Persistent Scatterer Interferometry Technique Research Articles

Automated Python workflow for generating Sentinel-1 PSI and SBAS interferometric stacks using SNAP on Geospatial Computing Platform

Detecting and monitoring surface deformation using radar satellite data is vital in geohazard assessment. Sentinel-1 has provided unprecedented spatial and temporal resolution, but data processing is complicated and poses computational challenges. Although software and tools exist, each with its own limitations. SNAP-ESA is notable for its user-friendly interface and stable performance in Interferometric Synthetic Aperture Radar (InSAR). However, SNAP-ESA lacks a flexible approach for generating interferometric time series stacks for Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques and faces computational challenges over large areas. Here, we present an automated Python workflow, SNAPWF, using SNAP-ESA to enable efficient PSI and SBAS interferometric time series stacks generation using flexible network graphs. SNAPWF has been implemented on a dedicated geospatial computing platform, enabling efficient performance over large areas. Results confirm its ability to generate PSI and SBAS interferometric stacks using full Sentinel-1 scenes and achieve results comparable to existing software.

Surface Displacement Evaluation of Canto Do Amaro Onshore Oil Field, Brazil, Using Persistent Scatterer Interferometry (PSI) and Sentinel-1 Data

This study aims to investigate the occurrence of surface displacements in the Canto do Amaro (CAM) onshore oil field, situated in Rio Grande do Norte, Brazil, using Sentinel-1 data. The persistent scatterer interferometry (PSI) technique was used to perform the analysis based on 42 Sentinel-1 images, acquired from 23 July 2020 to 21 December 2021. Moreover, information regarding the structural geology of the study area was collected by referencing existing literature datasets. Additionally, a study of the water, gas, and oil production dynamics in the research site was conducted, employing statistical analysis of publicly available well production data. The PSI points results were geospatially correlated with the closest oil well production data and the structural geology information. The PSI results indicate displacement rates from −20.93 mm/year up to 14.63 mm/year in the CAM region. However, approximately 90% of the deformation remained in the range of −5.50 mm/year to 4.95 mm/year, indicating low levels of ground displacement in the designated research area. No geospatial correlation was found between the oil production data and the zones of maximum deformation. In turn, ground displacement demonstrates geospatial correlation with geological structures such as strike-slip and rift faults, suggesting a tectonic movement processes. The PSI results provided a comprehensive overview of ground displacement in the Canto do Amaro field, with millimeter-level accuracy and highlighting its potential as a complementary tool to field investigations.

Persistent scatterer interferometry (PSI) technique for road infrastructure monitoring: a case study of the roadway network of the Nile Delta (Egypt)

A dependable transportation infrastructure is an essential local resource, linking communities and facilitating economic expansion. The Nile Delta, renowned for its high population density, is experiencing subsidence as a result of natural forces exerting pressure on its sediments. Land subsidence exerts a substantial influence on infrastructure, economic, and social progress. This study examines the evaluation of land subsidence in roads experiencing various issues in the Nile Delta. Each year, subsidence results in significant damage and, in certain instances, complete disruption of numerous road networks, with potential durations ranging from a few days to several months. Conventional methods for identifying and monitoring subsidence on a large and intricate road network can be challenging due to the substantial time and resource requirements. Satellite-based monitoring is essential as it can effectively detect ground displacements caused by human activities (such as subterranean diggings and disruption of other infrastructures) or environmental dangers, specifically those associated with crucial hydrogeological incidents. Moreover, it possesses the benefit of efficiently encompassing extensive regions with precision. This study utilized the persistent scatterer interferometry (PSI) of SNAP-StaMPS workflow to employ differential interferometry synthetic aperture radar (DInSAR) processing methods for investigating the area. By utilizing Sentinel-1 data, we successfully acquired a comprehensive understanding of the deformation occurring in the study area, specifically in the roads where subsidence is persistently taking place. The data has demonstrated its value in enhancing our understanding of the safety status of infrastructure and facilitating maintenance actions. The accuracy of the radar interferometry results was confirmed through the use of in situ methods, including Global Navigation Satellite System (GNSS), Global Positioning System (GPS), smart phone, and unmanned aerial vehicle. This approach has demonstrated its capability to consistently detect vertical displacement in critical areas of roads. Furthermore, there is a strong correlation between the PSI interferometry and the in situ measurements when absolute values are taken into consideration. The findings of this study also demonstrate that open-source solutions are more effective, which encompass freely available data and software, offered through the COPERNICUS program, possess significant potential for monitoring vertical displacements in roads.

