Interferometric Point Target Analysis Research Articles

An improved time series SAR interferometry (TSInSAR) for investigating earthquake-induced active unstable slopes (AUS) in Pakistan

ABSTRACT Time series interferometric SAR (TSInSAR) techniques face challenges related to the availability of highly coherent point targets in non-urban, vegetated, and mountainous regions. In this study, we tested an improved TSInSAR approach with the help of distributed scatterers (DS) detected through the fast statistically homogeneous pixel selection (faSHP) approach and jointly processed with persistent scatterers (PS) within the interferometric point target analysis (IPTA) framework. The method is applied to a seismically active and highly gradient terrain to investigate the earthquake-induced active unstable slopes (AUS) in the Muzaffarabad-Balakot region of northern Pakistan, particularly using sixteen PALSAR-2 images acquired between October 2014 and May 2020. However, its effectiveness was also assessed by using Sentinel-1 data from the same time period. The quantitative assessment showed the effectiveness of the method in delineating the sliding surfaces of the unstable slopes and achieving approximately four times higher point density as compared to the standard PSI approach when applied to PALSAR-2 data and approximately nine times higher point targets when Sentinel-1 data is used. Exhibiting an average deformation rate varying between −40 mm yr−1 and 20 mm yr−1 along the line of sight (LOS) direction, the obtained results from PALSAR-2 data revealed the existence of 451 AUS in the region. The study also reports the discovery of a giant destabilized slope (~2.30 sq. km) located near Patika Village along the Neelam River. The majority of these unstable slopes are found at altitudes above 780 m, with slope angles ranging from 15 to 50 degrees. The study also found a significant correlation between deformation patterns and local precipitation. The rainwater gradually penetrates into the joints and cracks, accelerating the processes of deformation and slope failure. The research and its findings could help decision-making entities by providing first-hand information for managing the risks associated with slope failure.

Revealing the Ground Deformation and Its Mechanism in the Heihe River Basin by Interferometric Synthetic Aperture Radar and Optical Images.

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to -70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone.

Analysis of surface deformation and related factors over mining areas based on InSAR: A case study of Fengcheng mine

Abstract. Ground surface deformation in mines affects mining production, damages the ecological environment, and endangers human safety. Mastering the detailed time series deformation and related triggering factors can provide key information for the safety of the mining area. Therefore, the Fengcheng mining area, a large and ancient coal mine in Jiangxi Province, China, was selected as the study area in this work, and the following research was conducted: 1. The accuracy and applicability of the Stacking, Small-Baseline Subset InSAR (SBAS-InSAR), and Interferometric Point Target Analysis (IPTA) methods were preliminarily explored while monitoring the annual deformation rate based on Sentinel-1A data from October 2019 to November 2022. 2. The time-series deformation of the Fengcheng mining area was obtained with SBAS-InSAR technology, and the sedimentation was validated with leveling results. 3. The correlation factors of deformation, such as rainfall and land cover, were studied, and the relationship between the influencing factors, such as coal mining dip angle, digital elevation, coal mining elevation, and deformation, was quantitatively explored with the Grey correlation model and Pearson correlation analysis method. The following conclusions were drawn: The SBAS method has the best adaptability in the dense vegetation mining area, and the root-mean-square error of the difference between deformation results and leveling data does not exceed 4mm. The evolution process of deformation centers is mainly divided into the stages of initial deformation, constant velocity deformation, accelerated deformation, and stable condition. Compared with the natural factors, the settlement of the Fengcheng mining area is mainly affected by human-induced mining and construction of artificial facilities.

Monitoring and Analysis of Land Subsidence in Cangzhou Based on Small Baseline Subsets Interferometric Point Target Analysis Technology

Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this area is significant. In this paper, we used the Small Baseline Subsets Interferometric Point Target Analysis (SBAS-IPTA) technique to process the Envisat-ASAR, Radarsat-2, and Sentinel-1A data and obtained the land subsidence of Cangzhou from 2004 to 2020. Additionally, we obtained winter wheat distribution information in Cangzhou using the Pixel Information Expert Engine (PIE-Engine) remote sensing cloud platform. On this basis, we analyzed the relationship between ground water level, winter wheat planting area, and the response of land subsidence according to the land use type and groundwater level monitoring data near the winter wheat growing area. The results show that during 2004–2020, the average annual subsidence rate of many places in Cangzhou was higher than 30 mm/year, and the maximum subsidence rate was 115 mm/year in 2012. From 2004 to 2020, the area of the subsidence funnel showed a trend of first increasing and then decreasing. In 2020, the subsidence funnel area reached 6.9 × 103 km2. The winter wheat planting area in the urban area showed a trend of first decreasing, then increasing and then decreasing, and it accounted for a large proportion in the funnel area. At the same time, we studied the relationship between the land subsidence rate and the water level at different burial depths and the response of winter wheat planting area. The results showed that the change of confined water level had a stronger response with the other two variables.

