A digital terrain model (DTM) is a three-dimensional representation of the terrain relief created from discrete points related to each other through their elevations. New technologies such as ...satellite remote sensing, airborne laser scanning and radar interferometry are efficient methods for constructing high-quality DTMs in a cost-effective manner. The accuracy of a DTM is influenced by a number of factors, including the accuracy, density and spatial distribution of elevation points, the terrain surface characteristics, etc. In this paper, direct comparisons of absolute and relative vertical accuracies are made between data reduction algorithms for the generalisation of DTM extracted from airborne Light Detection and Ranging (LiDAR) data. The absolute vertical accuracies are presented in terms of the mean error (ME), the mean absolute error (MAE) and the root mean square error (RMSE) and the relative vertical accuracies are characterised as per cent slope over Mount St Helens in southwest Washington State. The results show that LiDAR datasets can be reduced to 50 per cent density level by a uniform data reduction algorithm using triangulation with a linear interpolation method for the generalisation of DTM while still maintaining the quality of the original data.
The paper presents the campaigns of mobile satellite measurements, carried out in 2009–2015 on the railway and tram lines. The accuracy of the measurement method has been analysed on the basis of the ...results obtained in both horizontal and vertical planes. The track axis deviation from the defined geometric shape has been analysed in the areas clearly defined in terms of geometry, i.e. on the straight sections and sections with constant longitudinal inclination. The values of measurement errors have been estimated on the basis of signals subjected to appropriate processes of filtration. The paper attempts to evaluate the changing possibilities of using the GNSS techniques to determine the shape of the railway track axis from 2009 to 2015. The determined average value of the measurement error now equals a few millimetres. This achievement is very promising for the prospects of mobile satellite measurements in railway engineering.
Coal production in opencast mining generates substantial waste materials, which are typically delivered to an on-site waste dump. As a large artificial loose pile, such dumps have a special ...multi-berm structure accompanied by some security issues due to wind and water erosion. Highly accurate digital surface models (DSMs) provide the basic information for detection and analysis of elevation change. Low-cost unmanned aerial vehicle systems (UAS) equipped with a digital camera have become a useful tool for DSM reconstruction. To achieve high-quality UAS products, consideration of the number and configuration of ground control points (GCPs) is required. Although increasing of GCPs will improve the accuracy of UAS products, the workload of placing GCPs is difficult and laborious, especially in a multi-berm structure such as a waste dump. Thus, the aim of this study is to propose an improved GCPs configuration to generate accurate DSMs of a waste dump to obtain accurate elevation information, with less time and fewer resources. The results of this study suggest that: (1) the vertical accuracy of DSMs is affected by the number of GCPs and their configuration. (2) Under a set number of GCPs, a difference of accuracy is obtained when the GCPs are located on different berms. (3) For the same number of GCPs, the type 4 (GCPs located on the 1st and 4th berms) in the study is the best configuration for higher vertical accuracy compared with other types. The principal objective of this study provides an effective GCP configuration for DSM construction of coal waste dumps with four berms, and also a reference for engineering piles using multiple berms.
In the diverse domains of earth observation, elevation data are essential for a wide range of applications with various technical requirements and use cases. The Advanced Spaceborne Thermal Emission ...and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Shuttle Radar Topography Mission (SRTM), and other projects have made a large number of global Digital Elevation Model (DEM) datasets for environmental modelling and studies freely available. Global DEMs have undergone an accuracy review to measure their inherent vertical uncertainty to show how accurate information should be considered while planning and analysing. Comparing the DEMs with highly accurate geodetic control points as the independent reference data one of the best methods in the evaluation process. SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM are among the worldwide DEMs that were examined. Comparisons are made between 793 geodetic control points values and those from SRTM 30m, SRTM 90m, ALOS World 3D-30, Aster-GDEM, GMTED2010, and NASADEM. The statistical analysis of global DEMs from GPS reference elevations gave us that the accuracy of the ALOS World 3D-30m is much better than other models with RMSE and STD values of 1.2497 and 1.235 m, respectively. In contrast, Aster-GDEM exhibited the highest RMSE and residual error of STD values of 5.793 m and 3.394 m, respectively.
A hybrid theoretical–empirical model has been developed for modelling the error in LiDAR-derived digital elevation models (DEMs) of non-open terrain. The theoretical component seeks to model the ...propagation of the sample data error (SDE), i.e. the error from light detection and ranging (LiDAR) data capture of ground sampled points in open terrain, towards interpolated points. The interpolation methods used for infilling gaps may produce a non-negligible error that is referred to as gridding error. In this case, interpolation is performed using an inverse distance weighting (IDW) method with the local support of the five closest neighbours, although it would be possible to utilize other interpolation methods. The empirical component refers to what is known as “information loss”. This is the error purely due to modelling the continuous terrain surface from only a discrete number of points plus the error arising from the interpolation process. The SDE must be previously calculated from a suitable number of check points located in open terrain and assumes that the LiDAR point density was sufficiently high to neglect the gridding error. For model calibration, data for 29 study sites, 200×200 m in size, belonging to different areas around Almeria province, south-east Spain, were acquired by means of stereo photogrammetric methods. The developed methodology was validated against two different LiDAR datasets. The first dataset used was an Ordnance Survey (OS) LiDAR survey carried out over a region of Bristol in the UK. The second dataset was an area located at Gador mountain range, south of Almería province, Spain. Both terrain slope and sampling density were incorporated in the empirical component through the calibration phase, resulting in a very good agreement between predicted and observed data (R2=0.9856;p<0.001). In validation, Bristol observed vertical errors, corresponding to different LiDAR point densities, offered a reasonably good fit to the predicted errors. Even better results were achieved in the more rugged morphology of the Gador mountain range dataset. The findings presented in this article could be used as a guide for the selection of appropriate operational parameters (essentially point density in order to optimize survey cost), in projects related to LiDAR survey in non-open terrain, for instance those projects dealing with forestry applications.
