Digital Elevation Model Error Research Articles

Reducing systematic dome errors in digital elevation models through better UAV flight design

AbstractIt is well established that digital elevation models (DEMs) derived from unmanned aerial vehicle (UAV) images and processed by structure from motion may contain important systematic vertical errors arising from limitations in camera geometry modelling. Even when significant, such ‘dome’‐shaped errors can often remain unnoticed unless specific checks are conducted. Previous methods used to reduce these errors have involved: the addition of convergent images to supplement traditional vertical datasets, the usage of a higher number of ground control points, precise direct georeferencing techniques (RTK/PPK) or more refined camera pre‐calibration. This study confirms that specific UAV flight designs can significantly reduce dome errors, particularly those that have a higher number of tie points connecting distant images, and hence contribute to a strengthened photogrammetric network. A total of 22 flight designs were tested, including vertical, convergent, point of interest (POI), multiscale and mixed imagery. Flights were carried out over a 300 × 70 m2 flat test field area, where 143 ground points were accurately established. Three different UAVs and two commercial software packages were trialled, totalling 396 different tests. POI flight designs generated the smallest systematic errors. In contrast, vertical flight designs suffered from larger dome errors; unfortunately, a configuration that is ubiquitous and most often used. By using the POI flight design, the accuracy of DEMs will improve without the need to use more ground control or expensive RTK/PPK systems. Over flat terrain, the improvement is especially important in self‐calibration projects without (or with just a few) ground control points. Some improvement will also be observed on those projects using camera pre‐calibration or with stronger ground control. © 2020 John Wiley & Sons, Ltd.

A new method to improve the accuracy of remotely sensed data for wetland water balance estimates

Study regionThirlmere Lakes National Park, New South Wales, Australia Study focusUncertainties in water balance calculations can arise from errors associated with each of the budget input terms: precipitation, evapotranspiration, and inflows/outflows. However, uncertainties associated with the accuracy of the surface storage calculation have seldom been the focus of previous water balance studies. Digital elevation models (DEMs) used in water balance studies typically rely on bathymetric/topographic surveys, with remote-sensing techniques including satellite imaging processing, Light-Detection-and-Ranging (LiDAR), and unmanned-aerial-vehicle photogrammetry. This study investigates the vertical errors in bathymetric DEMs obtained from various remote-sensing techniques and its implication on water balance estimates in an intermittent wetland under drying conditions with vegetation encroachment. New hydrological insightsWhen bathymetry data obtained from different remote-sensing survey methods were adopted to calculate the water balance of a lake, variations in the model-predicted levels were attributed to the poor quality of photogrammetric DEMs. To improve the photogrammetric data, a new ground-filtering approach is developed, which reduces vertical errors induced by vegetation interference. The correlation (R2) of the DEMs, as compared to ground-truthed elevations, was improved from 0.5 before ground filtering to 0.9 after ground filtering. Using the ground-filtered DEM in the water balance calculation, a 70 % improvement was achieved in the water balance residuals. As such, uncertainties in lake and wetland bathymetry should be assessed in future water balance studies.

Accuracy assessment of the global TanDEM-X digital elevation model in a mountain environment

The past two decades have been prolific in production of global or near-global Digital Elevation Models (DEMs) derived from satellite data. The most recent addition to the family of global DEMs is the TanDEM-X DEM with resolution of 0.4 arc sec. DEMs are essential for a wide range of environmental applications, many of which are related to mountains including studies on natural hazards, forestry or glacier mass changes. However, synthetic aperture radar interferometry used for acquisition of TanDEM-X DEM is especially challenging over steep and irregular mountain surfaces due to shadowing and foreshortening effects. In this study, we assessed the absolute vertical accuracy of TanDEM-X DEM in European mountains. We compared it with both a Digital Terrain Model (DTM) and a Digital Surface Model (DSM) derived from airborne laser scanning data. Our results indicate that the height error of TanDEM-X DEM expressed as absolute deviation at the 90% quantile is consistent with the 10 m mission specification benchmark. We further concentrated on the absolute height error with respect to environmental characteristics (i.e. forested and non-forested areas, slope, and aspect). The comparison of TanDEM-X DEM with a reference DTM showed a positive vertical offset; however, the mean error differed greatly between forested and non-forested areas. When compared to reference DSM, our results showed a slight underestimation. We observed the highest underestimation in deciduous forests, followed by coniferous forests and non-forested areas. A significant decrease in accuracy was observed with increasing slope, especially for slopes above 10°. In mountains where the imagery was acquired only in one orbit direction (i.e. ascending for Northern hemisphere), the largest TanDEM-X DEM error when compared to DSM was recorded for the west-facing slopes (i.e. slopes facing the sensor); however, the association with terrain orientation diminished in mountains, the imagery of which was acquired from both the ascending and descending orbit. Finally, we evaluated the effect of data acquisition characteristics provided with TanDEM-X DEM as auxiliary data. Our results show that two coverages might not be sufficient in mountain environment. Additional acquisitions, especially those with different acquisition geometry, improved the absolute vertical accuracy of TanDEM-X DEM and eliminated areas of inconsistency. We discourage from using the Height Error Map (HEM) to estimate the error magnitude. On the other hand, auxiliary data (COM, COV) provide valuable information that should be always used in pre-analyses to identify possible problematic areas.

Estimation of Variance and Spatial Correlation Width for Fine-Scale Measurement Error in Digital Elevation Model

In this article, we borrow from the blind noise parameter estimation (BNPE) methodology early developed in the image processing field an original and innovative no-reference approach to estimate digital elevation model (DEM) vertical error parameters without resorting to a reference DEM. The challenges associated with the proposed approach related to the physical nature of the error and its multifactor structure in DEM are discussed in detail. A suitable multivariate method is then developed for estimating the error in gridded DEM. It is built on a recently proposed vectorial BNPE method for estimating spatially correlated noise using noise informative areas and fractal Brownian motion. The new multivariate method is derived to estimate the effect of the stacking procedure and that of the epipolar line error on local (fine-scale) standard deviation and autocorrelation function width of photogrammetric DEM measurement error. Applying the new estimator to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM2 and Advanced Land Observing Satellite (ALOS) World 3D DEMs, good agreement of derived estimates with results available in the literature is evidenced. Adopted for TanDEM-X-DEM, estimates obtained agree well with the values provided in the height error map. In future works, the proposed no-reference method for analyzing DEM error can be extended to a larger number of predictors for accounting for other factors influencing remote sensing (RS) DEM accuracy.

An Adaptive Terrain-Dependent Method for SRTM DEM Correction Over Mountainous Areas

This paper presents a terrain-related method to simultaneously correct the global error trends and local linear/nonlinear terrain-related errors of the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), which have not been focused in most of the previous methods. To meet this goal, an adaptive strategy for modelling the SRTM DEM errors is first proposed, especially over mountainous areas, based on the Bayesian information criterion. Then, the M-estimator, instead of the ordinary least squares solver, is utilized to estimate the model parameters of the constructed model to improve the estimation robustness. The proposed method was tested over the Zhangjiajie areas of China, where the ground surface terrain varies from plains to steep mountains. The results show that the errors of the SRTM DEM over the region of interest decrease from 10.1 m to approximately 8.1 m after correction with the proposed method, indicating an improvement of approximately 20%. In addition, compared with two existing and common methods that can respectively correct the global error trends and local linear terrain-related errors of the SRTM DEM, the accuracy of the corrected SRTM DEM improves by approximately 29% and 27.9% over mountainous areas with slopes larger than 20° when the proposed method is used. Moreover, the proposed method will be beneficial to the correction of other airborne or spaceborne DEM products, especially over mountainous areas.

Nonparametric Estimation of DEM Error in Multitemporal InSAR

Isolating phase residuals due to inaccurate external digital elevation model (DEM) is important in retrieval and interpretation of deformation behavior from interferometric synthetic aperture radar (InSAR) observations. Multitemporal InSAR (MTInSAR), by taking DEM error as a parameter, can make the isolation possible. However, due to the presence of atmospheric artifacts in observations and improper deformation model employed in the observation system, accurate retrieval of DEM error cannot be guaranteed in current MTInSAR techniques. Considering that the DEM error has a fixed spatial pattern and its contribution to interferometric phase-only changes with spatial baselines, we propose here a nonparametric method that can estimate the DEM error in a more robust way. To retrieve signals having a fixed spatial pattern from unwrapped MTInSAR measurements, the independent component analysis (ICA) is used. Experiments with synthetic and real data sets indicate the proposed method is able to estimate DEM error with no $a$ priori information about deformation. Moreover, experiments also show that the method can provide a more robust estimation when the observed phase observations are affected by atmospheric delays and/or the number of interferograms used is limited.

Minimizing Height Effects in MTInSAR for Deformation Detection Over Built Areas

Removing the topographic component in the interferometric synthetic aperture radar (InSAR) phase is conventionally conducted using an external digital elevation model (DEM). However, with an increasing spatial resolution of SAR data, the external DEM is becoming less qualified for this purpose, resulting in notable phase residues and even decorrelation in differential interferograms. Although topographic residuals can be parameterized and estimated by multi-temporal InSAR (MTInSAR) techniques, its accuracy is limited by several factors. Instead of providing accurate height information, shortening the length of baselines is an alternative for DEM phase mitigation. We propose here an MTInSAR processing framework that can retrieve the deformation time series without the estimation of topographic residuals. Within the framework, we generate a set of pseudo interferograms with near-zero baselines by integer combination and take these pseudo interferograms as observations of MTInSAR model, where deformation becomes the only signal that needs to be parameterized. The deformation time series is then retrieved directly from wrapped phases by ridge estimation with an integer ambiguity detector. It is noted that although atmospheric artifacts might be magnified during the combination, their differential components at arcs constructed with neighboring points that are not significantly enlarged. The proposed method is particularly suitable for infrastructure deformation monitoring in urban areas where no accurate external DEM is available. It also has promising potential for retrieving deformation from SAR data stacks with short acquisition intervals since the combination can enlarge the signal of interests in pseudo-observations. Semisynthetic and real data tests indicate that the proposed method has satisfied performance on DEM error mitigation and deformation time series estimation.

Ensemble Neural Networks for Modeling DEM Error

Digital elevation models (DEMs) have become ubiquitous and remarkably effective in the field of earth sciences as a tool to characterize surface topography. All DEMs have a degree of inherent error and uncertainty that is propagated to subsequent models and analyses, which can lead to misinterpretation and inaccurate estimates. A new method was developed to estimate local DEM errors and implement corrections while quantifying the uncertainties of the implemented corrections. The method is based on the flexibility and ability to model complex problems with ensemble neural networks (ENNs). The method was developed to be applied to any DEM created from a corresponding set of elevation points (point cloud) and a set of ground truth measurements. The method was developed and tested using hyperspatial resolution terrestrial laser scanning (TLS) data (sub-centimeter point spacing) collected from a marsh site located along the southern portion of the Texas Gulf Coast, USA. ENNs improve the overall DEM accuracy in the study area by 68% for six model inputs and by 75% for 12 model inputs corresponding to root mean square errors (RMSEs) of 0.056 and 0.045 m, respectively. The 12-input model provides more accurate tolerance interval estimates, particularly for vegetated areas. The accuracy of the method is confirmed based on an independent data set. Although the method still underestimates the 95% tolerance interval, 8% below the 95% target, results show that it is able to quantify the spatial variability in uncertainties due to a relationship between vegetation/land cover and accuracy of the DEM for the study area. There are still opportunities and challenges in improving and confirming the applicability of this method for different study sites and data sets.

