Advanced Land Observing Satellite World Research Articles

Detection of karst depression in Brazil comparing different semantic and instance segmentations and global digital elevation models

This research assessed the efficacy of deep segmentation in identifying and measuring natural karst depressions in the Bambuí Group's carbonate rocks in Western Bahia, Brazil. The investigation used five Global Digital Elevation Models (DEMs) with 30-m resolution: Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model version 3 (ASTER-GDEM v3), Advanced Land Observing Satellite World 3D – 30 m version 3.2 (AW3D30 v3.2), Copernicus 30 m global DEM (GLO-30), NASA Digital Elevation Model version 1 (NASADEM v1), and Shuttle Radar Topography Mission version 3 (SRTM v3). The karst feature detection analysis compared five semantic segmentation architectures: Feature Pyramid Networks (FPN), LinkNet, UNet, UNet++, DVL3+, using an EfficientNet-B7 backbone, one instance segmentation model (Mask-RCNN), and a semantic-to-instance conversion method. This comparison considers different datasets, including one, two, or eleven variables (DEM, DEM-based sink depth, and nine terrain attributes). Besides, we performed a combinatorial analysis of the DEMs to evaluate the improvement in karst feature detection. The methodology involved the generation of geomorphometric attributes, sample labeling from Sentinel-2 and Operational Land Imager-Landsat 8 datasets, training-validation-testing steps with 128 × 128 samples, reconstruction of large images for semantic and instance segmentation, and accuracy analysis. The findings revealed that GLO-30 and AW3D30 data were the most accurate, while ASTER GDEM performed poorly in both segmentation forms. Among the semantic models, FPN showed the highest accuracy. The 11-variable models preferentially outperformed those with fewer in both types of segmentation. The approach of semantic-to-instance conversion with Geographic Information System tools favored individualizing karst depressions and obtaining efficient quantification, consisting of an easy alternative to achieve instance segmentation. Reconstruction of large remote sensing images through sliding windows considering the specifics of instance and semantic segmentation demonstrated that smaller stride enhances object coverage and reduces the risk of losing crucial information, effectively improving the predictions. The combinatorial analysis for semantic and instance segmentation indicates that models incorporating more DEMs (4- and 5-DEM models) and variables achieve generally higher accuracy. The best result in semantic segmentation was combining the five DEM datasets using 11 variables, reaching an F-score of 85.06 and IoU of 74.00. In instance segmentation, the best result for the bounding box was the model that integrates AW3D30, GLO-30, NASADEM, and SRTM with eleven variables reaching Average Precision (AP) of 45.64, AP50 of 88.40 and AP75 of 39.38, while the best result for the segmentation mask was the model that integrates the five models with eleven variables with AP of 42.61, AP50 of 86.79 and AP75 of 35.43. These results demonstrate that integrating a wide range of DEM data, even the lowest performing ones, improves model generalization and accuracy in segmentation, leveraging strengths and mitigating individual weaknesses. Future work could explore high-resolution DEMs and the integration of various deep-learning methods.

Vertical accuracy evaluation free access digital elevation models (DEMs): case Fergana Valley in Uzbekistan

In this study, the vertical accuracy of the Shuttle Radar Topography Mission Digital Elevation Model Version 2.0 (SRTM30), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM Version 2.0 (ASTER GDEM2), and Advanced Land Observing Satellite World 3D Digital Surface Model Version 2.1 (ALOS AW3D30) was statistically assessed using GPS data. The Fergana Valley area was chosen as a study region, where the land surface can reflect tectonic processes. The values of ellipsoidal heights of 27 points of the regional GPS network were chosen as reference data. The geometric approach using GPS/leveling data and EGM96 global geopotential model-based geoid undulations was applied for geoid surface fitting. The geoid height corrections range ranged from –0.66 m to 0.87 m. Root-Mean-Square errors of ~10.0 m, ~16.4 m, and ~6.6 m was obtained for SRTM30, ASTER GDEM2, and ALOS AW3D30, respectively. It was found that compared with the reference model, all the global DEMs in mountainous areas generally overestimated elevation and the value of vertical accuracy at a 90% confidence level by 3-6 meters exceeded the declared by distributors. But ALOS AW3D30 proved to be the most accurate DEM that best represents the topography of the earth’s surface and could be used for some engineering applications in Fergana Valley.