Irshalwadi landslide in Western Ghats of India: Observations from precursory slope movement, rainfall and soil moisture

The Western Ghats of India, a UNESCO world heritage site, is gradually becoming a hotspot for catastrophic landslides. On July 19, 2023, the entire Irshalwadi village in the Raigad district of Maharashtra, India, was obliterated due to a single catastrophic landslide. Of the 228 residents of the village, the landslide killed 27 people and 57 are still reported to be missing. The landslide occurred on a slope which exhibited no visual precedence of disturbance or creep. However, analysis of potential precursory movements prior to slope failure using the Persistent Scatterer Interferometry (PSI) technique reveals active movement near the base of the slope (in order of ∼12 mm/y). Sentinel-2 satellite imagery acquired post-event characterises the landslide as a bifurcated debris flow possibly triggered by heavy rainfall in the region. Peak cumulative rainfall, estimated by the Indian Meteorological Department (IMD), was observed in this region on 17, 18 and July 19, 2023 (∼500) mm and was the highest recorded rainfall in the region during the given period. This caused significant water percolation into the porous basaltic soil, leading to increased soil moisture, as supported by the Soil Moisture Active Passive (SMAP) data. The resultant increase in pore pressure caused the slope material to fail and eventually trigger the landslide.

ARTIFICIAL NEURAL NETWORK FOR PREDICTION OF LAND SUBSIDENCE IN MUDSLIDES REGION THROUGH INSAR AND RAIN DATA

Abstract. Mudslides are powerful and fast-moving mass movements that pose significant risks to human lives, infrastructure, and natural environments. They are commonly triggered by intense rainfall and their impact is particularly severe in mountainous regions. Synthetic Aperture Radar (SAR) technology can be used to calculate the subsidence of the territory over time by means of a temporal series of SAR images through the Persistent Scatterer Interferometry (PSI) technique. In some research Interferometric SAR (InSAR PSI) data were used to train Long Short-Term Memory (LSTM) based Artificial Neural Network (ANN) to provide movements forecasting. This paper proposes a new LSTM based ANN to forecast future territory movements considering both the past InSAR PSI data, the rain forecasting of the next acquisition and the past cumulative amount of rain since the movements of mudslides are strictly dependent to the quantity of rainfall accumulated in the terrain. The results of the proposed ANN are shown in terms of Mean Square Error (MSE) and Mean Absolute error (MAE) by comparing them with a LSTM-based ANN trained with only the InSAR PSI data.

Remote monitoring of a concrete bridge using PSInSAR

AbstractStructural health monitoring (SHM) can support decision makers in structural integrity management by providing information about occurrence of damage and its evolution in time. Recently, due to new Synthetic Aperture Radar (SAR) missions, remote sensing through active satellites provides a promising methodology capable of monitoring infrastructure assets. This paper investigates the use of Multi‐Temporal Synthetic Aperture Radar Interferometry (MT‐InSAR) processing to monitor the structural health of bridges. The full interferometry processing chain is applied using Sentinel‐1 synthetic aperture radar data and a combination of open‐source routines from the Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS). Displacement time‐histories are derived from single look complex products by employing the Persistent Scatterer Interferometry (PSInSAR) technique. The PS‐InSAR technique is applied to study the behavior of a concrete bridge located over the Oglio river in Isola Dovarese, Italy, on which also GNSS receivers are installed. The GNSS data are then used to validate the PS‐InSAR measurements. The effects on PS‐InSAR results of the number of SAR images used as well of the target structure are investigated.

