Vertical Accuracy Of Digital Elevation Models Research Articles

Vertical Accuracy of Google Earth Data

Digital Elevation Models (DEMs) are an important data source used in many engineering and Geographic Information System (GIS) applications. This paper illustrates a strategy for creating a DEM by utilizing elevation data from Google Earth and evaluating the vertical positional accuracy of the generated DEM adopting a well-defined methodology. To ensure the accuracy of the elevation data obtained from Google Earth, a thorough evaluation was done in three diverse small districts of the northern shoreline in Egypt. The evaluation process involved determining the ground coordinates of reference points utilizing two surveying techniques: total station and Real-Time Kinematic (RTK) Global Positioning System (GPS) surveys. These coordinates were compared with the ones predicated by the DEM generated by putting into service Google Earth's elevation data. Furthermore, the vertical accuracy was assessed using Shuttle Radar Topographic Mission (SRTM) data of Google Earth collected at two different periods in 2015 and 2023. The vertical accuracy of the Google Earth data is detailed utilizing Mean Error (ME), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results, Google Earth's elevation data accuracy remains consistent from 2015 to 2023, and refining SRTM data does not improve the vertical accuracy. The vertical accuracy of the total station survey surpasses the one of the RTK GPS survey, and the elevation accuracy of the RTK GPS survey decreases with increasing height difference. In addition, the vertical accuracy of DEMs was found to be sufficient for some engineering applications but not accurate enough for precise engineering studies. The accuracy achieved in small height difference terrain can be utilized to produce large-scale cadastral maps, city plans, or land use maps. Finally, the elevation data offered by Google Earth can be utilized for preliminary studies at a low cost. However, to ensure the accuracy of these data, it is recommended that users compare them with reference data before implementation.

Aerial characterization of surface depressions in urban watersheds

Digital elevation models (DEM) are one of the most fundamental inputs for hydrological modeling. It has been a common practice to remove all surface depressions in a DEM as they are assumed to be data errors. The emerging technology of unmanned aircraft systems (UAS) provides an opportunity to re-examine this assumption at the hyperspatial resolution. This study was the first attempt to characterize small surface depressions in urban environments using UAS imagery. Using an urban area in south Texas as the study site, UAS flights were conducted to yield hybrid DEMs at the resolution of 8–14 cm, coupled with comprehensive ground truth collection. Surface depressions identified from the UAS DEMs were first corrected based on the vertical accuracy of DEMs and then validated through field surveys, with comparisons to two existing LiDAR DEMs (1-m and 10-m). The hydrological impacts of different DEM-derived estimates of catchment depression storage were examined using the Curve Number method across different design storms. Results show that the UAS DEMs outperformed the LiDAR DEMs in describing the microtopographic control of urban overland flow and associated hydrological connectivity across built and natural features. The 8-cm UAS DEM revealed 926% more depression storage than the 10-m LiDAR DEM. This demonstrates a compelling correlation between increasing DEM resolution and enhanced quantification of depression volume. Consequently, the increased depression storage reduced surface runoff by 41% under a two-year design storm and 13% under a 200-year design storm. The results suggest a strong relationship between the DEM resolution and the derived depression estimates, aligning with the fractal nature of watershed systems. Also, the results indicate that the centimeter-level UAS DEMs were not immune from problems. They could yield fake depressions caused by factors such as vegetation, temporary street objects, and underground sewer pipes. The findings of this study suggest the need to quantify the relationships between DEM resolution and associated hydrological attributes and develop new digital drainage analysis algorithms that could effectively incorporate UAS data into urban hydrological modeling.

