Satellite Interferometry Data Interpretation and Exploitation: Case Studies from the European Ground Motion Service (EGMS) [1 ed.] 0443133972, 9780443133978

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SATELLITE INTERFEROMETRY DATA INTERPRETATION AND EXPLOITATION

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SATELLITE INTERFEROMETRY DATA INTERPRETATION AND EXPLOITATION CASE STUDIES FROM THE EUROPEAN GROUND MOTION SERVICE (EGMS)

MICHELE CROSETTO Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Geomatics Research Unit, Castelldefels, Barcelona, Spain

LORENZO SOLARI Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Geomatics Research Unit, Castelldefels, Barcelona, Spain European Environment Agency, København, Denmark

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-443-13397-8 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Candice Janco Acquisitions Editor: Peter Llewellyn Editorial Project Manager: Teddy Lewis Production Project Manager: Sruthi Satheesh Cover Designer: Miles Hitchen Typeset by MPS Limited, Chennai, India

Dedication This book is dedicated to Eva, Martı´, Jordi, and Irene.

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Contents

Acknowledgments

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1. Introduction

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1.1 Motivation 1.2 Target readers 1.3 Content of the book Disclaimer Reference

1 2 2 5 5

2. Synthetic aperture radar interferometry

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2.1 Interferometric synthetic aperture radar 2.2 Interferometric synthetic aperture radar 2.3 A deformation estimation procedure 2.4 Interferometric synthetic aperture radar 2.5 Interferometric synthetic aperture radar 2.6 Interferometric synthetic aperture radar References

basics measurement points products pros and cons applications

7 9 10 14 18 19 20

3. InSAR technical aspects

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3.1 SAR data acquisition 3.2 Nature of the measurement points 3.3 Measurement point density 3.4 SAR geometric effects 3.5 LOS measurement 3.6 Reference point 3.7 Nonlinear and fast deformation 3.8 Deformation time series 3.9 Time series and thermal expansion 3.10 Measurement point positioning 3.11 Quality of the InSAR estimates 3.12 InSAR validation results 3.13 Artificial reflectors 3.14 InSAR versus in situ measurements 3.15 Examples of data analysis tools 3.16 Open-source InSAR software References

27 29 31 32 33 36 37 41 43 44 45 47 49 51 53 57 58

vii

viii

Contents

4. European Ground Motion Service

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4.1 Introduction 4.2 European Ground Motion 4.3 European Ground Motion 4.4 European Ground Motion 4.5 European Ground Motion 4.6 European Ground Motion 4.7 European Ground Motion 4.8 European Ground Motion References

63 66 69 73 76 77 79 84 86

Service Service Service Service Service Service Service

Basic product Calibrated product Ortho product validation applicability Explorer dissemination

5. Subsidence and uplift

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5.1 Subsidence related to groundwater exploitation in the Firenze
Prato
Pistoia basin 5.2 Mining subsidence in the Upper Silesian Coal Basin 5.3 Tips and tricks to interpret interferometric data in mining areas 5.4 Other applications of InSAR for subsidence detection References

91 104 117 119 127

6. Landslides 6.1 Landslide state of activity evaluation on the Granada coast (Spain) 6.2 Landslide mapping in Troms og Finnmark county (Norway) 6.3 Some considerations on the use of EGMS data for landslide studies References

7. Volcanoes and earthquakes 7.1 Volcanoes 7.2 Earthquakes References

8. Urban area: infrastructure, buildings, and cultural heritage 8.1 Rules Dam and Reservoir 8.2 Nice Coˆte d’Azur Airport 8.3 Blackfriars Railway Bridge, London (United Kingdom) 8.4 Railway in Finland 8.5 Levees in Bregenz (Austria) 8.6 Port of Antwerp (Belgium) 8.7 Thyborøn port (Denmark) 8.8 Historic center of Sighi¸soara (Romania) 8.9 Solnitsata-Provadia archeological site (Bulgaria) References

133 136 145 159 165

169 169 184 190

195 197 202 204 208 211 214 217 220 223 225

Contents

9. Conclusions

ix 231

9.1 List of main interferometric SAR limitations 9.2 Some considerations about EGMS and its products 9.3 Lessons learnt from the case studies

232 233 234

Index

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Acknowledgments We acknowledge the contribution of our colleagues and friends Oriol Monserrat, Bruno Crippa, Marı´a Cuevas, Anna Barra, and Riccardo Palama` in reviewing this book. We want to thank the Copernicus Land Monitoring Service and the European Environment Agency for making the EGMS products available to users. We are grateful to Henrik Steen Andersen for his support. The preparation of this book was part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/AEI/10.13039/501100011033.

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C H A P T E R

1 Introduction

1.1 Motivation This book is about interferometric synthetic aperture radar (InSAR), a remote-sensing technique to retrieve information from multiple satellite radar images acquired over the same area, yielding millimeter-scale ground deformation measurements from space. In the book, InSAR encompasses the terms differential InSAR (DInSAR), advanced DInSAR, time series InSAR, multitemporal InSAR, persistent scatterer interferometry, etc. In addition, the term deformation is used as a synonym of displacement. To be more precise, the book is focused on the interpretation and exploitation of the data obtained from InSAR. What is the main driver behind this book? First, InSAR is a clear example of technology push. Since its first description (Gabriel et al., 1989), the technique has undergone intense research and development that has produced advanced data processing and analysis tools. At the same time, several space agencies, especially the European Space Agency, have launched and operated missions carrying SAR sensors, guaranteeing the constant availability of InSAR primary data. In parallel to this, the computational capability has grown significantly. All these factors make InSAR mature enough to deliver deformation measurement on which to build up monitoring services that cover wide areas. The most emblematic of such services is the European Ground Motion Service (EGMS), which is the protagonist of this book. As it will be described later, the service opens a wide range of new applications based on EGMS products. It is worth noting that these products are solely InSAR deformation data, that is, they do not provide any interpretation of the causes or effects of the motion observed. In other words, they act as the starting point for investigations into the underlying causes of movement. This involves added-value activities based on such products, which in turn require correct InSAR data interpretation and exploitation. This book aims to contribute to this effort.

Satellite Interferometry Data Interpretation and Exploitation DOI: https://doi.org/10.1016/B978-0-443-13397-8.00004-2

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1. Introduction

Therefore this text will attempt to meet these objectives: • Understand what InSAR and EGMS offer. • Realize their potential and limitations. • Illustrate case studies based on the EGMS, promote data interpretation and exploitation, and ease the development of new applications.

1.2 Target readers The potential audience is broad, encompassing those who are connected to known InSAR applications, as well as those related to new potential applications of InSAR and EGMS. For this reason, the book does not require any specific a priori knowledge or technical background. In the following section, we list some of the most relevant types of readers. The first group would be the scientists and technicians working in the field of deformation monitoring of land, structures, and infrastructures. The second includes a wide spectrum of civil engineers, structural engineers, architects, and technicians working in the management of buildings, cultural heritage, linear infrastructures (roads, highways, railways, canals, dykes, levees, pipelines, etc.), bridges and viaducts, dams, airports and ports, industrial installations, etc. A third class includes technicians involved in the planning and management of major construction works, for example, tunnels and large excavations. A fourth and still significant group comprises the scientists and technicians working with phenomena that cause subsidence and uplift: hydrogeologists, people involved in gas and hydrocarbon extraction, mining engineers, geotechnicians, geologists, and geoscientists. A fifth class would be made up of professionals involved in the detection and monitoring of landslides and unstable slopes: geotechnicians, engineering geologists, geologists, geoscientists, etc. A sixth area covers people involved in the insurance industry, where the motion of buildings and industrial assets matters a great deal. The seventh group consists of scientists from different fields of geophysics, like tectonics, crustal deformation, vulcanology, glaciology, etc. Last, an open and potentially wide-ranging class encompasses anyone working with any other application that is directly or indirectly concerned with the deformation of land, structures, infrastructures, assets, etc.

1.3 Content of the book The book is organized as follows. It includes two main parts. The first part introduces the key concepts related to InSAR and EGMS, while the second part discusses several case studies based on the EGMS products.

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More in detail, Section 2.1 starts with the InSAR basics and the main InSAR observation equation. It then describes the InSAR measurement points, which are classified into two families: the point-like and the distributed scatterers. Then, Section 2.3 explains how the deformation is estimated with InSAR. This section refers to a general approach that is related to the experience of the authors, which includes input data, image coregistration, interferogram generation, selection of the measurement points, phase unwrapping, atmospheric component estimation, densification of the measurement points, and geocoding. Section 2.4 outlines the two main InSAR products: the deformation velocity and the deformation time series. Section 2.5 describes the main advantages and disadvantages of the InSAR technique. Finally, Section 2.6 lists some of the most important InSAR applications. Chapter 3 discusses the most important InSAR technical aspects. These are needed to perform a correct interpretation and exploitation of the InSAR products. The chapter starts with acquisition of the SAR data, and then it discusses the nature of the measurement points, and the density of such points. Section 3.4 describes the SAR geometric effects, while Section 3.5 discusses the implication of the line-of-sight InSAR measurement. Section 3.6 describes the relative nature of the InSAR results, and the issue related to the selection of the reference point. Section 3.7 outlines the characteristics of the InSAR results in the presence of nonlinear and fast deformation. Section 3.8 discusses the content of the deformation time series, while Section 3.9 describes the thermal expansion component of such time series. Section 3.10 reports the issue of positioning of the InSAR measurement points (geolocation). Section 3.11 details the quality of the InSAR estimates, and Section 3.12 addresses the validation of the InSAR results. Section 3.13 deals with the use of artificial reflectors, while Section 3.14 discusses the comparison of InSAR results and data coming from in situ measurements. Section 3.15 introduces some important postprocessing tools to analyze the InSAR results. This chapter ends with a description of available open-source InSAR software. Chapter 4 is entirely devoted to the EGMS. It starts with the main features of the service. It then discusses in detail the three main EGMS products, that is, Basic, Calibrated, and Ortho. Section 4.2 describes the Basic product, its main characteristics, and the key issue of the spatial and temporal reference for the deformations. Section 4.3 reports on the Calibrated product, which is considered the star product of the Service. The section introduces its main characteristics and outlines the calibration procedure based on InSAR and Global Navigation Satellite System (GNSS) data. Section 4.4 describes the Ortho product, its features, and the estimation of the deformation components. Section 4.5 outlines the EGMS validation activities, while Section 4.6 discusses the applicability

