Digital Elevation Models Uncertainty Research Articles

Bayesian estimation of glacier surface elevation changes from DEMs

Accurate estimates of glacier surface elevation changes are paramount for various aspects of the study of the cryosphere, from glacier flow and thickness estimates to hydrological forecasts and projections of sea-level-rise. We present a novel probabilistic framework to filter outliers and estimate uncertainties in glacier surface elevation changes computed from the subtraction of digital elevation models (DEM). Our methodology frames outlier filtering as a Bayesian inference problem, thus characterizing the state of knowledge on glacier surface elevation changes through the posterior distribution as the combination of glacier volume variation observations and prior knowledge arising from previously collected data and/or modeled results. We validate this technique with experiments using Gaussian random fields to generate artificial noise in glacier surface elevation variation observations and show that the model satisfactorily culls the simulated outliers. Surface elevation change estimates are consistent with results computed from widely-used outlier filtering and uncertainty estimation techniques. The Bayesian framework allows unifying DEM error models with physical considerations on glacier surface elevation changes within a simple, statistically coherent model preventing temporal correlation and additional biases in other techniques. On the basis of these results, we discuss the implications of DEM uncertainty and offer suggestions for the glaciological community.

Reducing vertical bias and error in tidal marsh digital elevation models with machine learning and LiDAR derivatives

The vertical bias and uncertainty of LiDAR-derived elevation data in tidal marshes is a major challenge for researchers in these critical ecosystems. Small positive bias errors in land surface elevation can lead to large underestimates of coastal inundation. Previous studies have shown that the bias and uncertainty of elevation data can be reduced using a variety of statistical techniques. However, many studies cover a relatively small extent, cover a large extent at coarse resolution, or rely on local data products that may not be widely available or are of unknown quality. This study aimed to develop and compare bias reduction approaches for digital elevation models (DEMs) using LiDAR derivatives in tidal marshes along Delaware Bay, Delaware, USA, a region that experiences high rates of relative sea-level rise and frequent coastal flooding. In this study, several statistical and machine-learning approaches were evaluated based on their ability to reduce vertical DEM error using field GPS survey data and LiDAR and terrain derivatives as inputs. The evaluated approaches for reducing DEM error included ensembles of multiple linear regressions, kernel-K Nearest Neighbors, gradient boosted regression trees, random forests, and deep neural networks (DNNs), which were compared based on statistical performance metrics. An ensemble of deep neural networks performed best at removing vertical DEM bias and reducing DEM uncertainty, though other approaches also performed well. GPS survey data indicated a mean absolute error, mean bias, and root mean square error in GPS surveys of 11.9 cm, 10.9, and 14.8 cm, respectively, in the original DEM. These error metrics were reduced to 3.5 cm, −0.2 cm, and 5.1 cm in the DNN ensemble-corrected DEM. The DNN ensemble was then applied to all tidal marshes throughout the study area. This corrected DEM clearly showed how interpretations of marsh platform inundation based on uncorrected DEM surfaces could be misguided. The approach used in this study only requires LiDAR-derived products and field surveys, so it may be applied readily in other regions.

Image Texture as Quality Indicator for Optical DEM Generation: Geomorphic Applications in the Arid Central Andes

The generation of Digital Elevation Models (DEMs) through stereogrammetry of optical satellite images has gained great popularity across various disciplines. For the analysis of these DEMs, it is important to understand the influence of the input data and different processing steps and parameters employed during stereo correlation. Here, we explore the effects that image texture, as well as the use of different matching algorithms (Block Matching (BM) and More Global Matching (MGM)), can have on optical DEMs derived from the flexible, open-source Ames Stereo Pipeline. Our analysis relies on a ∼2700 km2 clip of a SPOT6 tristereo scene covering the hyperarid, vegetation-free Pocitos Basin and adjacent mountain ranges in the northwestern Argentine Andes. A large, perfectly flat salt pan (paleolake bed) that covers the center of this basin is characterized by strong contrasts in image texture, providing a unique opportunity to quantitatively study the relationship between image texture and DEM quality unaffected by topography. Our findings suggest that higher image texture, measured by panchromatic variance, leads to lower DEM uncertainty. This improvement continues up to ∼103 panchromatic variance, above which further improvements in DEM quality are independent of local image texture but instead may have sensor or geometric origins. Based on this behavior, we propose that image texture may serve as an important proxy of DEM quality prior to stereo correlation and can help to set adequate processing parameters. With respect to matching algorithms, we observe that MGM improves matching in low-texture areas and overall generates a smoother surface that still preserves complex, narrow (i.e., ridge and valley) features. Based on this sharper representation of the landscape, we conclude that MGM should be preferred for geomorphic applications relying on stereo-derived DEMs. However, we note that the correlation kernel selected for stereo-matching must be carefully chosen depending on local image texture, whereby larger kernels generate more accurate matches (less artifacts) at the cost of smoothing results. Overall, our analysis suggests a path forward for the processing and fusion of overlapping satellite images with suitable view-angle differences to improve final DEMs.

