DEM-based topographic change detection considering the spatial distribution of errors

Interferometric Synthetic Aperture Radar (InSAR) has become an essential tool for monitoring surface deformation with high precision and wide spatial coverage. Among various InSAR techniques, the Stacking InSAR approach is widely used for geological hazard assessments due to its computational efficiency and robustness against decorrelation noise. However, Digital Elevation Model (DEM) errors remain a significant challenge, introducing spurious deformation signals and degrading deformation rate estimates. We present here a rigorous analysis of the impact of DEM errors on Stacking InSAR-derived deformation rates and introduce an enhanced method that effectively mitigates these errors. Unlike conventional correction techniques that require explicit DEM error estimation, the proposed method leverages perpendicular baseline averaging, eliminating DEM-induced biases while maintaining computational simplicity. The method is validated using both simulated and real Sentinel-1A datasets from two tracks, with results compared against conventional Stacking and Small Baseline Subset (SBAS) approaches. The findings demonstrate that the proposed method significantly improves deformation rate estimation by suppressing DEM-induced artifacts, thereby enhancing the reliability of InSAR applications in geological hazard monitoring and deformation assessment.

The Dead Sea water‐level has been dropping at an exceedingly increasing rate since 1960, and between 1993 and 2001, the interval of the InSAR data examined in this study, it has dropped at an average rate of 0.88 m per year. Such a water‐level change could potentially give rise to a resolvable lithospheric rebound and regional uplift, with spatial extent and amplitude that are controlled by the effective mechanical properties of the crust and upper mantle combined. We measure that deformation for the years 1993 to 2001, using 149 short baseline interferograms made of 31 ERS‐1 and ERS‐2 Synthetic Aperture Radar (SAR) images and continuous GPS data from the Survey of Israel recorded between 1997 and 2011. The uplift rate at the Dead Sea is small (up to 4 mm/year), and the basin topography is almost a mirror of the displacement, introducing a strong trade‐off between uplift and stratified atmosphere noise. To overcome this complication, we impose a linearity constraint on the satellite to ground Line Of Sight (LOS) phase changes based on the steady uplift observed by a continuous GPS station in the area of interest, and simultaneously solve for the LOS change rate, Digital Elevation Model (DEM) errors and the elevation‐phase correlation. While the LOS rate and DEM errors are solved for each pixel independently, the elevation‐phase correlation is solved for each SAR acquisition independently. Using this approach we separated the stratified atmospheric delay from the ground displacement. We observed a regional uplift around the Dead Sea northern basin, with maximum uplift close to the shorelines, and diminishing to zero by the Mediterranean coast. We modeled the effect of water load changes using a homogeneous elastic half‐space, and found a good agreement between modeled and observed ground displacements using elastic properties that are compatible with seismic and gravity data down to a depth of 15 km below the Dead Sea basin, suggesting that the response of the crust to the sea level drop is controlled mainly by the elastic properties of the upper‐crust immediately below the Dead Sea basin.

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.

Abstract. The Antarctic Peninsula (AP) is one of the widely studied polar regions because of its sensitivity to climate change and potential contribution of its glaciers to global sea level rise. Precise digital elevation models (DEMs) at a high spatial resolution are much demanded for investigating the complex glacier system of the AP at fine scales. However, the two most recent circum-Antarctic DEMs, the 12 m TanDEM-X DEM (TDM DEM) from bistatic interferometric synthetic aperture radar (InSAR) data acquired between 2013 and 2014 and the Reference Elevation Model of Antarctica mosaic (REMA mosaic) at an 8 m spatial resolution derived from optical data acquired between 2011 and 2017 have specific individual limitations in this area. The TDM DEM has the advantage of good data consistency and few data voids (approx. 0.85 %), but there exist residual systematic elevation errors such as phase-unwrapping errors in the non-edited DEM version. The REMA mosaic has high absolute vertical accuracy, but on the AP it suffers from extended areas with data voids (approx. 8 %). To generate a consistent, gapless and high-resolution topography product of the AP, we fill the data voids in the TDM DEM with newly processed TDM raw DEM data acquired in austral winters of 2013 and 2014 and detect and correct the residual systematic elevation errors (i.e., elevation biases) in the TDM DEM with the support of the accurately calibrated REMA mosaic. Instead of a pixelwise replacement with REMA mosaic elevations, these provide reference values to correct the TDM elevation biases over entire regions detected through a path propagation algorithm. The procedure is applied iteratively to gradually correct the errors in the TDM DEM from a large to small scale. The proposed method maintains the characteristics of an InSAR-generated DEM and is minimally influenced by temporal or penetration differences between the TDM DEM and REMA mosaic. The performance of the correction is evaluated with laser altimetry data from Operation IceBridge and ICESat-2 missions. The overall root mean square error (RMSE) of the corrected TDM DEM has been reduced from more than 30 m to about 10 m which together with the improved absolute elevation accuracy indicates comparable values to the REMA mosaic. The generated high-resolution DEM depicts the up-to-date topography of the AP in detail and can be widely used for interferometric applications as well as for glaciological studies on individual glaciers or at regional scales.