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1. Introduction

of the EGMS results. Section 4.7 introduces the EGMS explorer, which is the platform to view and distribute the EGMS products. It includes a WebGIS and an interface to search and download the EGMS products. Finally, the chapter finalizes by addressing the EGMS dissemination. Chapter 5, which is the first chapter of the book dedicated to real data examples, discusses some case studies related to subsidence and uplift. Section 5.1 analyzes the subsidence related to groundwater exploitation in the Firenze
Prato
Pistoia basin (Italy). It reports the deformation studied with old SAR imagery (ERS and Envisat) and complements this with the results from the EGMS. It analyzes in depth some areas that are particularly relevant for the high deformation rates and the temporal evolution of the motion. Section 5.2 considers the mining subsidence in the Upper Silesian Coal Basin (Poland and Czech Republic). It includes the discussion of deformations measured with old SAR imagery and with EGMS. It performs the analysis focused on specific areas of interest. Section 5.3 provides tips and tricks to interpret interferometric data in mining areas. Finally, Section 5.4 treats a miscellaneous of subsidence cases, which include gas extraction, an airport area, other types of mining activity, and a geothermal field. Chapter 6 is devoted to landslides. Section 6.1 analyzes a case of evaluation of a landslide state of activity in the Granada coast (Spain). Section 6.2 considers landslide mapping in the Troms og Finnmark county (Norway). The analysis discusses in depth different deformation areas, which include some fjords. This section specifically addresses the potential limitation in the usage of calibrated data in areas with strong GNSS signal. The chapter finalizes with a discussion on the use of EGMS data for landslide studies. Chapter 7 presents some examples of EGMS data in the context of volcanoes and earthquakes. Section 7.1 is devoted to volcanoes. It describes in detail the emblematic case of Mount Etna (Italy), the largest active onshore volcano in Europe. Then it considers the Campi Flegrei (Italy), a peculiar and unique active volcanic system in a densely urbanized area. Section 7.2 is focused on earthquakes. It describes the 2016 Central Italy seismic sequence and shows how InSAR can measure the coseismic deformation of an earthquake and how major earthquakes impact a time series of deformation. Chapter 8 aims at demonstrating the usage and drawbacks of EGMS products for urban area investigation, with a focus on infrastructure, groups of buildings, and cultural heritage. This chapter only includes local-scale examples. It starts in Section 8.1 with the dam and reservoir of Rules (Spain). Section 8.2 is focused on the airport of Nice Coˆte d’Azur (France). Section 8.3 is devoted to the railway bridge of Blackfriars, London (United Kingdom), complemented by a railway case study in Finland. Section 8.5 analyzes the levees of the Rhine River

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in Bregenz (Austria). Section 8.6 considers the Port of Antwerp (Belgium), while the following section is focused on the fishing town of Thyborøn (Denmark). Section 8.8 shows an example of cultural heritage, the UNESCO world heritage of the historic city center of Sighi¸soara (Romania). The chapter concludes with a second example of cultural heritage, which considers the archeological site of Solnitsata-Provadia (Bulgaria). The book finalizes with the conclusions that sum up the book and provide a list of lesson learnt and take-home messages from the EGMS use cases.

Disclaimer The views expressed in book are solely those of the authors and its content does not necessarily represent the views or position of the European Environment Agency.

Reference Gabriel, A.K., Goldstein, R.M., Zebker, H.A., 1989. Mapping small elevation changes over large areas: differential radar interferometry. Journal of Geophysical Research: Solid Earth 94 (B7), 9183
9191.

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C H A P T E R

2 Synthetic aperture radar interferometry The goal of this chapter is to introduce the basic concepts of InSAR. Although the Sentinel-1 sensors are mentioned in several sections, most of the concepts in this chapter are valid for any type of SAR sensor.

2.1 Interferometric synthetic aperture radar basics As stated in Chapter 1, interferometric SAR (InSAR) provides deformation measurements from a set of SAR images. How is this done? A SAR is an active radar sensor, which emits microwave signal and records the signal backscattered by the illuminated area. A complex SAR image contains several million picture elements (pixels), each of which contains two values. The first is the amplitude, which is related to the electromagnetic energy backscattered toward the radar by the given pixel footprint on the ground. The second is the phase φM , which is related to the distance between the sensor M and the same pixel footprint P along the radar line-of-sight (LOS): φM 5 φgeom2M 1 φscatt2M 5

 

4 π MP 1 φscatt2M λ

(2.1)

where MP is the sensor-to-footprint distance, λ is the radar wavelength, and φscatt2M is a phase component introduced during the interaction between the microwaves and the footprint P. Let us assume that the footprint P moves from P to P0 (see Fig. 2.1). SAR interferometry requires at least a second acquisition of the same scene. Let us assume, the footprint P0 is observed from a second viewpoint S: φS 5 φgeom2S 1 φscatt2S 5

Satellite Interferometry Data Interpretation and Exploitation DOI: https://doi.org/10.1016/B978-0-443-13397-8.00008-X

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4 π SP0 1 φscatt2S λ

(2.2)

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FIGURE 2.1 Scheme of the DInSAR deformation measurement. DInSAR, Differential InSAR.

The phase difference, φS 2 φM ; which is called interferometric phase, is given by: ΔφInt 5 φS 2 φM 5

SP0 2 MP λ 4 π



1 φscatt2S 2 φscatt2M

(2.3)

Eq. (2.3) can be written as: ΔφInt 5 φS 2 φM 5

SP 2 MP λ



1

SP0 2 SP λ 4 π



4 π

1 φscatt2S 2 φscatt2M

(2.4)

The first term is φTopo , which depends on the topography of the observed scene. The second term is the deformation phase component φDefo , where φDefo 5 4 λ π defo. Let us assume that the last two terms cancel each other out. The component φTopo can be simulated using a digital elevation model (DEM) of the observed scene, obtaining φTopo sim . This can then be subtracted from the interferometric phase, obtaining the socalled differential interferometric phase:



ΔφD2Int 5 ΔφInt 2 φTopo

sim

5 φDefo

(2.5)

According to this equation, the differential interferometric phase can be directly exploited to estimate the deformation PP0 . This is a simplified equation. In fact, a more comprehensive equation includes: ΔφD2Int 5 φDefo 1 φTopo

res

1 φAtm

S

2 φAtm

M

 

1 φNoise 1 2 k π

(2.6)

where φTopo res is the residual topographic component due to error in the computation of φTopo sim , φAtm is the atmospheric phase component for M and S, due to the propagation of microwaves through ionosphere and

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troposphere, and φNoise is the phase noise. In Eq. (2.6), φAtm includes the phase component due to orbital error of each image. The last term is due to the ambiguous nature of the observed phases, that is, the fact that they are bounded in the range (-π, π]. k is an integer value called phase ambiguity. All the components are measured in the radar LOS, which comprises the imaginary line that connects the sensor and the footprint on the ground. Eq. (2.6) is the main InSAR observation equation.1 Starting from the SAR images, ΔφD2Int for each pixel is obtained. From this observed value, the estimation of φDefo is derived. To do so, this component must be separated from the others. Estimating deformations is not a straightforward task. It can only be resolved by making assumptions and deploying appropriate estimation procedures. This is a relevant factor that must be considered when exploiting any InSAR result. The estimation procedures are discussed in Section 2.3.