Analysis of Deformation Dynamics in Guatemala City Metropolitan Area Using Persistent Scatterer Interferometry

The analysis of deformation dynamics in Guatemala city and its surrounding region presented in this paper holds significant relevance due to the high vulnerability of this area to natural disasters, combined with its rapid urbanization, similar to most Central American cities, contrasting with a lack of InSAR and deformation studies in the region. A total of 226 SAR images from Sentinel-1 A and B satellites in both ascending and descending geometries were processed with the Persistent Scatterer Interferometry (PSI) technique employing the SNAP-StaMPS integrated processing chain. The study area encompasses the Metropolitan Region of Guatemala, which is characterized by a diverse and active geological framework, with a historical record of earthquakes, intense groundwater extraction, and local subsidence phenomena, causing fissures and sinkholes. Four active areas were identified in the study area, each covering more than 50 hectares, with subsidence velocities greater than 10 mm/yr. This study provides valuable insights into fostering the sustainable development of this region by identifying deformation patterns, characterizing main active areas, and evaluating associated risks for disaster management and prevention. The results can also aid informed decision-making processes and guide urban planning and resource management strategies in other Central American countries. The application of InSAR studies is crucial for improving safety and sustainability in urban environments and natural resource management in vulnerable regions.

Hydrocarbon production induced land deformation over Permian Basin; analysis using persistent scatterer interferometry and numerical modeling

The Permian Basin, encompassing Southeastern New Mexico and Western Texas, has been an important source of oil and natural gas for over a century for the United States. With recent advancements in petroleum extraction methods, such as hydraulic fracturing and horizontal drilling, production has increased exponentially. Such rapid developments, along with the disposal of wastewater back into the subsurface, have caused significant changes in the stress regime of the region’s subsurface, resulting in various geohazards including land subsidence. In this work, we used the Sentinel-1C-band SAR dataset acquired between 2016 and 2021 to measure the basin-wide deformation over the entire Permian Basin. We utilized Persistent Scatterer Interferometry (PSI) technique to produce the time-series deformation at millimeter-level accuracy. The average monthly production and injection volumes and well depths over the Delaware Basin in New Mexico were calculated for each well and the impact of the hydrocarbon activities on the deformation was modeled using a distributed point source model from fluid volume and well depth data. The results show that the Permian Basin was deforming at a rate of 3–4 cm/yr., with two large pockets of deformation to the north of the Grisham Fault Zone (GFZ) in the Delaware Basin. The region south of the GFZ shows complex deformation features with linear patterns, indicating the presence of faults hindering fluid flow. Some areas in the Delaware Basin also show an uplift, especially along the Texas side of the border driven by shallow wastewater injection. The Midland Basin is also affected by subsidence with several localized subsidence zones identified in the region. Further, the Midland Basin experienced an increase in intensity and the spatial distribution of the deformation over the last few years. Given both production and injection well data, the modeled deformation results agree with the observed except in two pockets. When only production data is used for the modeling, the modeled deformation shows better agreement with the observed, especially in the interior of New Mexico. The modeling results suggest that the deep wastewater injection has minimal impact on surface deformation in the region. Additionally, it is also possible that the region has undergone inelastic deformation. While we used an elastic model in this preliminary study, a more accurate study requires regional poroelastic modeling to study the interaction between the solid subsurface and fluid hydrocarbons in detail.