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density

Light detection and ranging (lidar) scanning systems can be used to provide a point cloud with high quality and point density. Gridded digital elevation models (DEMs) interpolated from laser scanning point clouds are widely used due to their convenience, however, DEM uncertainty is rarely provided. This paper proposes an end-to-end workflow to quantify the uncertainty (i.e., standard deviation) of a gridded lidar-derived DEM. A benefit of the proposed approach is that it does not require independent validation data measured by alternative means. The input point cloud requires per point uncertainty which is derived from lidar system observational uncertainty. The propagated uncertainty caused by interpolation is then derived by the general law of propagation of variances (GLOPOV) with simultaneous consideration of both horizontal and vertical point cloud uncertainties. Finally, the interpolated uncertainty is then scaled by point density and a measure of terrain roughness to arrive at the final gridded DEM uncertainty. The proposed approach is tested with two lidar datasets measured in Waikoloa, Hawaii, and Sitka, Alaska. Triangulated irregular network (TIN) interpolation is chosen as the representative gridding approach. The results indicate estimated terrain roughness/point density scale factors ranging between 1 (in flat areas) and 7.6 (in high roughness areas), with a mean value of 2.3 for the Waikoloa dataset and between 1 and 9.2 with a mean value of 1.2 for the Sitka dataset. As a result, the final gridded DEM uncertainties are estimated between 0.059 m and 0.677 m with a mean value of 0.164 m for the Waikoloa dataset and between 0.059 m and 1.723 m with a mean value of 0.097 m for the Sitka dataset.

Quantification of global Digital Elevation Model (DEM) – A case study of the newly released NASADEM for a river basin in Central Vietnam

Study regionLai Giang River basin, Central Vietnam Study focusThe Digital Elevation Models (DEM) is essential in hydrological modeling and water cycle understanding. There are several unanswered questions regarding the impact of DEM on the models’ quality of hydrological applications. This study develops a comprehensive framework for evaluating impacts of six global DEM products: MERIT, NASA, SRTM, ASTER GDEM2, AW3D30, and TanDEM-X using the semi-distributed model SWAT for the Lai Giang River basin, Central Vietnam, in which four criteria have been used (i) statistical analysis, (ii) river basin characteristics, (iii) effects of DEM uncertainty on streamflow using hydrological simulations, and (iv) cross-validation’s impacts on hydrological applications. New hydrological insights for the regions(1) NASA and SRTM 30 DEM were ranked as most accurate DEMs among the chosen products; (2) ASTER GDEM2 performed the worst, with more uncertainty in river networks delineation; (3) MERIT, ASTER GDEM2, and SRTM 90 m provided inaccurate basin delineations, particularly for the intersection subbasin; (4) DEM resolution and source are insensitive to most of the hydrological components, except for lateral flow, groundwater flow, water percolation, and sediment yield. Moreover, a proposed cross-validation approach for the DEM performance has been introduced, showing that the calibrating parameters of higher-resolution DEM would positively impact the hydrological simulations of lower-resolution DEMs and the opposite, especially in terms of flood peak observation.

Quantifying the impacts of DEM uncertainty on clear-sky surface shortwave radiation estimation in typical mountainous areas