The impact of horizontal errors on the accuracy of freely available Digital Elevation Models (DEMs)

ABSTRACT Many freely available global or quasi-global digital elevation model (DEM) datasets, including Advanced Land Observing Satellite (ALOS) World 3 Dimensions (3D) 30 m DEM (AW3D30 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer DEM (ASTER DEM), Shuttle Radar Topography Mission DEM (SRTM DEM), Multiple-Error-Removed-Improved-Terrain DEM (MERIT DEM), and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) DEM (TanDEM-X DEM), are extremely useful products for many applications, so it is necessary to make a reasonable analysis of the quality of these DEMs. The quality of these DEMs is affected by both horizontal and vertical errors. However, most related researches have paid little attention to the effects of horizontal errors on the vertical accuracy of DEMs. Herein, after analysing the horizontal errors caused in the data acquisition process from optical stereo pairs and interferometry, experiments were proposed to assess the horizontal errors in DEMs. Then, we used the high accuracy digital surface model (DSM) of the Changsha area generated from the ZiYuan-3 triplet stereos to investigate the impact of horizontal errors on the accuracy of the five freely available DEMs. The subpixel level horizontal errors of the five DEMs were calculated, showing that the TanDEM-X DEM has systematic offsets and the other four DEMs have strip-like geometric errors. All the DEMs, except ASTER DEM, attained an accuracy increase of approximately 10% after the horizontal errors were removed. In mountainous areas, correcting the horizontal errors could eliminate the elevation errors caused by the imaging geometry of the optical camera and radar system. Generally, AW3D30 DEM has the best performance in terms of overall elevation accuracy and geometric accuracy after eliminating horizontal errors, and so it can have wide-ranging applications. These findings demonstrate the importance of removing horizontal errors to improve the elevation accuracy in practical applications using these global freely available DEMs.

Assessment of open source digital elevation models (SRTM-30, ASTER, ALOS) for erosion processes modeling

The relief has a major impact on the landscape`s hydrological, geomorphological and biological processes. Many geographic information systems used elevation data as the primary data for analysis, modeling, etc. A digital elevation model (DEM) is a modern representation of the continuous variations of relief over space in digital form. Digital Elevation Models (DEMs) are important source for prediction of soil erosion parameters. The potential of global open source DEMs (SRTM, ASTER, ALOS) and their suitability for using in modeling of erosion processes are assessed in this study. Shumsky district of Ternopil region, which is located in the Western part of Ukraine, is the area of our study. The soils of Shumsky district are adverselyaffected by erosion processes. The analysis was performed on the basis of the characteristics of the hydrological network and relief. The reference DEM was generated from the hypsographic data(contours) on the 1:50000 topographical map series compiled by production units of the Main Department of Geodesy and Cartography under the Council of Ministers. The differences between the reference DEM and open source DEMs (SRTM, ASTER and ALOS) are examined. Methods of visual detection of DEM defects, profiling, correlation, and statistics were used in the comparative analysis. This research included the analysis oferrors that occurred during the generation of DEM. The vertical accuracy of these DEMs, root mean square error (RMSE), absolute and relative errors, maximum deviation, and correlation coefficient have been calculated. Vertical accuracy of DEMs has been assessed using actual heights of the sample points. The analysis shows that SRTM and ALOS DEMs are more reliable and accurate than ASTER GDEM. The results indicate that vertical accuracy of DEMs is 7,02m, 7,12 m, 7,60 mand 8,71 m for ALOS, SRTM 30, SRTM 90 and ASTER DEMs respectively. ASTER GDEM had the highest absolute, relative and root mean square errors, the highest maximum positive and negative deviation, a large difference with reference heights, and the lowest correlation coefficient. Therefore, ASTER GDEM is the least acceptable for studying the intensity and development of erosion processes. The use of global open source DEMs, compared with the vectorization of topographic maps,greatly simplifies and accelerates the modeling of erosion processes and the assessment of the erosion risk in the administrative district.

