Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

https://www.mdpi.com/2072-4292/12/21/348

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

\ua9 2024 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. 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

Quality assessment of DEM derived from topographic maps for geomorphometric purposes

Digital elevation models (DEMs) play a significant role in geomorphological research. For geomorphologists reconstructing landform and drainage structure is frequently as important as elevation accuracy. Consequently, large-scale topographic maps (with contours, height points and watercourses) constitute excellent material for creating models (here called Topo-DEM) in fine resolution. The purpose of the conducted analyses was to assess the quality of Topo-DEM against freely-available globalDEMs and then to compare it with a reference model derived from laser scanning (LiDAR-DEM). The analysis also involved derivative maps of geomorphometric parameters (local relief, slope, curvature, aspect) generated on the basis of Topo-DEM and LiDAR-DEM. Moreover, comparative classification of landforms was carried out. It was indicated that Topo-DEM is characterised by good elevation accuracy (RMSE <2 m) and reflects the topography of the analyzed area surprisingly well. Additionally, statistical and percentage metrics confirm that it is possible to generate a DEM with very good quality parameters on the basis of a large-scale topographic map (1:10,000): elevation differences between Topo-DEM and: 1) topographic map amounted from−1.68 to +2.06 m,MAEis 0.10 m, RMSE 0.16 m; 2) LiDAR-DEM (MAE 1.13 m, RMSE 1.69 m, SD 1.83 m); 3) GPS RTK measurements amounted from−3.6 to +3.01 m, MAE is 0.72 m, RMSE 0.97 m, SD 0.97 m. For an area of several dozen km2 Topo-DEM with 10×10 m resolution proved more efficient than detailed (1×1 m) LiDAR-DEM

Positional Accuracy Assessment of Historical Google Earth Imagery

Google Earth is the most popular virtual globe in use today. Given its popularity and usefulness, most users do not pay close attention to the positional accuracy of the imagery, and there is limited information on the subject. This study evaluates the horizontal accuracy of historical GE imagery at four epochs between year 2000 and 2018, and the vertical accuracy of its elevation data within Lagos State in Nigeria, West Africa. The horizontal accuracies of the images were evaluated by comparison with a very high resolution (VHR) digital orthophoto while the vertical accuracy was assessed by comparison with a network of 558 ground control points. The GE elevations were also compared to elevation data from two readily available 30m digital elevation models (DEMs), the Shuttle Radar Topography Mission (SRTM) v3.0 and the Advanced Land Observing Satellite World 3D (AW3D) DEM v2.1. The most recent GE imagery (year 2018) was the most accurate while year 2000 was the least accurate. This shows a continuous enhancement in the accuracy and reliability of satellite imagery data sources which form the source of Google Earth data. In terms of the vertical accuracy, GE elevation data had the highest RMSE of 6.213m followed by AW3D with an RMSE of 4.388m and SRTM with an RMSE of 3.682m. Although the vertical accuracy of SRTM and AW3D are superior, Google Earth still presents clear advantages in terms of its ease of use and contextual awareness.Comment: 36 page

Global digital elevation models for terrain morphology analysis in mountain environments: insights on Copernicus GLO-30 and ALOS AW3D30 for a large Alpine area

This study focuses on the quality evaluation of two of the best 1 arc-second public global digital elevation models (DEMs), Copernicus GLO-30 DEM and ALOS AW3D30 DSM, from the perspective of their capability to represent the terrain fine-scale morphology of a complex alpine landscape, located in the Italian Trentino Province. The analysis is performed on an area of 6210 km(2), considering a reference DEM derived from a high resolution and accurate airborne Lidar survey. The quality assessment goes beyond a conventional approach based on elevation differences statistics, computed on a pixels-by-pixel basis. An ad hoc approach for evaluating the capability to represent fine-scale morphology, including surface roughness, is adopted. Moreover, the quality analysis is performed considering the influence of local morphology and of the different land covers. The findings show that although the two global DEMs have comparable overall quality, their relative performances change according to local landscape characteristics. Copernicus DEM performance is on average better than ALOS in correspondence of urbanized areas as well as in areas without vegetation cover, with gentle slopes and relatively low short-range roughness. Meanwhile, ALOS DEM performance is slightly better than Copernicus in rougher terrain and steeper slopes. In general, both DEMs have poor performances in steep slopes, with a limited capability to describe fine-scale morphology. The adoption of these global DEMs for terrain analysis and modelling of earth surface processes should be performed carefully, considering the impact of different land covers and of local morphology, including surface roughness

Evaluation of open-access global digital elevation models (AW3D30, SRTM and ASTER) for flood modelling purposes

Elevation data in the form of Digital Elevation Models (DEMs) has been recognised as a basic piece of information for the accurate representation of topographic controls exerted in hydrologic and hydraulic models. Yet many practitioners rely on open-access global datasets usually obtained from space-borne survey due to the cost and sparse coverage of sources of higher resolution. In may 2016 the Japanese Aerospace eXploration Agency (JAXA) publicly released an open-access global DEM at an horizontal resolution of 30 m, the ALOS World 3D-30m (AW3D30). So far no published study assessed the flood modelling capabilities of this new product. The purpose of this investigation is twofold. Firstly, to present an assessment of the capacity of the AW3D30 DEM for flood modelling purposes and secondly, to compare its performance with regards to computed water levels and flood extent maps calculated using other freely available 30 m DEMs for model setup (e.g. SRTM and ASTER GDEM). For this comparison, the reference to reality is given by the water levels and flood extent maps computed with the same numerical model but using a LiDAR based DEM (5 m of spatial resolution re-sampled to 30 m). The numerical model employed in this investigation is based on a damped partial inertia approximation of the Saint-Venant equations on a regular raster grid, which is forced with a simple and synthetic rainfall storm event. Numerical results using different elevation data in model setup are compared for two regions with contrasting topographic gradients (steep and smooth). Results with regards to water depth and flood extent show that AW3D30 DEM performs better than the SRTM DEM. Notably, in the case of mountainous regions results derived with the AW3D30 DEM are comparable in skill to those obtained with a LiDAR derived DSM, suggesting its suitability in the numerical reproduction of flood events. This encouraging performance paves the way to more accurate modelling for both data-scarce regions and global flood models

A pipeline for automated processing of declassified Corona KH-4 (1962-1972) stereo imagery

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100300) and the Swiss National Science Foundation (200021E 177652/1) within the framework of the DFG Research Unit GlobalCDA (FOR2630).The Corona KH-4 reconnaissance satellite missions acquired panoramic stereo imagery with high spatial resolution of 1.8–7.5m from 1962-1972. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents the Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utilizes deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time-dependent exterior orientation parameters is employed. Using the entire frame of the Corona image, bundle adjustment with well-distributed GCPs results in an average standard deviation or σ0 of less than two pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the 3D reconstruction accuracy. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long-term storage likely cause systematic deviations of up to six pixels. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation of elevation differences of ≈ 4m over an area of approx. 4000km2 after a tile-based fine coregistration of the DEMs. We further assess CoSP on complex scenes involving high relief and glacierized terrain and show that the resulting DEMs can be used to compute long-term glacier elevation changes over large areas.PostprintPeer reviewe

Evaluation and normalization of topographic effects on vegetation indices

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas