Advanced Synthetic Aperture Radar Research Articles

Application of Time Series INSAR (SBAS) Method Using Sentinel-1 for Monitoring Ground Deformation of the Aegina Island (Western Edge of Hellenic Volcanic Arc)

This study employs advanced synthetic aperture radar (SAR) techniques, specifically the small baseline subset (SBAS) method, to analyze ground deformation dynamics on Aegina, a volcanic island within the Hellenic Volcanic Arc. Using Sentinel-1 satellite data spanning January 2016 to May 2023, this research reveals different deformation behaviors. The towns of Aegina and Saint Marina portray regions of stability, contrasting with central areas exhibiting subsidence rates of up to 1 cm/year. The absence of deformation consistent with volcanic activity on Aegina Island aligns with geological records and limited seismic activity, attributing the observed subsidence processes to settlement phenomena from past volcanic events and regional geothermal activity. These findings reinforce the need for continuous monitoring of the volcanic islands located in the Hellenic Volcanic Arc, providing important insights for local risk management, and contributing to our broader understanding of geodynamic and volcanic processes.

Doppler Factor in the Omega-k Algorithm for Pulsed and Continuous Wave Synthetic Aperture Radar Raw Data Processing

Synthetic aperture radar (SAR) raw data do not have a direct application; therefore, SAR raw signal processing algorithms are used to generate images that are used for various required applications. Currently, there are several algorithms focusing SAR raw data such as the range-Doppler algorithm, Chirp Scaling algorithm, and Omega-k algorithm, with these algorithms being the most used and traditional in SAR raw signal processing. The most prominent algorithm that operates in the frequency domain for focusing SAR raw data obtained by a synthetic aperture radar with large synthetic apertures is the Omega-k algorithm, which operates in the two-dimensional frequency domain; therefore, in this paper, we used the Omega-k algorithm to produce SAR images and modify the Omega-k algorithm by adding the Doppler factor to improve the accuracy of SAR raw data processing obtained by the continuous wave and pulsed frequency modulated linear frequency modulated radar system from the surfaces of interest. On the other hand, for the case of unmanned aerial vehicle-borne linear frequency modulated continuous wave (LFM-CW) SAR systems, we added motion compensation to the modified Omega-k algorithm. Finally, the testing and validation of the developed Omega-k algorithm used simulated and real SAR raw data for both pulsed synthetic aperture and continuous wave radars. The real SAR raw data used for the validation of the modified Omega-k algorithm were the raw data obtained by the micro advanced synthetic aperture radar (MicroASAR) system, which is an LFM-CW synthetic aperture radar installed on board an unmanned aerial system and the raw data obtained by European remote sensing (ERS-2) satellite with a synthetic aperture radar installed.

Evolution of the magma system at Makushin volcano, Alaska, from 2004 to 2021

Located in the northeast of Unalaska Island in the central part of the Aleutian, Alaska, Makushin volcano is one of the more active volcanoes in the United States, with the most recent eruption occurred in 1995. Pre-eruptive inflation and post-eruptive deflation were identified from 1993 to 1995 and 1996 to 2000, respectively, and were interpreted as volume changes associated with magma movement within the plumbing system. In this study, we investigate the surface deformation of Makushin from 2004 to 2021 to evaluate its present-day activity, using Synthetic Aperture Radar (SAR) interferometry technique. SAR images acquired from C-band Envisat and Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 satellites are analyzed using an advanced multiple temporal Interferometric SAR (InSAR) algorithm. The produced deformation time series are then used to track the temporal evolution of the volcanic system with Ensemble Kalman Filter (EnKF) assuming Mogi sources. The results show that Makushin is characterized by time-varying deformation episodes including quiescence, and inflation/deflation cycles at varied rates since 2004. The derived deformation histories can be well reproduced by a spatially stationary pressure source located to the east-northeast of the summit, at a depth about 6 km beneath the sea level (BSL). The inflation/deflation cycles with distinct temporal patterns are interpreted as results from volatile-dominated volume accumulation/loss in the magmatic reservoir. A secondary reservoir connected to the main magma chamber is revealed from the deformation time series. No surface displacement associated with the large earthquake swarm in 2020 is identified from the InSAR-derived deformation. The cumulative volume change in the magma reservoir is about −6×106m3 from 2004 to 2021, indicating a volume/pressure deficit in the magma plumbing system.