On the use of global DEMs in ecological modelling and the accuracy of new bare-earth DEMs

It is now widely acknowledged that the increasing availability of remotely sensed data facilitates ecological modelling. Digital elevation models (DEMs) are arguably one of the most common remote sensing products used in this context. Topographic indices (e.g. slope, orientation, rugosity) derived from DEMs are widely used as surrogates for field-measured environmental variables. Available global DEMs, such as those from the shuttle radar topography mission (SRTM), however, do not provide information on bare-earth elevation as they measure elevation of the highest objects above the ground (e.g. canopy). This affects the derived topographic indices and limits the use of global DEMs in ecological modelling. Unfortunately, most ecological studies ignore this limitation despite the fact that methods to remove the vegetation offset have been developed. We used high resolution LiDAR DTM to assess the accuracy of two newly available global bare-earth DEMs where such methods were applied and to compare them with the SRTM DEM. Furthermore, we assessed the effect of DEMs’ vertical error on species distribution models (SDMs) by calculating slope and topographic wetness index (TWI) from these different models and evaluating their suitability for SDMs by adopting a virtual species approach. We simulated virtual species based on slope and TWI derived from accurate LiDAR DTM at three resolutions (30 m, 90 m and 900 m) and developed univariate generalized models to assess the performance of the bare-earth and SRTM DEMs. Our results show that the vertical error in both newly available, vegetation-corrected global DEMs is indeed successfully reduced. The overall vertical root mean squared error (RMSE) was 10.52 m for SRTM, while it was 6.80 m and 6.25 m for the two global bare-earth DEMs. The effect of the vertical error on SDMs was most significant at finer spatial resolutions. Using SRTM DEM, as opposed to a more accurate bare-earth DEM, led to a decline in area under curve (AUC) values from 0.94 to 0.77. SDMs fitted with slope and TWI derived from new global bare-earth DTMs performed slightly better than SRTM. Since methods for vegetation-offset removal in DEMs exist and corrected DEMs are freely available, we argue that the vertical accuracy of DEMs should be more consistently considered. Local, high-accuracy DEMs should be used where available; in remaining instances, however, global DEMs where vertical bias was minimized should be used in ecological modelling. Further improvement of global DEMs at 30 m and better resolutions are needed to enhance accuracy of derived indices and ecological models.

Uncertainty of soil erosion modelling using open source high resolution and aggregated DEMs

Digital Elevation Model (DEM) is one of the important parameters for soil erosion assessment. Notable uncertainties are observed in this study while using three high resolution open source DEMs. The Revised Universal Soil Loss Equation (RUSLE) model has been applied to analysis the assessment of soil erosion uncertainty using open source DEMs (SRTM, ASTER and CARTOSAT) and their increasing grid space (pixel size) from the actual. The study area is a part of the Narmada river basin in Madhya Pradesh state, which is located in the central part of India and the area covered 20,558 km2. The actual resolution of DEMs is 30 m and their increasing grid spaces are taken as 90, 150, 210, 270 and 330 m for this study. Vertical accuracy of DEMs has been assessed using actual heights of the sample points that have been taken considering planimetric survey based map (toposheet). Elevations of DEMs are converted to the same vertical datum from WGS 84 to MSL (Mean Sea Level), before the accuracy assessment and modelling. Results indicate that the accuracy of the SRTM DEM with the RMSE of 13.31, 14.51, and 18.19 m in 30, 150 and 330 m resolution respectively, is better than the ASTER and the CARTOSAT DEMs. When the grid space of the DEMs increases, the accuracy of the elevation and calculated soil erosion decreases. This study presents a potential uncertainty introduced by open source high resolution DEMs in the accuracy of the soil erosion assessment models. The research provides an analysis of errors in selecting DEMs using the original and increased grid space for soil erosion modelling.

Effect of sensor modelling methods on computation of 3-D coordinates from Cartosat-1 stereo data

The orbital and the rational polynomial coefficients (RPC) models are the two most commonly used models to compute a three-dimensional coordinates from an image stereo-pair. But it is still confusing that with the identical user provided inputs, which one of these two models provides more accurate digital elevation model (DEM), especially for mountainous terrain. This study aimed to find out the answer by evaluating the impact of used models on the vertical accuracy of DEM extracted from Cartosat-1 stereo data. We used high-accuracy photogrammetric DEM as the reference DEM. Apart from general variations in statistics, surprisingly in a few instances, both the DEMs provided contrasting results, thus proving the significance of this study. The computed root mean square errors and linear error at 90% (LE90) were lower in case of RPC DEM for various classes of slope, aspect and land cover, thus suggesting its better relative accuracy.