In this contribution, we assess the vertical accuracy of the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30) digital elevation model (DEM) using the runway method (RWYM). The RWYM utilizes the longitudinal profiles of runways which are reliable and ubiquitous reference data. A reference dataset used in this project consists of 36 runways located at various points throughout the world. We found that AW3D30 has a remarkably low root mean square error (RMSE) of 1.78 m (one sigma). However, while analysing the results, it has become apparent that it also contains a widespread elevation anomaly. We conclude that this anomaly is the result of uncompensated sensor noise and the data processing algorithm. Also, we note that the traditional accuracy assessment of a DEM does not allow for identification of these type of anomalies in a DEM.

The accuracy of global digital elevation models (GDEMs or DEMs), i.e., the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Shuttle Radar Topography Mission (SRTM), has been reassessed using the “runway” method. The runway method uses the vertical profiles of runways as reference data to which corresponding profiles extracted from investigated DEMs are compared. The major advantage of the runway method is that only the source of instrument-induced errors of DEMs is captured. This ultimate effect is achieved due to the flatness and the homogeneous materials from which runways are constructed. A cross-comparison of the two DEMs has helped to highlight a few important facts: both exhibit a negative elevation bias, and the ASTER DEM contains outliers that may be relatively easy to detect and correct, particularly in flat terrain.

Accuracy assessment of a global digital elevation model (DEM) is an important and challenging task primarily because of the difficulties and costs associated with securing a reliable and representative reference dataset. In this article, we report on the vertical accuracy assessment of the WorldDEM™, the latest global DEM using the synthetic aperture radar interferometry (InSAR) method, based on the German TanDEM-X mission data. For reference data we use vertical profiles along the centerline of 47 paved runways located in different areas around the world. Our accuracy statement is based on the analysis of discrepancies between the reference data and the corresponding vertical profiles extracted from the WorldDEM™ dataset. Since the runways are nearly flat and have homogenous surfaces, the observed discrepancies are mainly due to instrument-induced error. Therefore, the derived accuracy statement has a universal character, e.g., it is not biased by other error sources including target- or environment-induced errors. Our main conclusions are that the WorldDEM™ is the most accurate global DEM to date in terms of its vertical accuracy; it appears that the accuracy is spatially independent.

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth’s surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth’s surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progress for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~ 88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning.