Digital Elevation Model Generation Research Articles

Landslide Exposure Mapping Using GIS Utilization

There are various methods that had been implemented by many researchers in the previous studies regarding to the monitoring of landslides. At present, is it possible to predict the location of potential landslide occurrence. However, there limitations and constraints in methods that are being implemented and consequently better methods are necessary to improve the monitoring of landslides. Most of the methods discussed cannot be implemented in areas that are inaccessible and dangerous taking as example steep slopes nearby a highway. In addition, monitoring of slopes using existing methods require extensive manpower in the study area, the period of time to obtain complete information is quite long and the total budget is high. Therefore, in order to effectively address the concerns mentioned above, this research study proposes a novel method of monitoring landslides by Close Range Photogrammetry (CRP) as a platform for collecting data especially in areas that are not accessible. Finally, the slope profile depicting soil displacement and physical changes is extracted and analyzed using the DEM covering the potential landslide area. The information is obtained through accurate assessment and used to create a 3D model which would be used for monitoring technique applications. The landslide can be detected by calculation of two different epoch data achieved from Digital Elevation Model (DEM) generation. The paper focuses on observations of the studied area based on DEM area and volume generated from 3D surface analysis.

Study on Elimination Algorithms for Line Segment Mismatches

Image matching is a key step for remotely sensed image registration and digital elevation model (DEM) generation. Compared with point matching, few studies have focused on line matching for images, especially elimination algorithm of mismatched line segments. Therefore, this work systematically studies elimination algorithms of line segment mismatches by combining 2 transformation models (i.e., affine and homography) with 2 M-estimators or 2 sample consensus methods (i.e., random sample consensus, RANSAC, and least median of squares, LMedS). The main idea is as follows. After line segments are extracted and matched, the proposed algorithms can automatically remove mismatched line segments based on an error function of line segment. Aerial images with panchromatic bands and standard false color synthesis were selected for testing. Experiments were performed to compare different combinations of these models and methods and to quantitatively evaluate the performance of the algorithms in terms of accuracy and run time. The results show that the proposed algorithm can be effectively applied to automatically eliminate mismatched line segments, and among all combinations the homography model with LMedS performs the best. The algorithm can also ensure and control the quality of line segment matching from stereo pairs.

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning’s n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning’s n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions.

Glacier Mass Balance in the Manas River Using Ascending and Descending Pass of Sentinel 1A/1B Data and SRTM DEM

Mountain glaciers monitoring is important for water resource management and climate changes but is limited by the lack of a high-quality Digital Elevation Model (DEM) and field measurements. Sentinel 1A/1B satellites provide alternative data for glacier mass balance. In this study, we tried to generate DEMs from C-band Sentinel 1A/1B ascending and descending pass SLC images and evaluate the overall accuracy of INSAR DEMs based on Shuttle Radar Topography Mission (SRTM) DEM and ICESat/GLAS. The low Standard Deviation (STD)and Root Means Square Error (RMSE) displayed the feasibility of Sentinel 1A/1B satellites for DEM generation. Glacier elevation changes and glacier mass balance were estimated based on INSAR DEM and SRTM DEM. The results showed that the most glaciers have exhibited obvious thinning, and the mean annual glacier mass balance between 2000 and 2020 was −0.18 ± 0.1 m w.e.a−1. The south-facing and-east facing aspects, slope and elevation play an important role on glacier melt. This study demonstrates that ascending and descending orbit data of Sentinel-1A/1B satellites are promising for the detailed retrieval of surface elevation changes and mass balance in mountain glaciers.