An Automatic Approach to Extracting Large-Scale Three-Dimensional Road Networks Using Open-Source Data

3D road networks are amongst the indispensable elements of a smart city, which has been explored in various ways. However, researchers still faces challenges extracting 3D networks on a large scale. The global digital surface models (DSMs) with relatively high spatial resolution make it possible to extract 3D road networks. Nevertheless, the complete and accurate elevation of road networks cannot be obtained directly because of the limitation in sensors on the DSM production platform. Thus, we proposed a novel approach to extract large-scale 3D road networks, integrating terrain correction and road engineering rule constraint, by using the Advanced Land Observing Satellite World 3D-30 m DSM, OpenStreetMap and FABDEM. The simplification and terrain correction algorithm were applied to remove most of the edges with excessive grades and reduced the negative impact of the built-up environment in DSM on the extraction accuracy. Moreover, the tunnel parts of the 3D road networks were refined based on road engineering standards. Nanjing of China, Aalborg of Denmark and Los Angeles of the United States are selected as study areas. Using 3D road networks from unmanned aerial vehicle photogrammetry, light detection and ranging and Google Earth as references, we validated the road elevation accuracy of our method and obtained an overall root-mean-square error of 3.80 m and a mean absolute error of 1.94 m. The 3D topology of interchanges with different radii was reconstructed completely. Overall, our work is an endeavour to utilise multiple open-source data to extract large-scale 3D road networks and benefits future research related to smart city reconstruction and 3D urban analysis.

Positional accuracy assessment of historical Google Earth imagery in Lagos State, Nigeria

The horizontal accuracy of historical Google Earth (GE) images at four epochs between the years 2000 and 2018, and the vertical accuracy of its elevation data within Lagos State, in Nigeria, are respectively evaluated by comparison with a very high–resolution digital orthomosaic and comparison with 558 ground control points. Two readily available 30-m digital elevation models (DEMs) — the Shuttle Radar Topography Mission (SRTM) v3.0 and the Advanced Land Observing Satellite World 3D (AW3D) DEM v2.1. — were also compared with GE elevations. A novel approach for assessing the space–time variations in the magnitude and direction of errors in GE imagery is presented. For horizontal accuracy, the root mean square errors (RMSEs) are as follows — year 2000 (16.9 m), year 2008 (16.4 m), year 2012 (6.1 m) and year 2018 (6.1 m). The most recent GE imagery (year 2018) had the least horizontal error while year 2000 had the largest horizontal error. The horizontal shift was skewed towards the western and north-western directions, indicative of systematic error. In terms of the vertical accuracy, GE elevation data had the lowest accuracy and highest RMSE of 6.21 m followed by AW3D with an RMSE of 4.39 m and SRTM with an RMSE of 3.68 m.

Comparative analysis of freely available digital elevation models for applications in multi-criteria environmental modeling over data limited regions

Remotely sensed elevation data obtained from high-resolution Digital Elevation Models (DEMs) is one of the main sources of data required for environmental modeling and remediation especially when it has to do with identification of erosion sites and flood-prone areas. The focus of this study is to evaluate four recently improved and freely available high-resolution elevation data obtained from the Advanced Land Observing Satellite World 3D Digital Surface Model version 2.1 (ALOS W3D30), TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X 90), Multi-Error-Removed Improved-Terrain (MERIT) DEM and EarthEnv-DEM90 in order to determine the most suitable DEM that could be used as an input data in a multi-criteria environmental modeling to identify erosion sites and flood-prone areas in data limited regions such as Southeastern Nigeria. Intensive performance evaluation of these four DEMs was rigorously carried out using statistical and surface analyses tools (Root-Mean-Square Error (RMSE), Land Use/Land Cover, slope, and contour maps). The obtained RMSE range from 5.49 m to 19.79 m depending on the surface of contact. Slope values range from 0.00 to 89.49° depicting the complexity and ruggedness of the terrain. Analyses of the obtained RMSE showed that EarthEnv-DEM90 is more accurate (in terms of RMSE value) than the other three DEMs but further terrain analyses using slope and contour maps generated from each of the DEMs revealed that ALOS W3D30 represent the topography of the terrain (terrain variation) better than the other three DEMs. This study demonstrates the importance of carrying out rigorous and intensive performance evaluation of DEMs using many terrain parameters or attributes in order to avoid large errors that may be contributed by the use of a DEM in applications such as environmental modeling and remediation.