The effect of error in gridded digital elevation models on the estimation of topographic parameters

In this paper, we propose a method for estimating the position of an airborne platform using a digital elevation model (DEM) through data fusion. Given information of a terrain, the localization of the platform is possible through a template matching process if a local DEM under the platform can be measured. For better position estimation, we calculate the gradients of DEM and use it in the matching process. The position of the platform is determined as a point that has a minimum matching error in DEM and its gradients. To combine the error costs, we employed information theory to adaptively determine the weight between data. Based on the suggested method, the matching error can be improved by 55%.

There is a growing interest in investigating the accuracy of digital elevation model (DEM). However people usually have an unbalanced view on DEM errors. They emphasize DEM sampling errors, but ignore the impact of DEM resolution and terrian roughness on the accuracy of terrain representation. This research puts forward the concept of DEM terrain representation error (Et) and then investigates the generation, factors, measurement and simulation of DEM terrain representation errors. A multi-resolution and multi-relief comparative approach is used as the major methodology in this research. The experiment reveals a quantitative relationship between the error and the variation of resolution and terrain roughness at a global level. Root mean square error (RMS Et) is regressed against surface profile curvature (V) and DEM resolution (R) at 10 resolution levels. It is found that the RMS Et may be expressed asRMS Et=(0.0061·V+0.0052)·V+0.2415. This result may be very useful in forecasting DEM accuracy, as well as in determining the DEM resolution related to the accuracy requirement of particular application.

The past decade has witnessed the rapid development of the SRTM (Shuttle Radar Topography Mission) DEM (digital elevation model) in engineering applications and scientific research. The near-global SRTM DEM was generated based on radar interference theory. The latest version of the SRTM DEM with a resolution of 1 arc-second has been widely used in various applications. However, many studies have shown the poor elevation accuracy of the SRTM DEM in forested areas. Recent developments in the field of spaceborne lidar have provided an additional chance to correct the elevation error of the SRTM DEM in forested areas. We developed an easy-to-use method to correct the elevation error of the SRTM DEM based on the spatial interpolation method using the recent Ice, Cloud and land Elevation Satellite-2 data. First, an ICESat-2 terrain control point selection criterion was proposed to reject some erroneous ICESat-2 terrains caused by many factors. Second, we derived the elevation correction surface based on the interpolation method using the refined ICESat-2 terrain. Finally, a corrected SRTM DEM of forested areas was generated through the obtained elevation correction surface. The proposed method was tested in the typical forested area located in Massachusetts, USA. The results show that the RMSE of the selected terrain control points in vegetation areas and non-vegetation areas are 1.03 and 0.68 m, respectively. The corrected SRTM DEM have an RMSE of 4.2 m which is significantly less than that of the original SRTM DEM with an RMSE of 9.8 m, which demonstrates the proposed method is feasible to correct the elevation error in forested areas. It can be concluded that the proposed method obviously decreases the elevation error of the original SRTM DEM.

This study attempts to investigate the relationship between the accuracy of different Digital Elevation Model (DEM) and fractal dimension D and to solve the problem of determining the optimal catchment area threshold in plain watersheds. In this study, the fractal dimensions of the Shuttle Radar Topographic Survey Digital Elevation Model (SRTM) V4.1 DEM, Hydrology 1K (HYDRO1K) DEM, and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) with 90 m horizontal resolution and 30 m ASTER GDEM were calculated using the box dimension method, and the relationship between the horizontal resolution and accuracy of three data sources and fractal dimension D was studied. The optimal catchment area threshold in the study area was determined. The response of river network similarity and topographic features to DEM accuracy was explored, and the optimal catchment area threshold for the study area was verified. The result shows that, with the increase in the catchment area threshold, the fractal dimension D shows three stages of rapid decline, gentle fluctuation, and tend to 1. Compared with the horizontal resolution of DEM, the vertical accuracy has more influence on the fractal dimension D. The fractal dimension D accuracy increases with the increase in the vertical accuracy of DEM. The main order of influence of the three data sources is SRTM V4.1 DEM > ASTER GDEM > HYDRO1K DEM. The fractal dimension of the digital river network extracted by SRTM V4.1 DEM is 1.0245, the same as the fractal dimension of the actual river network. The optimal catchment area threshold of the study area is 4.05 km2, which has the highest coincidence with the actual river network. In summary, using the SRTM V4.1 DEM as the DEM data source is feasible to determine the optimal catchment area threshold in plain watersheds.

