Search

This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Persistent Scatterer Interferometry (PSI) is a consolidated tool for detecting and monitoring ground surface displacements. The availability of satellite data with free access policy and high monitoring capabilities (in terms of resolution and acquisition frequency) is increasing. Moreover, the first continental displacement map of Europe will be freely available in quarter one 2022 by the latest Copernicus Service, the European Ground Motion Service (EGMS). The EGMS will provide ground displacement maps, updated every year, delivering valuable information to a wide range of users, such as public or governmental institutions, industry, academia, and citizens. This vast amount of information needs semi-automatic tools and methodologies to derive user-oriented products that can be easily used by land use and urban planning decision-makers, who are often unfamiliar with PSI. This work proposes a semi-automatic procedure to identify damage prone areas in urban environments from wide-area PSI displacement maps. The proposed method identifies the most significant Active Deformation Areas (ADAs) to calculate three products based on the displacement intensity gradient: the Gradient Intensity Map, the Gradient Vectors and Time Series, and the Potential Damage Map. These products allow identifying buildings and urban structures exposed to potential damage, which could be followed by a more detailed building-based vulnerability and risk assessment. The methodology has been applied to an area of the province of Granada (Andalucía, Spain) but it can be applied to any other urban environment where PSI displacement maps are available. To demonstrate the advantages and limitations of the proposed method, results are discussed in five coastal resorts (Cerro Gordo, Punta de la Mona, Marina del Este, Alfa Mar, and Monte de los Almendros), strongly affected by slope movements. The methodology allowed to derive 175 ADAs from about 200,000 measurement points. About 15% of the resulting area has been found to correspond to high or very high gradient intensity class, and 192 out of 633 buildings have been identified to be prone to moderate or high potential damage. A damage prediction test has been realized through Receiver Operating Characteristic (ROC) analysis, based on a damage inventory map derived from field surveys. The results demonstrate the effectiveness of the methodology to localize damaged or potentially damaged buildings, substantially reducing the time of analysis. © 2022

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS. © 2022 by the authors.

Displacement time series (TS) provides temporal and spatial information related to ground deformation. This study aims to investigate temporal behavior of ground deformation TS, including classification of displacement trends and periodicity evaluation, which ease the interpretation of movements. To this end, we propose several modifications to an existing automatic classification workflow of Persistent Scatterers Interferometry (PSI) TS using new tests to classify ground deformations into seven main trends: Stable, Linear, Quadratic, Bilinear, Phase Unwrapping Errors (PUE), Discontinuous with constant and different velocities. We illustrate our approach over 1500 km2 of the Granada region and the metropolitan area of Barcelona, which were monitored using Sentinel-1 images and a PSI technique. This study provided the spatial distribution of different ground movement types and was useful to detect several TS anomalies due to PUE. The proposed approach also identified stable targets, which were wrongly classified as moving scatterers by the existing classification method. A periodicity analysis was finally performed using the Welch’s power spectral density estimator to investigate seasonal and yearly fluctuations. The method was validated using simulated data, where the classified TSs characterized by probable phase unwrapping errors were verified by PSI experts. The overall classification accuracy was 77.8%, indicating that the proposed method has a considerable TS classification potential. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over largescale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based postprocessing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR (DInSAR) methods in particular estimate the spatio-temporal evolution of deformation by incorporating information from multiple SAR images. Moreover, opportunities from the DIn-SAR techniques are accompanied by challenges that affect the final outputs. Resolving the inherent ambiguities of interferometric phases, especially in areas with a high spatio-temporal deformation gradient, represents the main challenge. This brings the necessity of quality indices as important DInSAR data processing tools in achieving ultimate processing outcomes. Often such indices are not provided with the deformation products. In this work, we propose four scores associated with (i) measurement points, (ii) dates of time series, (iii) interferograms and (iv) images involved in the processing. These scores are derived from a redundant set of interferograms and are calculated based on the consistency of the unwrapped interferometric phases in the frame of a least-squares adjustment. The scores reflect the occurrence of phase unwrapping errors and represent valuable input for the analysis and exploitation of the DInSAR results. The proposed tools were tested on 432,311 points, 1795 interferograms and 263 Sentinel-1 single look complex images by employing the small baseline technique in the PSI processing chain, PSIG of the geomatics division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). The results illustrate the importance of the scores—mainly in the interpretation of the DInSAR outputs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

A C-band Low-cost Active Reflector (AR) has been tested in a real experimental campaign aimed at monitoring through multi-temporal InSAR an area threatened by a landslide that occurred in 2019. To monitor and characterize the movement, a network of eight Passive Corner Reflectors and one Active Reflector were installed along a forested slope. A set of 285 interferograms obtained combining 60 Sentinel-1 SAR images were processed to evaluate the stability of the area. The AR, installed in a stable location close to the landslide, was used to provide a reference point in this low coherence area. Despite the high sensitivity of the phase response of such devices to temperature changes, the device operates with a stability of ±2 mm in deformation retrieval, a value acceptable for monitoring purposes, with a moderate range of temperature values. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

