ADAPTIVE SPATIAL FILTERING OF INTERFEROMETRIC DATA STACK ORIENTED TO DISTRIBUTED SCATTERERS

This study presents the application of interferometric synthetic aperture radar (InSAR), small baseline subset (SBAS) algorithm and GIS for subsidence analysis in a mining area located at Gangwon-do, Korea. Several mining areas at the Samchuk coalfield had operating coalmines until 2002–2003. Ground deformation has occurred as a form of subsidence due to the continuous mining activity or the collapse of residual cavities. Twenty-three scenes from a JERS-1 satellite acquired from 1992 to 1998 are used to detect the ground movement by means of the differential InSAR process. Distinctive phase variations induced by subsidence were observed in several interferograms. The SBAS algorithm was applied for a detailed analysis. As a result, it was confirmed that deformation was occurring between September 21, 1992 and September 30, 1998 and attained 22 cm at the most significantly subsiding site. The mean and standard deviation of RMS error were 2.0 cm and 0.78 cm, respectively, showing the level of the error due to the used digital elevation model, unwrapping and atmospheric effect. The SBAS-derived cumulative subsidence map was validated with the underground workings and geological maps in order to find the spatial relations and subsiding mechanism. From the spatial comparison with these data, it was shown that the subsidence has taken place above excavations and fault lines.

Time series interferometric synthetic aperture radar (TS-InSAR) technique can be applied to identify the creep body. However, it has strict requirements regarding the quality of the interferograms and the interferometric baseline networks. In regions with intermittent coherence, the use of conventional methods to select interferometric pairs can easily lead to interferometric baseline disconnections, redundancy, or missing key pairs. This results in reduced accuracy and may even misidentify creep characteristics. To solve this problem, the segmented average coherence threshold method (SACTM) is proposed. SACTM is an interferometric baseline optimization method that considers monthly coherence variability. In this paper, the SACTM baseline optimization TS-InSAR technique was used to identify creep bodies in Wudongde Reservoir area located along the Jinsha River in China. Compared with the conventional methods, the SACTM method yields a more robust interferometric baseline network and a smaller standard deviation for deformation rate, which demonstrates the superiority of this method.

Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates.

Interferometric synthetic aperture radar (InSAR) technique is a proven technique for measuring 3-D surface deformations by combining InSAR measurements from different techniques (i.e., differential InSAR, multiaperture InSAR, and pixel offset-tracking) and different tracks (i.e., ascending and descending) on a pixel-by-pixel basis. However, it is difficult to obtain the exact a priori variances or weights for such different kinds of InSAR measurements, resulting in inaccurate estimations of 3-D deformations. This paper proposes a method to retrieve 3-D deformations with InSAR by integrating the strain model and variance component estimation algorithm, which can exploit the spatial correlation of the adjacent points' deformations and produce accurate weights for multiple InSAR measurements. The proposed method is assessed with both simulated and real data sets. The results have shown that the proposed method can accurately measure 3-D surface deformations associated with geohazards, and even those occurring in a transient or short-term period (e.g., earthquake and volcanic eruption). In the case study of the 2007 eruption of Kilauea Volcano (Hawai'i), improvements of 51.2%, 22.4%, and 18.5% have been achieved for the derived east, north, and up displacements, respectively, with respect to those derived from the classical weighted least squares method.