How Well can Spaceborne Digital Elevation Models Represent a Man-Made Structure: A Runway Case Study

In this case study, an active runway of a civilian airport in Zonguldak, Turkey was used to assess the suitability of spaceborne digital elevation models (DEMs) to model an anthropogenic structure. The tested DEMs include the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30), the Shuttle Radar Topography Mission (SRTM)-1”, the SRTM-3”, the SRTM-X, the TanDEM-3”, and the WorldDEMTM. A photogrammetric high accuracy DEM was also available for the tests. As a reference dataset, a line-leveling survey of the runway using a Leica Sprinter 150/150M instrument was performed. The selection of a runway as a testbed for this type of investigation is justified by its unique characteristics, including its flat surface, homogenous surface material, and availability for a ground survey. These characteristics are significant because DEMs over similar structures are free from environment- and target-induced error sources. For our test area, the most accurate DEM was the WorldDEMTM followed by the SRTM-3” and TanDEM-3”, with vertical errors (LE90) equal to 1.291 m, 1.542 m, and 1.56 m, respectively. This investigation uses a method, known as the runway method, for identifying the vertical errors in DEMs.

Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area

The TanDEM-X (TDX) mission launched by the German Aerospace Center delivers unprecedented global coverage of a high-quality digital elevation model (DEM) with a pixel spacing of 12 m. To examine the relationships of terrain, vegetation, and building elevations with hydrologic, geologic, geomorphologic, or ecologic factors, quantification of TDX DEM errors at a local scale is necessary. We estimated the errors of TDX data for open ground, forested, and built areas in a coastal urban environment by comparing the TDX DEM with LiDAR data for the same areas, using a series of error measures including root mean square error (RMSE) and absolute deviation at the 90% quantile (LE90). RMSE and LE90 values were 0.49 m and 0.79 m, respectively, for open ground. These values, which are much lower than the 10 m LE90 specified for the TDX DEM, highlight the promise of TDX DEM data for mapping hydrologic and geomorphic features in coastal areas. The RMSE/LE90 values for mangrove forest, tropical hardwood hammock forest, pine forest, dense residential, sparse residential, and downtown areas were 1.15/1.75, 2.28/3.37, 3.16/5.00, 1.89/2.90, 2.62/4.29 and 35.70/51.67 m, respectively. Regression analysis indicated that variation in canopy height of densely forested mangrove and hardwood hammock was well represented by the TDX DEM. Thus, TDX DEM data can be used to estimate tree height in densely vegetated forest on nearly flat topography next to the shoreline. TDX DEM errors for pine forest and residential areas were larger because of multiple reflection and shadow effects. Furthermore, the TDX DEM failed to capture the many high-rise buildings in downtown, resulting in the lowest accuracy among the different land cover types. Therefore, caution should be exercised in using TDX DEM data to reconstruct building models in a highly developed metropolitan area with many tall buildings separated by narrow open spaces.

Baseline Estimation Using DEM Prior Knowledge and Capability Analysis for Gaofen-3 Repeat-Pass SAR Interferometry

In this paper, the study on the digital elevation model (DEM) reconstruction using Gaofen-3 satellite repeat-pass synthetic aperture radar (SAR) interferometry is presented. The coherence of the single-look complex (SLC) images and residuals of interferogram provide a powerful guarantee for interferometric processing for Gaofen-3. Baseline estimation is the key factor to generate accurate DEM with SAR interferometry. This paper uses the coarse orbit data and the precise orbit data of Gaofen-3 to carry on the baseline estimation and uses the prior coarse DEM information to refine the baseline estimation. Then, the topographic height is reconstructed with more precise spatial baseline. An experiment is conducted to verify the conclusion, and some mountain and flatten region are chosen as the study area. The results show that the DEM errors based on the prior DEM method are rapidly converged compared with the ones based on the orbit data method. Therefore, benefiting from the precise orbit data of the Gaofen-3 satellite, the accuracy of the baseline estimation can be improved further by using the method based on prior DEM information; thus, higher accuracy of DEM can be achieved so that Gaofen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, and high correlation, which has a good repeat-pass SAR interferometry application prospect.

