Parallel Small BAseline Subset Research Articles

Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms.

Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency's GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity maps. Subsequently, we applied a high-pass filter to the wrapped interferograms to separate the displacement signal from the atmospheric errors. We successfully identified the patterns associated with slow and fast movements by discerning the unique distributions within the matrices representing each movement class. The experiments included three case studies (from Italy, Portugal, and the United States), noted for their high sensitivity to landslides. We found that the Cosine K-nearest neighbor model achieved the best test accuracy. It is important to note that the test sets were not merely hidden parts of the training set within the same region but also included adjacent areas. We further improved the performance with pseudo-labeling, an approach aimed at evaluating the generalizability and robustness of the trained model beyond its immediate training environment. The lowest test accuracy achieved by the implemented algorithm was 80.1%. Furthermore, we used ArcGIS Pro 3.3 to compare the ground truth with the predictions to visualize the results better. The comparison aimed to explore indications of displacements affecting the main roads in the studied area.

Assessing land subsidence and analyzing tidal flooding in Tangerang, Banten

The increase in ocean temperature causes the expansion of seawater volume, resulting in an increase in sea level rise. The phenomenon of land subsidence also exacerbates the occurrence of tidal floods in coastal areas of Indonesia. This has prompted the need for a study of land subsidence and the distribution of tidal floods in Tangerang as a basis for taking anticipatory steps to reduce the negative impacts. The methods used for estimating land subsidence involved the SAR Sentinel-1A. The research utilized a total of 170 data points, spanning from 2017 until 2022. Data processing was carried out using the Parallel Small Baseline Subset method. The supporting data used in this study included SRTM data, tidal range, rainfall data, wind speed and direction. The results of this study reveal that the city of Tangerang has a maximum deformation value of -10.8 cm per year in the Periuk Sub District. Meanwhile, Tangerang Regency experienced land subsidence at a rate of -8.6 cm per year in Kosambi Sub District. Significant subsidence deformations occurred on the northeast side of Tangerang District and the southeast side of Tangerang City. Based on data analysis, it is evident that the total area inundated by tidal floods in Tangerang covers 33.267 hectares, with the largest affected area being in Pakuhaji District, spanning 9,262 hectares.

Dam Deformation Monitoring using Cloud-Based P-SBAS Algorithm: The Kramis Dam Case (Algeria)

This paper presents the application of the Parallel Small Baseline Subset (P-SBAS) algorithm, provided by the Geohazards Exploitation Platform for the precise monitoring of an earth dam's ground deformation using C-band Sentinel-1 data. The test site object of this study was the Kramis dam, located in the Mostaganem State, Western Algeria. Among the multiple advanced DInSAR techniques, SBAS is very adequate for long-term displacement monitoring in areas with changing terrain and vegetation. Ten corner reflectors were installed as a backscattered radar signal amplification tool to reduce the effect of temporal decorrelation and delineate the dam area. Four Sentinel-1 A and B satellite tracks were available (T30, T37, T103, and T110) to measure displacements, in the Line of Site (LoS) direction, for two years since the installation of the CRs in July 2019. The results showed a subsiding area on the left bank of the dam dike, with a velocity of 4mm/yr, and an uplifting rate of 3-4mm/yr in the upper part of the dike. The entire 3-dimensional vector of displacement of the dam vicinity was estimated using the least-squares method, proving a better understanding of the dam's temporal deformation, particularly for dams with a high exposure factor and associated risk.

A Procedure for the Quantitative Comparison of Rainfall and DInSAR-Based Surface Displacement Time Series in Slow-Moving Landslides: A Case Study in Southern Italy

Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area.