Optimization of the Vertical Wavenumber for PolInSAR Inversion Performance Based on Numerical CRLB Analysis

A number of advanced SAR missions have been planned to launch, which can operate in fully polarimetric SAR interferometry mode to acquire structural parameters of global forests. Before the PolInSAR mission, the system configuration of vertical wavenumber kz must be carefully designed because it has a significant impact on the inversion performance. To minimize the estimation error of forest height caused by the system error from the future PolInSAR campaigns, it is valuable for us to optimize the vertical wavenumber. To quantitatively investigate the impact of kz on PolInSAR inversion performance, this paper proposes the optimization of kz based on the Cramér–Rao Lower Bound (CRLB) analysis. Extensive numerical CRLB simulations have been conducted to analyze the impact of several parameters, including extinction level, incident angle, and system decorrelation, etc., on the optimum kz. Finally, by minimizing the simulated CRLB, the numerical optimum kz maps are provided for the system engineers to easily design the system parameters.

A Multi-Channel Attention Network for SAR Interferograms Filtering Applied to TomoSAR

Tomographic synthetic aperture radar (TomoSAR) is an advanced synthetic aperture radar (SAR) interferometric technique that can retrieve 3-D spatial information. However, the performances of 3-D reconstruction could be degraded due to the noise in interferograms, which makes the filtering crucial before the tomographic reconstruction. As known, filters for single-channel interferograms are common, but those for multi-channel interferograms are still rare. In this paper, we propose a multi-channel attention network to denoise the multi-channel interferograms applied for TomoSAR, which is built on the basis of multi-channel attention blocks. An important feature of the block is the local context mixing before the computation of attention maps across channels, which explores the intra-channel local information and the inter-channel relationship of the multi-channel interferograms. Based on this architecture, the proposed method can effectively filter the noise while preserving the structures in interferograms, thus improving the performance of tomographic reconstruction. The network is trained by simulated data and the promising results of both simulated and real data validate the effectiveness of our proposed method.

Satellite Observations and a Numerical Study of Recurring Atmospheric Gravity Waves Along the South China Sea Coast

AbstractSeveral groups of diurnal recurring atmospheric gravity waves (AGWs) were observed on an Environmental Satellite advanced synthetic aperture radar image and six Moderate Resolution Imaging Spectroradiometer (MODIS) images acquired along the South China Sea coast for 4 days from 3 to 6 March 2010. The unique observations are that the previously observed AGWs seldom recurred in a region for multiple days. Each cycle of these observed AGWs had undergone three stages: generation, propagation, and dissipation daily. Both SAR‐imaged sea surface roughness patterns and MODIS‐observed cloud imprints show that the AGWs were a group of transverse waves along the coast. We implemented the Weather Research and Forecasting (WRF) model to understand the physical characteristics and dynamical mechanisms of these AGWs. The WRF simulated results indicated that the AGWs were upstream waves triggered by the interaction of the southerly wind and topography and dissipated by the diurnal warming. We applied the spectral analysis to the model‐simulated vertical wind field and explored that the leading wave crest length and crest‐to‐crest wavelength are 156.25 and 7.14 km, respectively. The wave period and phase speed of the upstream AGWs within the vital observation of SAR are 1.81 hr and 1.26 m/s, respectively. The fifth‐generation European Centre for Medium‐Range Weather Forecasts reanalysis hourly data was used to explore the rarity of the recurring AGWs and proved that the stable air‐sea temperature difference was the main reason for the rarity of this event. And sufficient cloud content (>50%) was necessary for the MODIS to capture the AGWs' imprints.