An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm

InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation.

InSAR Displacement with High-Resolution Optical Remote Sensing for the Early Detection and Deformation Analysis of Active Landslides in the Upper Yellow River

Frequent landslides and other geological disasters pose a serious threat to human life and infrastructure in the Upper Yellow River. Detecting active landslides and ascertaining their impact necessitate the determination of deformation characteristics. In this study, we developed an integrated method combining interferometric synthetic aperture radar and high-resolution optical satellite remote sensing to detect active landslides in the Upper Yellow River region from Longyang Gorge to Lijia Gorge. Sentinel-1 satellite data from January 2019 to April 2021 with ascending and descending orbits were adopted to obtain deformation using the STACKING and interferometric point target analysis techniques. A 97.08% overlap rate in the detected results from the two InSAR technologies confirmed the suitability of both approaches. The missing detection rates (6.79% & 8.73%) from single line-of-sight (LOS) InSAR results indicate the necessity of different orbit direction data. Slight deformation rate changes (<4 mm/month) before and after rainy seasons of the Lijia Gorge landslide group indicate that precipitation exerted little impact on slope activity. This study supports the feasibility of integrated methods for the detection and analysis of active landslides in the Upper Yellow River and other regions.

Groundwater over-exploitation driven ground subsidence in the himalayan piedmont zone: Implication for aquifer health due to urbanization

Unsustainable groundwater exploitation often triggers inelastic aquifer compaction, endangering future groundwater potential. The Himalayan piedmont fan regions are undergoing fast urbanization and experiencing rapid groundwater depletion and water table drop. The piedmont fans have laterally varying aquifer geometry with slope-dominated aquifer water flow and experience variable spatiotemporal changes in response to groundwater depletion. We estimated the ground deformation over piedmont fans around Chandigarh (India), a growing urban centre, during 2014–2022 using satellite-based Interferometric Point Target Analysis (IPTA). We used both ascending and descending Differential Interferometric Synthetic Aperture Radar (DInSAR) acquisition modes to derive the vertical ground deformation by combining their line of sight (LOS) velocities. The region is experiencing vertical subsidence up to ∼60 mm/yr with prominent hotspots, where the temporal subsidence is compared with the groundwater cone of depression and urbanization over decades. Comparing the subsidence with the aquifer parameters from the bore wells suggests that the clay confining aquifer level III and semiconfined level II in the distal fan are experiencing the highest subsidence. The stress-strain relationship of these hotspot regions reveals the inelastic compaction of the aquifers producing severe subsidence. The piedmont zones of the Himalayas with identical aquifer geometry and population growth are facing similar challenges, and the combined DInSAR-IPTA and observational groundwater data modeling could provide a robust assessment for effective groundwater-aquifer health monitoring and management.

Understanding the Spatial-Temporal Characteristics of Land Subsidence in Shenzhen under Rapid Urbanization Based on MT-InSAR

Land subsidence is a common geological hazard in urban areas. It can be caused by human activities such as land reclamation and subway construction. However, rapid urbanization can accelerate the progress of these activities. Therefore, spatial-temporal analysis of land subsidence can provide a guarantee for rapid urbanization progress. In this study, Shenzhen, one of the fastest urbanizing cities in China, is selected for continued land subsidence monitoring. We collected 153 Sentinel-1A images from December 2015 to July 2021 and utilized an interferometric point target analysis (IPTA) method to generate the land subsidence in Shenzhen. Additionally, we analyzed the relationship between land subsidence and typical urbanization progress: land reclamation, transportation network, and urban construction. The results show that land subsidence mostly occurs in the western reclamation area, with a maximum rate of -74.9 mm/a in Qianhai Bay. Areas with larger quaternary marine sediment thickness in the range of 10∼20 m are more likely to experience subsidence. Obvious subsidence (<-20 mm/a) along the subway mainly occurred on Lines 11 and Line 5, which is inversely proportional to the distance from the metro. The correlation coefficient is -0.197. Areas with low road densities have a high probability of experiencing great subsidence. A relationship between land subsidence and building construction in the Guangming district is found. Obvious subsidence (<-20 mm/a) mainly occurred in the building areas, with a percentage of 43.48%.