InSAR-based mapping of ground deformation caused by industrial waste disposals: the case study of the Huelva phosphogypsum stack, SW Spain

Close to the city of Huelva, SW Spain, and near the Atlantic Ocean, there is a phosphogypsum (PG) stack that accumulates 100 Mt of wastes and extends over 1000 ha. The stack lies directly over estuarine unconsolidated sediments with no protective layer in between. Here, we evaluate for the first time the structural stability of the PG stack, monitoring the deformation suffered by the salt-marsh basement. Through the web-based Geohazard Exploitation Platform (GEP) of the European Space Agency (ESA), a specific differential SAR interferometry (DInSAR) algorithm known as Parallel Small Baseline Subset (P-SBAS) has been used to process 279 ESA Sentinel-1 images acquired between October 2016 and June 2021. Resulting displacement maps and time-series curves reveal vertical displacements of up to 16 cm/year. This vertical motion has been associated to subsidence. In parallel with subsidence, horizontal movements > 2.5 cm/year have been also accounted and linked to talus destabilization. The analysis also demonstrates that the Huelva PG stack is vulnerable to adverse weather condition. The present study demonstrates that the InSAR-based methods are effective tools for monitoring the stability and ground motion of large waste stockpiles.

Nation-wide mapping and classification of ground deformation phenomena through the spatial clustering of P-SBAS InSAR measurements: Italy case study

The rising availability of satellite-based multi-temporal interferometric datasets covering large areas of the Earth surface constitutes a huge asset in the context of operational workflows aimed at improving land risk assessment and management. In order to cost-effectively handle huge amount of data, we design a semi-automatic procedure to quickly identify, map and inventory ground and infrastructures displacements by means of spatial clustering performed over very large-scale Differential Synthetic Aperture Radar Interferometry (DInSAR) datasets. The detected deforming areas are then evaluated against the Line of Sight (LOS) velocity vector decomposition and the accessible ancillary layers for a preliminary classification of the triggering factors. We apply our methodology to the mean ascending and descending deformation maps covering the whole Italian territory resulting from 3294 and 2868 Sentinel-1 (S1) acquisitions respectively, spanning from March 2015 to December 2018 and processed through the Parallel Small BAseline Subset (P-SBAS) technique. By setting a displacement rate threshold of ± 1 cm/year, a total number of 14,638 areas resulting from both geometries are found to suffer from instability phenomena, the origin of which are in turn preliminary sorted in 11 classes split between natural causes and man-made activities. With 2 degrees of confidence, we classified landslide and subsidence events as the main causes of deformation within the Italian territory, constituting respectively 31% and 27% of the total unstable areas, followed by volcanic-related processes (22%). Lastly, we provide a complete overview of the deformation phenomena which have recently occurred on the Italian Peninsula starting from national scale statistical analysis and ending up with local scale investigations according to the deformation patterns visible through the vertical and East-West components of motion.

Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth’s surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.

Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment

We present in this work an advanced processing pipeline for continental scale differential synthetic aperture radar (DInSAR) deformation time series generation, which is based on the parallel small baseline subset (P-SBAS) approach and on the joint exploitation of Sentinel-1 (S-1) interferometric wide swath (IWS) SAR data, continuous global navigation satellite system (GNSS) position time-series, and cloud computing (CC) resources. We first briefly describe the basic rationale of the adopted P-SBAS processing approach, tailored to deal with S-1 IWS SAR data and to be implemented in a CC environment, highlighting the innovative solutions that have been introduced in the processing chain we present. They mainly consist in a series of procedures that properly exploit the available GNSS time series with the aim of identifying and filtering out possible residual atmospheric artifacts that may affect the DInSAR measurements. Moreover, significant efforts have been carried out to improve the P-SBAS processing pipeline automation and robustness, which represent crucial issues for interferometric continental scale analysis. Then, a massive experimental analysis is presented. In this case, we exploit: (i) the whole archive of S-1 IWS SAR images acquired over a large portion of Europe, from descending orbits, (ii) the continuous GNSS position time series provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL) available for the investigated area, and (iii) the ONDA platform, one of the Copernicus Data and Information Access Services (DIAS). The achieved results demonstrate the capability of the proposed solution to successfully retrieve the DInSAR time series relevant to such a huge area, opening new scenarios for the analysis and interpretation of these ground deformation measurements.