Monitoring artificial canals with multiple SAR satellites: A case study of the Changge Canal of the South-to-North Water Diversion Project in China

Surface monitoring of artificial canals is essential to ensure safe operation. Traditional geodetic tools are commonly employed to monitor the canal of China’s South-to-North Water Diversion Project (SNWDP), which is time-consuming and high-cost. The advanced satellite Synthetic Aperture Radar Interferometry (InSAR) is an effective tool to detect subtle surface deformation, while its application for monitoring canals is short of fine deformation interpretation on a structure scale. In this study, we applied multiple SAR satellites to monitor a typical canal of the Middle Route of the SNWD Project (MRP), the Changge Canal, by extracting the historical deformation, inverting two-dimensional (2D) deformation, and analyzing distortions. The result suggests that the focused canal section is experiencing continuous deformation caused by near coal mining activity. Its vertical and horizontal distortions both reach ±1.5 mm/100 m. The InSAR applicability, including visibility and sensitivity, is applied to the structure-level interpretation of deformation. The InSAR MPs distribution highly depends on canal structures' aspect and surface coverings. The canal structures with high coherence and lower R-Index (i.e., foreshortening) are favorable for detecting InSAR MPs; meanwhile, a few MPs are detected for the structures with high coherence but high R-index. For the sensitivity of the deformation vector along the downslope, its dependency on the aspect opposes InSAR MPs distribution. The structure-level interpretation considering InSAR applicability helps deploy targeted maintenance and analyze deformation mechanisms. The procedure of fine InSAR monitoring applies to similar water diversion projects.

Geomorphological characterization, remote sensing monitoring, and modeling of a slow-moving landslide in Alcoy (Southern Spain)

In the present paper, an active landslide located to the southeast of the urban area of Alcoy (Alicante, Spain) is analysed. This landslide affects the N-340 road, as well as an area in which some industrial buildings dated from the fifteenth century and catalogued as Places of Cultural Interest are placed. This region is characterised by the existence of a steep relief composed of materials from the Miocene to the present, on which many gravitational movements are developed. The displacements have been analysed using high-resolution spotlight mode SAR images captured by the Spanish SAR satellite PAZ (which operates at X-band), which were processed by advanced differential SAR Interferometry (A-DInSAR). The behaviour of the instability has been determined using InSAR-derived time series displacement maps. Additionally, the study has been completed with a detailed geomorphological mapping at 1:2000 scale and an analysis of structural damage carried out in situ. The landslide was modelled in 3D, from a deterministic and probabilistic point of view, confirming the global instability of the area and the relationship of the landslide with rainfall and the construction of a new road. The integration of all this information obtained and its subsequent analysis has made possible to precisely define the extension of the instability, its kinematics, and to identify the main triggering factors. This information will be crucial for the proper management of this landslide by the local authorities.

Internal Wave Signature Extraction From SAR and Optical Satellite Imagery Based on Deep Learning

Internal waves (IWs) are a common characteristic of oceans and serve a crucial role in transmitting energies between large-scale tides and small-scale mixing. This study developed a deep-learning-based method for extracting IW signatures on multiple satellite imagery from synthetic aperture radar (SAR) and optical sensors in sun-synchronous and geostationary orbits with varying spatial resolution. We collected 1115 satellite images, including 116 ENVISAT ASAR (Advanced SAR), 839 MODIS (MODerate-resolution Imaging Spectroradiometer), and 160 Himawari-8 AHI (Advanced Himawari Imager) images with clear IW signatures in the South China Sea (SCS), Sulu Sea, and Celebes Sea for model training. Considering the distinct IW characteristics under different imaging mechanisms, the specially tailored IW Extraction network (IWE-Net) leverages three modifications to improve the accuracy and robustness: online data augmentation, squeeze and excitation blocks, and Matthews correlation coefficient loss. The overall mean Precision, Recall, and F1-score of the IWE-Net model are 85.75%, 85.67%, and 85.71%, demonstrating the model is accurate for IW signature extraction.We also proved the transferability of our method to sea areas worldwide, long-term periods, and Sentinel satellite sensors completely independent of the model training. Globally, the number of IW images and extracted pixels show an obvious tidal-related double-peak distribution. Furthermore, we processed 15461 MODIS images in the northeastern SCS to present a holistic IW distribution map over the past 22 years. An unreported IW silent zone caused by drastic topography changes has been discovered, indicating the great potential of deep learning in information retrieval from remote sensing imagery.

Image-Based Baseline Correction Method for Spaceborne InSAR With External DEM

An accurate baseline of synthetic aperture radar (SAR) interferometry (InSAR) is an important parameter for the geodetic application of the InSAR data. Although some advanced SAR satellites have precise orbit determination, there are still many SAR satellites suffering from baseline inaccuracies, such as GF-3. In this article, an image-based estimator for baseline correction is proposed, which requires only the external digital elevation model (DEM) data. The idea of the method is to project the orbit error phase onto the phase components carrying the baseline error information, which is called orbit error phase bases in this article, and to correct the baseline according to the projection coefficients. Since the pure orbit error phase is unavailable, the residual phase of the interferogram is used to approximate the orbit error phase, and a series of processes are introduced to weaken the effect of this approximation. Both the simulated and real data from GF-3 SAR are used to validate the proposed method, and a comparison with the conventional nonlinear least-square and the latest proposed flat-Earth phase-based baseline refinement methods are made. The results indicated the superior accuracy and robustness of our method, especially in areas with higher relief and wider coverage.

An Unsupervised CNN-Based Multichannel Interferometric Phase Denoising Method Applied to TomoSAR Imaging

Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3D spatial information. However, the standard deviation in the reconstructed elevation could be high due to the noise in the interferometric phases, which makes the denoising filter crucial before tomographic reconstruction. In this paper, we propose an unsupervised multichannel SAR interferometric phase denoising method based on the Convolution Neural Network (CNN). It utilizes the Weighted Least-Squares (WLS) regularization combining with the covariance of multichannel interferometric phases to minimize the standard deviation of phase noise, which leads to the accurate and complete TomoSAR reconstruction. This network is trained by real SAR images and the results of both simulated and real observations verify the effectiveness of our proposed method.

UltraHi-PrNet: An Ultra-High Precision Deep Learning Network for Dense Multi-Scale Target Detection in SAR Images

Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences coexist in SAR images. In particular, it is difficult for existing target detection methods to detect both ultra-large-scale targets and ultra-small-scale targets in SAR images, resulting in poor detection results for these two types of targets. To solve these problems, this paper proposes an ultra-high precision deep learning network (UltraHi-PrNet) to detect dense multi-scale targets. Firstly, a novel scale transfer layer is constructed to transfer the features of targets of different scales from bottom networks to top networks, ensuring that the features of ultra-small-scale, small-scale, and medium-scale targets in SAR images can be extracted more easily. Then, a novel scale expansion layer is constructed to increase the range of the receptive field of feature extraction without increasing the feature resolution, ensuring that the features of large-scale and ultra-large-scale targets in SAR images can be extracted more easily. Next, the scale expansion layers with different expansion rates are densely connected to different stages of the backbone network, and the features of the target with ultra-large-scale differences are extracted. Finally, the classification and regression of targets were achieved based on Faster R-CNN. Based on the SAR ship detection dataset (SSDD), AIR-SARShip-1.0, high-resolution SAR ship detection dataset-2.0 (high-resolution SSDD-2.0), the SAR-ship-dataset, and the Gaofen-3 airport dataset, the experimental results showed that this method can detect similar-scale, large-scale, and ultra-large-scale targets more easily. At the same time, compared with other advanced SAR target detection methods, the proposed method can achieve higher accuracy.