InSAR-derived land subsidence in Wuhan between 2015 and 2020

Land subsidence has become a challenging problem with urbanization and underground exploitation. The land subsidence in Wuhan, China, was inferred from 47 Sentinel-1A images acquired between June 2015 to October 2020 based on Synthetic Aperture Radar (SAR) Interferometric Point Target Analysis (IPTA) technology. Results show that significant subsidence mainly occurred in Houhu, Qinshan, Baishazhou, and Zhuankou areas, and the most significant subsidence occurred in Baishazhou, reaching an annual subsidence rate of 41.0 mm/a with accumulated subsidence of 219.4 mm., The subsidence was more significant in the carbonate solution zone with the overlying Holocene strata compared to the rest carbonate karst belts. Analysis shows thatland building construction is a major reason for land subsidence in Wuhan. The construction of subway underground stations is another factor causing land subsidence. The effect of water level fluctuations of the Yangtze River on long-term trends is negligible. Our study provides further insight into the temporal-spatial pattern of Wuhan’s displacements and suggests that the land subsidence should be continuously monitored for alleviation of potential destructive hazards, especially during the period of construction of a land building, such as residential, public facilities, and subway.

Application of Optimized Grey-Markov Model to Land Subsidence Monitoring With InSAR

Land subsidence prediction in mining areas is one of the most important applications of land deformation monitoring, which is significance for safe production. We used interferometric point target analysis (IPTA) timing series interferometry synthetic aperture radar (InSAR) processing technology to analyze the land subsidence results for the Xinfa mining area from 2017 to 2020; and compared them to global positioning system (GPS) static monitoring data. We proposed a residual correction theory based on deviation coefficient using the grey prediction and Markov models, and an optimized Grey-Markov model (RGM-M model) was established to predict the land subsidence of the mining area. Our results show that: (1) The maximum difference between InSAR timing processing results and GPS monitoring data in the same period is 10.91mm; they have roughly the same subsidence trend, indicating that IPTA timing series InSAR technology are strongly reliable in mining deformation. (2) Compared to the traditional Grey-Markov model, the improved residual correction and dynamic assignment of the Grey-Markov model improves the prediction accuracy. The optimized residual correction and dynamic empowerment of the Grey-Markov model prediction results are more suitable for the actual fluctuation of land subsidence value in the mining area. The maximum root mean square error of the prediction results is 0.751mm, and the maximum average absolute percentage error is 7.46%, which has a certain guiding significance for the work of monitoring, prediction and safety management of land deformation in the mining area.

Application of Gray-Markov Model to Land Subsidence Monitoring of a Mining Area

Land subsidence monitoring in mining areas is one of the main applications of surface deformation monitoring, which is of great significance for safety production. Using the IPTA (Interferometric Point Target Analysis) time-series InSAR (Interferometry Synthetic Aperture Radar) method, land subsidence data from the new exploration area in the Weizhou mining area were analyzed and compared with static GPS (Global Positioning System) monitoring data for 2017–2020. Gray-Markov model was established by combining the gray prediction model with the Markov model to predict the surface subsidence of the mining area. The results show that (1) InSAR data have high accuracy and application potential in prediction of long-term surface deformation in mining areas; (2) The Gray-Markov model can better reflect the volatility and practicality of subsidence data in mining areas; (3) The prediction results have high accuracy, and the Gray-Markov model can serve as an effective guide for long-term surface deformation monitoring and safety management.

An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas

Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.

Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR

Land subsidence, as one of the engineering geological problems in the world, is generally caused by compression of unconsolidated strata due to natural or anthropogenic activities. We employed interferometric point target analysis (IPTA) as a multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique on ascending and descending Sentinel-1A the terrain observation with progressive scans SAR (TOPSAR) images acquired between January 2015 and December 2018 to analyze the spatio-temporal distribution and cause of subsidence in Abbottabad City of Pakistan. The line of sight (LOS) average deformation velocities along ascending and descending orbits were decomposed into vertical velocity fields and compared with geological data, ground water pumping schemes, and precipitation data. The decomposed and averaged vertical velocity results showed significant subsidence in most of the urban areas in the city. The most severe subsidence was observed close to old Karakorum highway, where the subsidence rate varied up to −6.5 cm/year. The subsidence bowl profiles along W–E and S–N transects showed a relationship with the locations of some water pumping stations. The monitored LOS time series histories along an ascending orbit showed a close correlation with the rainfall during the investigation period. Comparative analysis of this uneven prominent subsidence with geological and precipitation data reflected that the subsidence in the Abbottabad city was mainly related to anthropogenic activities, overexploitation of water, and consolidation of soil layer. The study represents the first ever evidence of land subsidence and its causes in the region that will support the local government as well as decision and policy makers for better planning to overcome problems of overflowing drains, sewage system, littered roads/streets, and sinking land in the city.