Evaluating Vertical Accuracies of Open-Source Digital Elevation Models over Multiple Sites in China Using GPS Control Points

Digital elevation models (DEMs) are widely used across a range of fields. Several open-source global DEMs have been released, including the advanced land observing satellite world 3D 30 m DEM (AW3D30DEM), advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM), shuttle radar topography mission DEM (SRTMDEM), and TerraSAR-X for digital elevation measurement (TanDEM-X). ASTER and SRTM are the most widely used DEMs, while the newer models AW3D30DEM and TanDEM-X are becoming increasingly popular. Many studies have evaluated the qualities of these DEMs; however, few multi-regional studies have been conducted in China. To comprehensively and systematically evaluate the qualities of these DEMs in China, the vertical accuracies of AW3D, ASTER, STRM (all 30 m), and TanDEM-X (90 m) were tested across 16 regions in China. Using high-precision global positioning system control points for reference, error values were determined by subtracting these reference values from corresponding global DEM elevation values. As the study only covered flat areas (slope < 5°), slope was treated as a controlled variable. After assessing the impacts of the slope aspect and land cover type, variations in vertical accuracy were examined with respect to longitude and latitude. Overall, TanDEM-X exhibited the highest stability and accuracy, AW3D30 and SRTM also performed well, while ASTER exhibited the worst accuracy. The DEMs showed relationships with the slope aspect and land cover type, assuming that slope had no influence on vertical accuracy. In general, vertical accuracy in high latitudes was slightly better than that in low latitudes, and no evident variations were observed with respect to longitude. This study is the first to conduct DEM analysis across many regions in China from open sources. Since most of the users rely on public domain DEM datasets, this work contributes to their analysis in academic and engineering fields.

Land Cover and Land Use Mapping of the East Asian Summer Monsoon Region from 1982 to 2015

Owing to the recent intensification of the East Asian summer monsoon, the frequency of floods and dry spells, which commonly affect more than one billion people, is continuously increasing. Thus, understanding the causes of changes in the EASM is paramount. Land cover and land use change can perturb a regional climate system through biogeophysical and biogeochemical processes. However, due to the scarcity of temporally continuous land cover and land use maps, the impact of land cover and land use change on the EASM is still not thoroughly explored. In the present study, this limitation was addressed via the production of annual land cover and land use maps of the East Asian summer monsoon region covering a period of 34 years (1982–2015). This was achieved through a random forest classification of phenological information derived from the Advanced Very High-Resolution Radiometer Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index dataset and terrain information from the Advanced Land Observing Satellite World 3D—30 m Digital Surface Model data. Nine ecological zones were involved in the random forest classification and the classified map in 2015 was validated using very high-resolution images obtained from Google Earth. The overall accuracy (73%) of the classification map surpasses the Moderate Resolution Imaging Spectroradiometer and Global Land Surface Satellite land cover products for the same year by ~7% and 4%, respectively. According to our classified maps, croplands and forests significantly increased in the East Asian summer monsoon region from 1982 to 2015. The dominant transition in these three decades was from croplands to forests.