Land Subsidence Evolution and Simulation in the Western Coastal Area of Bohai Bay, China

Groundwater overexploitation and loading of buildings have been the main factors triggering land subsidence along the west coast of Bohai Bay, China, since the 2000s. Uneven subsidence has been causing damage to buildings and civil facilities, loss of elevation, increasing the risk of flood and seawater intrusion, and threatening the safety of people’s lives and property. This paper analyzed the spatial and temporal features of land subsidence along the coastal area from 2003 to 2010 and from 2015 to 2020, respectively. The relations between the initiating factors and land subsidence were explored. Then, the simulation model of land subsidence was constructed through a deep learning method. During the process, multiple data were collected, including land satellite (Landsat), environmental satellite advanced synthetic aperture radar (ENVISAT ASAR) and Sentinel-1 images, leveling data, lithological data, and groundwater level data. The area occupied by buildings and vertical displacement were extracted by using supervised classification, small baseline subset (SBAS), and persistent scatterer interferometry (PSI) technologies. The gated recurrent unit (GRU) neural network was adopted to simulate the evolution of land subsidence. Results showed that the maximum annual vertical displacement rate decreased from −94 mm/yr during 2003–2010 to −87 mm/yr during 2015–2020. The correlation efficiency between the groundwater level of the third confined aquifer group and land subsidence was larger than the area occupied by buildings and the compressible layer thickness with subsidence. The constructed GRU neural network model can simulate subsidence from September 2019 to December 2019, with the overall RMSE and MAE being 3.16 mm and 2.19 mm, respectively. This work can facilitate an understanding of the evolution and prevention of land subsidence along the west coast of Bohai Bay, which will provide information for policy decisions and flood-fighting plans of the worldwide coastal cities.

Automated classification of A-DInSAR-based ground deformation by using random forest

ABSTRACT Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model

High-resolution snow water equivalent studies are important for obtaining a clear picture of the potential of water resources in arid areas, and SAR-based sensors can achieve meter-level snow water equivalent inversion. The advanced C-band SAR satellite Gaofen-3 (GF-3) can now achieve meter-level observations of the same area within one day and has great potential for the inversion of the snow water equivalent. The EQeau model is an empirical method for snow water equivalent inversion using C-band SAR satellites, but the model has major accuracy problems. In this paper, the EQeau model is improved by using classification of underlying surface types and polarization decomposition, and the inversion of the snow water equivalent was also completed using the new data source GF-3 input model. The results found that: (1) the classification of underlying surface types can significantly improve the fit between the snow thermal resistance and the backscattering coefficient ratio; (2) the accuracy of the snow density extracted by the GF-3 satellite using the Singh–Cloude Three-Component Hybrid (S3H) decomposition is better than IDW spatial interpolation, and the overall RMSE can reach 0.005 g/cm3; (3) the accuracy of the optimized EQeau model is significantly improved, and the overall MRE is reduced from 27.4% to 10.3%. Compared with the original model, the optimized model is superior both in terms of verification accuracy and image detail. In the future, with the combination of advanced technologies such as the Internet of Things (IoT), long, gapless, all-weather, and high-resolution snow water equivalent inversion can be achieved, which is conducive to the realization of all-weather monitoring of the regional snow water equivalent.

Estimation of Sea Ice Production in the North Water Polynya Based on Ice Arch Duration in Winter During 2006–2019