Copernicus Sentinel-1 MT-InSAR, GNSS and Seismic Monitoring of Deformation Patterns and Trends at the Methana Volcano, Greece

The Methana volcano in Greece belongs to the western part of the Hellenic Volcanic Arc, where the African and Eurasian tectonic plates converge at a rate of approximately 3 cm/year. While volcanic hazard in Methana is considered low, the neotectonic basin constituting the Saronic Gulf area is seismically active and there is evidence of local geothermal activity. Monitoring is therefore crucial to characterize any activity at the volcano that could impact the local population. This study aims to detect surface deformation in the whole Methana peninsula based on a long stack of 99 Sentinel-1 C-band Synthetic Aperture Radar (SAR) images in interferometric wide swath mode acquired in March 2015–August 2019. A Multi-Temporal Interferometric SAR (MT-InSAR) processing approach is exploited using the Interferometric Point Target Analysis (IPTA) method, involving the extraction of a network of targets including both Persistent Scatterers (PS) and Distributed Scatterers (DS) to augment the monitoring capability across the varied land cover of the peninsula. Satellite geodetic data from 2006–2019 Global Positioning System (GPS) benchmark surveying are used to calibrate and validate the MT-InSAR results. Deformation monitoring records from permanent Global Navigation Satellite System (GNSS) stations, two of which were installed within the peninsula in 2004 (METH) and 2019 (MTNA), are also exploited for interpretation of the regional deformation scenario. Geological, topographic, and 2006–2019 seismological data enable better understanding of the ground deformation observed. Line-of-sight displacement velocities of the over 4700 PS and 6200 DS within the peninsula are from −18.1 to +7.5 mm/year. The MT-InSAR data suggest a complex displacement pattern across the volcano edifice, including local-scale land surface processes. In Methana town, ground stability is found on volcanoclasts and limestone for the majority of the urban area footprint while some deformation is observed in the suburban zones. At the Mavri Petra andesitic dome, time series of the exceptionally dense PS/DS network across blocks of agglomerate and cinder reveal seasonal fluctuation (5 mm amplitude) overlapping the long-term stable trend. Given the steepness of the slopes along the eastern flank of the volcano, displacement patterns may indicate mass movements. The GNSS, seismological and MT-InSAR analyses lead to a first account of deformation processes and their temporal evolution over the last years for Methana, thus providing initial information to feed into the volcano baseline hazard assessment and monitoring system.

Ground Subsidence Triggered by the Overexploitation of Aquifers Affecting Urban Sites: The Case of Athens Coastal Zone along Faliro Bay (Greece)

Land subsidence in the coastal zone of the Neo Faliro, Moschato, and Kallithea municipalities, along the Faliro bay, has been recorded since the mid 1960’s. This phenomenon has caused damage to buildings, pavements, and roads. Aiming to identify the main causes of the observed ground deformations, data referring to the geological, geotechnical, and hydrogeological settings of the study area has been evaluated. Subsidence has been quantified by the use of space-born Synthetic Aperture Radar interferometry (InSAR) techniques. SVD (Singular Value Decomposition) and IPTA (Interferometric Point Target Analysis) techniques have been applied for the production of deformation maps, referring to the time period between 2002 and 2010. Furthermore, aiming to extend the study of the phenomenon further to the past, Persistent Scatterer Interferometry (PSI) data for the time period from 1992 to 2001 were also evaluated. Finally, the results of the InSAR analysis have been crosschecked with measurements acquired by a vertical geodetic control network as well as by ground truth data, referring to damage inventory of the site. The current research presents an interesting case study of an urban site affected for a long-lasting period by the activities of a neighboring industrial zone. The development of an extensive depression cone, mainly due to the overexploitation of the aquifers for industrial use, is the main cause of the land subsidence phenomenon, without excluding a component of motion due to the natural compaction of the compressible soil in the area of interest. The complexity of the geological, hydrogeological, and geotechnical conditions and the interaction of the numerous land use activities make this study far more interesting.

Monitoring of large-scale landslides in Zongling, Guizhou, China, with improved distributed scatterer interferometric SAR time series methods

The Zongling landslide (Nayong, Guizhou, China) is dominated by a unique karst landscape area with many landslide masses. In this paper, an improved Interferometric Point Target Analysis (IPTA) method is proposed to identify and monitor the Zongling landslide. In this method, the Anderson-Darling test is applied to distributed scatterer (DS) selection, and DS and persistent scatterer (PS) are combined to improve the density of measurement points in vegetation area. Moreover, this method is also characterized by the appropriate combination of differential interferograms produced by a small baseline subsets and the employment of the phase triangulation algorithm to estimate the optimal phase. Combining 105 scenes of C-band Sentinel-1A ascending and descending data acquired during 2014–2018, the method is applied to retrieve time series displacement for the large-scale landslide in Zongling Town. Finally, three accelerating landslides are identified from our result, which is consistent with ALOS PALSAR differential interferometry synthetic aperture radar (DInSAR) results and field investigation. The influencing factors and deformation mechanism of the Zongling landslide are also analysed. Our monitoring results will help the local government to conduct regular inspections and strengthen disaster prevention in mountain areas.

Land Subsidence Response to Different Land Use Types and Water Resource Utilization in Beijing-Tianjin-Hebei, China

The long-term overexploitation of groundwater leads to serious land subsidence and threatens the safety of Beijing-Tianjin-Hebei (BTH). In this paper, an interferometric point target analysis (IPTA) with small baseline subset InSAR (SBAS-InSAR) technique was used to derive the land subsidence in a typical BTH area from 2012 to 2018 with 126 Radarsat-2 and 184 Sentinel-1 images. The analysis reveals that the average subsidence rate reached 118 mm/year from 2012 to 2018. Eleven subsidence features were identified: Shangzhuang, Beijing Airport, Jinzhan and Heizhuanghu in Beijing, Guangyang and Shengfang in Langfang, Wangqingtuo in Tianjin, Dongguang in Cangzhou, Jingxian and Zaoqiang in Hengshui and Julu in Xingtai. Comparing the different types of land use in subsidence feature areas, the results show that when the land-use type is relatively more complex and superimposed with residential, industrial and agricultural land, the land subsidence is relatively more significant. Moreover, the land subsidence development patterns are different in the BTH areas because of the different methods adopted for their water resource development and utilization, with an imbalance in their economic development levels. Finally, we found that the subsidence changes are consistent with groundwater level changes and there is a lag period between land subsidence and groundwater level changes of approximately two months in Beijing Airport, Jinzhan, Jingxian and Zaoqiang, of three months in Shangzhuang, Heizhuanghu, Guangyang, Wangqingtuo and Dongguang and of four months in Shengfang.

INTERFEROMETRIC POINT TARGET ANALYSIS (IPTA) FOR LANDSLIDE MONITORING

Abstract. By the advent of Persistent Scatterer Interferometry (PSI) time-series analysis, this technique has demonstrated high performance in producing accurate measurements of ground displacements. However, due to several limitations such as high deformation rate, lack of man-made features and rough topographic characteristic, the efficiency of the PSI significantly decreases. The main goal of this paper is to illustrate the potential of one of the PSI methods namely the Interferometric Point Target Analysis (IPTA) to measure the deformation caused by landslide. The landslide occurs in an area lacking man-made features with rough topography. To this end, 28 Sentinel-1A SLC images spanning from October 14 2014 to October 27 2016, were used to generate single-master interferograms. The PS pixels were identified using amplitude dispersion and spectral diversity criteria. The PS pixels were unwrapped considering a linear model for the deformation behavior in an iterative manner. The residual topography and atmospheric contributions were estimated in each iteration and subtracted from the PS pixels phases. The results were finally compared to those extracted from conventional Small Baseline Subset (SBAS) time series analysis applied on the same datasets. A good agreement existed between two methods in some locations whereas unwrapping errors probably due to improper deformation model were observed in a couple of points.

InSAR Monitoring of Italian Coastline Revealing Natural and Anthropogenic Ground Deformation Phenomena and Future Perspectives

In this work, we use X and C-band SAR data provided by the COSMO-SkyMed and ENVISAT missions to detect and measure some ground deformation phenomena along six coastal areas of Italy. In particular, we exploit multi-temporal interferometric synthetic aperture radar (InSAR), i.e., small baseline subsets (SBAS) and interferometric point target analysis (IPTA) methods, to retrieve the deformation rate maps and time series for each investigated area. Multi-temporal InSAR analysis revealed local subsidence and uplifting effects in Ravenna Coastal Areas, Fiumicino, Campi Flegrei, Sibari Plain, Augusta Bay, and Taranto Gulf. Our work is meant as a demonstrator to show how InSAR-based analysis can provide a detailed understanding of the coastal hazards. Such analysis also opens up new monitoring scenarios such as the possibility of designing a near real-time surveillance service based on Sentinel-1 SAR data.

Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method

Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.