Underground gas storage facilities are an important element of the natural gas supply system. They compensate for seasonal fluctuations in natural gas consumption. Their expected lifetime is in tens of years. Continuous monitoring of underground gas storage is therefore very important to ensure its longevity. Periodic injection and withdrawal of natural gas can cause, among other things, vertical movements of the terrain surface. Radar interferometry is a commonly used method for tracking changes in the terrain height. It can register even relatively small height changes (mm/year). The primary aim of our research was to verify whether terrain behavior above a relatively deep underground gas storage can be monitored by this method and to assess the possibility of detecting the occurrence of anomalous terrain behavior in an underground gas storage area such as reactivation of faults in the area. The results show a high correlation between periodic injection and withdrawal of natural gas into/from the underground reservoir and periodic changes in terrain height above it (the amplitude of the height changes is in centimeters), which may allow the detection of anomalous phenomena. We documented special behavior of storage structures in the Vienna Basin: the areas adjacent to the underground gas storages show exactly the opposite phase of vertical movements, i.e., while the terrain above the underground reservoirs rises as natural gas is injected, the adjacent areas subside, and vice versa. Based on the analysis of geological conditions, we tend to conclude that this behavior is conditioned by the tectonic fault structure of the studied area.

The space–time series carry information on temporal and spatial patterns in observed phenomena. The reported research integrates computational, visual and cartographic methods to support visual analysis of space–time series describing terrain surface movement. The proposed methodology for space–time series visualisation can support their analysts in investigating space–time patterns using transformation, clustering, filtration and visualisation. The presented approach involves spiral graphs for representation time dimension and cartographic visualisation through proportional point symbol map for representation of spatial dimension. The result is an intuitive visualisation of space–time series, conveying the sought-after spatio-temporal information. For practical tests, we used space–time series obtained by permanent scatterers interferometry (PS InSAR) to monitor the Earth’s surface movement above the underground gas storage (UGS) Tvrdonice, the Czech Republic. An UGS is characterised by periodic injection and withdrawal of natural gas, which induces periodic movement of the terrain above it. We have verified that our visualisation method provides the required pattern information and is easy to use.

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Several methods allow accurate measurement of terrain surface motions. Global navigation satellite systems (GNSSes) and interferometry with synthetic aperture radar (InSAR) stand out in terms of measurement accuracy among them. In principle, both methods make it possible to evaluate a three-dimensional vector of the motion of points on the terrain surface. In this work, we dealt with the evaluation of motions in the up–down (U–D) and east–west direction (E–W) over underground gas storage (UGS) from InSAR. One crucial step in breaking down PSInSAR line of sight (LOS) measurements to U–D and E–W components is getting time series derived from individual tracks to the same time frame. This is usually performed by interpolation, but we used an innovative approach: we analyzed individual time series using the Lomb–Scargle periodogram (LSP), which is suitable for periodic noisy and irregularly sampled data; we selected the most significant period, created LSP models, and used them instead of the original time series. Then, it was possible to derive time series values for any arbitrary time step. To validate the results, we installed one GNSS receiver in the Tvrdonice UGS test area to perform independent measurements. The results show a good agreement in the evaluation of motions by both methods. The correlation coefficient between horizontal components from both PSInSAR and GNSS was 0.95 in the case of the E–W component, with an RMSE of 1.75 mm; for U–D they were 0.78 and 2.35 mm, respectively. In addition to comparing the motions in the U–D and E–W directions, we also created a comparison by converting GNSS measurements to a line of sight of the Sentinel-1 satellite to evaluate the conformity of InSAR and GNSS measurements. Based on descending track, the correlation coefficient between LOS from both methods is, on average, 0.97, with an RMSE of 2.70 mm.

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<p><strong>Abstract.</strong> With the growing population, there is a growing demand for quality drinking water. Especially in developing parts of the world, this is a serious problem. The aim of this work is to test remote sensing methods for water quality monitoring. The presented part of the project is focused on introducing the process of water pollution assessment using vegetation indices, which are derived only using RGB images. Water quality monitoring is based on satellite imagery Landsat 8 and UAV images Phantom 3. As reference data was used in-site measurements in profiles points. In-site measurements were repeated every month in the vegetation period from April to September. Based on regression analysis, the equation for the calculation of the amount of chlorophyll and the statistical evaluation of the quality of these equations is derived for each vegetation index. The best results were achieved using the ratio aquatic vegetation index (RAVI) and ExG (Excess green) indices of 97% and 96.8% respectively.</p>

With the growing availability of accurate and long-term measurements of displacements of technical infrastructure elements, there is a growing interest in the automated processing of acquired data. Various methods can be used for monitoring; however, radar interferometry and Global Navigation Satellite Systems (GNSS) measurements are among the best for long-term monitoring. GNSS allows for continuous monitoring of individual points, while radar interferometry allows only periodic data collection but with an areal coverage. Radar interferometry can also reach a better precision under certain conditions; therefore, it appears to be more appropriate. Automated systems are being developed that allow not only to process radar data but also to detect anomalies in vertical displacement. It is advisable to have a testing polygon for their verification, enabling the comparison of the automated processing of radar interferometry with an independent GNSS measurement. In autumn 2019, a testing polygon was built at the Department of Geoinformatics, HGF VSB -Technical University of Ostrava, consisting of three corner reflectors. Two are fixed, but one, complemented by a GNSS receiver, has an adjustable height. The article describes its construction and presents the first results of comparing automated radar interferometry processing with GNSS measurements.

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