Tracking Deformation Processes at the Legnica Glogow Copper District (Poland) by Satellite InSAR—II: Żelazny Most Tailings Dam

The failures of tailings dams have a major negative impact on the economy, surrounding properties, and people’s lives, and therefore the monitoring of these facilities is crucial to mitigate the risk of failure, but this can be challenging due to their size and inaccessibility. In this work, the deformation processes at Żelazny Most tailings dam (Poland) were analyzed using satellite Ad-vanced Differential SAR Interferometry (A-DInSAR) from October 2014 to April 2019, showing that the dam is affected by both settlements (with a maximum rate of 30 mm/yr), and horizontal sliding in radial direction with respect to the ponds. The load of the tailings is pushing the dam forward along the glacio-tectonic shear planes located at depth, in the Pliocene clays, causing horizontal displacements at a rate up to 30 mm/yr, which could lead to a passive failure of the dam. The measured displacements have been compared with the ones observed by in situ data from the 90s to 2013, available in the literature. The outcomes indicate that intense localized deformations occur in the eastern and northern sectors of the dam, while the western sector is deforming evenly. Moreover, although the horizontal deformation had a slowdown from 2010 until 2013, it continued in 2014 to 2019 with recovered intensity. The upper and the recent embankments are affected by major settlements, possibly due to a lower consolidation degree of the most recent tailings and a larger thickness of compressible materials.

Tracking Deformation Processes at the Legnica Glogow Copper District (Poland) by Satellite InSAR—I: Room and Pillar Mine District

Mining exploitation leads to slow or rapid ground subsidence resulting from deformation until the collapse of underground post-mining voids following excavation activities. Satellite SAR interferometry capabilities for the evaluation of ground movements allows the monitoring of intensive surface mine subsidence and can provide new knowledge about the risks in the mining industry. This work integrates both conventional and advanced Differential SAR Interferometry (DInSAR) to study the ground subsidence in the Legnica Glogow Copper District (LGCD, Poland) by processing about 400 Sentinel-1 images from October 2014 to April 2019. Even without field data and information on past and ongoing excavation activities, the DInSAR approach allowed us to identify 30 troughs of subsidence, ranging from 500 m to 2.5 km in diameter, which in some cases, took place several times during the analyzed time span. The cumulative subsidence in 4 years and 7 months exceeds 70 cm in several zones of the LGCD. The sub-centimetric precision achieved by advanced analysis (A-DInSAR), allowed us to monitor the real extent of the mining influence area on the surface, with deformation velocities of up to 50 mm/year. The ground deformation detected at LGCD can be due to both mining-induced tremors and roof subsidence above the underground excavation rooms. As deformations do not occur concurrently with tremors, this can be related to excavation activities or to degradation of abandoned mines.

Comment on “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry” by Ansari et al. (2021)

<p>In a recent publication Ansari et al. (2021) [1] claim (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommend a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors”. In particular, among various techniques, Ansari et al. (2021) [1] identify the solution presented by Manunta et al. (2019) [2] as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) [1] is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019) [2]. In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021) [1], we demonstrate that the generated InSAR products do not show any significant bias.</p>

Comment on “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry” by Ansari et al. (2021)

<p>In a recent publication Ansari et al. (2021) [1] claim (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommend a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors”. In particular, among various techniques, Ansari et al. (2021) [1] identify the solution presented by Manunta et al. (2019) [2] as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) [1] is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019) [2]. In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021) [1], we demonstrate that the generated InSAR products do not show any significant bias.</p>

Active landslides deformations mapping and monitoring in rural areas using satellite radar interferometry