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

AbstractThe application of structure from motion (SfM) photogrammetry for digital elevation model (DEM) and orthophoto generation from visible imagery enjoys ever‐growing popularity in geomorphological research. Photogrammetry experts, however, urge that a rigorous approach is a prerequisite for reliable results—a requirement that may conflict with real‐world survey. We present a method that unites the two disciplines, using the example of a challenging SfM photogrammetric survey at a Scottish river. Using simultaneous geometric pre‐calibration of a multi‐sensor remotely piloted aircraft system (RPAS), the method facilitates time‐efficient topography mapping and the integration of other wavelengths to create orthophotos providing additional surface information. The approach utilizes an on‐site 3D structure—for example, a building, as calibration object, by extracting coordinates of natural features from lidar scans and sensor imagery. We assess the workflow with specialized calibration software (VMS) and widely applied commercial SfM photogrammetric software (AM), using a DJI Phantom optical and a Workswell thermal sensor. We achieved calibration accuracies below one‐third (optical) and one‐quarter (thermal) of a pixel. Subsequently, we transfer the sensor parameters to pre‐calibrate the SfM application and compare the results to a self‐calibrated workflow. In a systematic experiment using the optical river survey dataset, we assess the effectiveness of pre‐calibration, oblique imagery, scale variation and masking to mitigate systematic DEM errors. Opposing trends show between the calibration strategies. Decreasing network complexity (i.e., flying heights/view angles) improves pre‐calibrated but compromises self‐calibrated scenarios. Pre‐calibrating (VMS) imagery from a single height (30 m nadir) yielded the best results. This finding could have implications for geomorphological surveys, in which single‐scale datasets are widespread practice, despite the literature's urge towards more complex imaging networks. The self‐calibrated results legitimise this insistence: The same dataset resulted in pronounced dome‐shaped DEM distortion, indicating systematic errors, whereas additional flying heights and angles significantly improved the results.

Surface Characteristics, Elevation Change, and Velocity of High-Arctic Valley Glacier from Repeated High-Resolution UAV Photogrammetry

Unmanned Aerial Vehicles (UAVs) are being increasingly used in glaciology demonstrating their potential for the generation of high-resolution digital elevation models (DEMs) that can be further used for the evaluation of glacial processes in detail. Such investigations are especially important for the evaluation of surface changes of small valley glaciers, which are not well-represented in lower-resolution satellite-derived products. In this study, we performed two UAV surveys at the end of the ablation season in 2019 and 2021 on Waldemarbreen, a High-Arctic glacier in NW Svalbard. We derived the mean annual glacier surface velocity of 5.3 m. The estimated mean glacier surface elevation change from 2019 to 2021 was −1.46 m a−1 which corresponds to the geodetic mass balance (MB) of −1.33 m w.e. a−1. The glaciological MB for the same period was −1.61 m w.e. a−1. Our survey includes all Waldemarbreen and demonstrates the efficiency of high-resolution DEMs produced from UAV photogrammetry for the reconstruction of changes in glacier surface elevation and velocity. We suggest that glaciological and geodetic MB methods should be used complementary to each other.

Initial insights into the development and implementation of a citizen-science drone-based coastal change monitoring program in the Great Lakes region

High-resolution spatio-temporal data are needed to improve coastal management programs, particularly along the Great Lakes where lake level fluctuations pose challenges to coastal decision-making and planning. Unfortunately, there is a paucity of coastal change monitoring datasets, particularly those that document event-scale changes over a large spatial scale. This paucity of data is compounded by the large size and range of shore types throughout the region.Unoccupied aerial vehicle (UAV) or drone data collected by citizen scientists are a potential solution to this challenge. However, no citizen science coastal change monitoring program exists in the Great Lakes region, nor does a comprehensive drone-based coastal change monitoring programs exist anywhere in the United States. To inform the development of drone-based citizen science programs, the goal of this paper is to describe the development and implementation of a citizen science coastal change monitoring program along the Great Lakes shores of Michigan. The citizens participating in this project generate imagery in two ways: (1) the submission of photos of coastal changes or hazards via a web app developed for the project called PicShores and (2) drone collection of survey-quality aerial imagery for use in the generation of orthomosaic images and digital elevation models (DEMs). This paper presents the methods utilized to develop the citizen science monitoring program, some initial findings from the citizen science monitoring, and explores some challenges and next steps for the program.

Processing Techniques for Nadir Echo Suppression in Staggered Synthetic Aperture Radar

Synthetic aperture radar (SAR) is a class of high-resolution imaging radar particularly suitable for satellite remote sensing with diverse applications, such as biomass and ice monitoring, generation of digital elevation models, and measuring of subsidence. Staggered SAR is a novel mode of operation under consideration for the next-generation SAR missions such as Tandem-L and NASA-ISRO SAR. It uses digital beamforming and a continuous variation of the pulse repetition interval (PRI) to achieve high azimuth resolution over a much wider, continuous swath than is traditionally possible. This PRI variation renders infeasible use of existing methods to avoid nadir echoes, which might impair the quality of staggered SAR images. This work proposes processing techniques that mitigate the impact of nadir echoes in staggered SAR through localization and thresholding-and-blanking of these echoes in range-compressed data and recovery of part of the underlying useful signal through interpolation. The performance of these processing techniques is evaluated through simulations using real TerraSAR-X data. The proposed technique can be implemented as an optional stage in the processing chain of future staggered SAR missions and leads to improved image quality at a reasonable additional computational cost.