Accurate estimation of shortwave radiation in mountains will advance our knowledge of climate change effects, especially on mountain ecosystems. Recently, some approaches have been developed to estimate shortwave radiation parameters in mountains with satellite data, but few attempts were made to understand the impacts of digital elevation model (DEM) uncertainty on estimates. Our study investigates such impacts quantitatively in clear-sky conditions at multiple spatial and temporal scales (30–3000 m, instantaneous to daily). We employed a retrieval algorithm to estimate instantaneous and daily mean clear-sky downward shortwave radiation (DSR) and net shortwave radiation (NSR), as a proxy for our evaluation. The accuracy of our method based on accurate terrain data was verified against in-situ measurements with root-mean-square errors (RMSEs) of 65.9 W/m2 and 65.1 W/m2 for instantaneous DSR and NSR, and 21.2 W/m2 and 22.5 W/m2 for daily mean values, respectively. When using satellite DEM products, the DSR estimation uncertainty could increase by 64.0% for instantaneous values and 46.2% for daily mean values. Using AW3D30 and SRTM DEMs for DSR estimation led to a maximum difference of 16.8% (103.6 W/m2) and 13.0% (25.8W/m2) on instantaneous and daily mean values, respectively. That estimation difference of shortwave radiation decreased with an increase in spatial scale, with RMS deviation lower than 2% for spatial resolution beyond 3000 m. In addition, the evaluation of introducing random errors into AW3D30 DEM showed that the shortwave radiation uncertainty caused by DEM may exceed the algorithm uncertainty itself with DEM mean absolute error (MAE) equaling about 5.0 m. Considering the current DEM accuracy, the impacts of DEM errors on shortwave radiation in mountains cannot be ignored. This study emphasizes the potential impacts of DEM uncertainty on surface shortwave radiation estimation, which is crucial in using satellite-derived datasets for energy balance calculation and climate change applications in mountains.

A new Greenland digital elevation model derived from ICESat-2 during 2018–2019

Abstract. Greenland digital elevation models (DEMs) are indispensable to fieldwork, ice velocity calculations, and mass change estimations. Previous DEMs have provided reasonable estimations for all of Greenland, but the time span of applied source data may lead to mass change estimation bias. To provide a DEM with a specific time stamp, we applied approximately 5.8×108 ICESat-2 observations from November 2018 to November 2019 to generate a new DEM, including the ice sheet and glaciers in peripheral Greenland. A spatiotemporal model fit process was performed at 500 m, 1 km, 2 km, and 5 km grid cells separately, and the final DEM was posted at the modal resolution of 500 m. A total of 98 % of the grids were obtained by the model fit, and the remaining DEM gaps were estimated via the ordinary Kriging interpolation method. Compared with IceBridge mission data acquired by the Airborne Topographic Mapper (ATM) lidar system, the ICESat-2 DEM was estimated to have a maximum median difference of −0.48 m. The performance of the grids obtained by model fit and interpolation was similar, both of which agreed well with the IceBridge data. DEM uncertainty rises in regions of low latitude and high slope or roughness. Furthermore, the ICESat-2 DEM showed significant accuracy improvements compared with other altimeter-derived DEMs, and the accuracy was comparable to those derived from stereophotogrammetry and interferometry. Overall, the ICESat-2 DEM showed excellent accuracy stability under various topographic conditions, which can provide a specific time-stamped DEM with high accuracy that will be useful to study Greenland elevation and mass balance changes. The Greenland DEM and its uncertainty are available at https://doi.org/10.11888/Geogra.tpdc.271336 (Fan et al., 2021).

Towards ice-thickness inversion: an evaluation of global digital elevation models (DEMs) in the glacierized Tibetan Plateau

Abstract. Accurate estimates of regional ice thickness, which are generally produced by ice-thickness inversion models, are crucial for assessments of available freshwater resources and sea level rise. A digital elevation model (DEM) derived from surface topography of glaciers is a primary data source for such models. However, the scarce in situ measurements of glacier surface elevation limit the evaluation of DEM uncertainty. Hence the influence of DEM uncertainty on ice-thickness modeling remains unclear over the glacierized area of the Tibetan Plateau (TP). Here, we examine the performance of six widely used and mainly global-scale DEMs: AW3D30 (ALOS – Advanced Land Observing Satellite – World 3D – 30 m; 30 m), SRTM-GL1 (Shuttle Radar Topography Mission Global 1 arc second; 30 m), NASADEM (NASA Digital Elevation Model; 30 m), TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement, synthetic-aperture radar; 90 m), SRTM v4.1 (Shuttle Radar Topography Mission; 90 m), and MERIT (Multi-Error-Removed Improved-Terrain; 90 m) over the glacierized TP by comparison with ICESat-2 (Ice, Cloud and land Elevation Satellite) laser altimetry data while considering the effects of glacier dynamics, terrain factors, and DEM misregistration. The results reveal NASADEM to be the best performer in vertical accuracy, with a small mean error (ME) of 0.9 m and a root mean squared error (RMSE) of 12.6 m, followed by AW3D30 (2.6 m ME and 11.3 m RMSE). TanDEM-X also performs well (0.1 m ME and 15.1 m RMSE) but suffers from serious errors and outliers on steep slopes. SRTM-based DEMs (SRTM-GL1, SRTM v4.1, and MERIT) (13.5–17.0 m RMSE) had an inferior performance to NASADEM. Errors in the six DEMs increase from the south-facing to the north-facing aspect and become larger with increasing slope. Misregistration of the six DEMs relative to the ICESat-2 footprint in most glacier areas is small (less than one grid spacing). In a next step, the influence of six DEMs on four ice-thickness inversion models – GlabTop2 (Glacier bed Topography), Open Global Glacier Model (OGGM), Huss–Farinotti (HF), and Ice Thickness Inversion Based on Velocity (ITIBOV) – is intercompared. The results show that GlabTop2 is sensitive to the accuracy of both elevation and slope, while OGGM and HF are less sensitive to DEM quality and resolution, and ITIBOV is the most sensitive to slope accuracy. NASADEM is the best choice for ice-thickness estimates over the whole TP.