The paper presents a multi-source approach tailored for the analysis of ground movements affecting the village of Barberà de la Conca (Tarragona, Catalonia, Spain), where cracks on the ground and damage of different severity to structures and infrastructure was recorded. For this purpose, monitoring of ground displacements performed by topographic survey, geotechnical monitoring and remote sensing techniques (ground-based synthetic aperture radar, GBSAR) are combined with multi-temporal damage surveys and monitoring of cracks (crackmeters) to get an insight into the kinematics of the urban slope. The obtained results highlight the correspondence between the monitoring data and the effects on the exposed facilities induced by ground displacements, which seem to occur predominantly in the horizontal plane with diverging directions (northward and southward) from the main ground fracture crossing the centre of the village. The case study stands as a further contribution to fostering this kind of integrated approaches that via cross-validations can improve data reliability as well as enrich datasets for slope instability recognition and analysis, which are crucial to plan risk mitigation works. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

This study is focused on wide-area deformation monitoring initiatives based on the differential interferometric SAR technique (DInSAR). In particular, it addresses the use of advanced DInSAR (A-DInSAR) techniques, which are based on large sets of synthetic aperture radar (SAR) and Copernicus Sentinel-1 images. Such techniques have undergone a dramatic development in the last twenty years: they are now capable to process big sets of SAR images and can be exploited to realize a wide-area A-DInSAR monitoring. The study describes several initiatives to establish wide-area ground motion services (GMS), both at county- and region-level. In the second part of the study, some of the key technical aspects related to wide-area A-DInSAR monitoring are discussed. Finally, the last part of the study is devoted to the European ground motion service (EGMS), which is part of the Copernicus land monitoring service. It represents the most important wide-area A-DInSAR deformation monitoring system ever developed. The study describes its main characteristics and its main products. The end of the production of the first EGMS baseline product is foreseen for the last quarter of 2021. © 2020 by the authors.

An assessment of the National Aeronautics and Space Administration NASA's Cyclone Global Navigation Satellite System (CyGNSS) mission for biomass studies is presented in this work on rain, coniferous, dry, and moist tropical forests. The main objective is to investigate the capability of Global Navigation Satellite Systems Reflectometry (GNSS-R) for biomass retrieval over dense forest canopies from a space-borne platform. The potential advantage of CyGNSS, as compared to monostatic Synthetic Aperture Radar (SAR) missions, relies on the increasing signal attenuation by the vegetation cover, which gradually reduces the coherent scattering component scoh,0. This term can only be collected in a bistatic radar geometry. This point motivates the study of the relationship between several observables derived from Delay Doppler Maps (DDMs) with Above-Ground Biomass (AGB). This assessment is performed at different elevation angles ?e as a function of Canopy Height (CH). The selected biomass products are obtained from data collected by the Geoscience Laser Altimeter System (GLAS) instrument on-board the Ice, Cloud, and land Elevation Satellite (ICESat-1). An analysis based on the first derivative of the experimentally derived polynomial fitting functions shows that the sensitivity requirements of the Trailing Edge TE and the reflectivity G reduce with increasing biomass up to ~ 350 and ~ 250 ton/ha over the Congo and Amazon rainforests, respectively. The empirical relationship between TE and G with AGB is further evaluated at optimum angular ranges using Soil Moisture Active Passive (SMAP)-derived Vegetation Optical Depth (VOD), and the Polarization Index (PI). Additionally, the potential influence of Soil Moisture Content (SMC) is investigated over forests with low AGB. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Underground mining is one of the human activities with the highest impact in terms of induced ground motion. The excavation of the mining levels creates pillars, rooms and cavities that can evolve in chimney collapses and sinkholes. This is a major threat where the mining activity is carried out in an urban context. Thus, there is a clear need for tools and instruments able to precisely quantify mining-induced deformation. Topographic measurements certainly offer very high spatial accuracy and temporal repeatability, but they lack in spatial distribution of measurement points. In the past decades, Multi-Temporal Satellite Interferometry (MTInSAR) has become one of the most reliable techniques for monitoring ground motion, including mining-induced deformation. Although with well-known limitations when high deformation rates and frequently changing land surfaces are involved, MTInSAR has been exploited to evaluate the surface motion in several mining area worldwide. In this paper, a detailed scale MTInSAR approach was designed to characterize ground deformation in the salt solution mining area of Saline di Volterra (Tuscany Region, central Italy). This mining activity has a relevant environmental impact, depleting the water resource and inducing ground motion; sinkholes are a common consequence. The MTInSAR processing approach is based on the direct integration of interferograms derived from Sentinel-1 images and on the phase splitting between low (LF) and high (HF) frequency components. Phase unwrapping is performed for the LF and HF components on a set of points selected through a “triplets closure” method. The final deformation map is derived by combining again the components to avoid error accumulation and by applying a classical atmospheric phase filtering to remove the remaining low frequency signal. The results obtained reveal the presence of several subsidence bowls, sometimes corresponding to sinkholes formed in the recent past. Very high deformation rates, up to -250 mm/yr, and time series with clear trend changes are registered. In addition, the spatial and temporal distribution of velocities and time series is analyzed, with a focus on the correlation with sinkhole occurrence. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.

This paper presents an assessment of the correlation between CyGNSS-derived global navigation satellite systems reflectometry (GNSS-R) bistatic reflectivity, {\Gamma _{rl}}, and soil moisture active passive (SMAP) derived brightness temperature, T{}_I/2, over land surfaces. This parametric study is performed as a function of soil moisture content (SMC), vegetation opacity \tau , and albedo \omega . Several target areas, classified according to the International Geosphere-Biosphere Program (IGBP) land cover types, are selected to evaluate potential differentiated geophysical effects on 'active' (as many transmitters as navigation satellites are in view) and passive approaches. Although microwave radiometry has potentially a better sensitivity to SMC, the spatial resolution achievable from a spaceborne platform is poor, ~40 km. On the other hand, GNSS-R bistatic coherent radar pixel-size is limited by half of the first Fresnel zone, which provides about ~150 m of spatial resolution (depending on the geometry). The main objective of this 'active'/passive combination is twofold: a) downscaling the SMC, b) complement the information of microwave radiometry with GNSS-R data to improve the accuracy in SMC determination. The Pearson linear correlation coefficient of {\Gamma _{rl}} and T{}_I/2 obtained over Thailand, Argentinian Pampas, and Amazon is ~-0.87, ~-0.7, and ~-0.26, respectively, while the so-called tau-omega model is used to fit the data. Results over croplands are quite promising and deserve special attention since the use of GNSS-R could benefit agricultural and hydrological applications because of: a) the high spatio-temporal sampling properties, b) the high spatial resolution, and c) the potential combination with microwave radiometry to improve the accuracy of the measurements. © 2008-2012 IEEE.

In this paper, persistent scatterer interferometry and Synthetic Aperture Radar (SAR) tomography have been applied to Sentinel-1 data for urban monitoring. The paper analyses the applicability of SAR tomography to Sentinel-1 data, which is not granted, due to the reduced range and azimuth resolutions and the low resolution in elevation. In a first part of the paper, two implementations of the two techniques are described. In the experimental part, the two techniques are used in parallel to process the same Sentinel-1 data over two test areas. An intercomparison of the results from persistent scatterer interferometry and SAR tomography is carried out, comparing the main parameters estimated by the two techniques. Finally, the paper addresses the complementarity of the two techniques, and in particular it assesses the increase of measurement density that can be achieved by adding the double scatterers from SAR tomography to the persistent scatterer interferometry measurements. © 2018 by the authors.

In recent years, the measurement of dam displacements has benefited from a great improvement of existing technology, which has allowed a higher degree of automation. This has led to data collection with an improved temporal and spatial resolution. Robotic total stations and GNSS (Global Navigation Satellite System) techniques, often in an integrated manner, may provide efficient solutions for measuring 3D displacements on precise locations on the outer surfaces of dams. On the other hand, remote-sensing techniques, such as terrestrial laser scanning, ground-based SAR (synthetic aperture radar) and satellite differential interferometric SAR offer the chance to extend the observed region to a large portion of a structure and its surrounding areas, integrating the information that is usually provided in a limited number of in-situ control points. The design and implementation of integrated monitoring systems have been revealed as a strategic solution to analyze different situations in a spatial and temporal context. Research devoted to the optimization of data processing tools has evolved with the aim of improving the accuracy and reliability of the measured deformations. The analysis of the observed data for the interpretation and prediction of dam deformations under external loads has been largely investigated on the basis of purely statistical or deterministic methods. The latter may integrate observation from geodetic, remote-sensing and geotechnical/structural sensors with mechanical models of the dam structure. In this paper, a review of the available technologies for dam deformation monitoring is provided, including those sensors that are already applied in routinary operations and some experimental solutions. The aim was to support people who are working in this field to have a complete view of existing solutions, as well as to understand future directions and trends. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Soil creep is characterised by slow displacement, with depths of a few meters and loosely defined limits. Buildings and infrastructure located on slopes affected by such landslides may suffer significant damages if their foundations are poorly dimensioned. The presence of soil creep in urban areas makes it necessary to develop landslide activity maps, derive hazard maps, and implement risk management plans. Even though both geological and geomorphological analyses can provide basic information, it is often necessary to use additional techniques to obtain information about ground displacements. This paper proposes a method to derive a soil creep activity map using a multi-approach analysis based on geological, geomorphological, and persistent scatterer interferometry (PSI) data. PSI is a satellite-based technique to estimate land displacement velocity. The work described in this paper was carried out in the town of El Papiol, in the metropolitan area of Barcelona (Spain). This is an urban area that has been heavily affected by soil creep over the past decades. The results achieved show that PSI data substantially improve the information provided by the geological and geomorphological analyses and make it possible to accurately define the landslide area and estimate its activity. © 2018, The Author(s).