Satellite and UAV-based remote sensing for assessing the flooding risk from Tibetan lake expansion and optimizing the village relocation site

Climate change in recent decades led to the remarkable expansions for most lakes in endorheic basins of the Tibetan Plateau (TP). Enlarged lake inundation areas may pose adverse effects and potential threats on the local human living environment, especially for high-risk villages adjacent to rapidly expanding lakes. Taking a rapidly expanding lake, Angzi Co in the central TP as a study case, we investigated the flooding risk of lake growth on the local living environment and proposed an optimized solution of village relocation selection on the basis of satellite and unmanned aerial vehicle (UAV) remote sensing. The detection of spatiotemporal variations of Angzi Co using optical and altimetric satellite observations revealed a significant area and water level increase by 81.28 km2 and 5.78 m, respectively, from 2000 to 2020. We also assessed the vertical accuracy of multi-source digital elevation model (DEM) products using Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) altimetry data and further examined the flooding risk and potential influences of lake expansion on adjacent settlements (Guozha Village). Results indicated that UAV-DEM achieves excellent advantages in depicting details of lake shoreline variations and simulating potential submergence regions, followed by Advanced Land Observing Satellite World 3D DEM (AW3D DEM). Moreover, assuming that Angzi Co maintains the water level at a growth rate of 0.29 m/a (the average change rate during 2000–2020), the village will be submerged in approximate 10 years based on our assessment. Furthermore, we designed an optimal relocation site southwest of Guozha Village and approximately 3 km away based on the GIS-MVDA method and field investigations. An initial remote sensing-based approach for assessing the flooding risk from dramatic lake expansions in the TP and optimizing the village relocation site was proposed in this study to provide an essential scientific reference for formulating risk mitigation solutions under future climate change scenarios.

Vertical Accuracy Assessment of Open Access Digital Elevation Models Using GPS

In this study, the vertical accuracy of the Shuttle Radar Topography Mission Digital Elevation Model Version 2.0 (SRTM30), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM Version 2.0 (ASTER GDEM2), and Advanced Land Observing Satellite World 3D Digital Surface Model Version 2.1 (ALOS AW3D30) was statistically assessed using GPS data. The Tashkent geodynamical polygon was chosen as a study region, where the land surface can reflect as both natural (tectonic) and man-made (Charvak reservoir load effect) processes. The geometric approach using GPS/leveling stations and EGM96 GGM-based geoid undulations was applied for local geoid modeling. RMSE of ~7.48 m, ~6.90 m, and ~8.24 m was obtained for SRTM30, ASTER GDEM2, and ALOS AW3D30, respectively. ASTER GDEM2 proved to be the most accurate DEM with absolute vertical accuracy LE90 of 11.35 m and ME of 5.25 m and it is very suitable for geodetic and hydrological studies in this mountainous area.

Reconstruction and Evaluation of DEMs From Bistatic Tandem-X SAR in Mountainous and Coastal Areas of China

TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) mission is designed to generate 3-D images of the Earth as the first bistatic synthetic aperture radar (SAR). However, few quantitative studies of TanDEM-X digital elevation model (DEM) quality validation have been conducted specifically in China. This article presents an iterative method to generate high-resolution TanDEM-X DEMs and assesses the vertical accuracy with high-accuracy GPS observations, 1 arc second global DEMs available [Advanced Land Observing Satellite World 3-D-30 m v2.2, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) v3, Shuttle Radar Topography Mission (SRTM) v3.1, NASADEM, and X-band SRTM], and TanDEM-X 90 m DEM. The results demonstrate remarkable elevation quality and consistency in coastal areas with root mean square error of 1.7 m and 90% linear error (LE90) of 0.4 m, whereas 3–4 times weaker accuracies in steep mountainous areas. A positive bias of 1–2 m for an overall LE90 measure exists in the dense vegetation and steep-slope mountainous areas. TanDEM-X DEM-based SAR interferometry deformation uncertainty simulation indicates a low or even negligible topographic error contribution of 2–4 mm in mountainous areas and less than 1 mm in coastal areas. It indicates that the TanDEM-X DEM performs better than other global DEMs overall and shows a better elevation consistence with SRTM C-band DEM in the coastal area. As an excellent source of up-to-date information, the TanDEM-X DEM are expected be an advantage for understanding dynamic land use changes and improving identification and delineation of coastal lands, mountainous landslides, and earthquakes disasters.

