training functions, gradient descent backpropagation and gradient descent with momentum and adaptive learning rule backpropagation, were the worst. Thus, this study contributes to the investigation of parameter selection of ANN for spatial interpolation in DEM height estimation for different terrain types and point distributions.

Study on the Differences between the Extraction Results of the Structural Parameters of Individual Trees for Different Tree Species Based on UAV LiDAR and High-Resolution RGB Images

Light Detection and Ranging (LiDAR) points and high-resolution RGB image-derived points have been successfully used to extract tree structural parameters. However, the differences in extracting individual tree structural parameters among different tree species have not been systematically studied. In this study, LiDAR data and images were collected using unmanned aerial vehicles (UAVs) to explore the differences in digital elevation model (DEM) and digital surface models (DSM) generation and tree structural parameter extraction for different tree species. It was found that the DEMs generated based on both forms of data, LiDAR and image, exhibited high correlations with the field-measured elevation, with an R2 of 0.97 and 0.95, and an RMSE of 0.24 and 0.28 m, respectively. In addition, the differences between the DSMs are small in non-vegetation areas, whereas the differences are relatively large in vegetation areas. The extraction results of individual tree crown width and height based on two kinds of data are similar when all tree species are considered. However, for different tree species, the Cinnamomum camphora exhibits the greatest accuracy in terms of crown width extraction, with an R2 of 0.94 and 0.90, and an RMSE of 0.77 and 0.70 m for LiDAR and image points, respectively. In comparison, for tree height extraction, the Magnolia grandiflora exhibits the highest accuracy, with an R2 of 0.89 and 0.90, and an RMSE of 0.57 and 0.55 m for LiDAR and image points, respectively. The results indicate that both LiDAR and image points can generate an accurate DEM and DSM. The differences in the DEMs and DSMs between the two data types are relatively large in vegetation areas, while they are small in non-vegetation areas. There are significant differences in the extraction results of tree height and crown width between the two data sets among different tree species. The results will provide technical guidance for low-cost forest resource investigation and monitoring.

A Comparative Assessment of Multi-Source Generation of Digital Elevation Models for Fluvial Landscapes Characterization and Monitoring

Imaging and measuring the Earth’s relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of the land surface on digital elevation models is crucial. The main objective of the study was to assess and compare the accuracy of state-of-the-art remote sensing technologies in generating DEMs for riverscape characterization and fluvial monitoring applications. In particular, we were interested in DAP and LiDAR techniques comparison, and UAV applicability. We carried out field surveys, i.e., GNSS-RTK measurements, UAV and aircraft flights, on islands and sandbars within a nature reserve on a braided section of the Vistula River downstream from the city of Warsaw, Poland. We then processed the data into DSMs and DTMs based on four sources: ULS (laser scanning from UAV), UAV-DAP (digital aerial photogrammetry), ALS (airborne laser scanning), and satellite Pléiades imagery processed with DAP. The magnitudes of errors are represented by the cross-reference of values generated on DEMs with GNSS-RTK measurements. Results are presented for exposed sediment bars, riverine islands covered by low vegetation and shrubs, or covered by riparian forest. While the average absolute height error of the laser scanning DTMs oscillates around 8–11 cm for most surfaces, photogrammetric DTMs from UAV and satellite data gave errors averaging more than 30 cm. Airborne and UAV LiDAR measurements brought almost the perfect match. We showed that the UAV-based LiDAR sensors prove to be useful for geomorphological mapping, especially for geomorphic analysis of the river channel at a large scale, because they reach similar accuracies to ALS and better than DAP-based image processing.

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.

Fast geolocation solution for bistatic interferometric synthetic aperture radar configuration of inclined geosynchronous transmitter with low earth orbit receivers

AbstractSpaceborne interferometric synthetic aperture radar (InSAR) technology is an effective method to obtain digital elevation model (DEM) data. The bistatic InSAR configuration of the inclined‐geosynchronous (InGEO) transmitter with low earth orbit (LEO) receivers (InGEO‐LEO) is a novel InSAR system to acquire terrain information. This novel system is characterised with high resolution, wide swath and timeliness, but the Newton iterative method is time‐consuming to solve the bistatic InSAR equations for fast DEM generation. For the conventional LEO bistatic InSAR system, the closed‐form solution is an effective method to improve the efficiency of solving InSAR equations, which is invalid in the InGEO‐LEO InSAR system because of the significant geometry difference caused by the orbits of InGEO transmitter and LEO receivers. To address this issue, we analyse the bistatic InGEO‐LEO geometry in detail and exploit the bistatic InSAR equations to propose an approximate closed‐form solution (ACS) for the novel system. Compared with the general Newton iterative method, the ACS significantly improves the efficiency of geolocation for the bistatic InGEO‐LEO InSAR system with high precision. Simulation experiments are carried out to verify the effectiveness and superiority of ACS.