2.2 Interferometric synthetic aperture radar measurement points Let us call measurement point (MP) a pixel where Eq. (2.6) can be resolved to allow φDefo to be estimated. A SAR system performs a regular and dense sampling of the observed scene.2 However, the MPs are usually irregular and much less dense than the original SAR images. In other words, not all the pixels of a SAR image correspond to a measurement point. This is an important aspect. Let us work through an example to illustrate this property: a Sentinel-1 image has a pixel footprint of approximately 14 by 4 m: this equals 17860 pixels/km2. A deformation map can typically have 5000
8000 MPs in urban areas, and fewer than 1000 MPs in agricultural areas (Larsen et al., 2020). Why is there such a significant reduction in MP density? The reason can be found in the term φNoise of Eq. (2.6). This component is the main source of ambiguities of the phase unwrapping operation, thus the φDefo can only be estimated if φNoise is small enough. What are the typical InSAR MPs? They belong to two main families of pixels. All of them have a constant component φscatt over time, hence providing a coherent response, that is, they are coherent targets. • The first family is given by the point-like scatterers (PS), where the response to the radar wavelengths is dominated by a strong reflecting object located within the pixel footprint, that is constant 1 Note that even though Eq. (2.6) refers to differential InSAR, in this work, the term InSAR is used. 2 This is true in the geometry of the SAR images, while it is not for geometry on the ground, see Section 3.4.

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over time (Ferretti et al., 2000, 2001). The same PS acronym is used to refer to such scatterers as permanent or persistent scatterers, emphasizing their constant response over time. Typical examples of PS are poles, antennas, fences, metallic objects in general, or objects with sharp edges, such as parts of buildings and man-made structures, rock outcrops, etc. These objects typically make good MPs. • The second family is made up of the so-called distributed scatterers (DS). They maintain a constant response over time, which is due to different small scattering objects distributed within the pixel footprint, without the presence of a dominant scatterer (Ferretti et al., 2011). The response of DS is weaker than that of PS; however, their information content can be improved by spatially averaging neighboring pixels that show similar properties. DS can be found in bare soil, homogeneous ground, debris, and desert areas. Many InSAR approaches only exploit PS. However, the most advanced techniques can exploit both PS and DS. It is worth noting that adding DS to PS offers an improvement of the MP density, which depends on the land cover. For instance, this improvement is rather modest in urban areas, but it can be remarkable in different types of nonurban areas.

2.3 A deformation estimation procedure How is deformation estimated with InSAR? There is not a straightforward approach to doing it. In fact, during the last two decades, there has been intense research and development in this field, which has yielded several InSAR approaches. To cite just a few: Ferretti et al. (2000, 2001), Berardino et al. (2002), Mora et al. (2003), Crosetto et al. (2005), Costantini et al. (2008), Hooper (2008), Ferretti et al. (2011), Perissin and Wang (2011), and Devanthe´ry et al. (2014). The different approaches are not discussed in this book. Rather, a general approach is discussed that is related to the experience of the authors. A similar approach that includes the main characteristics of the InSAR techniques used in the production of the European Ground Motion Service (EGMS) is described in Ferretti et al. (2021). The flow chart of the general InSAR approach is shown in Fig. 2.2. Each step is discussed next. • Input data. The InSAR procedure requires three types of input data: (1) a stack of complex SAR images covering the same area, (2) the precise orbits3 corresponding to each SAR image, and (3) a DEM of the covered area. 3 The Precise Orbit Determination is the process of accurately tracking the position and velocity of a satellite in orbit.

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FIGURE 2.2 Flow chart of a general InSAR approach. InSAR, Interferometric SAR.

• Image coregistration. To perform the InSAR analysis, the stack of SAR images needs to be coregistered, that is, a pixel with image coordinates (i, j) of any image must correspond to the same footprint on the ground. This does not occur due to the slightly different acquisition geometries of the images. This step involves the choice of one image, to be used as a geometric reference for all the other images, and coregistration. Coregistration requires the resampling of all images onto the grid of the reference image, to ensure pixel-topixel alignment along the entire image stack. All steps of the procedure are performed within the geometry of the reference image. • Interferogram generation. This step implements Eqs. (2.4) and (2.5). First, the phase difference is computed considering pairs of images (φS 2 φM in Eq. 2.4), by which the interferometric phase is obtained. Then, using the DEM and the orbits associated with the given image pair, the topographic term is simulated and subtracted from the interferometric phase (Eq. 2.5). It is worth noting that in Eq. (2.5) both the topographic term (φTopo sim ) and the deformation term (φDefo ) appear. In the DEMs currently used, the topography is known with a precision which ranges from decimeters (e.g., a lidar DEM4) to several meters (e.g., the SRTM DEM5). With such precision values, how is it possible to estimate the deformations with millimetric 4 What is the difference between lidar data and a DEM? https://www.usgs.gov/faqs/ what-difference-between-lidar-data-and-digital-elevation-model-dem. 5 https://www2.jpl.nasa.gov/srtm/.

