Single-pair differential synthetic aperture radar interferometry (DInSAR) as well as more advanced methods, such as persistent scatterer interferometry (PSI), allow vertical displacements to be detected at the sub-centimeter level. Since 2014 free SAR data—Sentinel-1—have been collected systematically under the COPERNICUS program at a high temporal resolution and with global coverage. Such an open-access policy greatly helps build a wide user-community and develop diverse SAR-based applications. In this study conventional single-pair DInSAR and the PSI techniques were employed to monitor the vertical displacements of the newly constructed D8 highway, more specifically the part passing through the České Středohoří Mountains, where, during highway construction, a massive landslide occurred in June 2013. For both DInSAR and PSI, free Sentinel-1 radar data were used; moreover, the conventional single-pair DInSAR workflow was processed using freely available SNAP software. Results from the radar interferometry were validated using in situ techniques, such as geodetic measurements, 3D inclinometers, and laser scanning. Both approaches proved their ability to achieve reliable results in detecting vertical displacement “hotspots”. Additionally, in terms of absolute values, the PSI interferometry corresponds very well with the in situ measurements. This study also shows that open-source solutions (free data and SW) provided under the COPERNICUS program bring a great potential for monitoring vertical displacements.

During the last decade, remote sensing methods have developed significantly. The technological progress in development of new sensors and techniques opened up a large scope of new applications including near-field data collecting using Unmanned Aerial Vehicles (UAVs). State-of-the-art UAV technologies provide advantages including cost-effectiveness and temporal flexibility. For our case study, we acquired the high-resolution UAV data over the archaeological site near Černouček, the Czech Republic. This site was discovered at the beginning of 1990s as a result of low altitude aerial reconnaissance carried out by the Institute of Archaeology, Czech Academy of Sciences. Two ditched enclosures were identified due to vegetation marks in late spring and early summer, as higher moisture and presence of some chemical constituents in the secondary infill of the ditches give better conditions for plants above them. In 2017, new UAV data (Red, Green and Blue: RGB and Red and Near-infrared data: Red + NIR) were acquired over the Černouček site in June to find out whether there are some other objects hidden under ground. Using the RGB data digital elevation models were derived while the Red + NIR data were used to compute vegetation indices (VI), further spatial filtering allowing enhancing the local anomalies in the VI values was employed. As a result, several small objects were detected and suggested for the further investigations.

Post-earthquake analysis using radar interferometry has become a standard procedure for assessing earthquakes with significant damages. Sentinel-1 satellite provides 6-day revisiting time, and Sentinel-2 data has 5-day revisiting time and the same viewing angle that can enable the detection of changes in surface/land-cover after a major seismic event. Using Sentinel-2 alongside Sentinel-1 could bring new benefits when gathering spatial information about a post seismic event. In our study, we focused on analyzing a major earthquake, which occurred on 14 November 2016 with 7.8 magnitude near the city of Kaikōura, New Zealand, using both Sentinel-1 radar images and Sentinel-2 optical data. Hundreds of landslides were reported as a result of this earthquake. In addition, substantial land uplift was detected in some parts of the sea shore. Differential interferometry allowed us to estimate earthquake strength analyzing the distribution of absolute vertical displacement values. Sentinel-2 pre- and post-earthquake images were used in order to assess land-cover changes and automatically detect landslides, which occurred after the earthquake. Linking DInSAR results with Sentinel-2 change detection analysis helped us to get a more complex perspective on the earthquake impact, to create landslide inventory maps, and to subsequently develop workflows for quick post-event analysis.