Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

Urban flood models that use Digital Elevation Models (DEMs) to simulate extent and depth of flood inundation rely on the accuracy of DEMs for predicting flood events. Despite recent advances in developing vegetation corrected DEMs, the effect of building height and density errors in global DEMs in urban areas are still poorly understood, and their correction remains a challenge. In this research we developed a methodology for building error correction that can be applied to any other case study, where building density data and a local reference DEM data are available. This methodology was applied to Nairobi, Kenya using six global DEMs (SRTM, MERIT, ALOS, NASADEM, TanDEM-X 12 m, and TanDEM-X 90 m DEM). Our results show building error at highest building density varying between 1.25 m and 5.07 m for the DEMs used, with the MERIT DEM showing the smallest vertical height error from the reference DEM. The six DEMs were corrected by deriving a linear relationship between building density and DEM error. Our findings show that the removal of building density error resulted in the improvement of the vertical height accuracy of the global DEMs of up to 45% for MERIT and 40% for ALOS. This methodology was also applied to the Central Business District (CBD) area of Nairobi, characterized by taller buildings and high building density. The error parameters in the CBD area resulted to be between 15 to 45% higher than those of the Nairobi city wide area for the six global DEMs, thus providing further insights into the contribution of building heights to errors in global DEMs. Building height data is still unavailable on a global scale and our results show that global DEMs can be usefully corrected for building density errors in urban areas, even where specific building height data are not available.

TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) mission is designed to generate 3-D images of the Earth as the first bistatic synthetic aperture radar (SAR). However, few quantitative studies of TanDEM-X digital elevation model (DEM) quality validation have been conducted specifically in China. This article presents an iterative method to generate high-resolution TanDEM-X DEMs and assesses the vertical accuracy with high-accuracy GPS observations, 1 arc second global DEMs available [Advanced Land Observing Satellite World 3-D-30 m v2.2, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) v3, Shuttle Radar Topography Mission (SRTM) v3.1, NASADEM, and X-band SRTM], and TanDEM-X 90 m DEM. The results demonstrate remarkable elevation quality and consistency in coastal areas with root mean square error of 1.7 m and 90% linear error (LE90) of 0.4 m, whereas 3–4 times weaker accuracies in steep mountainous areas. A positive bias of 1–2 m for an overall LE90 measure exists in the dense vegetation and steep-slope mountainous areas. TanDEM-X DEM-based SAR interferometry deformation uncertainty simulation indicates a low or even negligible topographic error contribution of 2–4 mm in mountainous areas and less than 1 mm in coastal areas. It indicates that the TanDEM-X DEM performs better than other global DEMs overall and shows a better elevation consistence with SRTM C-band DEM in the coastal area. As an excellent source of up-to-date information, the TanDEM-X DEM are expected be an advantage for understanding dynamic land use changes and improving identification and delineation of coastal lands, mountainous landslides, and earthquakes disasters.

Formulation of the problem. Water erosion of soils is the most widespread and dangerous soil degradation process in Ukraine. The development of an effective system of soil protection measures requires the use of spatially distributed mathematical models of soil erosion losses. This, in turn, highlights the problem of spatially distributed source data, which adequately reflect the spatial differentiation of factors of the erosion process, among which the main one is the relief. The purpose of the article. Assessing the adequacy of available spatially distributed source data, including cartographic and freely distributed global digital elevation models (DEMs), for spatially distributed quantitative assessment of soil erosion losses at the local level of territorial coverage is the aim of the article. Assessing from this point of view the scale of the original cartographic data, different global DEMs and their spatial resolution, as well as the degree of spatial generalization of the original data. Materials and methods. The solution of the set tasks was performed by the method of simulation modeling with the use of physical-statistical GIS-realized mathematical model of soil erosion-accumulation, developed at Odessa I. I. Mechnikov National University. Source data arrays were tested with DEMs SRTM90 and SRTM30 with a spatial resolution of 3 and 1 angular seconds, respectively, and AW3D30 with a spatial resolution of 1 angular second, as well as with cartographic DEMs based on topographic maps of scale 1:10000 and 1:25000. For testing the initial data, three test plots with an area of 2.67, 0.59 and 0.21 km2 were selected. The plots are located in the Balta district of Odessa region on the southern spurs of the Podolska upland. Results. It is established that freely distributed global digital elevation models SRTM and AW3D30 in the conditions of flat terrain do not always allow to adequately display the structure of slope runoff and, accordingly, to correctly perform calculations of soil erosion losses. The maximum deviation of the average soil erosion losses calculated for the test plots using global DEMs from the soil losses calculated using the reference DEM for SRTM30 and AW3D30 was 27%, for SRTM90 – almost 70%. The distribution of soil losses over the area of test plots obtained using different global DEMs differs even more. When using DEM based on topographic maps, reducing the scale of the original maps from 1: 10000 to 1: 25000 leads to a decrease in the average value of soil erosion losses by about 20% due mainly to reducing the magnitude and area of distribution of maximum soil losses, and on slopes of complex shape also due to changes in the area of accumulation zones. The degree of spatial generalization of the initial data significantly affects the results of the assessment of soil erosion losses both in relation to the average values and their distribution over the area. For small areas, the use of raster cells larger than 50 m is impractical. Scientific novelty and practical significance. It has been shown for the first time that in the conditions of flat terrain at the local level of spatial coverage, the freely distributed global DEM SRTM and AW3D30 are not always hydrologically correct. The reasons and conditions of violation of this correctness are specified. It has been established that the global DEM AW3D30 has local instrumental errors that may make it impossible to use it. The most realistic values of soil erosion losses are provided by DEM SRTM with a spatial resolution of 1 angular second.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation [1]–[4]. With a typical separation between the satellites of 120 to 500 m a global Digital Elevation Model (DEM) with 2 m relative height accuracy at 12 m posting is being generated. While the main mission phase for DEM data acquisition has been finished in 2014, the processing of the global TanDEM-X DEM will be concluded in autumn 2016. Final DEMs are well within specifications and feature a low percentage of void areas. Following the DEM data acquisition the capabilities of this unique mission for new scientific application have been demonstrated. In the meantime the continuation of the joint TerraSAR-X/TanDEM-X mission was approved and will be dedicated to the generation of DEMs with even higher accuracy for selected areas and further scientific experiments.

Digital Elevation Models (DEMs) contribute to geomorphological and hydrological applications. DEMs can be derived using different remote sensing-based datasets, such as Interferometric Synthetic Aperture Radar (InSAR) (e.g., Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) and Shuttle Radar Topography Mission (SRTM) DEMs). In addition, there is also the Digital Surface Model (DSM) derived from optical tri-stereo ALOS Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) imagery. In this study, we evaluated satellite-based DEMs, SRTM (Global) GL1 DEM V003 28.5 m, ALOS DSM 28.5 m, and PALSAR DEMs 12.5 m and 28.5 m, and their derived channel networks/orders. We carried out these assessments using Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) and their derived channel networks and Strahler orders as reference datasets at comparable spatial resolutions. We introduced a pixel-based method for the quantitative horizontal evaluation of the channel networks and Strahler orders derived from global DEMs utilizing confusion matrices at different flow accumulation area thresholds (ATs) and pixel buffer tolerance values (PBTVs) in both ±X and ±Y directions. A new Python toolbox for ArcGIS was developed to automate the introduced method. A set of evaluation metrics—(i) producer accuracy (PA), (ii) user accuracy (UA), (iii) F-score (F), and (iv) Cohen’s kappa index (KI)—were computed to evaluate the accuracy of the horizontal matching between channel networks/orders extracted from global DEMs and those derived from LiDAR DTMs and DSMs. PALSAR DEM 12.5 m ranked first among the other global DEMs with the lowest root mean square error (RMSE) and mean difference (MD) values of 4.57 m and 0.78 m, respectively, when compared to the LiDAR DTM 12.5 m. The ALOS DSM 28.5 m had the highest vertical accuracy with the lowest recorded RMSE and MD values of 4.01 m and −0.29 m, respectively, when compared to the LiDAR DSM 28.5 m. PALSAR DEM 12.5 m and ALOS DSM 28.5 m-derived channel networks/orders yielded the highest horizontal accuracy when compared to those delineated from LiDAR DTM 12.5 m and LiDAR DSM 28.5 m, respectively. The number of unmatched channels decreased when the PBTV increased from 0 to 3 pixels using different ATs.

Over the last years the TanDEM-X mission acquired data for a second global digital elevation model (DEM) the TanDEM-X Change DEM. This new DEM is temporally independent of the former global TanDEM-X DEM and therefore yields the possibility of change detection. In order to decrease the phase noise level the interferometric processing for the Change DEM has been upgraded. This also allows a more accurate change detection. Currently, the processing of the global data is performed operationally. It includes the detection of terrain changes and first examples of detected terrain changes can be presented.

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.