Ground deformation is the direct manifestation of the earth-rock dam's hazard potential. Therefore, it is essential to monitor deformation for dam warning and security evaluation. The Liuduzhai Dam, a clay-core dam of a large reservoir in China, was reinforced with plastic concrete cut-off walls between 13 January 2009 and 29 May 2010, as it was subject to leakage and deformation. However, the deformation development and the mechanism of the dam are still unclear. In this study, the deformation fields before and after the reinforcement of the Liuduzhai Dam were yielded by using the Interferometric Synthetic Aperture Radar (InSAR) technique. Furthermore, a numerical simulation method was employed to obtain the dynamic seepage field of the dam during the InSAR observation period. The results indicated that the average deformation velocity and maximum deformation velocity are −11.7 mm/yr and −22.5 mm/yr, respectively, and the cumulative displacement exceeds 100 mm, which shows typical continuous growth characteristics in a time series. In contrast, the dam deformation tended to be stable after reinforcement, with the average deformation velocity and maximum deformation velocity being −0.4 mm/yr and −1.2 mm/yr, respectively, behaving as cyclical deformation time series. According to the results of InSAR and seepage analysis, it is shown that: (1) dynamic seepage was the main mechanism controlling dam deformation prior to reinforcement; (2) the concentrated load caused by construction and the rapid dissipation of pore water pressure caused by the sudden drop of the infiltration line were the reasons for the acceleration of deformation during and after construction; and (3) the plastic concrete cut-off walls effectively reduced the dynamic seepage field, while the water level fluctuations were the main driving factor of elastic deformation of the dam after reinforcement. This study provides a novel approach to investigating the deformation mechanism of earth-rock dams. Furthermore, it has been confirmed that InSAR can identify the seepage deformation of dams by detecting surface movements. It is recommended that InSAR deformation monitoring should be incorporated into future dam safety programs to provide detailed deformation signals. By analyzing the temporal and spatial characteristics of the deformation signal, we can identify areas where dam performance has degraded. This crucial information aids in conducting a comprehensive dam safety assessment.

Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes.

The development of satellite technology is rapidly increasing the evolution of remote sensing. Satellite images give extensive useful information about the land structure that is easily manageable in the process of generating true, high-speed information which allows the forecasting of future environmental and urban planning. Remote sensing comprises active and passive systems. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding area being observed. Active systems which produce their own electromagnetic energy and their main properties are their ability of collecting data in nearly all atmospheric conditions, day or night. These systems are frequently used to generate a digital elevation model (DEM) because they cover large areas. DEM supplies essential data for applications that are concerned with the Earth’s surface and DEMs derived from survey data are accurate but very expensive and time consuming to create. However, the use of satellite remote sensing to provide images to generate a DEM is considered to be an efficient method of obtaining data. Interferometric Synthetic Aperture Radar (InSAR) is a new geodetic technique for determining earth topography. InSAR measurements are highly dense and they only give information in Line of Sight of Radar. In the study, interferograms were produced from the InSAR images taken by ERS satellites in 1992 and 2007 and we developed the methods to generate a DEM using the InSAR technique and present the results relating to Kayseri Province in Turkey. The accuracy of the DEM derived from the InSAR technique is evaluated in comparison with a reference DEM generated from contours in a topographical map.

Abstract. The local distribution of water vapour in the urban area of Rome has been studied using both a high resolution mesoscale model (MM5) and Earth Remote Sensing-1 (ERS-1) satellite radar data. Interferometric Synthetic Aperture Radar (InSAR) techniques, after the removal of all other geometric effects, estimate excess path length variation between two different SAR acquisitions (Atmospheric Phase Screen: APS). APS are strictly related to the variations of the water vapour content along the radar line of sight. To the aim of assessing the MM5 ability to reproduce the gross features of the Integrated Water Vapour (IWV) spatial distribution, as a first step ECMWF IWV has been used as benchmark against which the high resolution MM5 model and InSAR APS maps have been compared. As a following step, the high resolution IWV MM5 maps have been compared with both InSAR and surface meteorological data. The results show that the high resolution IWV model maps compare well with the InSAR ones. Support to this finding is obtained by semivariogram analysis that clearly shows good agreement beside from a model bias.

Human activity detection plays an important role in social security monitoring. Since human activity is very weak, it is necessary to employ the repeat-pass Interferometric Synthetic Aperture Radar (InSAR) technique to detect the potential activity between two data acquisitions; a high level of coherence is required for detection. With the object of detecting human activity of interest, this paper presents a coherence improvement approach based on sub-aperture InSAR for human activity detection. Different sub-apertures contain different scattering information of the target, as they represent the backscatter of the target from a different range of angles. Integrating corresponding sub-aperture interferometric results can improve the coherence between two complex images compared to the entire synthetic aperture, as well as removing a little disturbance in some circumstances. To validate the method presented in this paper, the actual airborne Ka-band frequency modulated continuous wave (FMCW) InSAR data acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS) are utilized. The experimental results demonstrate that the proposed method can effectively improve the coherence between two complex SAR images and can validly detect human activity of interest.