AbstractThe North Water Polynya (NOW) is the largest recurrent Arctic coastal polynya. The formation of the NOW is critically dependent on the development of an ice arch that defines its northern boundary. In this study, high‐resolution ENVISAT Advanced Synthetic Aperture Radar data, Sentinel‐1A data, and Moderate Resolution Imaging Spectroradiometer data were employed to identify the spatio‐temporal characteristics of the ice arch during 2006–2019. Polynya pixels were identified based on the thin ice thickness (TIT), using a threshold of TIT <0.2 m, from which the polynya extent, heat flux, and ice production (IP) were estimated. The results show the different locations of the ice arch in different years, with a mean duration of 132 ± 69 days. The average annual polynya extent over the 14 years is ∼38.8 ± 8 × 103 km2, and we found that it is more closely correlated with wind speed during the winter and air temperature during early spring. The average heat flux drops from about 248 W/m2 in the winter months to about 34 W/m2 in May. The average accumulated IP varies significantly every year, with an average of 144 ± 103 km3, and peak values in March in most years. No apparent interannual trends are shown for the polynya area, heat flux, and IP during 2006–2019. The results also show that IP calculated based on the ice arch data is approximately 25% lower than that obtained by assuming a fixed time, location, and duration for the polynya.

SBAS-DInSAR analysis of January 2020 eruption of La cumbre Volcano, Galapagos Archipelago

The Galapagos Archipelago in the Pacific Ocean, situated 1000 km from the Ecuadorian coast, has been considered one of the UNESCO World Heritage sites. Its geographical location, ongoing seismo_volcanic activities, and the island's extreme isolation have led to the development of a wide variety of unique plant and animal species, which inspired Charles Darwin's theory of evolution. La cumbre volcano is one of the most active volcanoes in the Galapagos archipelago and is situated closest to the mantle plume. Eruptions are frequent here, with a record of 30 eruptions since 1800. Recently La Cumbre volcano erupted on 2020 January 12, with 11 seismic tremors recorded on that day with a maximum magnitude of 4.7. The Small Baseline Subset (SBAS) is an advanced Differential Interferometric SAR technique used to generate the deformation time series from the sequence of SAR datasets with a reduced spatiotemporal decorrelation. It is one of the best approaches for monitoring and analyzing surface deformations from space with a millimeter level of accuracy. In this paper, we analyze the January 2020 eruption of the La cumbre volcano using the SBAS technique. 9 Sentinel-1C band SAR data granules from 2019 November 17 to 2020 February 21 were used to generate the deformation time series map for estimating the degree of deformation at the volcanic edifice. As per the analysis, it is evident that the recent eruption has caused a displacement of up to −38 cm at the South Eastern flank of the crater, which has increased initially and then decreased on approaching the steady-state of eruption.

Structural lineament mapping of Central-Eastern high atlas, Morocco, using ASAR/Envisat and SAR/sentinel 1B data

The Central-Eastern part of the High Atlas is dominated by carbonate of Mesozoic age which is affected by the Alpine Orogeny. The deformation manifest in area by different geological structures. The analysis of these later lead to their classification in different category for understanding the geodynamic of the area. This study focuses to map structural lineaments in the Central-Eastern High Atlas using two types of Radar data: Synthetic Aperture Radar (SAR)/Sentinel 1B with polarization (VV + VH) and Advanced Synthetic Aperture Radar (ASAR)/Envisat with polarization (VV + HH). This research aims to demonstrate the utility of Radar data for extracting structural lineaments in carbonate deposits. In this work, two techniques of processing data are used: First, the automatic extraction technique, we are employing the Algorithm Line Module of PCI Geomatica program, after the enhancement of images by applying the mean Co-occurrence filter. Second, the manual extraction technique, it was accomplished by applying directional filters with four directions (N00°, N45°, N90° and N135°) in order to highlight linear structures, then trace the detected lineaments. The synthetic maps of structural lineaments are developed by the two techniques. They are validated by using the lithologic maps extracted from Landsat Operational Land Imager (OLI), the previous researches in the study area, shaded relief extracted from ASTER Global Digital Elevation Model (GDEM), and field investigation data. The results present a good correlation between the direction of structural lineaments extracted from the satellite images and the field measurements. Therefore, the directions of lineaments extracted are N-S and E-W, NE-SW and NW-SE, especially the abundance of the direction N-S. This last one is perpendicular with the major faults. The density of structural lineaments in the study area is controlled particularly by the lithological nature and dynamic evolution, where the concentration is high in the Liassic carbonates. The geostatistical and spatial distribution of the structural lineaments led us to conclude that different tectonic factors are responsible for the distribution of lineaments in the surface of the study area. The comparison of two types of radar data shows that Envisat data is more efficient in the manual extraction than Sentinel 1B data. However, Sentinel 1B data is more efficient in the automatic extraction than Envisat data. This work identifies several families of structural lineaments which can be determined the tectonic phases of the Alpine orogeny affected the intracontinental Atlas basin.