Generation of Dense and High-Precision Digital Elevation Model Using Low-Cost Unmanned Aerial Vehicle and Space-Borne TanDEM-X to Measure Exposed Area Change Due to Tidal Invasion

Tidal flats are internationally protected areas because of their environmental, geological, and economic value. Nevertheless, these are areas where casualties are common among visitors. To ensure the safety of visitors, efforts must be made to understand the geomorphologic characteristics of tidal flats. It is necessary to use three-dimensional topographic data to clearly determine when an exposed tidal flat rapidly disappears as a result of periodic tidal fluctuations. Digital elevation models (DEMs) created using unmanned aerial vehicle (UAV) imaging and commercial photogrammetric software to measure tidal flats are produced using ground control points (GCPs). It is difficult to conduct field surveys and readings of image coordinates that correspond to these because tidal flat areas are difficult to access. The distribution of GCPs affects the accuracy of a DEM because the entire test region cannot be covered during a survey. Without GCPs, the DEM generated with the UAV images and photogrammetric software using the structure from motion (SfM) technique probably has nonlinear distortion such as a bowl shape. This paper proposes a practical method of least-squares three-dimensional (3D) surface matching with a polynomial model (LS3D-PM) to correct a distorted DEM. For this LS3D-PM matching, a global TanDEM-X DEM was used instead of a field survey to collect the GCPs in a tidal flat. Practical testing was conducted using the proposed method. The root mean square error with respect to the height decreased from approximately 1.0 m to 0.1 m, and the bowl-effect error was eliminated. With the DEM corrected using the proposed method and the classified orthoimages, the exposed tidal flat changes due to seawater invasion were estimated at different times during flood tide. This method has non-trivial technical values for quick and relatively precise DEM generations at a low cost compared to that of other techniques, particularly in emergency situations such as tsunamis, landslides, and earthquakes.

Riparian Zone DEM Generation From Time-Series Sentinel-1 and Corresponding Water Level: A Novel Waterline Method

Topography data are essential for land management, hydrology, and earth science applications. The topography in a riparian zone varies with time. Available global digital elevation models (GDEMs) do not satisfy the hydrological and environmental modeling requirements for riparian zones. In this study, a novel waterline method was proposed to acquire the digital elevation model (DEM) of riparian zones. Synthetic aperture radar (SAR) images are used to extract waterlines and corresponding water level is applied to acquire the elevation. The uneven water surfaces are considered to acquire the elevation of waterlines and Shuttle Radar Topography Mission (SRTM) DEM is used to interpolate the elevation of pixels without waterlines. The proposed method is applied in the riparian zone of Three Gorges Reservoir and Dongting Lake to test its feasibility. More than 80 Sentinel-1 SAR images at different water levels within three years are used to detect waterlines. Based on corresponding water level of hydrological stations, the elevation of waterlines are acquired via inverse distance weighting method. The distance layer and slope derived from the original SRTM DEM are used to interpolate the elevation of pixels between waterlines. The DEM generated by the proposed method is compared against the DEM derived in 2017, SRTM DEM, the DEM which does not include the uneven effects, and Ice, Cloud and land Elevation Satellite -2 (ICESat-2) ATL08. The good performance of the proposed method implies its high efficiency in revealing riparian zone topography.