Mud Volcanism at the Taman Peninsula: Multiscale Analysis of Remote Sensing and Morphometric Data

Mud volcanism is observed in many tectonically active regions worldwide. One of the typical areas of mud volcanic activity is the Taman Peninsula, Russia. In this article, we examine the possibilities of multiscale analysis of remote sensing and morphometric data of different origins, years, scales, and resolutions for studying mud volcanic landscapes. The research is exemplified by the central-northern margin of the Taman Peninsula, where mud volcanism has only been little studied. The data set included one arc-second gridded Advanced Land Observing Satellite World three-dimensional (3D) digital surface model (AW3D30 DSM); a Corona historical declassified satellite photography; high-resolution imagery from an unmanned aerial survey (UAS) conducted with a multi-copter drone DJI Phantom 4 Pro, as well as a series of 1-m gridded morphometric models, including 12 curvatures (minimal, maximal, mean, Gaussian, unsphericity, horizontal, vertical, difference, vertical excess, horizontal excess, accumulation, and ring one) derived from UAS-based images. The data analysis allowed us to clarify the conditions of neotectonic development in the central-northern margin of the Taman Peninsula, as well as to specify manifestations of the mud volcanism in this region. In particular, we were able to detect minor and weakly topographically expressed mud volcanic features (probably, inactive gryphons, and salses), which are hidden by long-term farming practice (e.g., ploughed and covered by soil).

On the Reliable Generation of 3D City Models from Open Data

A 3D model communicates more effectively than a 2D model, hence the applications of 3D city models are rapidly gaining significance in urban studies. However, presently, there is a dearth of free of cost, high-resolution 3D city models available for use. This paper offers potential solutions to this problem by providing a globally replicable methodology to generate low-cost 3D city models from open source 2D building data in conjunction with open satellite-based elevation datasets. Two geographically and morphologically different case studies were used to develop and test this methodology: the Chinese city of Shanghai and the city of Nottingham in the UK. The method is based principally on OpenStreetMap (OSM) and Advanced Land Observing Satellite World 3D digital surface model (AW3D DSM) data and use GMTED 2010 DTM data for undulating terrain. Further enhancement of the resultant 3D model, though not compulsory, uses higher resolution elevation models that are not always open source, but if available can be used (i.e., airborne LiDAR generated DTM). Further we test and develop methods to improve the accuracy of the generated 3D models, employing a small subset of high resolution data that are not open source but can be purchased with a minimal budgets. Given these scenarios of data availability are globally applicable and time-efficient for 3D building generation (where 2D building footprints are available), our proposed methodology has the potential to accelerate the production of 3D city models, and thus to facilitate their dependent applications (e.g., disaster management) wherever commercial 3D city models are unavailable.

Accuracy validation and bias assessment for various multi-sensor open-source DEMs in part of the Karakoram region

ABSTRACT The present study evaluate horizontal and vertical accuracy of seven open-source digital elevation models (DEMs) having moderate-to-high resolutions viz. 30 m Shuttle Radar Topography Mission (SRTM1), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), Advanced Land Observing Satellite World 3D (AW3D30), and Cartosat DEM (CartoDEM), 90 m TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), 12.5 m terrain corrected Phased Array L-band Synthetic Aperture Radar (PALSAR) from Advanced Land Observing Satellite (ALOS) and 8 m High Mountain Asia (HMA) over the rugged mountainous terrain of the Karakoram region. Horizontal accuracy (specified in x and y) assessed by referring photogrammetrically generated master DEM from Cartosat-1 revealed AW3D30 as the most consistent DEM with a slight shift of +2.80 m and −4.89 m in x and y direction, respectively. However, vertical accuracy analysis showed that both HMA and AW3D30 DEMs are quite close to each other with MAE of 3.01 m and 3.46 m, RMSE of 5.6 m and 7.5 m, and NMAD of 4.09 m and 5.5 m, respectively. We also examined the influence of slope on DEM errors and associated elevation-dependent bias over non-glaciated surface which can be valuable input for geodetic mass balance estimations.