&lt;p&gt;Landslide hazards pose as one of the greatest risks in today&amp;#8217;s context of climate change and settlement expansion. The later process occurs both in the urban and rural areas and significantly changes the terrain morphology and contributes as a conditioning factor for the triggering of new landslide events or reactivation of old dormant ones. Usually, the urban areas are of a greater interest to assess the activity of landslides and their associated risks. On the other hand, the remote areas such as the rural settlements are not as much investigated and monitored, mostly because the in-situ investigations requires additional costs for the deployment of various instruments.&lt;/p&gt;&lt;p&gt;In the last decades, the development of Advanced Differential SAR Interferometry techniques permits to identify and monitor these geomorphological processes from space. They rely on the microwave&amp;#8217;s signal properties to quantify with millimeter accuracy possible deformations in time. The advances of satellite&amp;#8217;s acquisition capabilities and the increase of computational power allow the mapping of active landslides over wide areas and even detection of failure precursors.&lt;/p&gt;&lt;p&gt;In our case, we used the DInSAR techniques to identify the active landslides over a large area in the Moldavian Plateau that affects the human settlements. Even though for the urban areas was much easier to detect the landslide induced deformations, in the case of the rural communities this task was much more challenging. We used the COMET-LiCS Sentinel-1 InSAR data (LiCSAR) and the LiCSBAS software for processing the data for the Moldavian Plateau, Northeastern Romania. Based on the results post-processing we classified the landslides activity based on their velocity and we created an active landslide inventory of the area.&lt;/p&gt;

MAPPING AND MONITORING GROUND INSTABILITIES WITH SENTINEL-1 DATA: THE EXPERIENCE OF SERNAGEOMIN

The application of Satellite Differential SAR interferometry (DInSAR) has become a reliable solution as a tool for mapping and monitoring geohazards. Few years ago, the main applications of these techniques were devoted to science. However, nowadays, the easy access to SAR imagery and the maturity of the techniques to exploit these type of data has widened the user’s spectrum from only scientists to professional and decision makers. The advent of Sentinel-1 satellites has significantly contributed to this achievement. In particular, in the field of geohazard risk management, Sentinel-1 has solved one of the main constraining factors that hindered the operational use of interferometric techniques in the past: the lack of systematic acquisition plans. In this context, Sentinel-1 assures worldwide coverage with short temporal baselines (6 to 24 days). This has supposed a definitive step towards the implementation of DInSAR based techniques to support decision makers against geohazards. In this work, we show the first experiences of the remote sensing unit of the Geological and Mining Survey of Chile (Sernageomin) with Sentinel-1 data. Three different case studies in different areas of the Chilean territory are presented. The examples illustrate how DInSAR based techniques can provide different levels of information about geohazard activity in different environments.

InSAR techniques to determine mining-related deformations using Sentinel-1 data: the case study of Rydułtowy mine in Poland.

&lt;p&gt;Since launching Sentinel 1 satellites, the European Space Agency has been providing a huge amount of repeated SAR data. Thanks to 6-days revisiting time, it creates a perfect possibility for the monitoring of ground deformation, caused by underground mining activity, by using Differential SAR interferometry (DInSAR).&lt;/p&gt;&lt;p&gt;Because, DInSAR exploits single interferometric SAR pairs, the accuracy of this technique is limited by spatial and temporal decorrelation and atmospheric artifacts. To minimize the atmospheric influence on DInSAR results, we investigated precipitation and relative humidity data acquired from the Institute of Meteorology and Water Management (IMGW). Theoretically, the summed atmospheric LOS errors due to relative humidity for 106 ascending and 112 descending images are -3.5 cm and 7,5 cm, respectively. &amp;#160;In fact, we observed that there is a moderate correlation between precipitation/relative humidity and &amp;#8220;bad&amp;#8221; acquisition in relatively small study area. Nevertheless, we were able to remove 33 ascending and 15 descending images from the queue of consecutive DInSAR. Finally, it allowed to estimate up to 1m subsidence in the period of 1 Jan 2017&amp;#8211;8 Oct 2018 in the Rydu&amp;#322;towy mine located in the southwest part of the Upper Silesian Coal Basin (USCB), Poland.&lt;/p&gt;&lt;p&gt;To evaluate our DInSAR accuracy due to atmospheric artefacts, we decided to compare the results with &amp;#8220;atmospheric-free&amp;#8221; results acquired by SBAS technique. SBAS separates diverse interferometric components that correspond to deformation, topographic error, atmospheric error, and orbital errors.&lt;/p&gt;&lt;p&gt;The Root-Mean-Square Error (RMSE) has been calculated between SBAS and DInSAR for selected subsidence profiles. The maximal RMSE was found to be 3.6 cm and 4.1cm for ascending and descending LOS displacements, respectively. This shows that DInSAR cannot be used for monitoring millimeter-level deformation. On the contrary, it can be effectively used to assess quick nonlinear deformations reaching several decimeters /year such as in the presented study case.&lt;/p&gt;

Drone-borne Differential SAR Interferometry

Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.