On the Derivation of Volume Decorrelation From TanDEM-X Bistatic Coherence

The bistatic interferometric coherence is affected by different sources of error, among which volume decorrelation, which quantifies the amount of noise caused by volume scattering mechanisms. The magnitude of volume decorrelation can be derived from the total interferometric coherence by properly compensating for all other decorrelation sources. In the scientific community, it is common practice to approximate the volume decorrelation with the coherence or to compute it by compensating for the signal-to-noise ratio (SNR) decorrelation only, which typically represents the predominant decorrelation component. The aim of this work is to assess the impact of different decorrelation sources in detail and to provide the readers with a practical procedure for a precise computation of the volume decorrelation from TanDEM-X bistatic data. In particular, we concentrate on the two most relevant decorrelation components: the SNR and the quantization components. Regarding the former, we estimate the noise equivalent sigma nought directly from real SAR data and we provide the users with a set of polynomial coefficients for the retrieval of the system noise floor for each operational TanDEM-X StripMap beam used for the generation of the global Digital Elevation Model (DEM). Concerning the latter, we analyze its dependency on the backscatter local statistics and quantization rate and we provide the reader with a set of empirical look-up-tables (LUT) for quantifying its impact on the coherence. Finally, we provide reasonable assumptions for all other remaining decorrelation sources, discussing two application scenarios.

Knowledge-Aided InSAR Phase Unwrapping Approach

2-D phase unwrapping (PU) is one of the biggest challenges in synthetic aperture radar (SAR) interferometry (InSAR) processing. As an ill-posed problem, the performance of the traditional algorithmic model-based 2-D PU algorithms is not guaranteed to be correct with rapid ground deformation or topographic changes. An increasing number of remote sensing observations collected by different sensors (e.g., LiDAR and GPS) provides new opportunities to assist the traditional 2-D InSAR PU by reducing the nondeterminacy. In this article, we propose a novel knowledge-aided PU (KAPU) approach. KAPU compiles different prior knowledge from different sources with InSAR observations simultaneously through an integer programming model. More importantly, the mathematical proof demonstrates that the constraint of the optimization model of KAPU is totally unimodular, so KAPU can be efficiently solved without having to have the constraint that the ambiguity number is an integer. Theoretical analysis and extensive experimental results illustrate that KAPU outperforms the existing model-based 2-D InSAR PU algorithms on digital elevation model (DEM) generation and surface deformation estimation.

Statistical Analysis for Tidal Flat Classification and Topography Using Multitemporal SAR Backscattering Coefficients

Coastal zones are very dynamic natural systems that experience short-term and long-term morphological changes. Their highly dynamic behavior requires frequent monitoring. Tidal flat topography for a large spatial coverage has been generated mainly by the waterline extraction method from multitemporal remote sensing observations. Despite the efficiency and robustness of the waterline extraction method, the waterline-based digital elevation model (DEM) is limited to representing small scale topographic features, such as localized tidal tributaries. Tidal flats show a rapid increase in SAR backscattering coefficients when the tide height is lower than the tidal flat topography compared to when the tidal flat is covered by water. This leads to a tidal flat with a distinct statistical behavior on the temporal variability of our multitemporal SAR backscattering coefficients. Therefore, this study aims to suggest a new method that can overcome the constraints of the waterline-based method by using a pixel-based DEM generation algorithm. Jenks Natural Break (JNB) optimization was applied to distinguish the tidal flat from land and ocean using multitemporal Senitnel-1 SAR data for the years 2014–2020. We also implemented a logistic model to characterize the temporal evolution of the SAR backscattering coefficients along with the tide heights and estimated intertidal topography. The Sentinel-1 DEM from the JNB classification and logistic function was evaluated by an airborne Lidar DEM. Our pixel-based DEM outperformed the waterline-based Landsat DEM. This study demonstrates that our statistical approach to intertidal classification and topography serves to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales.

DEM Generation with ICESat-2 Altimetry Data for the Three Antarctic Ice Shelves: Ross, Filchner–Ronne and Amery

The ice shelf is an important component of the Antarctic system, and the interaction between the ice sheet and the ocean often proceeds through mass variations of the ice shelf. The digital elevation model (DEM) of the ice shelf is particularly important for ice shelf elevation change and mass balance estimation. With the development of satellite altimetry technology, it became an important data source for DEM research of Antarctica. The National Aeronautics and Space Administration (NASA) Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) launched in 2018 is a significant improvement in along-track sampling rate and measurement accuracy compared with previous altimetry satellites. This study uses ordinary kriging interpolation to present new DEMs (ICESat-2 DEM hereinafter) for the three ice shelves (Ross, Filchner–Ronne and Amery) in Antarctica with ICESat-2 altimetry data. Two variogram models (linear and spherical) of ordinary kriging interpolation are compared in this paper. The result shows that the spherical model generally shows better performance and lower standard deviation (STD) than the linear models. The precision of the ultimate DEM was evaluated by NASA Operation IceBridge (OIB) data and compared with five previously published Antarctic DEM products (REMA, TanDEM-X PolarDEM, Slater DEM, Helm DEM, and Bamber DEM). The comparison reveals that the mean difference between ICESat-2 DEM of the Ross ice shelf and OIB is −0.016 m with a STD of 0.918 m, and the mean difference between ICESat-2 DEM of the Filchner–Ronne ice shelf and OIB is −0.533 m with a STD of 0.718 m. The three ICESat-2 DEMs show higher spatial resolution and elevation accuracy than five previously published Antarctic DEMs.