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precision? The answer can be found in Eq. (2.4): the topographic component depends on the positions of the sensors M and S, while the deformation component is independent from such positions. This implies a very different sensitivity to deformation and topography. The difference is of approximately three orders of magnitude: thanks to a wavelength of a few centimeters (e.g., 5.55 cm for Sentinel-1), deformation can be estimated in the millimeter scale, while topography is only known at meter scale (i.e., 1000 times less precise). • Selection of MP candidates. As mentioned earlier, not all the pixels of the image stack can be used to estimate the deformation. In this step the so-called MP candidates are selected. They are candidates because their actual quality is evaluated later, during the processing. It is important to properly identify good MP candidates because this affects the subsequent steps, especially phase unwrapping and the estimation of the atmospheric component. In this step the PS and the DS are identified. The former can be identified by exploiting the SAR amplitude of the image stack (Ferretti et al., 2000, 2001). The DS can be identified with more complex algorithms (e.g., see Ferretti et al., 2011). • Model estimation. The objective of this module is to estimate and remove φTopo res , and partially estimate and remove φDefo from ΔφD2Int (see Eq. 2.6). For a given MP, Topo res represents the height of the pixel footprint with respect to the elevation indicated for the same location by the DEM. If an MP corresponds to the roof of a building, and the DEM is at ground level, then the Topo res represents the building height. The second term is φDefo . In this step, Defo is usually approximated by a linear term, Defo lin, that is, assuming that over the observed period the MP has a constant deformation velocity (the velocity is zero for stable MPs). However, more complex model can be used, for example, piecewise linear and polynomials. The two terms are computed over pairs of neighboring MPs and then are integrated over the whole MP set. Note that this operation is performed over ambiguous (wrapped) phases (see Biescas et al., 2007 and Section 3.7 for greater detail). It is worth mentioning that Topo res is key information with which to compute the 3D location of the MPs (see the Geocoding module) • Phase unwrapping. This operation involves the estimation of the phase ambiguity k (see Eq. 2.6). This is the most critical step of the entire procedure. Several approaches have been proposed for this estimation (e.g., see Ghiglia and Pritt, 1998; Costantini, 1998; and Chen and Zebker, 2001). For single interferograms, the condition to correctly unwrap the phases is that the difference between unwrapped phases over neighboring MPs should be less than π.

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Assuming that all the terms of Eq. (2.6) but φDefo are zero, this means that the differential deformation over such MPs must be less than λ/4, that is, approximately 13.87 mm for Sentinel-1. This is an important limitation of InSAR, see Section 3.7. • Atmospheric component estimation. In this module the atmospheric phase component φAtm is estimated and subsequently removed from the interferometric phase ΔφD2Int : This operation requires temporally ordered phases: in some approaches, such as the small baseline subset (SBAS) approach (Berardino et al., 2002), this involves a transformation from interferometric phases into image phases. The φAtm estimation is usually based on assumptions about the spatiotemporal characteristics of the data: φAtm is spatially correlated, but temporally uncorrelated, while the φDefo is typically correlated over time. The two components φDefo and φAtm are separated using low-pass6 and high-pass filters.7 In some cases, such filters are applied after estimating and removing a temporal polynomial plus a seasonal component from the deformation (Ferretti et al., 2021). It bears noting that the atmospheric component estimation is based on assumptions. Generally, it is useful to remove the φAtm component, at least partially. However, if the assumptions are not fully satisfied, the estimation can be biased: some part of φAtm can be wrongly estimated as a spatially correlated φDefo , or conversely, a part of spatially correlated φDefo can be estimated as φAtm and then removed. This possibility must be considered in the interpretation of the InSAR results. • MP densification. Once the φAtm component is estimated and removed, a new analysis is performed to find additional MPs, hence increasing the MP density. The goal is to select all MPs where reliable deformation information can be estimated. This operation is done over wrapped phases (see Section 3.7) and therefore again requires (1) the estimation of Defo lin and Topo res; and (2) phase unwrapping. A given pixel is considered to be an MP if its temporal coherence γ t exceeds a given threshold, where:     1 X N j Δφ 2Δφ D2Inta i D2Intmod i   (2.7) γt 5  e   N i51 And where ΔφD2Int a i is the observed interferometric phase in the ith interferogram after removing the atmospheric component, and ΔφD2Int mod i is the estimated phase from the model, which is usually 6 https://en.wikipedia.org/wiki/Low-pass_filter. 7 https://en.wikipedia.org/wiki/High-pass_filter.

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a function of Defo lin and Topo res, and N is the number of interferograms. Note that in the EGMS the ΔφD2Int mod i is a thirdorder polynomial, plus a seasonal component. Let us consider the simplest case, where the model is linear. γ t varies between 0 and 1, where 1 indicates a perfect agreement between the observed and modeled phases. Since γ t refers to a linear model (to estimate Defo lin), γ t 5 1implies that the deformation is perfectly linear over time, and that φNoise is zero in all N interferograms. By contrast, low values of γ t can indicate noisy data and/or a nonlinear deformation. The deformation time series (TS) for each selected MP is generated in this module. • Geocoding. The MPs coming from the previous module are represented in the radar geometry of the reference image by two coordinates: azimuth and range. The geocoding or geolocation procedure is used to estimate the geographical or cartographic coordinates of the MPs. This operation makes use of the azimuth and range coordinates of the given MP, the orbits of the reference image, the Topo res of the MP, and the DEM. This is a key step to enable the interpretation and exploitation of the InSAR products.