Google Earth (GE) provides accurate and reliable global high-resolution images and can obtain the coordinates of any point on Earth's surface. A digital elevation model (DEM) is a dataset that ...quantitatively reflects the elevation of Earth's surface. Although GE and DEMs can be used to obtain the coordinates of any position on Earth, their open-access data can be affected by various factors, thereby inducing undesirable precision variability. Therefore, it is essential to estimate the accuracy of GE and open-source DEMs. In this study, 325 high-precision GPS survey points in 16 regions in China were used to evaluate the horizontal and vertical accuracies of GE and the elevation accuracy of two open-source DEMs. GE had a high horizontal accuracy with a root mean square error (RMSE) of 2.495 m and an error range of 1.090–4.844 m. The elevation accuracy of GE (RMSE = 2.610 m) was lower than those of TanDEM-X (RMSE = 2.055 m) and AW3D30 (RMSE = 2.373 m) DEMs. At the same time, the impacts of slope, aspect, and feature type on the accuracy of these data are studied and analyzed. The results show that the accuracy of control data are closely related to the characteristics of the study area. Overall, these findings indicate that for future studies in China, GE can be used to acquire horizontal data, whereas TanDEM-X and AW3D30 are more suitable for elevation data that have higher precision and provide a reference for relevant research on geographic information.
The first near‐global high‐resolution digital elevation model (DEM) of the Earth has recently been released following the successful Shuttle Radar Topography Mission (SRTM) of 2000. This data set ...will have applications in a wide range of fields and will be especially valuable in the Earth sciences. Prior to widespread dissemination and use, it is important to acquire knowledge regarding the accuracy characteristics. In this work a comprehensive analysis of the vertical errors present in the data set and the assessment of their effects on different hydrogeomorphic products is performed. In particular, the work consisted of (1) measuring the vertical accuracy of the data set in two areas with different topographic characteristics; (2) characterizing the error structure by comparing elevation residuals with terrain attributes; (3) assessing a wavelet‐based filter for removing speckle; and (4) assessing the effects of vertical errors on hydrogeomorphic products and on slope stability modeling. The results indicate that in the two sites, relief has a strong effect on the vertical accuracy of the SRTM DEM. In the high‐relief terrain, large errors and data voids are frequent, and their location is strongly influenced by topography, while in the low‐ to medium‐relief site, errors are smaller, although the hilly terrain still produces an effect on the sign of the errors. Speckling generates deviations in the drainage network in one of the investigated areas, but the application of a wavelet filter proved to be an effective tool for removing vertical noise, although further fine tuning is necessary. Vertical errors cause differences in automatically extracted hydrogeomorphic products that range between 4 and 1090.
Majuro Atoll in the central Pacific has high coastal vulnerability due to low-lying islands, rising sea level, high wave events, eroding shorelines, a dense population center, and limited freshwater ...resources. Land elevation is the primary geophysical variable that determines exposure to inundation in coastal settings. Accordingly, coastal elevation data (with accuracy information) are critical for assessments of inundation exposure. Previous research has demonstrated the importance of using high-accuracy elevation data and rigorously accounting for uncertainty in inundation assessments. A quantitative analysis of inundation exposure was conducted for Majuro Atoll, including accounting for the cumulative vertical uncertainty from the input digital elevation model (DEM) and datum transformation. The project employed a recently produced and validated DEM derived from structure-from-motion processing of very-high-resolution aerial imagery. Areas subject to marine inundation (direct hydrologic connection to the ocean) and low-lying lands (disconnected hydrologically from the ocean) were mapped and characterized for three inundation levels using deterministic and probabilistic methods. At the highest water level modeled (3.75 ft, or 1.143 m), more than 34% of the atoll study area is likely to be exposed to inundation (68% chance or greater), while more than 20% of the atoll is extremely likely to be exposed (95% chance or greater). The study demonstrates the substantial value of a high-accuracy DEM for assessing inundation exposure of low-relief islands and the enhanced information from accounting for vertical uncertainty.
India presents among the world’s most topographically complex geomorphologies, with land elevations ranging from –2 m to + 8586 m and terrain gradients sometimes exceeding 45°. Here, we present an ...evaluation of four freely available digital surface models (DSMs) on a model-to-model basis, as well as a validation using independent ground-truth data from levelled benchmarks in India. The DSMs tested comprise SRTM1″, SRTM3″, ASTER1″ and Cartodem1″ an India-only model. Along with these four DSMs, the MERIT3″ digital elevation model (DEM) is also tested with the ground-truth data. Our results for India indicate some mismatch of these DEMs/DSMs from their claimed accuracies/precisions. All DSMs/DEMs (except for ASTER) have > 90% of pixels satisfying ± 16 m at the one-sigma level, but only in the low-lying (< 500 m) parts of India, i.e. the Gangetic plains and the Thar desert.
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.
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