INTEGRATED MINING IMPACT MONITORING (EU-PROJECT I2MON) FOR OPEN-PIT AND UNDERGROUND MINES

Abstract. Monitoring mining impact has become increasingly important as the awareness of safety and environmental protection is rising. Our project Integrated Mining Impact Monitoring (i2Mon), funded by European Commission – Research Fund for Coal and Steel, intends to monitor the mining-induced impact, in particular, ground movement. The monitoring system comprises terrestrial measurement and remote sensing: levelling, GPS, LiDAR scanning, UAV survey, and SAR interferometry. The aim is to launch an interactive GIS-based platform as an early warning and decision making service for mining industry. This study has developed a scheme based on advanced SAR interferometry to monitor the ground movement over an extensive area at millimetre level. The first test site is a deactivated open-pit mine in Cottbus, Germany owned by Lausitz Energie Bergbau AG. The whole area was reclaimed into a post-mining lake and must be monitored for the safety. The second test site is located in Poland, where the underground mining operated by POLSKA GRUPA GÓRNICZA began in June 2021. We have monitored the in-situ ground movement carefully as part of the influenced area covers the human settlement. The ground movement of our test sites was analysed from Sentinel-1 images. The crucial parameters include stepwise movement series, instantaneous velocities and accelerations, and significance index. In addition, six corner reflectors along with sensors like GPS were installed across the region in Cottbus. They were observed in the Sentinel-1 series and the GPS readings will be used for validation. Finally, all the data will be integrated into DMT’s platform – SAFEGUARD.

Representation of sea ice regimes in the Western Ross Sea, Antarctica, based on satellite imagery and AMPS wind data

Sea ice drift data at high spatial resolution and surface wind model output are used to explore atmosphere-sea ice interactions in the Western Ross Sea including the three main polynyas areas; McMurdo Sound polynya (MSP), Terra Nova Bay polynya (TNBP), and the Ross Sea polynya (RSP). This study quantifies the relationship between the winds and sea ice drift and observes the average and annual anomalies across the region. Sea ice drift velocities are based on high-resolution (150 m) Advanced Synthetic Aperture Radar (ASAR) images from Envisat for winters between 2002 and 2012. Sea ice motion vectors were first correlated with the corresponding Antarctic Mesoscale Prediction System (AMPS) surface wind velocities, and the sensitivity of the spatial correlations and residuals were examined. Four drift parameters were selected (mean drift, the correlation between drift and wind, drift to wind scaling factor, and the directional drift constancy) to perform an unsupervised k-means classification to automatically distinguish six zones of distinctive sea ice characteristics solely based on ice drift and wind information. Results indicate a heterogeneous pattern of sea ice movement at a rate ranging from 0.41 to 2.24% of the wind speed in different areas. We also find that the directional constancy of sea ice drift is closely related to the wind fields. Sea ice drift and wind velocities display the highest correlation in free-drift areas (R = 0.70), followed by deformational drift zones (R = 0.54), and more random drift areas (R = 0.28). The classification illustrates the significance of localized wind-driven sea ice drift in this coastal area resulting in zones of convergence, shear, and free drift. The results also indicate that the most persistent patterns of sea ice motion are near the RSP and TNBP areas, both being driven by strong localized winds. Our findings identify that large-scale sea ice motion is predominantly wind-driven over much of the study area while ocean currents play only a minor role.