2.4 Interferometric synthetic aperture radar products The procedure described in the previous section generates two main InSAR products: the deformation velocity and the deformation TS. The average deformation velocity represents the mean deformation velocity over the whole observation period. It is usually derived by linear regression from the deformation TS and typically expressed in mm/ year or cm/year. It is worth observing that this parameter represents a simplification of the TS behavior. In fact, it only captures the linear deformation velocity term. However, it comes from a robust estimation procedure. In fact, it is a single parameter, which, for each MP, is computed using M observations, where M is the number of images. An example of a deformation velocity map is shown in Fig. 2.3. In this figure, the velocity values are color-coded between 220 and 20 mm/year. The negative values indicate LOS displacements away from the sensor, while positive values correspond to displacements toward the sensor. Fig. 2.4 shows a second example of deformation velocity map, which was derived using very high-resolution TerraSAR-X data. There is a different scale with respect to Fig. 2.3. In this figure, one may appreciate the high MP density. The deformation TS displays the time history of a given MP over the observed period. It is usually expressed in mm or cm. The deformation values are usually referred to the date of the first SAR image used in

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2.4 Interferometric synthetic aperture radar products

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FIGURE 2.3 Deformation velocity map of the airport and port of Barcelona (Spain). Source: Period covered: March 2015 to December 2020. The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

FIGURE 2.4 Deformation velocity map over the coast of Barcelona (Spain). Source: Period covered: 2007
09. The image was derived using very high-resolution data from TerraSAR-X.

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the processing: the deformation value of such an image is usually set to zero. This is not the case of the EGMS products, see Section 4.2.2. An example of a deformation TS is shown in Fig. 2.5. The TS describes the behavior of discrete MPs that cannot be identified by simply observing the mean annual velocity. The entire deformation pattern can be identified in the TS, including, for example, the time when a given phenomenon starts, seasonal trends, and accelerations. The TS is a fundamental tool to identify the driving mechanism of a given phenomenon. An example is shown in Fig. 2.6. The two deformation TSs were derived using very high-resolution TerraSAR-X data. In the covered period (2007
2012), there is a first period where the two MPs are basically stable. Then a deformation period occurs, where the two MPs have a rather constant deformation velocity. Finally, the deformation stops and there is a period with a small uplift. This behavior was due to water extraction. Using the TSs from Fig. 2.6, it is possible to determine the start and the end of the water pumping. As it is mentioned later, the SAR images of snow-covered areas cannot be exploited for InSAR purposes. In the EGMS the winter SAR scenes over North Europe are removed from the processing. This results in TS like the one shown in Fig. 2.7.

FIGURE 2.5 Example of deformation time series from the port of Barcelona. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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2.4 Interferometric synthetic aperture radar products 10.00 5.00 Deformation [mm]

0.00 -5.00 -10.00 -15.00 -20.00 -25.00 -30.00

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FIGURE 2.6 Example of two deformation time series from the Barcelona metropolitan area. Source: The time series were derived using very high-resolution TerraSAR-X data.

FIGURE 2.7 Example of deformation time series from North Europe, where the winter SAR scenes are discarded for the processing. SAR, Synthetic aperture radar. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

The TSs are usually accompanied by metadata obtained from the InSAR processing: identification number, geographical coordinates, cartographical coordinates, orthometric height, ellipsoidal height, azimuth range, temporal coherence, etc. Such information can be visualized in the TS viewer of the EGMS Explorer.

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2.5 Interferometric synthetic aperture radar pros and cons The InSAR technique offers unique features in the field of deformation measurement and monitoring. Some of its main advantages are briefly described next. • InSAR is a remote-sensing technique. As such, it provides worldwide global coverage and regular repeat observation capabilities. The latter aspect is key to perform monitoring activities. Additionally, it provides remote monitoring, and fully automatic data acquisition at a low cost. • InSAR provides wide area coverage. In the early days of the technique in the 1990s, results were obtained for several thousand square kilometers. Now the coverage of an entire continent is possible. • InSAR is sensitive to small deformations, which in terms of TS are at millimetric scale. This aspect is of paramount importance from the deformation monitoring viewpoint. • InSAR can be used to cover a wide area, and at the same time, it is able to focus on individual buildings and structures. This capability clearly depends on the resolution of the SAR images used. However, even the midresolution data of Sentinel-1 can be used, at least partially, to measure single urban elements. • Using InSAR, the investigation of past deformation phenomena is possible. Its capability to measure historical deformation where other monitoring data do not exist is unmatched. Sentinel-1 data start in 2015 and are still being acquired. Using the ERS data, it is possible to monitor deformation since 1991. It is worth observing that the availability of archive data is not homogeneous worldwide. Europe has the highest level of data coverage. • Using InSAR, it is possible to obtain a potential reduction in the amount of ground-based observation. This results in targeted and simplified logistics operations, reduced maintenance costs, personnel time and cost savings, and the capability to avoid insecure, hostile, or inaccessible environments. A list of some of the most important InSAR limitations is as follows. • InSAR MPs are not available everywhere, and their availability is often unknown before performing the processing. This limitation is severe in vegetated and forested areas but can also be important for buildings, structures, infrastructures, etc. In fact, the availability of MPs depends on the response to the microwave of such objects. Importantly, in specific cases, the presence MPs can be guaranteed by installing in situ artificial reflectors8 (Crosetto et al., 2013; Luzi et al., 2021; Xia et al., 2002). 8 Artificial reflectors are devices that are installed in situ to guarantee a good response to the microwaves. They represent good MPs.

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• A second limitation is the LOS measurement. Given a generic 3D deformation, InSAR only provides the estimate of one component of this deformation, which is obtained by projecting the 3D deformation onto the LOS direction. As described later, by using ascending and descending SAR data,9 one can retrieve the vertical and east-to-west horizontal components of deformation. However, InSAR is basically blind to south-to-north horizontal displacements. • InSAR suffers severe limitations in the capability to measure fast deformation phenomena due to the ambiguous nature of its observations (see Section 2.3). It is worth observing that the adjective fast is to be intended from the specific InSAR viewpoint (i.e., several cm/year). In addition, many InSAR approaches use a linear deformation model in their estimation procedures. This assumption can have a negative impact on the deformation estimates of all phenomena characterized by nonlinear deformation behavior, for which the assumption is not valid. The InSAR products based on the linear assumption typically lack MPs in all areas where the deformation shows significantly nonlinear motion. This can be critical because it affects those areas where the need to measure deformation is highest. It is worth noting that nonlinear approaches are more computationally demanding and complex to apply.