The Landsat Collection-2 distribution introduces a new global Digital Elevation Model (DEM) for scene orthorectification. The new global DEM is a composite of the latest and most accurate freely available DEM sources and will include reprocessed Shuttle Radar Topographic Mission (SRTM) data (called NASADEM), high-resolution stereo optical data (ArcticDEM), a new National Elevation Dataset (NED) and various publicly available national datasets including the Canadian Digital Elevation Model (CDEM) and DEMs for Sweden, Norway and Finland (SNF). The new DEM will be available world-wide with few exceptions. It is anticipated that the transition from the Collection-1 DEM at 3 arcsecond to the new DEM will be seamless because processing methods to maintain a seamless transition were employed, void filling techniques were used, where persistent gaps were found, and the pixel spacing is the same between the two collections. Improvements to the vertical accuracy were realized by differencing accuracies of other elevation datasets to the new DEM. The greatest improvement occurred where ArcticDEM data were used, where an improvement of 35 m was measured. By using theses improved vertical values in a line of sight algorithm, horizontal improvements were noted in some of the most mountainous regions over multiple 30-m Landsat pixels. This new DEM will be used to process all of the scenes from Landsat 1-8 in Collection-2 processing and will be made available to the public by the end of 2020.

Abstract. Digital Elevation Models (DEMs) are fundamental for ensuring the geolocation accuracy of Synthetic Aperture Radar (SAR) imagery, as vertical errors in elevation data propagate into horizontal displacements. This study examines the impact of DEM accuracy on the geolocation performance of Sentinel-1 Ground Range Detected (GRD) products over an urban area in Ankara, Türkiye. A comparative analysis was conducted using four global DEMs (SRTM 1″, SRTM 3″, AW3D30, Copernicus 30 m), a high- resolution national DEM (YÜKPAF), and two interferometrically derived DEMs generated from Sentinel-1 Single Look Complex (SLC) pairs. Vertical accuracy was validated against ICESat-2 ATL08 reference elevations, with Copernicus (2.2 m RMSE) and YÜKPAF (3.2 m RMSE) providing the highest reliability, while the InSAR-derived DEMs showed larger errors (>12 m) due to coherence loss and phase unwrapping inconsistencies. Horizontal accuracy was evaluated using Ground Control Points (GCPs) obtained from HGM-Küre. The results demonstrated that high-quality DEMs, particularly YÜKPAF, achieved the lowest horizontal RMSE (8.2 m), whereas InSAR-based DEMs produced the largest errors, approaching 15 m. Despite these variations, all orthorectified outputs remained below 15 m geolocation error, owing to the precise orbital information of Sentinel-1 and the flat topography of the study area. Overall, the study confirms that DEM quality is a decisive factor for SAR geolocation accuracy and offers practical guidance for dataset selection in operational SAR orthorectification workflows.

Artefact detection in global digital elevation models (DEMs): The Maximum Slope Approach and its application for complete screening of the SRTM v4.1 and MERIT DEMs

<p>With growing urbanisation and climate change, flooding is likely to become even more frequent and severe.  Therefore, it is essential to constantly monitor water level changes at a large scale. Synthetic Aperture Radar (SAR) images are often used for flood mapping as they allow a rather straightforward detection of water bodies, at almost all weather and illumination conditions. As the number of satellite observations and global digital elevation models (DEMs) are becoming more available, this mapping approach is gaining in popularity.</p><p>In this study, we propose three different approaches to retrieve water level maps based on the combination of satellite and topographic data, hereby referred to as the global Height Above Nearest Drainage (HAND), the local HAND and the local DEM methods.</p><p>The three approaches are based on the optimization of a threshold applied on the topography (HAND or DEM) data enabling a best fit with the SAR-extracted flood map. The optimized threshold values provide at the same time the normalized water levels (with respect to the drainage network). The water depth map is thus computed from the difference between the water level and the DEM. The global HAND method applies a single optimized threshold to the HAND map, over the entire area of interest. The local HAND method is based on the same concept but optimizes and applies the HAND threshold value locally, using a sliding window. The local DEM thresholding method employs the same principle as the second method but directly on the DEM.</p><p>We evaluate these methods using hydraulic model simulation results and ground truth data, and we carry out several experiments using various SAR images (Envisat, TerraSAR-X and Sentinel-1) and topographic datasets (SRTM, CopDEM and LiDAR).</p><p>The best results are obtained while combining a high-resolution image (e.g:TerraSAR-X) with: 1) a high-resolution dataset (e.g: LiDAR DEM), using the local DEM approach, or 2) a coarse-resolution dataset (e.g: Srtm DEM), using the global HAND approach. RMSDs on the derived water depth maps reach respectively 0.52m and 0.93m.</p>