Improving the Accuracy of Open Source Digital Elevation Models with Multi-Scale Fusion and a Slope Position-Based Linear Regression Method

The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall accuracy of public domain DEMs by data fusion. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods—the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD), and nonlinear adaptive multi-scale decomposition (N-AMD)—are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D—30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the Ice Cloud and Land Elevation Satellite (ICESat) global land surface altimetry data as a reference. The fused DEMs offer significant improvements of 29.6% and 19.3% in RMSE at a mountainous site, and 27.4% and 15.5% over a low-relief region, compared to the SRTM-1 and AW3D30, respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region, respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

Estimating Coastal Digital Elevation Model Uncertainty

Amante, C.J., 2018. Estimating coastal digital elevation model uncertainty.Integrated bathymetric–topographic digital elevation models (DEMs) are representations of Earth's solid surface that extend across the coastal land–water interface. DEMs are fundamental to the modeling of coastal processes, including tsunami, storm-surge, and sea-level-rise inundation. Vertical errors in coastal DEMs are deviations in elevation values from the actual seabed or land surface, which originate from the (1) elevation measurements, (2) datum transformation that converts bathymetric and topographic measurements to a common vertical reference system, (3) spatial resolution of the DEM, and (4) interpolative gridding technique that estimates elevations in areas unconstrained by measurements. The magnitude and spatial distribution of the vertical errors are typically unknown, and a DEM uncertainty surface is a statistical assessment of the likely magnitude of these errors. The National Oceanic and Atmospheric Administration National Centers for Environmental Information develops DEMs for United States' coastal communities. This study describes a methodology to derive uncertainty surfaces that estimate coastal DEM vertical errors at the DEM cell–level. A coastal DEM south of Sarasota, Florida is the case study for deriving uncertainty surfaces. Results indicate that large vertical uncertainty exists in deeper waters offshore with sparse echo-sounder measurements, and small vertical uncertainty exists on flat terrains with dense light detection and ranging measurements. The estimated uncertainty can be propagated into the modeling of coastal processes that utilize DEMs by deriving numerous plausible DEM realizations within the uncertainty bounds. The numerous DEMs realizations can then produce an ensemble of coastal modeling results, and in turn, better-informed coastal management decisions.

Comparison of bathymetric data sources used in hydraulic modelling of floods

Topographic data plays an essential role in hydraulic modelling of floods. A high‐precision digital elevation model (DEM) including river bathymetry (bed topography) is required. DEMs can be derived from such various data sources as ground surveying or remote sensing techniques. This study is focused on (a) the DEM error that results from the inability to scan the morphology of the channel using remote sensing methods, and (b) assessment of its impact on the results of a one‐dimensional (1D) hydraulic model. DEMs produced by remote sensing techniques were tested in combination with ground surveying and by software‐updated remote sensing data. Differences in riverbed representation and thalweg position were evaluated. The 1D hydraulic model HEC−RAS was chosen to determine the impact of various DEM sources on the hydraulic quantities (water surface elevation, inundation area). The study was carried out on a reach of the River Vltava (Czech Republic). The best results were achieved by DEMs that combined remote sensing data with ground mapping. Good results also were obtained using software‐updated remote sensing data. Neglecting of cross‐sectional area in remote sensing data has an important impact on the results of hydrodynamic models.