Identification of Debris‐Flow Channels Using High‐Resolution Topographic Data: A Case Study in the Quebrada del Toro, NW Argentina

AbstractResolving Earth's surface at the meter scale is essential for an improved understanding of the dynamics of mass‐movement processes. In this study, we explore the applicability and potential of digital elevation models (DEMs) derived from stereophotogrammetry to detect debris‐flow channels in the Quebrada del Toro in the northwestern Argentine Andes. Our analysis relies on a high‐resolution (3 m) DEM created from SPOT‐7 tri‐stereo satellite data. We carefully validated DEM quality with ∼6,000 differential GPS points and identified optimal parameters for DEM generation in high‐relief terrain. After multiple processing steps, we achieved an accuracy of 0.051 ± 1.915 m (1σ) using n = 3,139 control points with cm precision. Previous studies have used the drainage area and slope framework to identify topographic signatures of debris flows within a catchment. We built upon this and investigated individual river‐channel segments using connected‐component (CC) analysis on meter‐scale topographic data. We define CC as segments of similar slope along the channel profile. Based on seven manually identified debris‐flow catchments, we developed a debris‐flow similarity index using component length and mean channel‐segment slope and identified channel segments that have likely been shaped by debris flows. The presented approach has the potential to resolve intra‐catchment variability of transport processes, allows to constrain the extent of debris‐flow channels more precisely than slope‐area analysis, and highlights the versatility of combined space‐ and field‐based observations for natural‐hazard assessments.

TanDEM-X PolarDEM 90 m of Antarctica: generation and error characterization

Abstract. We present the generation and validation of an updated version of the TanDEM-X digital elevation model (DEM) of Antarctica: the TanDEM-X PolarDEM 90 m of Antarctica. Improvements compared to the global TanDEM-X DEM version comprise filling gaps with newer bistatic synthetic aperture radar (SAR) acquisitions of the TerraSAR-X and TanDEM-X satellites, interpolation of smaller voids, smoothing of noisy areas, and replacement of frozen or open sea areas with geoid undulations. For the latter, a new semi-automatic editing approach allowed for the delineation of the coastline from DEM and amplitude data. Finally, the DEM was transformed into the cartographic Antarctic Polar Stereographic projection with a homogeneous metric spacing in northing and easting of 90 m. As X-band SAR penetrates the snow and ice pack by several meters, a new concept for absolute height adjustment was set up that relies on areas with stable penetration conditions and on ICESat (Ice, Cloud, and land Elevation Satellite) elevations. After DEM generation and editing, a sophisticated height error characterization of the whole Antarctic continent with ICESat data was carried out, and a validation over blue ice achieved a mean vertical height error of just −0.3 m ± 2.5 m standard deviation. The filled and edited Antarctic TanDEM-X PolarDEM 90 m is outstanding due to its accuracy, homogeneity, and coverage completeness. It is freely available for scientific purposes and provides a high-resolution data set as basis for polar research, such as ice velocity, mass balance estimation, or orthorectification.

Integrated Denoised Synthetic Aperture Radar Images for Enhanced Digital Elevation Model Generation

Synthetic aperture radar (SAR) enables imaging of topographic surfaces day and night in different atmospheric conditions. SAR imaging systems record both intensity and phase information of the backscattered signals. Acquired intensity information is often exposed to speckle noise, and gathered phase information is usually corrupted by thermal and other types of noise. Thus, these types of noises have negative effects on interpretation of SAR images. Digital elevation model (DEM) can be generated by interferometric SAR using two SAR images, of the same area, with slightly different look angles. The generated DEM is affected by the corruption of both intensity and phase information. In this paper, a proposed framework of convolutional neural network (CNN) and modified Wiener filter (MWF) is suggested in DEM generation process. The main purpose of the proposed framework is minimizing not only speckle noise of input SAR images but also phase noise of the interferogram. Thus, an enhanced DEM can be generated. Extensive experiments are carried out and different DEMs are generated from original SAR and from both despeckled SAR images and filtered interferogram. Results and comparative analyses show significant improvements in both quality and vertical accuracy of the DEM generated by the proposed hybrid (CNN-MWF) framework.