The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment

We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

The Parallel SBAS-DInSAR Processing Chain for the Generation of National Scale Sentinel-1 Deformation Time-Series

In this work we present a fully parallel and automatic interferometric processing chain, based on the well-known advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the effective and massive generation of Sentinel-1 (S-1) Interferometric Wide Swath (IWS) displacement time-series and corresponding mean deformation velocity maps of very wide areas. The strategy chosen to implement such a parallel processing chain fully benefits from the Terrain Observation with Progressive Scans (TOPS) mode characterizing the IWS acquisitions of the S-1 constellation for systematic monitoring of large land and coastal areas. The TOPS mode consists of a series of bursts that can be considered as separate images to be independently processed. Such a data structure fosters the parallelization of the S-1 IWS interferometric data processing, which can be automatically carried out through the exploitation of both high-performance distributed computing infrastructures and efficient algorithms for tackling the huge data flow provided by the S-1 constellation.To demonstrate the capability of the presented S-1 P-SBAS processing chain to deal with the huge volume of data acquired by the S-1 constellation, we process a very extended dataset acquired over the Italian territory from which we compute the mean velocity maps and associated deformation time-series.

Large areas surface deformation analysis through a cloud computing P-SBAS approach for massive processing of DInSAR time series

We present in this work a methodology for computing surface deformation time series and mean velocity maps of large areas. Our approach relies on the availability of a multi-temporal set of synthetic aperture radar (SAR) data collected from ascending and descending orbits over an area of interest, and also permits us to estimate the vertical and horizontal (East-West) components of the Earth's surface deformation. The adopted methodology is based on an advanced cloud computing implementation of the differential SAR interferometry (DInSAR) Parallel Small Baseline Subset (P-SBAS) processing chain which allows the unsupervised processing of large SAR data volumes, from the raw data (level-0) imagery up to the generation of the corresponding DInSAR time series and maps. The solution presented, which is highly scalable, has been tested on ascending and descending ENVISAT SAR archives comprising approximately 400GB of data, which have been acquired over a large area of southern California (US) that extends over about 90,000km2. Such an input dataset has been processed in parallel by exploiting 280 computing nodes of the Amazon Web Services Cloud environment. The overall processing lasted about 8h and cost approximately $1900 USD. Moreover, to produce the final mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area, we also took advantage of the information available from external GPS measurements that permit us to account for possible regional trends not easily detectable by DInSAR and to refer the P-SBAS measurements to an external geodetic datum.The results presented clearly demonstrate the effectiveness of the proposed approach that paves the way to the extensive use of the available ERS-1/2 and ENVISAT SAR data archives. Furthermore, the proposed methodology can be particularly suitable to deal with the very large data flow provided by the Sentinel-1 constellation, thus permitting the extension of the DInSAR analyses at a nearly global scale.

A Cloud Computing Solution for the Efficient Implementation of the P-SBAS DInSAR Approach

We present an efficient Cloud Computing (CC) implementation of the Parallel Small BAseline Subset (P-SBAS) algorithm, which is an advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for the generation of Earth surface displacement time series through distributed computing infrastructures. The rationale of our approach consists in properly distributing the large data volumes and the processing tasks involved in the P-SBAS chain among the available (virtual and/or physical) computing nodes of the CC infrastructure, so that each one of these elements can concurrently work on data that are physically stored on its own local volume. To do this, both an ad hoc management of the data flow and an appropriate scheduling of the parallel jobs have been also implemented to properly handle the high complexity of the P-SBAS workflow. The proposed solution allows minimizing the overall data transfer and network load, thus improving the P-SBAS efficiency and scalability within the exploited CC environments. The presented P-SBAS implementation has been extensively validated through two experimental analyses, which have been carried out by exploiting the Amazon Web Services (AWS) Elastic Cloud Compute (EC2) resources. The former analysis involves the processing of a large (128 SAR images) COSMO-SkyMed dataset, which has been performed by exploiting up to 64 computing nodes, and is aimed at demonstrating the P-SBAS scalable performances. The latter allows us to show the P-SBAS capability to generate DInSAR results at a regional scale (150 000 km2 in Southern California) in a very short time (about 9 h), by simultaneously processing 18 ENVISAT frames that correspond to a total of 741 SAR images, exploiting in parallel 144 AWS computing nodes. The presented results confirm the effectiveness of the proposed P-SBAS CC solution, which may contribute to further extend the frontiers of the DInSAR investigation at a very large scale.