2.6 Interferometric synthetic aperture radar applications In this section, some of the most important InSAR applications are listed. The list is merely illustrative and does not intend to restrict any potential future applications. • Monitoring of subsidence and uplift areas. This is an important type of application, which can be related to water pumping (Schmidt and Bu¨rgmann, 2003, Zerbini et al., 2007, Bell et al., 2008), gas and hydrocarbon extraction (Vasco et al., 2008, 2010, Teatini et al., 2011), mining activity (Colesanti et al., 2005, Wegmu¨ller et al., 2009), abandoned mines (Du et al., 2021), reclaimed land (Kim et al., 2010, Jiang et al., 2011), soil consolidation (Da Lio et al., 2018, Sun et al., 2018, Liu et al., 2021), dissolution of saline layers (Nof et al., 2019), etc. This type of monitoring is of particular importance in low-land flood-prone areas (Dixon et al., 2006, Teatini et al., 2012). • Detection and monitoring of landslides and unstable slopes. This is a consolidated application, which however suffers from the InSAR limitation over vegetated and forested areas and steep relief. Examples 9 The ascending data are acquired by a satellite in polar orbit that moves from south to north, while the descending one corresponds to a satellite moving from north to south.

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•

•

•

•

2. Synthetic aperture radar interferometry

of applications concern landslide inventory (Notti et al., 2010, Righini et al., 2012, Rosi et al., 2018), monitoring (Farina et al., 2006, Tofani et al., 2013, Wasowski and Bovenga, 2022), and analysis and modeling (Herrera et al., 2011, Bovenga et al., 2012, Rosi et al., 2013). InSAR is being implemented as a near-real-time tool to forecast landslide failure (Intrieri et al., 2018) or to set up regional monitoring systems (Confuorto et al., 2021). Interferometric data are being implemented in the civil protection cycle as fundamental resources for the prevention and recovery phases (Raspini et al., 2017, Bianchini et al., 2021). Geophysical applications. InSAR is a valuable and consolidated information source for the study of tectonics (Bu¨rgmann et al., 2006, Funning et al., 2007, Reale et al., 2011, Lazecky´ et al., 2020), vulcanology (Hooper et al., 2004, Lundgren et al., 2004, Poland and Zebker, 2022), and glaciology (Strozzi et al., 2020, Nagler et al., 2021). Monitoring of structures and infrastructures. This is a broad application field, which includes • urban area monitoring (Herrera et al., 2009, Crosetto et al., 2010, Wasowski et al., 2015); • study of single buildings (Gernhardt and Bamler, 2012, Gernhardt et al., 2015; Zhu et al., 2018) and industrial installations; • cultural heritage (Tapete and Cigna, 2012, Pratesi et al., 2015); • roads and highways (Orellana et al., 2020, Fiorentini et al., 2020); • railways (Chang et al., 2016, 2018); ¨ zer et al., 2019, Seidel et al., 2019); • canals, dykes, and levees (O • pipelines (Bayramov et al., 2020, Tang et al., 2020); • bridges and viaducts (Crosetto et al., 2015; Jung et al., 2019); • dams (Toma´s et al., 2013; Di Martire et al., 2014); and • airports and ports (Bianchini Ciampoli et al., 2020; Gagliardi et al., 2021). Monitoring of construction works (e.g., tunnels and large excavations). Some examples are described in Roccheggiani et al. (2019) and Ramirez et al. (2022). InSAR is key to analyzing the cause
effect links between works and damages in the surroundings (Botey i Bassols et al., 2021). In addition, InSAR can be used to assess the suitability of an area for the construction of a new infrastructure. Monitoring ground motion of buildings and industrial assets can be a valuable input for the insurance industry.

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280. Available from: https://doi.org/10.1109/TGRS.2011.2160644. Poland, M.P., Zebker, H.A., 2022. Volcano geodesy using InSAR in 2020: the past and next decades. Bulletin of Volcanology 84 (3), 1
8. Available from: https://doi.org/10.1007/ s00445-022-01531-1. Pratesi, F., Tapete, D., Terenzi, G., Del Ventisette, C., Moretti, S., 2015. Structural assessment of case study historical and modern buildings in the florentine area based on a PSI-driven seismic and hydrogeological risk analysis, Engineering Geology for Society