The main city of Kunming is located on the north bank of Dianchi Lake. The complex geological environment, large-scale construction, and expansion of the city in recent years have caused uneven land surface subsidence and threatened public safety. In this study, Sentinel-1 ascending and descending orbit datasets were collected for the period of February 2018 to May 2021. The characteristics of surface displacement in the Kunming downtown area were monitored using the time-series interferometric synthetic aperture radar (InSAR) technique, and attribution analysis was performed. It was found that areas with more severe surface settlement were concentrated in the International Exhibition Center area and the large, newly built communities near Dianchi Lake and the Xiaobanqiao Region. The multifactor attribution analysis results demonstrated that the subsidence areas are concentrated in urban villages and engineered, construction-intensive areas in the lakeside sedimentary layer area, with the maximum displacement rate reaching −23.12 mm/a in the line-of-sight direction of the Sentinel-1 ascending dataset. The reliability of the InSAR results was cross-validated with ascending and descending results. This study provides a scientific reference for urban development planning and potential geological disaster detection in Kunming.

Peatlands are unique ecosystems that contain massive amounts of carbon. These ecosystems are incredibly vulnerable to human disturbance and climate change. This may cause the peatland carbon sink to shift to a carbon source. A change in the carbon storage of peatlands may result in surface deformation. This research uses the interferometric synthetic aperture radar (InSAR) technique to measure the deformation of the peatland’s surface in south Sweden in response to the seasonal and extreme weather conditions in recent years, including the unprecedented severe drought in the summer of 2018. The deformation map of the study area is generated through a time-series analysis of InSAR from June 2017 to November 2020. Monitoring the peatland areas in this region is very important as agricultural and human activities have already caused many peatlands to disappear. This further emphasizes the importance of preserving the remaining peat sites in this region. Based on the InSAR results, a method for calculating the carbon flux of the peat areas is proposed, which can be utilized as a regular monitoring approach for other remote areas. Despite the severe drought in the summer of 2018, our findings reveal a significant uplift in most of the investigated peat areas during the study period. Based on our estimations, 86% of the peatlands in the study area experienced an uplift corresponding to about 47 000 tons of carbon uptake per year. In comparison, the remaining 14% showed either subsidence or stable conditions corresponding to about 2300 tons of carbon emission per year during the study period. This emphasizes the importance of InSAR as an efficient and accurate technique to monitor the deformation rate of peatlands, which have a vital role in the global carbon cycle.

Optimization of InSAR based coseismic slip modeling for moderate earthquakes accounting for fore–aftershock sequence

Abstract. The integration of snow hydrology models and remote sensing observations via data assimilation is a promising method to capture the dynamics of seasonal snowpacks at a high spatial resolution and to reduce uncertainty with respect to snow water resources. In this study, we employ an interferometric synthetic aperture radar (InSAR) technique to quantify snow depth change using modeled snow density and assimilate the referenced and calibrated retrievals into the Multilayer Snow Hydrology Model (MSHM). Although the impact of assimilating snow depth change is local in space and time, the impact on snowpack mass properties (snow depth or snow water equivalent, SWE) is cumulative, and the InSAR retrievals are valuable to improve snowpack simulation and to capture the spatial and temporal variability in snow depth or SWE. Details on the estimation algorithm of InSAR snow depth or SWE changes, referencing, and calibration prove to be important to minimize errors during data assimilation.