VALIDATION OF FULL-RESOLUTION DINSAR-DERIVED VERTICAL DISPLACEMENT IN CULTURAL HERITAGE MONITORING: INTEGRATION WITH GEODETIC LEVELLING MEASUREMENTS

Abstract. Towards revealing the potential of satellite Synthetic Aperture Radar (SAR) Interferometry (InSAR) for efficient detection and monitoring of Cultural Heritage (CH) encouraging resilient built CH, this study is devoted to the validation of InSAR-derived vertical displacements with a full-resolution perspective taking advantage of high-precision geodetic levelling measurements. Considering the Cathedral of Como, northern Italy, as the case study, two different Persistent Scatterer Interferometry (PSI) techniques have been applied to Cosmo-SkyMed high-resolution SAR images acquired in both ascending and descending orbit tacks within the time interval of 2010–2012. Besides using the simplified approach for obtaining the vertical displacement velocity from Line of Sight (LOS) velocity, a weighted, localized, multi-track Vertical Displacement Extraction (VDE) approach is proposed and evaluated, which uses the technical outcome of Differential InSAR (DInSAR) and spatial information. The results, using a proper PSI technique, showed that the accuracy level of extracted vertical displacement velocities in a full-resolution application is ca. 0.6 [mm/year] with a dense concentration of InSAR-Levelling absolute errors lower than 0.3 [mm/year] which are reliable and reasonable levels based on the employed validation framework in this study. Also, the weighted localized VDE can significantly decrease the InSAR-Levelling errors, adding to the reliability of the InSAR application for CH monitoring and condition assessment in practice.

Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR

Abstract. Safety and environmental aspects are crucial beyond production goals in the hydropower industry. By monitoring landslides associated with the construction of a hydropower dam in the Santa Cruz River in Argentine Patagonia, this paper contributes to the assessment of the project structural integrity of the construction and safety risks. Ground deformation is monitored using synthetic aperture radar (SAR) satellite data and the persistent scatterer interferometry technique, and it is contrasted with optical imagery, geological and technical reports, and fieldwork. The results include maps of accumulated deformation and deformation time series for the locations of the anchorages of the dam, providing a new and independent dataset to assess the integrity of the construction.

Extending polarimetric optimization of multi-temporal InSAR techniques on dual polarized Sentinel-1 data

The rationale of combining polarimetric SAR data with multi-temporal InSAR techniques, which is based on polarimetric optimization, is to improve their performance in identifying coherent pixels and increasing the accuracy of deformation monitoring. This paper extended the polarimetric optimization of Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques on dual-polarized Sentinel-1 data. For this purpose, 20 dual-polarization (VV/VH) SAR images acquired by the Sentinel-1A sensor were used. The results indicated that by applying polarimetric optimization on dual-polarized data in the SBAS technique, the number of candidate and final coherent pixels increased 2.95 and 1.42 times, respectively. Similarly, the number of candidate and final coherent pixels (CP) increased 3.12 and 1.45 times in the PSI technique, respectively. Also, the effect of the polarimetric optimization on increasing the density of coherent points in different regions was investigated. In this framework, Sentinel-2 time-series images were classified into four classes (bare soil, vegetation, build-up, and others) using Google Earth Engine by applying remote sensing indices. This demonstrated that polarimetric optimization of the SBAS technique was more effective in bare soil and vegetated regions than in urban areas. However, polarimetric optimization of the PSI technique was more successful in non-urban regions than in urban areas. An external comparative analysis was also performed between the deformation results derived by PSI and SBAS techniques and GPS observation. The evaluation demonstrated that polarimetric optimization coupled with multi-temporal InSAR analysis yielded more reliable deformation results than conventional single-polarimetric data.

Persistent Scatterer Interferometry (PSI) Technique for the Identification and Monitoring of Critical Landslide Areas in a Regional and Mountainous Road Network

A reliable road network is a vital local asset, connecting communities and unlocking economic growth. Every year landslides cause serious damage and, in some cases, the full disruption of many road networks, which can last from a few days to even months. The identification and monitoring of landslides with conventional methods on an extended and complex road network can be a rather difficult process, as it requires a significant amount of time and resources. The road network of the Chania regional unit on the island of Crete in Greece is a typical example, as it connects, over long distances, many remote mountainous villages with other local communities, as well as with the main urban centers, which are mainly located across the shore. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor slow-moving and ongoing landslides over large and complex areas such as those of the mountainous road networks. This study applied PSI in the Chania regional unit, using the novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing BEYOND of the Institute of Astronomy and Astrophysics, Space Applications and Remote Sensing of the National Observatory of Athens (BEYOND) for the rapid identification of the areas, most critical to landslide in a local road network. The application of P-PSI speeded up the total required processing time by a factor of five and led to the rapid identification and monitoring of 235 new slow-moving landslides. The identified landslides were correlated with a pre-existing landslide inventory and open access visual data to create a complete landslide inventory and a relative landslide inventory map, thus offering a valuable tool to local stakeholders.