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets

The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various spatial resolutions. It is often necessary to identify vegetated areas for masking purposes during the postprocessing phase, excluding them for the digital elevation models (DEMs) generation or change detection purposes. However, vegetation can be extracted using sensors capable of capturing the near-infrared part of the spectrum, which cannot be recorded by visible (RGB) cameras. In this study, after reviewing different visible-band vegetation indices in various environments using different UAV technology, the influence of the spatial resolution of orthomosaics generated by photogrammetric processes in the vegetation extraction was examined. The triangular greenness index (TGI) index provided a high level of separability between vegetation and nonvegetation areas for all case studies in any spatial resolution. The efficiency of the indices remained fundamentally linked to the context of the scenario under investigation, and the correlation between spatial resolution and index incisiveness was found to be more complex than might be trivially assumed.

PHOTOGRAMMETRIC MAPPING FROM HIGH-RESOLUTION STEREO IMAGERY AND COMPARATIVE ANALYSIS USING EXISTING DATA

The launch of the very high resolution (VHR) sensor satellites has paved the way for further exploitation of the capabilities of stereo satellite imagery for many applications and most importantly in the extraction of topographical details. Advances in digital technologies in the photogrammetric technique provide more accurate and up to date topographic representation, while traditional methods of topographic mapping are time-consuming and require large technical support. This paper explores the methods of production and an analysis of the capabilities of the high-resolution stereo imaging system for the extraction of features (topographic map) and generation of digital elevation model (DEM). This study uses high-resolution GeoEye-1 stereo pair images. For qualitative assessment, topographic features were extracted by digitizing the georeferenced stereo pair images and overlapped to form a DEM and orthophoto within the Summit Evolution software. Georeferencing was carried out using the existing Ground Control Points (GCPs) and Check Points (CPs) established using Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) technique. For quantitative assessment, a total of 7 existing GCPs were considered suitable for use and 5 CPs were established as a check for accuracy of the output. The accuracy of the CPs was evaluated by computing Root Mean Square Error (RMSE). The average horizontal RMSE for the left image and right image were 1.17150098m and 0.819334672m respectively and the average vertical RMSE was 1.605898565m. Additionally, the standard deviation for elevation data from generated DEM using photogrammetric technique and existing elevation data of RTK-GNSS technique were computed, which resulted in 15.91286667 and 16.59529228 respectively. The accuracy was evaluated by comparing the results of the data extracted using stereo satellite images and those extracted from ground measurement techniques (Total station and RTK-GNSS). The comparative analysis showed that stereo pair images provide a viable alternative to other ground mapping techniques providing results within the acceptable range. Based on error estimation and analysis, it concludes that, if strict photogrammetric processing model and ground control points are employed, high-resolution satellite imagery can be used for the accurate generation and update of the topographic maps, DEM and digital orthophotos, which can be further be used as a reference for comparative analysis applications.

Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations

AbstractWith the launch of Sentinel-1 in 2014, a new era of openly accessible spaceborne radar imagery was begun, and its potential has been demonstrated throughout all fields of applications. However, while interferometric approaches to detect surface deformations are continuously being published, only a few studies address the derivation of digital elevation models (DEMs) from Sentinel-1 data. This is mainly because of the narrow orbital tube, which was primarily designed for subsidence measurements using differential interferometry. Nonetheless, the technical conditions are provided for successful applications involving DEM generation. These are outlined in the first part of this article with a focus on potential error sources and the impact of the most important constraints, namely, temporal and perpendicular baselines. The second part evaluates 21 studies on this topic, their aims, and how they dealt with error sources and the necessity of validation. These studies are then discussed based on the main challenges and potentials including how these can be tackled in the future to lay a solid foundation for scientific discourse.