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

Digital Elevation Model (DEM) errors tend to be spatially correlated, inevitably affecting DEM-based topographic change detection. Traditional topographic change detection methods often ignore the spatial distribution of the DEM error. This paper aims to develop a workflow that considers the spatial autocorrelation of the error in topographic change detection. Firstly, the DEM of Difference (DoD) is obtained from two-period DEMs, and the Monte Carlo method is employed to evaluate the Spatially Distributed Errors (SDE) in DEMs. Secondly, DoD errors are calculated by propagation based on spatially distributed DEM errors. At the same time, its spatial distribution is quantified using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial distribution analysis and significance detection. The results in two small catchments indicate that DEM errors are spatially correlated, increasing the volume calculation errors. However, using Standard Deviation of Errors (SDE) instead of Root Mean Square Error (RMSE) can effectively reduce the sensitivity of the detection results in the significance threshold. When the significance threshold increases from 68% to 95%, the observations loss using the spatially distributed error is 4.67% −6.92% lower than that using the RMSE. The level of detection has little impact on the net topographic change and significantly influences gross erosion and deposition. In particular, the use of level of detection can effectively reduce the misclassification of erosion or deposition in stable topography areas. The proposed method can be effectively utilized in various applications like surface deformation monitoring, erosion monitoring, and sediment transport assessment.

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.

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.

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.

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

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

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 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.

High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old “legacy” data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003–2013 period changed from 3,237,896 m3 to 135,902 m3 in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m3. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

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.

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.

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%.

A proper understanding of the occurrence of depressions is necessary to understand how they affect the processing of a Digital Elevation Model (DEM) for hydrological analysis. While the effect of DEM cell resolution on common terrain derivatives has been well established, this is not well understood for depressions. The more widespread availability of high resolution DEMs derived through Light Detection and Ranging (LIDAR) technologies presents new challenges and opportunities for the characterization of depressions. A 6-meter LIDAR DEM for a study watershed in North Carolina was used to determine the effect of DEM cell resolution on the occurrence of depressions. The number of depressions was found to increase with smaller cell sizes, following an inverse power relationship. Scale-dependency was also found for the average depression surface area, average depression volume, total depression area and total depression volume. Results indicate that for this study area the amount of depressions in terms of surface area and volume is at a minimum for cell sizes around 30 to 61 meters. In this resolution range there will still be many artificial depressions, but their presence is less than at lower or higher resolutions. At finer scales, the (small) vertical error of the LIDAR DEM needs to be considered and introduces a large number of small and shallow artificial depressions. At coarser scales, the terrain variability is no longer reliably represented and a substantial number of large and sometimes deep artificial depressions is created. The results presented here support the conclusion that the use of the highest resolution and most accurate data, such as LIDAR-derived DEMs, may not result in the most reliable estimates of terrain derrivatives unless proper consideration is given to the scale-dependency of the parameters being studied.

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.

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.