Accurate measurements of glacier surface topography and their changes play an essential role in various glaciological studies related to glacier dynamics and mass balance. The focus of this study is on mapping glacier digital elevation model (DEM) and elevation changes in the western Qilian Mountains, northern Tibetan Plateau, by synergistically using the TanDEM-X (TDX) bistatic Interferometric Synthetic Aperture Radar (InSAR) data in 2013 and Shuttle Radar Topography Mission (SRTM) DEM in 2000. The first high-resolution and high-precision glacier DEM is derived in this region by a TDX InSAR procedure with a nonlocal (NL) filter. Validated against the Ice, Cloud, and land Elevation Satellite (ICESat) height references, the absolute height error of the TanDEM-X DEM derived with the NL filter and the Goldstein filter with the parameters investigated is, respectively, 1.493 ± 0.747 and 1.857 ± 1.709 m. Further, four combinations of differential phase method (DiffPha) and DEM differencing method (DiffDem) with Goldstein filter and NL filter are applied to estimate glacier elevation changes between 2000 and 2013. The synergistic use of the DiffPha method and the NL filter is superior to other three combinations in terms of uncertainty and noise reduction. Generally, a clear surface thinning can be found in most glacier tongue regions, the maximum value of elevation lowering up to approximately −40 m, whereas a slight thickening is detected in accumulation areas, which are in agreement with the height difference results between GPS measurements and SRTM DEM over Laohugou Glacier No.12. This study demonstrates the potential of the TanDEM-X bistatic InSAR in mapping surface topography and elevation changes of valley glaciers in the Tibetan Plateau.

Landscape-scale geomorphic change detection: Quantifying spatially variable uncertainty and circumventing legacy data issues

Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments

Errors in digital elevation models (DEMs) will introduce errors in geoid and quasi-geoid models, via their use in interpolating free-air gravity anomalies and (in the case of the quasi-geoid) their use in computing the Molodensky G1 term. The effects of these errors and those of datum shifts are assessed using three independent DEMs for a test region in South Africa. It is shown that these effects are significant and that it is important to choose the best-possible DEM for use in geoid and quasi-geoid modelling.

This paper describes the theoretical relation between interferometric phase and changes in snow water equivalent (SWE) and show results from experiments using ERS- 1/2 tandem data. The main scattering contribution from a dry snow cover is from the snow-ground interface. However, the radar wave will be refracted in the snow. Thus, only small changes in the snow properties between two interferometric synthetic aperture radar (SAR) images will change the interferometric phase. This phase change is shown to introduce a significantly increase in the digital elevation model (DEM) height error, although no effects are observed on the degree of coherence. The phase change is also shown to affect the differential interferometric results and may wrongly be interpreted as range displacement. The presented theory and results implies that light snowfall and/or small changes in snow properties between interferometric SAR (InSAR) image acquisitions, may introduce significant height errors in DEM derived from glaciers, ice sheets, or bare ground, even in the case of high degree of coherence. Thus, meteorological observations in addition to degree of coherence must be considered when generating DEM in areas covered with snow or where snow fall is likely to have occurred.

Interferometric synthetic aperture radar (InSAR) processing, if lacking high quality ground control points (GCPs), may produce large errors in the final DEM. Some kind of alignment or registration can reduce these errors. This paper evaluates the accuracy of InSAR processed digital elevation models (DEM), against a high resolution DEM. InSAR DEMs were aligned and least-squares registered with USGS, 1/3 arc second NED. The accuracy evaluations after registration show InSAR DEMs alignment and registration can eliminate DEM errors caused by lack of accurate GCPs. In some cases, this process improves the accuracy of the InSAR DEM even more efficiently than including GCPs into InSAR processing.