On the quality checking of persistent scatterer interferometry data by spatial-temporal modelling

Abstract Today, rapid growth in infrastructure development and urbanisation process increases the attention for accurate deformation monitoring on a relatively large-scale. Furthermore, such deformation monitoring is of great importance in the assessment and management of natural hazard processes like landslides, earthquakes, and floods. In this study, the Persistent Scatterer Interferometry (PSI) technique is applied using open-source Synthetic Aperture Radar (SAR) data from the satellite Sentinel-1. It allows point-wise deformation monitoring based on time series analysis of specific points. It also enables performing spatio-temporal area-based deformation monitoring. Currently, these data do not have a sophisticated quality assurance process to judge the significance of deformations. To obtain different quality classes of the Persistent Scatterer (PS) data points, the first step is to classify them into buildings and ground types using LoD2 building models. Next, time series analysis of the PS points is performed to model systematic and random errors. It allows estimation of the offset and the deformation rate for each point. Finally, spatio-temporal modelling of neighbourhood relations of the PS points is carried out using local geometric patches which are approximated with a mathematical model, such as, e.g., multilevel B-Splines. Subsequently, the quality of SAR data from temporal and spatial neighbourhood relations is checked. Having an appropriate spatio-temporal quality model of the PS data, a deformation analysis is performed for areas of interest in the city of Hamburg. In the end, the results of the deformation analysis are compared with the BodenBewegungsdienst Deutschland (Ground Motion Service Germany) provided by the Federal Institute for Geosciences and Natural Resources (BGR), Germany.

Land subsidence modeling and assessment in the West Pearl River Delta from combined InSAR time series, land use and geological data

West Pearl River Delta (WPRD) is sinking as a result of the jointed effect of natural and anthropogenic factors. Land subsidence has increasingly become a concern because of tremendous population growth and rapid urbanization over this region in the last few decades. In this study, sixty-seven Sentinel-1 images, acquired between 2016 and 2021, were analyzed with the persistent scatterer interferometry technique (PSI), to monitor and reveal the ground subsidence characteristics in the WPRD. It is found that the overall vertical deformation velocities observed in the WPRD ranged between −70 mm/year and 10 mm/year. Three subsidence bowls were found in the study area (Gaolan island of Zhuhai, the junction area of Zhuhai and Zhongshan, and the junction area of Zhongshan and Jiangmen). The spatial–temporal subsidence characteristics have been analyzed. It is discovered that the ground subsidence is mostly dispersed in Quaternary deposits and is highly relevant to the thickness of sediments, indicating that soft soil consolidation is one of the primary causes contributing to land subsidence. Furthermore, land use maps for 2016 and 2021 were generated using Landsat-8 images for the investigation on the relationship between land subsidence and land use. The results obtained from analysis demonstrated that the rapid subsiding areas mainly occurred in the land-use classes as follows: aquaculture, urban land, and agricultural land. The land use conversion pattern with more significant anthropogenic influence usually causes a higher subsidence rate. In addition, based on soft soil thickness, groundwater exploitation, land use, elevation, and strata lithology, a Random Forest Regression (RFR) model was used to predict subsidence rates (R2 = 0.631, RMSE = 2.7 mm/year). The importance of these influencing factors of land subsidence was calculated based on the RFR algorithm. The results indicated that soft soil thickness, elevation, groundwater exploitation, strata lithology, and landcover type are the most significant factors affecting subsidence. The applicability of geological data and land-use history for land subsidence prediction has been demonstrated with the use of the RFR algorithm.

