Digital Elevation Model Error Research Articles

New approach for satellite DEM accuracy enhancement by combing machine learning, fuzzy majority voting, and weighted interpolation techniques

The digital elevation model (DEM) is crucial in many global and regional scientific studies in civilian and military applications. The aim of this research is to develop and test a new DEM approach for correcting the various errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model. Firstly, the DEMs with the feature attributes from Sentinel-2 multispectral imagery are generated. Secondly, SRTM DEM with one band and attributes of a sentinel-2 image with eight bands are used as input data in supervised max-like hood, an artificial neural network (ANN), and support vector machine (SVM) classification models. Thirdly, ANN, supervised max-like hood, and SVM classification models, which have various properties, are fused by fuzzy majority voting (probability fusion). Finally, the fused probability is assigned for each pixel of the image, which has 12 fixed ground control points (GCPs), which is considered new input data for the inverse probability weighted interpolation (IPWI) approach to create the corrected SRTM elevations. The results were contrasted with a reference DEM (RD) created by image matching with Worldview-1 stereo satellite images, which had a 1-m vertical accuracy. The results of this study demonstrated that the RMSE of the original SRTM DEM was 5.95. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 1.98 compared with that of the MLR method (3.01). The study shows a series of significant improvements in the SRTM when assessed with the reference DEM, with an RMSE reduction of (66.72%) when compared to the widely utilized multiple linear regression (MLR) method. It can be concluded that the elevation error of the original SRTM DEM is clearly reduced by the suggested approach.

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

ABSTRACT 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.

Assessment of the global Copernicus, NASADEM, ASTER and AW3D digital elevation models in Central and Southern Africa

ABSTRACT Validation studies of global Digital Elevation Models (DEMs) in the existing literature are limited by the diversity and spread of landscapes, terrain types considered and sparseness of groundtruth. Moreover, there are knowledge gaps on the accuracy variations in rugged and complex landscapes, and previous studies have often not relied on robust internal and external validation measures. Thus, there is still only partial understanding and limited perspective of the reliability and adequacy of global DEMs for several applications. In this study, we utilize a dense spread of LiDAR groundtruth to assess the vertical accuracies of four medium-resolution, readily available, free-access and global coverage 1 arc-second (30 m) DEMs: NASADEM, ASTER GDEM, Copernicus GLO-30, and ALOS World 3D (AW3D). The assessment is carried out at landscapes spread across Cape Town, Southern Africa (urban/industrial, agricultural, mountain, peninsula and grassland/shrubland) and forested national parks in Gabon, Central Africa (low-relief tropical rainforest and high-relief tropical rainforest). The statistical analysis is based on robust accuracy metrics that cater for normal and non-normal elevation error distribution, and error ranking. In Cape Town, Copernicus DEM generally had the least vertical error with an overall Mean Error (ME) of 0.82 m and Root Mean Square Error (RMSE) of 2.34 m while ASTER DEM had the poorest performance. However, ASTER GDEM and NASADEM performed better in the low-relief and high-relief tropical forests of Gabon. Generally, the DEM errors have a moderate to high positive correlation in forests, and a low to moderate positive correlation in mountains and urban areas. Copernicus DEM showed superior vertical accuracy in forests with less than 40% tree cover, while ASTER and NASADEM performed better in denser forests with tree cover greater than 70%. This study is a robust regional assessment of these global DEMs.

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.

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas.

Mapping stream and floodplain geomorphometry with the Floodplain and Channel Evaluation Tool

AbstractBroad‐scale mapping of stream channel and floodplain geomorphic metrics is critical to improve the understanding of geomorphic change, biogeochemical processes, riverine habitat quality, and opportunities for management intervention. The Floodplain and Channel Evaluation Tool (FACET) was developed to provide an open‐source tool for automated processing of digital elevation models (DEMs) to generate regional‐scale estimates of bank height, channel width, floodplain width, and a suite of other fluvial geomorphic dimensions that can be summarized at the stream reach‐ or catchment‐scale. FACET was tested on 3‐m DEMs covering the Delaware River watershed and 85% of the Chesapeake Bay watershed in the United States (U.S.) and on 1‐m DEMs for a subset of the study area. Accuracy was assessed from data collected at 67 field sites in the study area. FACET successfully measured geomorphometry for over 270,000 stream reaches (88% of streams attempted) in the study area. Factors that reduced the ability of FACET to accurately estimate geomorphic metrics included errors in DEM hydro‐conditioning, gradually sloping banks, incised stream channels, and the use of fixed input parameters to define buffer lengths. Even with these limitations, FACET was able to map regional patterns in stream and floodplain geomorphometry providing a robust dataset that can enhance modeling and management efforts throughout the mid‐Atlantic region, U.S.

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.

Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research.

Evaluating the TOPODATA digital elevation model as a source of information for cycling planning purposes: a case study for a small-sized Brazilian city

The present study aims to evaluate the TOPODATA Digital Elevation Model (DEM) as a source of relevant altimetric information for urban cycling planning. A case study was conducted in the city of Bariri-SP. The Cartographic Accuracy Standard of Digital Cartographic Products (PEC-PCD), assessed by comparing the TOPODATA altitudes with homologous altitudes surveyed by a precise satellite method (GNSS), suggests that the DEM may not be adequate for phases of cycling planning that require greater detailing of the elements to be designed. A moderate to strong positive spatial autocorrelation was observed between the DEM errors. Regarding its usability for estimating the average slopes of the road segments, however, the results suggest that TOPODATA average slopes do not differ statistically from those estimated with field-surveyed data and, for the two criteria adopted for acceptable gradient lengths for cycling, more than 82% of the road segments were classified similarly using both sources of information.

Assessing slope uncertainties of martian Digital Elevation Models from numerical propagation of errors on synthetic geological surfaces

Digital Elevation Models (DEM) are widely used in planetary sciences, including for the specific case of Mars. DEMs allow us to extract topography parameters necessary in geomorphological studies. However, DEMs are not free from vertical errors, which yields uncertainties in calculations of parameters such as local slopes. In addition, slope maps computed from DEMs often display slope patterns which are not spatially correlated with the original images. We suspect such slope patterns to originate from DEM vertical errors. To investigate this question, we propose a fully numerical method to provide a quantitative analysis of slope errors based on DEM error propagation using synthetic models. We find that the addition of vertical errors following a normal distribution (random noise) leads to the occurrence of slope patterns comparable to those in observed data. Results are similar for the two models of spatially correlated errors. We also provide estimations of slope errors for four martian cameras: HiRISE (High Resolution Imaging Science Experiment), CaSSIS (Colour and Stereo Surface Imaging System), HRSC (High Resolution Stereo Camera) and MOC (Martian Orbiter Camera). These estimations aim to be used as first order uncertainty constraints on local slopes for geomorphological studies.

Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data

AbstractChemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth's land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth's mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo‐photogrammetry at ∼30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo‐photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root‐mean‐square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine‐scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth's mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth's surface.

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 Method for SRTM DEM Elevation Error Correction in Forested Areas Using ICESat-2 Data and Vegetation Classification Data

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

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

AbstractThe application of structure from motion (SfM) photogrammetry for digital elevation model (DEM) and orthophoto generation from visible imagery enjoys ever‐growing popularity in geomorphological research. Photogrammetry experts, however, urge that a rigorous approach is a prerequisite for reliable results—a requirement that may conflict with real‐world survey. We present a method that unites the two disciplines, using the example of a challenging SfM photogrammetric survey at a Scottish river. Using simultaneous geometric pre‐calibration of a multi‐sensor remotely piloted aircraft system (RPAS), the method facilitates time‐efficient topography mapping and the integration of other wavelengths to create orthophotos providing additional surface information. The approach utilizes an on‐site 3D structure—for example, a building, as calibration object, by extracting coordinates of natural features from lidar scans and sensor imagery. We assess the workflow with specialized calibration software (VMS) and widely applied commercial SfM photogrammetric software (AM), using a DJI Phantom optical and a Workswell thermal sensor. We achieved calibration accuracies below one‐third (optical) and one‐quarter (thermal) of a pixel. Subsequently, we transfer the sensor parameters to pre‐calibrate the SfM application and compare the results to a self‐calibrated workflow. In a systematic experiment using the optical river survey dataset, we assess the effectiveness of pre‐calibration, oblique imagery, scale variation and masking to mitigate systematic DEM errors. Opposing trends show between the calibration strategies. Decreasing network complexity (i.e., flying heights/view angles) improves pre‐calibrated but compromises self‐calibrated scenarios. Pre‐calibrating (VMS) imagery from a single height (30 m nadir) yielded the best results. This finding could have implications for geomorphological surveys, in which single‐scale datasets are widespread practice, despite the literature's urge towards more complex imaging networks. The self‐calibrated results legitimise this insistence: The same dataset resulted in pronounced dome‐shaped DEM distortion, indicating systematic errors, whereas additional flying heights and angles significantly improved the results.

Application of LiDAR-Derived Data using Multi-Criteria Evaluation (MCE) and Stochastic Modelling; A Flood Risk Analysis of the Mersey River, Nova Scotia

The Maritime province of Nova Scotia has seen coastal flooding become a more frequent phenomenon during the past decades due to the changing climate regime. This has influenced the interest provincial and federal governments have in flood risk modelling, who often incorporate Geographic Information Systems (GIS) as useful tools in their analysis. Incorporating LiDAR-derived digital elevation models (DEMs) in their workflows is the next step in hydrological analysis, as LiDAR-derived DEMs offer high resolution data for the analysis of flood risk without the need to rely on biotic or hydrological data. This study aims to follow this theme in order to model the effects of inland flooding in the low relief landscape of the Mersey River, located in Queen’s County, Nova Scotia, and its effects on the infrastructure built along the river network. The analysis included multi-criteria evaluation (MCE) methods coupled with a stochastic simulation approach in order to determine areas where vulnerability is the most certain. Results indicated that high flood risk is present in urbanized areas within 1 km of the Mersey River at a low degree of uncertainty, making them the best candidates for flood-preventive measures. The accuracy provided by LiDAR-derived DEMs supported a high-quality workflow for the MCE and DEM error analysis, proving their utility for floodplain delineation. The addition of historical and hydrological data to future projects could build on the results presented in this study, adding more to the literature on flood risk modelling along the Mersey River.