Designing and Building an Intelligent Pavement Management System for Urban Road Networks

Pavement maintenance plays a significant role in megacities. Managing complaints and scheduling road reviews are the two maintenance concerns under the intelligent pavement management system (PMS) plan. In contrast, if the damages are not treated immediately, they will increase over time. By leveraging accurate data from sensors, smart PMS will improve management capability, support sustainability, and drive economic growth in the road network. This research aimed to elaborate on the different modules of an intelligent city pavement network to advance to a sustainable city. First, a 3D mobile light detection and ranging (LiDAR) sensor, accompanied by a camera, was applied as the data collection tool. Although 3D mobile LiDAR data have gained popularity, they lack precise detection of pavement distresses, including cracks. As a result, utilizing RGB imaging may help to detect distresses properly. Two approaches were integrated alongside conducting the data analysis in this paper: (1) ArcGIS pro, developed by Esri Inc., which includes noise removal, digital elevation model (DEM) generation, and pavement and building footprint extraction; (2) the Mechanistic-Empirical Pavement Design Guide (AASHTOWare PMED), which was used to assess site specifications such as traffic, weather, subbase, and current pavement conditions in an effort to design the most appropriate pavement for each road section. For the 3D visualization module, CityEngine (a software from Esri) was used to provide the 3D city model. After implementing the research methodology, we drew the following conclusions: (1) using the AASHTOWare PMED method to make decisions about road maintenance and rehabilitation(M&R) actions can significantly speed up the decision-making process, essentially saving time and money and shortening the project’s duration; and (2) if the road conditions are similar, the smart geographical information system (GIS)-based PMS can make consistent decisions about road M&R strategies, i.e., the interference from human factors is less significant.

Radargrammetry DEM Generation Using High-Resolution SAR Imagery Over La Palma During the 2021 Cumbre Vieja Volcanic Eruption

This letter aims at investigating the potential of high-resolution (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7\times0.5\,\,\text{m}^{2}$ </tex-math></inline-formula> ) synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. In this work, we process two SAR images recorded by the Capella Space X-band satellite-borne radar sensor on two consecutive days, October 2 and 3, 2021, over La Palma (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption. We adopt an iterative point-aggregation algorithm to identify matching pixels between the two images; then, the height estimation is performed using a distance minimization routine over the previously identified pairs of matching points. The resultant radargrammetric DEM is validated against a lidar-based DEM for various land cover (LC) classes, showing a good agreement in the areas less affected by lava flow. An estimation of the lava thickness is performed, yielding profiles of the cone area, which are compared to the photogrammetry estimates obtained from the Pléiades mission data.

Image Texture as Quality Indicator for Optical DEM Generation: Geomorphic Applications in the Arid Central Andes

The generation of Digital Elevation Models (DEMs) through stereogrammetry of optical satellite images has gained great popularity across various disciplines. For the analysis of these DEMs, it is important to understand the influence of the input data and different processing steps and parameters employed during stereo correlation. Here, we explore the effects that image texture, as well as the use of different matching algorithms (Block Matching (BM) and More Global Matching (MGM)), can have on optical DEMs derived from the flexible, open-source Ames Stereo Pipeline. Our analysis relies on a ∼2700 km2 clip of a SPOT6 tristereo scene covering the hyperarid, vegetation-free Pocitos Basin and adjacent mountain ranges in the northwestern Argentine Andes. A large, perfectly flat salt pan (paleolake bed) that covers the center of this basin is characterized by strong contrasts in image texture, providing a unique opportunity to quantitatively study the relationship between image texture and DEM quality unaffected by topography. Our findings suggest that higher image texture, measured by panchromatic variance, leads to lower DEM uncertainty. This improvement continues up to ∼103 panchromatic variance, above which further improvements in DEM quality are independent of local image texture but instead may have sensor or geometric origins. Based on this behavior, we propose that image texture may serve as an important proxy of DEM quality prior to stereo correlation and can help to set adequate processing parameters. With respect to matching algorithms, we observe that MGM improves matching in low-texture areas and overall generates a smoother surface that still preserves complex, narrow (i.e., ridge and valley) features. Based on this sharper representation of the landscape, we conclude that MGM should be preferred for geomorphic applications relying on stereo-derived DEMs. However, we note that the correlation kernel selected for stereo-matching must be carefully chosen depending on local image texture, whereby larger kernels generate more accurate matches (less artifacts) at the cost of smoothing results. Overall, our analysis suggests a path forward for the processing and fusion of overlapping satellite images with suitable view-angle differences to improve final DEMs.