Monitoring Building Activity by Persistent Scatterer Interferometry

In many countries globally, information for new buildings is either scarce or incomplete. In an effort to bridge this information gap an approach based on public domain synthetic aperture radar (SAR) satellite data is introduced. The method is based on the persistent scatterer interferometry (PSI) technique in order to detect newly constructed buildings and estimate their heights in a selected case study area in Athens, Greece. The overall objective is to derive timely spatial information for building activity. A key concept of the approach is the residual height, i.e., the difference in height of each point compared to a reference point that is suitably selected. The data used are acquired by the Sentinel-1 satellite. Both ascending and descending orbits and dual polarizations (VV/VH) are used. The results show that as much as 70% of new buildings can be detected at a vertical accuracy approximately of 2.5 m, which is sufficient to determine the number of stories per building. Overall, the proposed method can provide an efficient insight regarding building activity and provide a significant information layer for urban studies in a rapidly changing world.

Risk Evaluation of the Sanalona Earthfill Dam Located in Mexico Using Satellite Geodesy Monitoring and Numerical Modeling

Dams are essential structures in the growth of a region due to their ability to store large amounts of water and manage it for different social activities, mainly for human consumption. The study of the structural behavior of dams during their useful life is a fundamental factor for their safety. In terms of structural monitoring, classic terrestrial techniques are usually costly and require much time. Interferometric synthetic aperture radar (InSAR) technology through the persistent scatterer interferometry (PSI) technique has been widely applied to measure millimeter displacements of a dam crest. In this context, this paper presents an investigation about the structural monitoring of the crest of the Sanalona dam in Mexico, applying two geodetic satellite techniques and mathematical modeling to extract the risk of the dam–reservoir system. The applicability of the InSAR technique for monitoring radial displacements in dams is evaluated and compared with both GPS systems and an analytical model based on the finite element method (FEM). The radial displacements of the Sanalona dam follow a seasonal pattern derived from the reservoir level, reaching maximum radial magnitudes close to 13 mm in November when the rainy season ends. GPS recorded and FEM simulated maximum displacements of 7.3 and 6.7 mm, respectively. InSAR derived radial displacements, and the reservoir water level presented a high similarity with a correlation index equal to 0.8. In addition, it was found that the Sanalona dam presents the greatest deformation in the central zone of the crest. On the other hand, based on the reliability analysis, the probability of failure values lower than 8.3 × 10−2 was obtained when the reservoir level was maximum, which means that the radial displacements did not exceed the limit states of the dam–reservoir system in the evaluated period. Finally, the extracted values of the probability of failure demonstrated that the Sanalona dam does not represent a considerable risk to society.

Monitoring land subsidence with the combination of persistent scatterer interferometry techniques and distributed fiber optic sensing techniques: a case study in Suzhou, China

Land subsidence is a global geo-hazard caused by various natural and human factors, and it directly threatens the safety of the environment and infrastructures. Investigating the mechanism of land subsidence is becoming more and more important. In this paper, we use the persistent scatterer interferometry (PSI) technique combining the distributed fiber optic sensing (DFOS) technique to detect the spatial–temporal distributions of land subsidence and investigate the stratum deformation characteristics in Shengze, Suzhou. By analyzing Sentinel-1A date between 2017 and 2019 with the PSI technique and the deformation date derived by the DFOS technique, we conclude that the land surface deformations are mostly affected by the transitional exploitation of groundwater. The average line-of-sight (LOS) deformation rate is mostly concentrated in − 3 to 2.2 mm/yr, and a maximum subsidence rate of up to − 16.9 mm/yr is observed in four industrial areas. Meanwhile, the DFOS-derived results reveal that the compression strata are mostly concentrated in 41.2–137.9 m depth, which is closely associated with the pore water pressure in the second confined aquifer. And it also reveals that the groundwater over-exploitation may be the significant triggering factor of the subsidence in the study area. The InSAR-derived results are also evaluated by the deformation time series obtained by the DFOS technique. The combination of those two new sensing and monitoring technologies enables us to highlight the large deformation area and reveal the mechanism of its subsidence, which is conducive to urban development, disaster risk management, and rational exploitation and management of groundwater in Suzhou, China.