The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.

Correcting Digital Elevation Models (DEM) from Unmanned Aerial Vehicles (UAV): A New Method Using Polynomial Model Matching Techniques

Lee, H.-S. and Oh, J.-H., 2021. Correcting Digital Elevation Models (DEM) from Unmanned Aerial Vehicles (UAV): A new method using polynomial model matching techniques. In: Lee, J. L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 434–438. Coconut Creek (Florida), ISSN 0749-0208. Korean coastal regions have large mudflats that shelter endangered migratory birds and many prosperous ecosystems inhabited by various species living in clusters. To recognize the topographical and environmental characteristics of the coastal regions and to systematically manage them, it is essential to obtain topographical maps of the geographical and environmental characteristics. In particular, the height information of the mudflats is critically important in monitoring changes caused by either natural or artificial influences. Recently, Unmanned Aerial Vehicles (UAV) are shown to be potential candidates in preparing accurate Digital Elevation Models (DEMs) of a terrain. But a major problem in using UAVs is that it is almost impossible to conduct a field survey of Ground Control Points (GCPs). This is because the mudflat areas are not easy to approach places where the proportion of silt and clay content is very high. Without GCPs, the DEM produced from the UAV images and photogrammetric software tend to show a nonlinear distortion due to the aerial triangulation errors. Therefore, we propose a least-square height-difference DEM matching with a polynomial model. This method uses a reference DEM obtained from aerial images to compensate for the location error and the aforementioned nonlinear distortion. Experimental results showed that this significant DEM error modeling was possible with curvilinear translation and constant rotation parameters along the diagonal direction of the test DEM that significantly reduce the location errors and almost eliminate the nonlinear distortion of the UAV-derived DEM.

Topographic Wetness Index as a Proxy for Soil Moisture: The Importance of Flow‐Routing Algorithm and Grid Resolution

AbstractThe Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow‐routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow‐routing algorithms at DEM resolutions 1–30 m. We analyze the relationship between TWI and field‐quantified soil moisture using statistical modeling methods and 5,200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow‐routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest r2 (23.7%) was reached using a multiple‐flow‐direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple‐flow‐direction algorithms were also more robust against DEM errors than single‐flow‐direction algorithms. The results provide field‐evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow‐routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture.

Applicability of Data Acquisition Characteristics to the Identification of Local Artefacts in Global Digital Elevation Models: Comparison of the Copernicus and TanDEM-X DEMs

Several global digital elevation models (DEMs) have been developed in the last two decades. The most recent addition to the family of global DEMs is the TanDEM-X DEM. The original version of the TanDEM-X DEM is, however, a nonedited product (i.e., it contains local artefacts such as voids, spikes, and holes). Therefore, subsequent identification of local artefacts and their editing is necessary. In this study, we evaluated the accuracy of the original TanDEM-X DEM and its improved edited version, the Copernicus DEM, in three major European mountain ranges (the Alps, the Carpathians, and the Pyrenees) using a digital surface model derived from airborne laser scanning data as a reference. In addition, to evaluate the applicability of data acquisition characteristics (coverage map, consistency mask, and height error map) and terrain characteristics (slope, aspect, altitude) to the localization of problematic sites, we modeled their associations with the TanDEM-X DEM error. We revealed local occurrences of large errors in the TanDEM-X DEM that were typically found on steep ridges or in canyons, which were largely corrected in the Copernicus DEM. The editing procedure used for the Copernicus DEM construction was evidently successful as the RMSE for the TanDEM-X and Copernicus DEMs at the 90 m resolution improved from 45 m to 12 m, from 16 m to 6 m, and from 24 m to 9 m for the Alps, the Pyrenees, and the Carpathians, respectively. The Copernicus DEM at the 30 m resolution performed similarly well. The boosted regression trees showed that acquisition characteristics provided as auxiliary data are useful for locating problematic sites and explained 28–50% of deviance of the absolute vertical error. The absolute vertical error was strongly related to the height error map. Finally, up to 26% of cells in the Copernicus DEM were filled using DEMs from different time periods and, hence, users performing multitemporal analysis or requiring data from a specific time period in the mountain environment should be wary when using TanDEM-X and its derivations. We suggest that when filling problematic sites using alternative DEMs, more attention should be paid to the period of their collection to minimize the temporal displacement in the final products.

High-resolution topography of the Antarctic Peninsula combining the TanDEM-X DEM and Reference Elevation Model of Antarctica (REMA) mosaic

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.