Shading aware DSM generation from high resolution multi-view satellite images

ABSTRACT In many cases, the Digital Surface Models (DSMs) and Digital Elevation Models (DEMs) are obtained with Light Detection and Ranging (LiDAR) or stereo matching. As an active method, LiDAR is very accurate but expensive, thus often limiting its use in small-scale acquisition. Stereo matching is suitable for large-scale acquisition of terrain information as the increase of satellite stereo sensors. However, underperformance of stereo matching easily occurs in textureless areas. Accordingly, this study proposed a Shading Aware DSM GEneration Method (SADGE) with high resolution multi-view satellite images. Considering the complementarity of stereo matching and Shape from Shading (SfS), SADGE combines the advantage of stereo matching and SfS technique. First, an improved Semi-Global Matching (SGM) technique is used to generate an initial surface expressed by a DSM; then, it is refined by optimizing the objective function which modeled the imaging process with the illumination, surface albedo, and normal object surface. Different from the existing shading-based DEM refinement or generation method, no information about the illumination or the viewing angle is needed while concave/convex ambiguity can be avoided as multi-view images are utilized. Experiments with ZiYuan-3 and GaoFen-7 images show that the proposed method can generate higher accuracy DSM (12.5–56.3% improvement) with sound overall shape and temporarily detailed surface compared with a software solution (SURE) for multi-view stereo.

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.

Revising digital elevation models generation from Sentinel-1 synthetic aperture radar and interferometric technique in densely-vegetated humid tropical environment

Generation of digital elevation models (DEM) over densely vegetated humid tropics using Sentinel-1 data and Interferometry synthetic aperture radar (InSAR) technique is revised based on recommended optimum perpendicular baselines, and accuracy assessment based on coherence cells is introduced. Six pairs of Sentinel-1 data within the optimum perpendicular baseline of 150 − 400 m and different temporal baselines acquired between 2015 and 2021 were processed using Sentinel Application Platform. Results revealed: that due to the extreme difficulty of achieving good coherence, interferograms are characterized by noise, generated DEMs are dominated by artefacts, higher elevation accuracy can be achieved within coherence cells, and within the recommended optimum perpendicular baseline range, elevation accuracy within coherence cells is mainly determined by the perpendicular baseline compared to temporal baseline. Therefore, this study can provide practical guide for InSAR generated DEMs evaluation in densely vegetated environments, and remote sensing method for regional-scale measurement and mapping of spots heights.

AN OPEN-SOURCE-BASED WORKFLOW FOR DEM GENERATION FROM SENTINEL-1 FOR LANDSLIDE VOLUME ESTIMATION

Abstract. Digital elevation models (DEMs) serve as a basis for various geomorphological applications, such as, landform mapping, natural hazard assessment, and landslide characterisation. The inter-comparison of DEMs can be used to provide estimates of topographic change, for example, volume changes. Sentinel-1 synthetic aperture radar (SAR) data can be used to generate multitemporal topographic datasets using interferometric SAR (InSAR). Such analyses are often conducted using commercial software; however, a well-structured workflow based on free and open-source software (FOSS) increases the applicability and transferability of the DEM generation method and can facilitate the use of multi-temporal topographic analysis within the Earth Sciences. In this study, we aim to develop an open-source-based workflow for the generation of multi-temporal DEMs from Sentinel-1 data, and to explore their potential for landslide volume estimation using FOSS. We implemented this semi-automated and transferable workflow bundled in the open-source Python package “SliDEM”, which relies on SNAP, SNAPHU, and several other open-source software for geospatial and geomorphological applications, all distributed within a Docker image. Two major landslides in Austria and Norway were selected to test, evaluate, and validate the workflow in terms of reliability, performance, reproducibility, and transferability. Even if the quality of the Sentinel-1-based DEMs varies among the study areas, the preliminary results are promising. However, there is a need for improvement and thorough analysis of the causes of elevation errors. A comprehensive evaluation of the influence of the perpendicular and temporal baselines, topography, land use/land cover, and other environmental conditions can be systematically assessed within the “SliDEM” workflow.

Processing CORONA image for generation of Digital Elevation Model (DEM) and orthophoto of Bilaspur district, Himachal Pradesh

Processing CORONA image for generation of Digital Elevation Model (DEM) and orthophoto of Bilaspur district, Himachal Pradesh

A Novel Topography Retrieval Algorithm Based on Single-Pass Polarimetric SAR Data and Terrain Dependent Error Analysis

Polarimetric synthetic aperture radar (PolSAR) data provide an alternative way for topography retrieval, especially when limited PolSAR data are available. This article proposes a novel topography retrieval algorithm based on the Lambertian backscatter model that further improves the vertical precision of digital elevation model (DEM) generation and requires only one flight. The key idea of the proposed algorithm is to avoid data fluctuations caused by the ratio of the azimuth slope angle to the polarimetric orientation angle (POA). The previous research has confirmed the feasibility of generating a DEM based on single-pass PolSAR data, but its effect on the quality of reference DEM has not been well-explained. To analyze this effect, a large number of experiments on DEM with different resolutions are conducted. In addition, an in-depth analysis of non-linear and terrain-dependent errors is performed. The L-band PolSAR data of NASA/JPL TOPSAR and ALOS-2 PALSAR-2 and interferometric SAR (InSAR) DEM data are used to verify the proposed algorithm. The experimental results show that PolSAR data can be used as an additional reliable information source for DEM fusion under certain conditions to improve the quality of public DEM.