Detecting Surface Displacement in Kathmandu Valley with Persistent Scatterer Interferometry

Kathmandu Valley has been susceptible to surface displacement due to natural as well as anthropogenic causes since a long time. Previous studies till 2017 suggest that displacement (specially subsidence) with rates of several centimeters per year have occurred in the Kathmandu Valley indicating uncontrolled groundwater withdrawal as the major cause. Owing to the history of surface displacement, this study aims at detecting the nature of land subsidencein Kathmandu for years: 2017 (18th January to 26th December) and 2019 (2nd January to 28th December) based on Persistent Scatterer Interferometry (PSI) technique using Synthetic Aperture Radar (SAR) datasets from Sentinel 1. PSI is able to detect persistently backscattering targets and evaluate respective displacements from the backscattered signal. The results of 2017 and 2019 revealed significant displacement of -100.54mm and -129.19mm along Line Of Sight (LOS) of radar during the study period at Baluwatar and Lazimpat area of Kathmandu district respectively. Similarly, New Baneshwor, Bode and Imadol exhibited a substantial displacement of -88.81mm,-103.55mm, -127.35mm respectively for year 2019.

Satellite based assessment of artificial reservoir induced landslides in data scarce environment: A case study of Baglihar reservoir in India

The monitoring of reservoir-induced landslides is essential for assessing and mitigating the associated geo-environmental hazards. The research utilises the Persistent Scatterer Interferometry (PSI) technique for mapping and monitoring Reservoir-Induced Landslides (RIL). The rim area of the Baglihar Dam Reservoir on the Chenab river (Jammu and Kashmir, India) along National Highway NH-244 is chosen for the analysis. The slope movements are necessarily impacted by the reservoir drawdown effect (RDE) and the rainfall. 46 images from ascending and 70 images from descending track of Sentinel 1C-band SAR satellite have been used for the analysis. Further, ascending and descending results have been combined to estimate the 3D movement. Processing of both the stacks have yielded the Line of Sight (LoS) slope movement along the reservoir rim. The velocities retrieved vary from −95.1 mm/year to +85.1 mm/year. Precipitation was derived from Tropical Rainfall Measuring Mission (TRMM) data, extracted from Google Earth Engine (GEE). The temporal correlation between the displacements and the local precipitation has been qualitatively analysed. The SAR interferometry results were used to identify the active zones.

A spatio-temporal temperature-based thresholding algorithm for underground coal fire detection with satellite thermal infrared and radar remote sensing

Coal fire is a global catastrophe that causes resource waste, economic loss, and environmental pollution. Accurate detection of surface abnormalities induced by coal fires (such as thermal anomalies and land deformation) is critical for coal fire monitoring and extinguishment. However, the conventional thermal anomaly detection methods do not consider the temporal variation of land surface temperature (LST). Moreover, previous studies have not applied the polarimetric persistent scatterer interferometry(PolPSI) technique for coal-fire-related ground deformation monitoring, which is supposed to have better performance than conventional persistent scatterer interferometry (PSI) approaches. To this end, in this study, a spatio-temporal temperature-based thresholding (STTBT) algorithm is proposed to detect thermal anomalies, firstly. Then, the ground deformation obtained by the PolPSI method is combined with the detected thermal anomalies for the coal fire detection. Besides, a two-stage bandpass filter is employed to assist in detecting the suspected coal fire. The results in the Fukang coal fire area, China, demonstrate that, compared with the conventional methods, the proposed STTBT mothed achieves higher accuracy of thermal anomalies detection, thereby can able to detect coal fire areas more effectively. In addition, PolPSI has better performance than PSI in ground deformation monitoring over coal fire areas. The proposed STTBT combined with PolPSI can serve as a powerful tool for detecting and monitoring underground coal fires.