The primary objective of the TanDEM-X mission is the generation of a global, consistent, and high-resolution digital elevation model (DEM) with unprecedented global accuracy. The goal is achieved by exploiting the interferometric capabilities of the two twin SAR satellites TerraSAR-X and TanDEM-X, which fly in a close orbit formation, acting as an X-band single-pass interferometer. Between December 2010 and early 2015 all land surfaces have been acquired at least twice, difficult terrain up to seven or eight times. The acquisition strategy, data processing, and DEM calibration and mosaicking have been systematically monitored and optimized throughout the entire mission duration, in order to fulfill the specification. The processing of all data has finally been completed in September 2016 and this paper reports on the final performance of the TanDEM-X global DEM and presents the acquisition and processing strategy which allowed to obtain the final DEM quality. The results confirm the outstanding global accuracy of the delivered product, which can be now utilized for both scientific and commercial applications.

Started in June 2010, the TanDEM-X satellite joined the TerraSAR-X satellite in space to perform the conjoint interferometric TanDEM-X mission to acquire a truly global Digital Elevation Model (DEM) of unprecedented accuracy [1]. Since the very first interferometric acquisitions, the Integrated TanDEM-X Processor (ITP) delivered operationally “Raw”-DEMs and complex products of mono- and bistatic data. The RawDEMs are scenes of about 50km × 30km, generated for a dedicated DEM Mosaicking and Calibration Processor (MCP) which produces the final DEM. The so-called Coregistered Single-look Slant-range Complex (CoSSC) products are provided for each of these scenes in different flavors for production internal purposes and system performance monitoring as well as for scientific use. The capabilities of the ITP go far beyond the primary mission objective of DEM generation alone: it also provides the operationally available end-user products from different experimental modes as e.g. pursuit monostatic, dual polarization bistatic data, alternating bistatic in single and dual polarization and different bistatic and alternating bistatic spotlight modes. This paper focuses on the accuracy of the generated products, the ITPs contribution to the achieved accuracy of the data and the direct effect of it on the use and interpretation of RawDEM heights for temporal change detection. Also the basic characteristics of the operational experimental products are introduced‥

Abstract. Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methods have been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive to topographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The study area was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correction method. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) and tasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, we evaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and a regional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimum and maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightness and had very small effect on the other models. Cosine of the solar incidence angle (cos i) derived from SRTM DEM showed stronger relationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive models based on common vegetation indices are not sensitive to topographic effects.

Accurate elevation data are essential for effective oil palm plantation planning, hydrological modeling, and environmental management particularly in tropical regions characterized by complex topography and dense vegetation. While LiDAR-derived Digital Elevation Models (DEMs) offer high vertical precision, their acquisition costs often hinder adoption in resource-limited settings. Open-source DEMs provide accessible alternatives but are frequently affected by vegetation interference and coarse resolution, leading to reduced vertical accuracy. This study proposes a hybrid correction framework that integrates a random forest (RF) machine learning algorithm and a geographically weighted regression (GWR) a spatially adaptive statistical method to enhance the vertical accuracy of open-source DEMs for terrain-sensitive applications. The study used 377 high-precision Ground Control Points (GCPs) and LiDAR data to evaluate and correct six global DEMs: Advanced Land Observing Satellite (ALOS), TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), Copernicus GLO-30, Shuttle Radar Topography Mission (SRTM), Forest And Buildings removed Copernicus DEM (FABDEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM, and a high-resolution drone-derived DEM. RF was used to identify key topographic predictors, including aspect, slope, curvature, Topographic Position Index (TPI), and Terrain Ruggedness Index (TRI), while GWR applied spatially adaptive corrections to the RF residuals. The integrated RF–GWR model significantly improved the vertical accuracy across all DEMs. The post-correction R² values reached 0.914 for TanDEM-X, 0.910 for ALOS, and increased from 0.608 to 0.914 for Copernicus, with the residual standard deviations reduced by up to 75% and near-zero mean bias. These results highlight the model’s ability to correct both systematic and spatially varying elevation errors. The framework presents a scalable and alternative to LiDAR for use in precision agriculture, flood risk modeling, and infrastructure planning. Future work should explore integration with deep learning to improve the temporal responsiveness and operational scalability.