A new multi-resolution based method for estimating local surface roughness from point clouds

From some empirical and theoretical research on the digital elevation model (DEM) accuracy obtained for different source data densities, it can be observed that when the same degree of data reduction is applied to a whole area, the rate of change in the DEM error is statistically greater in local areas where the surface is rougher. Based on this observation, it is possible to characterize surface roughness or complexity from the differences between two digital elevation models (DEMs) built using point clouds that represent the same terrain surface but are of different spatial resolutions (or data spacings). Following this logic, a new approach for estimating surface roughness is proposed in this article. Numerical experiments are used to test the effectiveness of the approach. The study datasets considered in this article consist of four elevation point clouds obtained from terrestrial laser scanning (TLS) and airborne light detection and ranging (LiDAR). These types of topographical data are now used widely in Earth science and related disciplines. The method proposed was found to be an effective means of quantifying local terrain surface roughness.

Evaluation of Atmospheric Effects on Interferograms Using DEM Errors of Fixed Ground Points.

High-resolution synthetic aperture radar (SAR) data are widely used for disaster monitoring. To extract damaged areas automatically, it is essential to understand the relationships among the sensor specifications, acquisition conditions, and land cover. Our previous studies developed a method for estimating the phase noise of interferograms using several pairs of TerraSAR-X series (TerraSAR-X and TanDEM-X) datasets. Atmospheric disturbance data are also necessary to interpret the interferograms; therefore, the purpose of this study is to estimate the atmospheric effects by focusing on the difference in digital elevation model (DEM) errors between repeat-pass (two interferometric SAR images acquired at different times) and single-pass (two interferometric SAR images acquired simultaneously) interferometry. Single-pass DEM errors are reduced due to the lack of temporal decorrelation and atmospheric disturbances. At a study site in the city of Tsukuba, a quantitative analysis of DEM errors at fixed ground objects shows that the atmospheric effects are estimated to contribute 75% to 80% of the total phase noise in interferograms.

Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM)

ABSTRACTIn the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty.

Modeling the precision of structure-from-motion multi-view stereo digital elevation models from repeated close-range aerial surveys

The accuracy of digital elevation models (DEMs) derived from structure-from-motion (SFM) multi-view stereo (MVS) 3D reconstruction is commonly computed for a single realization of model elevations. This approach may be adequate to estimate an overall measure of systematic error; however, it cannot provide a good estimation of measurement precision. Knowing measurement precision is crucial for measuring elevation surface changes observed by DEM comparisons. In this paper, we illustrate an approach to characterize spatial variation in the precision for SFM-MVS derived DEMs. We use a snow-covered surface of an active rock glacier located in the southern French Alps as the case study. A spatially varying precision estimate is calculated from repeated close-range aerial surveys for a single acquisition period by calculating the standard deviation per grid cell between the DEMs created for each flight repetition. Regression analysis using a generalized additive model (GAM) is performed to model the estimated precision and provide insights regarding how sensor, survey design and field site conditions may spatially influence the measurement precision. Additionally, we define how DEM error can be described differently depending on the available validation data. In our study image height above ground level and distance to ground control points had the greatest explanatory power for spatial variation in DEM precision. Image overlap, mean reprojection error and saturation were also useful for explaining spatially varying measurement precision of the DEMs. Field site characteristics, such as slope angle and shading, had the least importance in our model of precision. From a practical point of view, regression-modeled relationships between precision and image and site characteristics can be utilized to design future surveys with similar sensing platforms and site conditions for improved DEM precision.

Exploring the sensitivity of coastal inundation modelling to DEM vertical error

ABSTRACTAs sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.

A global corrected SRTM DEM product for vegetated areas

ABSTRACTIt has been found that the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is systematically higher than the actual land surface in vegetated areas. This study developed a new globally corrected SRTM DEM through reducing its systematic bias in vegetated areas. Over 150, 000 km2 airborne light detection and ranging (LiDAR) data along with spaceborne LiDAR, global canopy height data, global canopy cover data and global land cover data were collected to correct the SRTM DEM. A linear regression based method was used to estimate the original SRTM DEM error and therefore correct the SRTM DEM data. The results show that the original SRTM DEM data is around 6 m higher than the actual land surfaces on average across all vegetation types. Our corrected SRTM DEM data can significantly reduce the significant bias to near zero, and can also reduce the root-mean-square error by 1 m.