Planetary Rover Path Planning Method with Robustness against Digital Elevation Model's Uncertainty

Planetary rovers need to plan a safe route to ensure the success of their mission. Many missions to the Moon and Mars use existing remote sensing data to plan their exploration routes before launch under the assumption that the data is correct. For example, digital elevation models (DEMs) are biased in their accuracy depending on the density of their source data. Most studies of advance path planning using DEMs do not account for that uncertainty. Therefore, the path planned in advance is not necessarily safe for the actual rover. In this paper, we propose a path planning method that is robust against the uncertainty of terrain data generated from remote sensing data. Simulations using real data confirmed that the route planned by this method is safer than these two conventional methods: 1) route planning that assumes the topographic data is correct, and 2) route planning that adds Gaussian noise to the DEM to generate multiple DEMs and treats their variance as uncertainty.

Uncertainty of Drone-Derived DEMs and Significance of Detected Morphodynamics in Artificially Scraped Dunes

This work capitalises on the morphodynamic study of a scraped artificial dune built on the sandy beach of Porto Garibaldi (Comacchio, Italy) as a barrier to protect the touristic facilities from sea storms during the winter season and contributes to understanding of the role of elevation data uncertainty and uniform thresholds for change detection (TCDs) on the interpretation of volume change estimations. This application relies on products derived from unmanned aerial vehicle (UAV) surveys and on the evaluation of the uncertainty associated with volume change estimations to interpret the case study morphodynamics under non-extreme sea and wind conditions. The analysis was performed by comparing UAV-derived digital elevation models (DEMs)—root mean squared error (RMSE) vs. global navigation satellite system (GNSS) < 0.05 m—and orthophotos, considering the significance of the identified changes by applying a set of TCDs. In this case, a threshold of ~0.15 m was able to detect most of the morphological variations. The set of TCD ≤ 0.15 m was considered to discuss the significance of minor changes and the uncertainty of volume change calculations. During the analysed period (21 December 2016–20 January 2017), water levels and waves affected the front of the artificial dune by eroding the berm area; winds remodelled the entire dune, moving the loose sand around the dune and further inland; sediment volumes mobilised by sea and wind forcing were comparable. This work suggests that UAV-derived coastal morphological variations should be interpreted by integrating: (i) a set of uniform thresholds to detect significant changes; (ii) the uncertainty generated by the propagation of the original uncertainty of the elevation products; (iii) the characteristics of the morphodynamic drivers evaluated by adopting uncertainty-aware approaches. Thus, the contribution of subtle morphological changes—magnitudes comparable with the instrumental accuracy and/or the assessed propagated uncertainty—can be properly accounted for.

Sensitivity of various topographic data in flood management: Implications on inundation mapping over large data-scarce regions

Topographic data in the form of digital elevation models (DEMs) play a significant role in flood management. Despite the increasing availability of DEMs for large regions, there is a need to evaluate their performance at the inundation/flood level, while considering the overall complexity of flood models. The present study identifies, for the first time, the uncertainties generated in both river channel and overland flooding while considering a set of nine variants from various sources (LiDAR, Cartosat, SRTM, and ASTER) and grid resolutions (resampled versions) in the presence of discharge, rainfall, and tide boundary conditions for a severely flood-prone catchment in the Mahanadi River Basin, India. Extensive geostatistical analyses reveal the existence of significant biases with global DEMs i.e., SRTM and ASTER, whereas interestingly the LiDAR and Carto DEMs exhibit a high degree of isotropy. The global DEMs fail to capture several inundated spots; thus plummeting the flood inundation extents to a sufficient degree of unacceptability. Prominently, the inability in identifying high and very high flood depths (>1.5 m) over the coastal stretches results in large uncertainties in the majority of the grids. Our analysis reveals the existence of significant noise in global DEMs, which nullifies the hydrodynamic interaction during the coupling of 1-D and 2-D flood models in presence of tidal influence. We recommend that under unavailability of precise LiDAR DEMs, resampled and freely available Carto DEMs, that are as efficient as LiDAR if not more, be given higher preference. We caution against the copious usage of global DEMs for large data-scarce and flood-prone regions, as the DEM uncertainty may be substantially amplified at the inundation level during combined channel and overland flood simulations. Through this study, we would like to recommend the proposed framework as a guided step while selecting appropriate DEM for flood inundation mapping over large data scarce regions.

Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

Abstract. Digital elevation models (DEMs) representing topography are an essential input for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as on further topographic characteristics and the DEM error's variability. We further find that, in the absence of systematic bias in the DEM, a performant root-mean-square-error-based unconditional stochastic simulation yields similar results to a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability in the DEM error, which leads to an increase in the potential hazard area as well as extreme values of dynamic flow properties.

Accuracy of the UAV-Based DEM of Beach\u2013Foredune Topography in Relation to Selected Morphometric Variables, Land Cover, and Multitemporal Sediment Budget

Recent developments in unmanned aerial vehicles (UAVs) have resulted in high-resolution digital elevation models (DEMs) of vulnerable coastal environments, including beach–foredune topography. If performed repetitively, they can offer an excellent tool to determine the spatial and temporal changes in the sediment budget, which may be required for proper land management. However, the quality of a UAV, slope parameters, and vegetation significantly influence DEM accuracy. The aim of this study is to compare precise GPS-RTK transects across a section of the South Baltic coast in Poland with those obtained from a DEM based on high-resolution and high-accuracy images obtained by a wind-resistant, high-quality fixed-wing UAV during beyond visual line of sight operation (BVLOS). Different land cover classes, slope inclination, and general curvature, as well as surface roughness, were taken into consideration as possible factors influencing the uncertainty. The study revealed that marram grass greatly affects the accuracy of the UAV-derived model and that the uncertainty of the UAV-derived DEM increases together with increasing slope inclination and, to a lesser degree, with increasing general slope curvature. We showed that sediment budget determinations with the use of a UAV-based DEM are correct only where grass cover is sparse, in our study, up to 20% of the area.

Incorporating the effect of ALS-derived DEM uncertainty for quantifying changes due to the landslide in 2011, Mt. Umyeon, Seoul

ABSTRACT The landslide, which occurred at Umyeon mountain (Mt. Umyeon) in Seoul, Korea in 2011, was a prime example that raised awareness about the landslide in the highly urbanized area. Although many studies have been done on Umyeon landslide, there is a lack of research that detects the area where the landslide occurred and quantifies the elevation changes through remote sensing data. In this regard, this paper aims to detect and assess topographic changes quantitatively over Mt. Umyeon by using digital elevation models (DEMs) derived from airborne laser scanning (ALS) data. Since Mt. Umyeon was hilly and covered with dense trees during summer, traces of the landslide were detected by estimating the spatially distributed uncertainty of ALS-derived DEMs. The probabilistic analysis with Bayes'™ theorem considering the spatially distributed DEM of difference (DoD) uncertainty enabled to detect the landslide traces efficiently and was less affected by the influence of ALS errors. The results indicated that ALS-derived DEMs have the potential to detect landslides with their uncertainty estimation, although the ALS data were acquired in hilly and densely vegetated areas. Moreover, quantifying topographic changes due to landslides with high reliability is considered to be beneficial and practically helpful for disaster recovery.

Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset

Abstract. Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.

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.

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.

A new digital elevation model of Antarctica derived from CryoSat-2 altimetry

Abstract. We present a new digital elevation model (DEM) of the Antarctic ice sheet and ice shelves based on 2.5×108 observations recorded by the CryoSat-2 satellite radar altimeter between July 2010 and July 2016. The DEM is formed from spatio-temporal fits to elevation measurements accumulated within 1, 2, and 5 km grid cells, and is posted at the modal resolution of 1 km. Altogether, 94 % of the grounded ice sheet and 98 % of the floating ice shelves are observed, and the remaining grid cells north of 88∘ S are interpolated using ordinary kriging. The median and root mean square difference between the DEM and 2.3×107 airborne laser altimeter measurements acquired during NASA Operation IceBridge campaigns are −0.30 and 13.50 m, respectively. The DEM uncertainty rises in regions of high slope, especially where elevation measurements were acquired in low-resolution mode; taking this into account, we estimate the average accuracy to be 9.5 m – a value that is comparable to or better than that of other models derived from satellite radar and laser altimetry.

Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.

The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands

While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments.