Deformation measurement and monitoring with Ground-Based SAR

The Ground-Based Synthetic Aperture Radar (GB-SAR) is a relatively new technique, which in the last ten years has gained interest as deformation measurement and monitoring tool. The GB-SAR technique is based on an imaging radar-based sensor, which o ers high sensitivity to small displacements, in the region of sub-millimetres to millimetres, long-range measurements, which can work up to some kilometres, and massive deformation measurement capability. These features confer to the GB-SAR technique interesting advantages with respect to other point-wise deformation measurement techniques. The process of estimating deformation from the GB-SAR data is not straightforward: it requires complex data processing and analysis tools. This dissertation is focused on these tools, covering the whole deformation estimation process. This thesis collects the main research results achieved on this topic during my work at the Active Remote Sensing Unit of the Institute of Geomatics. Two di erent approaches for measuring deformation with GB-SAR data are described and discussed. The irst one is the interferometric approach, based on the exploitation of the phase component of the GB-SAR data, which is the commonly used GB-SAR method. The second one is a non-interferometric approach, which exploits the amplitude component of the GB-SAR data, o ering an interesting alternative way to exploit the GB-SAR data. This dissertation has two main objectives. The first one is presenting, step by step, a complete interferometric GB-SAR procedure for deformation measuring and monitoring. The second one is presenting two new algorithms, which represent the most innovative part of this thesis. The first algorithm faces the phase unwrapping problem, providing an automatic solution for detecting and correcting unwrapping errors, which is called 2+1D phase unwrapping. The second algorithm is the base of the above mentioned non- interferometric approach, which overcomes some of the most critical limitation of GB-SAR interferometry, at the expense of getting less precise deformation estimates. The dissertation is divided in 6 chapters. The first one is the introduction, while the second one provides an overview of GB-SAR interferometry, introducing the main aspects that are the basics of the subsequent chapters. Chapter 3 describes a complete GB-SAR processing chain. Chapters 4 and 5 contain the most original part of the dissertation, i.e. the 2D+1 phase unwrapping algorithm, and the non-interferometric approach. Finally, in Chapter 6 the conclusions are discussed and further research is proposed.El radar terrestre d’obertura sintètica (GB-SAR) és una tècnica relativament nova que, en els últims deu anys, ha guanyat interès com a eina per a mesurar i monitorar deformacions. La tècnica GB-SAR es basa en un sistema radar amb capacitat per proporcionar imatges, que ofereix una alta sensibilitat a petits desplaçaments, d’ordre mil·limètric o submil·limètric, que és capaç de mesurar a llargues distàncies (alguns km) i que té una alta capacitat per fer mesures massives. Aquestes característiques donen a la tècnica interessants avantatges respecte a altres tècniques clàssiques de mesura de deformacions, típicament basades en mesures puntuals. Derivar mesures de deformació a partir de dades GB-SAR no és un procés senzill, ja que requereix uns procediments complexos de processat i anàlisi de dades. Aquesta tesi es centra en aquests processos. Aquesta tesi recull alguns dels resultats més destacats de la investigació que he desenvolupat sobre aquest tema a la unitat de Teledetecció Activa de l'Institut de Geomàtica. Al llarg del document es descriuen dues aproximacions diferents per mesurar deformacions amb GB-SAR. Una es basa en la explotació de la tècnica de la interferometria, és a dir explotant la component de la fase de les imatges GB-SAR: és la tècnica GB-SAR usada habitualment. La segona, anomenada tècnica no-interferomètrica, es basa en la component de l’amplitud de les dades GB-SAR i ofereix una interessant alternativa a la primera. La tesi acompleix dos objectius principals. En primer lloc presenta un procediment complet per la mesura i monitoratge de deformacions mitjançant interferometria GB-SAR. En segon lloc, descriu dos nous algorismes que resolen problemes específics de la interferometria clàssica aplicada al GB-SAR i que representen la part més innovadora d’aquesta tesi. El primer algorisme aborda un dels problemes oberts de la interferometria, el phase unwrapping, proposant un mètode automàtic per detectar-ne i corregir-ne els errors. El segon algorisme proposa un nou mètode per a l'explotació de les dades GB-SAR per mesurar deformacions sense utilitzar la interferometria. La estructura de la tesi consisteix en sis capítols. Després de la introducció, el Capítol 2 proporciona una visió general de la interferometria GB-SAR, introduint els conceptes principals utilitzats en la tesi. En el tercer capítol es descriu una cadena de processament basada en GB-SAR interferomètric. Els capítols quart i cinquè contenen la part més original de la tesi: l'algorisme de phase unwrapping i el mètode no-interferomètric per la mesura de deformacions. Finalment, es discuteixen les conclusions principals i es proposen futures línies d’investigació

Pyrenees deformation monitoring using Sentinel-1 data and the Persistent Scatterer Interferometry technique

This study focuses on deformation mapping and monitoring using Sentinel-1 radar data and the DInSAR (Differential Interferometric Synthetic Aperture Radar) technique. In particular, a Persistent Scatterer Interferometry technique was used over 15000 ¿¿¿¿2 of the Pyrenees, located in Spain, Andorra and France. The main goal is to monitor deformations over a vegetated and mountainous region by exploiting the wide-area coverage, the high coherence and temporal sampling provided by the Sentinel-1satellites. All possible interferograms were generated using 150 images covering five years. The velocity map of the entire region is presented considering the characteristics of the study area, including vegetation and severe steep mountains. Two areas of deformation are shown, which are characterized by velocity values ranging between -20 to -40 mm/year.The work of S.M. Mirmazloumi has been funded by the Spanish State Research Agency, through a grant for a pre doctorate contract (Ref: PRE2018-083394). This work has been co-funded by the European Regional Development Fund through the Interreg V-A Spain, France and Andorra (POCTEFA 2014-2020), project EFA295/19Peer ReviewedPostprint (published version

Spatio-temporal quality indicators for differential interferometric Synthetic Aperture Radar data

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 DInSAR 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.This research was partially funded by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya—through a grant for the recruitment of early-stage research staff (Ref: 2021FI_B2 00186).Peer ReviewedPostprint (published version

Ground movement classification using statistical tests over persistent scatterer interferometry time series

This study proposes modifications to an existing automatic classification method of Persistent Scatterers Interferometry (PSI) time series (TS) and a new procedure to classify ground movements into seven classes. We also represent a technique to detect TSs affected by phase unwrapping errors and a reclassification part to detect stable points, which are incorrectly classified as moving points using the original method. Around 60 km2 of Catalunya were classified using Sentinel-1 images and a PSI technique. The proposed method classified 78359 PS TS. This study provided the spatial distribution of ground movement classes and detected several time series anomalies.Peer ReviewedPostprint (published version

Interferometric SAR deformation timeseries: a quality index

Estimating unknown absolute phase from a wrapped observation is a challenging and ill-posed problem that possibly leads to misinterpretation of interferometric SAR (InSAR) deformation results. In this study, we introduce a quality index to cluster post-phase unwrapping multi-master InSAR timeseries outputs based on the estimated phase residuals and redundancy of network of interferograms. The index is supposed to indicate the reliability of a timeseries, including the identification of persistent scatterers (PSs) possibly affected by phase unwrapping jumps. The algorithm was tested on two Sentinel-1 interferometric datasets with 622,991 and 95,398 PSs, generated from the PSI processing chain PSIG of the geomatics division of CTTC. Promising result have been achieved-especially in identifying erroneous PSs with phase unwrapping jumps. Along with existing temporal phase consistency checking algorithms, the approach could provide rich information toward a better interpretation of the deformation timeseries results.This work has been funded by AGAUR, Generalitat de Catalunya, in the framework of Resolution EMC/ 2459/2019, FI-2020.Peer ReviewedPostprint (published version

Supervised machine learning algorithms for ground motion time series classification from InSAR data

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the genera- tion of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deforma- tion 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.This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/ AEI/10.13039/501100011033. Additionally, it has been supported by the European Regional Devel- opment Fund (ERDF) through the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme. Additionally, this work has been co-funded by the European Union Civil Protection through the H2020 project RASTOOL (UCPM-2021-PP-101048474).Peer ReviewedPostprint (published version

Sentinel-1 A-DInSAR approaches to map and monitor ground displacements

Persistent scatterer interferometry (PSI) is a group of advanced interferometric synthetic aperture radar (SAR) techniques used to measure and monitor terrain deformation. Sentinel-1 has improved the data acquisition throughout and, compared to previous sensors, increased considerably the differential interferometric SAR (DInSAR) and PSI deformation monitoring potential. The low density of persistent scatterer (PS) in non-urban areas is a critical issue in DInSAR and has inspired the development of alternative approaches and refinement of the PS chains. This paper proposes two different and complementary data-driven procedures to obtain terrain deformation maps. These approaches aim to exploit Sentinel-1 highly coherent interferograms and their short revisit time. The first approach, called direct integration (DI), aims at providing a very fast and straightforward approach to screen-wide areas and easily detects active areas. This approach fully exploits the coherent interferograms from consecutive images provided by Sentinel-1, resulting in a very high sampling density. However, it lacks robustness and its usability lays on the operator experience. The second method, called persistent scatterer interferometry geomatics (PSIG) short temporal baseline, provides a constrained application of the PSIG chain, the CTTC approach to the PSI. It uses short temporal baseline interferograms and does not assume any deformation model for point selection. It is also quite a straightforward approach, which improves the performances of the standard PSIG approach, increasing the PS density and providing robust measurements. The effectiveness of the approaches is illustrated through analyses performed on different test sites.This work has been partially funded by AGAUR, Generalitat de Catalunya, through a grant for the recruitment of early-stage research staff (Ref: FI_B 00741) and through the Consolidated Research Group RSE, “Remote Sensing” (Ref: 2017-SGR-00729). It has been also partially funded by the Spanish Ministry of Economy and Competitiveness through the DEMOS project “Deformation monitoring using Sentinel-1 data” (Ref: CGL2017-83704-P) and by AGAUR.Peer ReviewedPostprint (published version

Geotechnics for rockfall assessment in the volcanic island of Gran Canaria (Canary Islands, Spain)

The island of Gran Canaria (Canary Islands, Spain) is characterized by a large variability of volcanic rocks reflecting its volcanic evolution. The geological map provided by Geological Survey of Spain at 1:25.000 scale shows more than 109 different lithologies and it is too complex for environmental and engineering purposes. This work presents a simplified geotechnical map with a small number of classes grouping up units with similar geotechnical behaviours. The lithologies were grouped using about 350 rock samples, collected in the seven major islands of the Archipelago. The geotechnical map was used to model rockfall hazard in the entire island of Gran Canaria, where rockfalls are an important threat. The rockfall map was validated with 128 rockfall events along the GC-200 road, located in the NW sector of Gran Canaria. About 96% of the events occurred along sections of the road where the number of expected trajectories is high or moderate.This work was carried out in the framework of two projects funded by the European Commission, Directorate-General Humanitarian Aid and Civil Protection (ECHO): SAFETY (Sentinel-1 for geohazard prevention and forecasting. Ref. ECHO/SUB/2015/718679/Prev02) and U-GEOHAZ (Geohazard Impact Assessment for Urban Areas. Grant Agreement No. 783169). This work has been partly funded by the University of Alicante in the framework of Quality Improvement Grant of PhD Program in Materials, Structures and Soil Engineering: Sustainable Construction, Salvador de Madariaga Mobility Program from the Spanish Ministry of Science (PRX18/00020) and the Industrial PhD Project GEODRON (IND2017/AMB-7789). We also appreciate the contribution of the MACASTAB project (Ref.: MAC/3.5b/027). The laboratory tests were carried out in the Laboratories of Building and Public Works from the Canarian Government. The methodology is also developed in the framework of the RISKCOAST project (Ref: SOE3/P4/E0868) funded by the European Regional Development Fund - Interreg programme (3rd call for proposals)

Evaluation of the SBAS InSAR Service of the European Space Agency’s Geohazard Exploitation Platform (GEP)

The analysis of remote sensing data to assess geohazards is being improved by web-based platforms and collaborative projects, such as the Geohazard Exploitation Platform (GEP) of the European Space Agency (ESA). This paper presents the evaluation of a surface velocity map that is generated by this platform. The map was produced through an unsupervised Multi-temporal InSAR (MTI) analysis applying the Parallel-SBAS (P-SBAS) algorithm to 25 ENVISAT satellite images from the South of Spain that were acquired between 2003 and 2008. This analysis was carried out using a service implemented in the GEP called “SBAS InSAR”. Thanks to the map that was generated by the SBAS InSAR service, we identified processes not documented so far; provided new monitoring data in places affected by known ground instabilities; defined the area affected by these instabilities; and, studied a case where GEP could have been able to help in the forecast of a slope movement reactivation. This amply demonstrates the reliability and usefulness of the GEP, and shows how web-based platforms may enhance the capacity to identify, monitor, and assess hazards that are associated to geological processes.Spanish “Juan de la Cierva” grants support part of the work of Jorge P. Galve. The expenses related to the hired researcher contract of Jorge P. Galve and the field surveying were funded by the project CGL2015-67130-C2-1-R (FEDER and Spanish Ministry of Economy and Competitiveness)