Accuracy Assessment of Alos W3d30, Aster Gdem and Srtm30 Dem: A Case Study of Nigeria, West Africa

Digital Elevation Models (DEMs) depict the configuration of the earth surface and are being applied in many areas in earth and environmental sciences. In this study, the accuracy of the Advanced Land Observing Satellite World 3D Digital Surface Model version 2.1 (ALOS W3D30), the Shuttle Radar Topography Mission Digital Elevation Model version 3.0 (SRTM30) and the Advanced Space borne Thermal Emission and Reflection Radiometer Global DEM version 2.0 (ASTER GDEM2) was statistically assessed using high accuracy GPS survey data. Root-Mean-Square errors of ~5.40 m, ~7.47 m and ~20.03 m were obtained for ALOS W3D30, SRTM30 and ASTER GDEM2 respectively. In further analyses, we discovered that ALOS W3D30 and SRTM30 were much more accurate in regions where the height intervals were within 201 m - 400 m and >801 m. ALOS W3D30 proved to be the most accurate DEM that best represents the topography of the earth’s surface and could be used for some earth and environmental applications in Nigeria. We recommend that this study should serve as a guide in the use of any of these DEMs for earth and environmental applications in Nigeria.

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

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

Characterizing Complex Surface Ruptures in the 2013 Mw 7.7 Balochistan Earthquake Using Three‐Dimensional Displacements

AbstractWe use satellite‐derived high‐resolution topography and orthoimages, namely, preearthquake Advanced Land Observing Satellite World 3‐D data and postearthquake Pleiades data, to retrieve 3‐D displacements in the 2013 Balochistan, Pakistan, earthquake. Previous studies of this earthquake have revealed many complex rupture patterns, such as off‐fault deformation (near‐field strike‐slip displacements smaller than the far field) and inelastic surface deformation (horizontal shortening on the surface significantly larger than that from simple elastic model). In this paper, we reanalyze the complexities of surface ruptures in a systematic way using the newly derived 3‐D displacements. In doing so, we made use of the vertical component of the curl and the horizontal divergence of the displacement field. By comparing the off‐fault deformation and curl field along the rupture, we found that curl is a good measure of the width of the deformation zone. The curl width ratio (CWR)—the ratio of the basic resolution of the curl field and curl width—was used to quantify the degree of surface slip localization. When CWR ≥ 0.9, there is no or very little off‐fault deformation, whereas when CWR ≤ 0.6, surface deformation is almost 100% distributed. The distributed deformation observed is controlled by both the fault geometry and local material types. Despite the overall strike slip with some thrusting, the divergence shows localized extension or enhanced shortening in the near field due to fault geometric variations at a spatial scale of tens to hundreds of meters, consistent with the 3‐D displacements and geological interpretations.

Comparative accuracy of the AW3D30 DSM, ASTER GDEM, and SRTM1 DEM: A case study on the Zaoksky testing ground, Central European Russia

ABSTRACTQuasi-global, medium-resolution land surface models – the Shuttle Radar Topography Mission digital elevation model (SRTM1 DEM), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), and the Advanced Land Observing Satellite World 3D digital surface model (AW3D30 DSM) – are widely used in geosciences. We carried out a comparative analysis of accuracy for these models exemplified by the Zaoksky testing ground, Central European Russia. First, we extracted related portions from the SRTM1 DEM, ASTER GDEM, and AW3D30 DSM for the study area. In the further analysis, we used 69 ground control points (GCPs) within the testing ground, as well as a reference DEM photogrammetrically generated from aerial stereo images. For each GCP, we determined an elevation value in four models and a related vertical error. For each model, we carried out a statistical analysis of vertical errors. To visualize the spatial distribution of vertical errors, we calculated matrices of residuals between the reference DEM and the quasi-global models. The results demonstrate that the AW3D30 DSM can be considered the most accurate quasi-global, medium-resolution land surface model. We do not recommend the use of the ASTER GDEM because it is marked by high errors with chaotically distributed hummock-like patterns.