<p>The Capo Colonna promontory in southern Italy has long been affected by ground instability involving not only coastal erosion and loss of land, but also a noticeable subsidence process, both posing risk to houses and roads built onto the promontory, alongside its archeological site including Hera Lacinia’s sanctuary. Tectonic-induced submergence of some formerly exposed structures and sites and landward retreat up to 200 m were recorded over the centuries along the coastlines of this region. Anthropogenic activities associated with hydrocarbon exploitation add onto Capo Colonna’s ground deformation drivers, with an influence zone that appears to be mostly limited to the shallow-marine terrace that defines the promontory. Subsidence at the site has been monitored since 2005 with geodetic and geophysical methods by the national hydrocarbons authority and the archaeological superintendence. More recent investigations included satellite Interferometric Synthetic Aperture Radar (InSAR) techniques, that revealed −1 to −2 cm/year subsidence rates in 1992−2014 [1-2]. Artificial corner reflectors were also installed to enhance the backscattering properties of the archaeological site and the coastline, trying to ease identification of persistent and coherent scatterers suitable to act as InSAR monitoring targets [2]. This work extends the temporal coverage of past InSAR surveys using two 6 year-long big data stacks of ~300 Sentinel-1 IW scenes each [3], allowing the estimation of subsidence rates and patterns to date, with an unprecedented weekly temporal sampling. The Parallel Small BAseline Subset (P-SBAS) method integrated in ESA’s Geohazards Exploitation Platform (GEP) is used to run the advanced image processing workflow using a cloud environment. Present-day vertical rates are found in the order of −0.7 to −1.5 cm/year, with peaks of −2.3 cm/year. Two clear bands of east-west deformation are identified, with rates reaching ±1 cm/year and pointing towards the maximum subsidence center, i.e. west of a gas production well. While Sentinel-1 data corroborate the spatial association between land subsidence and gas extraction infrastructure (that was already observed in previous studies), the new results suggest an acceleration of the subsidence process with respect to its long-term trend. Some previously unknown short-term trend variations that overlapped onto the main subsidence process over the last few years are also highlighted, owing to the temporal granularity of the Sentinel-1 acquisitions. These outcomes contribute to advance the understanding of a local phenomenon studied for years, and prove the benefits that technical improvements in satellite observations can bring to refine coastal subsidence rates and distinguish driving factors.</p><p> </p><p>[1] Tapete D., Cigna F. 2012. Site-specific analysis of deformation patterns on archaeological heritage by satellite radar interferometry. MRS Online Proceedings Library, 1374, 283-295. https://doi.org/10.1557/opl.2012.1397</p><p>[2] Cigna F. et al. 2016. 25 years of satellite InSAR monitoring of ground instability and coastal geohazards in the archaeological site of Capo Colonna, Italy. In: SAR Image Analysis, Modeling, and Techniques XVI, SPIE, Vol. 10003, id. 100030Q. https://doi.org/10.1117/12.2242095</p><p>[3] Cigna F., Tapete D. 2021. Sentinel-1 big data processing with P-SBAS InSAR in the Geohazards Exploitation Platform: an experiment on coastal land subsidence and landslides in Italy. Remote Sensing, 13, 885. https://doi.org/10.3390/rs13050885</p>

Time-lapse interferometric synthetic aperture radar (InSAR) remote sensing methods of surface deformation have proven their use in desert environments. The data are acquired frequently without the need to send personnel or equipment into the field. The quality and accuracy of the data is very high. The spatial resolution of the data is excellent and matches that of surface seismic data. These characteristics make the data well suited for a variety of time-lapse monitoring tasks. In this study, we describe the accuracy of the InSAR technique relative to other measurements such as the global positioning system and precise leveling (acquired at known stable locations). We illustrate two case studies of differing natures. In one case, gas production leads to reservoir compaction, which is tracked as surface subsidence with time using frequent InSAR data. The results are used to map zones of increased deformation and identify areas with localized changes. These insights are being used to influence decisions on new wells and well interventions, to provide support for management of facility integrity, and to advise building code and material selection that can withstand the expected rate of deformation. The second case of a shallow steam flood illustrates the use of InSAR data to identify areas of surface uplift following thermal expansion of the reservoir. These data are also used to support the monitoring of the steam chamber growth and confinement in the reservoir. The information from InSAR will become more valuable for reservoir management when the steam chamber matures and conventional downhole data acquisition consequently becomes challenging. In summary, oil and gas fields located in arid environments lend themselves well to remote sensing using the InSAR technique because (1) they are sizeable (from tens to hundreds of square kilometers); (2) they are free from vegetation, snow cover, and most atmospheric distortions, although cloud and pollution can affect the data quality; and (3) they benefit from highly repeatable long-term regular monitoring.