Automatic and Systematic Sentinel-1 SBAS-DInSAR Processing Chain for Deformation Time-series Generation

Sentinel-1 is the first of a family of satellites designed to provide a data stream for the European environmental monitoring program known as Copernicus. Sentinel-1 constellation has been specifically designed to perform, over land, advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) analyses for the investigation of Earth's surface displacements. In particular, owning to its 6-day revisit time and its innovative acquisition mode, which is referred to as Terrain Observation by Progressive Scans (TOPS) and is fundamental for guaranteeing a global spatial coverage, Sentinel-1 constellation is contributing to the creation of a framework for the exploitation of “Big Data” for Earth Observation (EO) applications.In this paper, we present an efficient and automatic implementation of the Parallel Small BAseline Subset (P-SBAS) DInSAR algorithm, specifically intended for the processing of Sentinel-1 SAR data. The algorithm is able to run on distributed computing infrastructures by effectively exploiting a large number of resources, and allows the generation of ground displacement time-series. The aim of this paper is to show that it is possible to automatically and continuously process, in a short time frame, very large sequences of Sentinel-1 data, thus allowing us to perform advanced interferometric analyses at an unprecedented large scale. In addition, the proposed Sentinel-1 P-SBAS algorithm has also been tested on commercial public cloud computing platforms, such as those provided by the Amazon Web Services.The presented Sentinel-1 P-SBAS processing chain is well suited to build up operational services for the easy and rapid generation of advanced interferometric products, which can be very useful not only for scientific purposes but also for the risk management and the natural hazard monitoring.

An On-Demand Web Tool for the Unsupervised Retrieval of Earth’s Surface Deformation from SAR Data: The P-SBAS Service within the ESA G-POD Environment

This paper presents a web tool for the unsupervised retrieval of Earth’s surface deformation from Synthetic Aperture Radar (SAR) satellite data. The system is based on the implementation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, within the Grid Processing on Demand (G-POD) environment that is a part of the ESA’s Geohazards Exploitation Platform (GEP). The developed on-demand web tool, which is specifically addressed to scientists that are non-expert in DInSAR data processing, permits to set up an efficient on-line P-SBAS processing service to produce surface deformation mean velocity maps and time series in an unsupervised manner. Such results are obtained by exploiting the available huge ERS and ENVISAT SAR data archives; moreover, the implementation of the Sentinel-1 P-SBAS processing chain is in a rather advanced status and first results are already available. Thanks to the adopted strategy to co-locate both DInSAR algorithms and computational resources close to the SAR data archives, as well as the provided capability to easily generate the DInSAR results, the presented web tool may contribute to drastically expand the user community exploiting the DInSAR products and methodologies.

A First Assessment of the P-SBAS DInSAR Algorithm Performances Within a Cloud Computing Environment

We present in this work a first performance assessment of the Parallel Small BAseline Subset (P-SBAS) algorithm, for the generation of Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) deformation maps and time series, which has been migrated to a Cloud Computing (CC) environment. In particular, we investigate the scalable performances of the P-SBAS algorithm by processing a selected ENVISAT ASAR image time series, which we use as a benchmark, and by exploiting the Amazon Web Services (AWS) CC platform. The presented analysis shows a very good match between the theoretical and experimental P-SBAS performances achieved within the CC environment. Moreover, the obtained results demonstrate that the implemented P-SBAS Cloud migration is able to process ENVISAT SAR image time series in short times (less than 7 h) and at low costs (about USD 200). The P-SBAS Cloud scalable performances are also compared to those achieved by exploiting an in-house High Performance Computing (HPC) cluster, showing that nearly no overhead is introduced by the presented Cloud solution. As a further outcome, the performed analysis allows us to identify the major bottlenecks that can hamper the P-SBAS performances within a CC environment, in the perspective of processing very huge SAR data flows such as those coming from the existing COSMO-SkyMed or the upcoming SENTINEL-1 constellation. This work represents a relevant step toward the challenging Earth Observation scenario focused on the joint exploitation of advanced DInSAR techniques and CC environments for the massive processing of Big SAR Data.