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and Territory, Volume 8. Springer International Publishing, pp. 345
349. Available from: https://doi.org/10.1007/978-3-319-09408-3_60. Ramirez, R.A., Lee, G.J., Choi, S.K., Kwon, T.H., Kim, Y.C., Ryu, H.H., et al., 2022. Monitoring of construction-induced urban ground deformations using Sentinel-1 PSInSAR: the case study of tunneling in Dangjin, Korea. International Journal of Applied Earth Observation and Geoinformation 108, 102721. Available from: https://doi.org/ 10.1016/j.jag.2022.102721. Raspini, F., Bardi, F., Bianchini, S., Ciampalini, A., Del Ventisette, C., Farina, P., et al., 2017. The contribution of satellite SAR-derived displacement measurements in landslide risk management practices. Natural Hazards 86 (1), 327
351. Available from: https://doi.org/10.1007/s11069-016-2691-4. Reale, D., Nitti, D.O., Peduto, D., Nutricato, R., Bovenga, F., Fornaro, G., 2011. Postseismic deformation monitoring with the COSMO/SKYMED constellation. IEEE Geoscience and Remote Sensing Letters 8 (4), 696
700. Available from: https://doi.org/10.1109/ LGRS.2010.2100364. Righini, G., Pancioli, V., Casagli, N., 2012. Updating landslide inventory maps using Persistent Scatterer Interferometry (PSI). International Journal of Remote Sensing 33 (7), 2068
2096. Available from: https://doi.org/10.1080/01431161.2011.605087. Roccheggiani, M., Piacentini, D., Tirincanti, E., Perissin, D., Menichetti, M., 2019. Detection and monitoring of tunneling induced ground movements using Sentinel-1 SAR interferometry. Remote Sensing 11 (6), 639. Available from: https://doi.org/10.3390/ rs11060639. Rosi, A., Vannocci, P., Tofani, V., Gigli, G., Casagli, N., 2013. Landslide characterization using satellite interferometry (PSI), geotechnical investigations and numerical modelling: the case study of Ricasoli Village (Italy). International Journal of Geosciences 4, 904
918. Available from: https://doi.org/10.4236/ijg.2013.45085. Rosi, A., Tofani, V., Tanteri, L., Tacconi Stefanelli, C., Agostini, A., Catani, F., et al., 2018. The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15 (1), 5
19. Available from: https://doi.org/10.1007/s10346-017-0861-4. Schmidt, D.A., Bu¨rgmann, R., 2003. Time-dependent land uplift and subsidence in the Santa Clara valley, California, from a large interferometric synthetic aperture radar data set. Journal of Geophysical Research: Solid Earth 108 (B9), 1978
2012. Available from: https://doi.org/10.1029/2002JB002267. Seidel, M., Marzahn, P., Ludwig, R., 2019. Monitoring of a sea-dike in Northern Germany by means of ERS-1, ENVISAT/ASAR, and Sentinel-1 SAR interferometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (11), 4351
4360. Available from: https://doi.org/10.1109/JSTARS.2019.2949244. Strozzi, T., Caduff, R., Jones, N., Barboux, C., Delaloye, R., Bodin, X., et al., 2020. Monitoring rock glacier kinematics with satellite synthetic aperture radar. Remote Sensing 12 (3), 559. Available from: https://doi.org/10.3390/rs12030559. Sun, Q., Jiang, L., Jiang, M., Lin, H., Ma, P., Wang, H., 2018. Monitoring coastal reclamation subsidence in Hong Kong with distributed scatterer interferometry. Remote Sensing 10 (11), 1738. Available from: https://doi.org/10.3390/rs10111738. Tang, Q., Xue, L., Zhang, S., Peng, C., Wu, Y., 2020. Monitoring and early warning system of geological hazards of regional mountain pipeline based on the combination of heaven and land. Signal and Information Processing, Networking and Computers. Springer, Singapore, pp. 647
654. Available from: https://doi.org/10.1007/978-981-154163-6_77. Tapete, D., Cigna, F., 2012. Rapid mapping and deformation analysis over cultural heritage and rural sites based on Persistent Scatterer Interferometry. International Journal of Geophysics 2012, 19. Available from: https://doi.org/10.1155/2012/618609.

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Teatini, P., Castelletto, N., Ferronato, M., Gambolati, G., Janna, C., Cairo, E., et al., 2011. Geomechanical response to seasonal gas storage in depleted reservoirs: a case study in the Po River basin, Italy. Journal of Geophysical Research: Earth Surface 116 (F2), 2003
2012. Available from: https://doi.org/10.1029/2010JF001793. Teatini, P., Tosi, L., Strozzi, T., Carbognin, L., Cecconi, G., Rosselli, R., et al., 2012. Resolving land subsidence within the Venice Lagoon by persistent scatterer SAR interferometry. Physics and Chemistry of the Earth, Parts A/B/C 40, 72
79. Available from: https://doi.org/10.1016/j.pce.2010.01.002. Tofani, V., Raspini, F., Catani, F., Casagli, N., 2013. Persistent Scatterer Interferometry (PSI) technique for landslide characterization and monitoring. Remote Sensing 5 (3), 1045
1065. Available from: https://doi.org/10.3390/rs5031045. Toma´s, R., Cano, M., Garcı´a-Barba, J., Vicente, F., Herrera, G., Lopez-Sanchez, J.M., et al., 2013. Monitoring an earthfill dam using differential SAR interferometry: La Pedrera dam, Alicante, Spain. Engineering Geology 157, 21
32. Available from: https://doi. org/10.1016/j.enggeo.2013.01.022. Vasco, D.W., Ferretti, A., Novali, F., 2008. Reservoir monitoring and characterization using satellite geodetic data: interferometric synthetic aperture radar observations from the Krechba field, Algeria. Geophysics 73 (6), WA113
WA122. Available from: https:// doi.org/10.1190/1.2981184. Vasco, D.W., Rucci, A., Ferretti, A., Novali, F., Bissell, R.C., Ringrose, P.S., et al., 2010. Satellite-based measurements of surface deformation reveal fluid flow associated with the geological storage of carbon dioxide. Geophysical Research Letters 37 (3). Available from: https://doi.org/10.1029/2009GL041544. Wasowski, J., Bovenga, F., Refice, A., Nitti, D., Nutricato, R., 2015. High resolution PSI for mapping ground deformations and infrastructure instability, Engineering Geology for Society and Territory, Vol. 2. Springer International Publishing, pp. 399
403. Available from: https://doi.org/10.1007/978-3-319-09057-3_63. Wasowski, J., Bovenga, F., 2022. Remote sensing of landslide motion with emphasis on satellite multi-temporal interferometry applications: an overview. Landslide Hazards, Risks, and Disasters 365
438. Available from: https://doi.org/10.1016/B978-0-12818464-6.00006-8. Wegmu¨ller, U., Walter, D., Spreckels, V., Werner, C.L., 2009. Nonuniform ground motion monitoring with TerraSAR-X persistent scatterer interferometry. IEEE Transactions on Geoscience and Remote Sensing 48 (2), 895
904. Available from: https://doi.org/ 10.1109/TGRS.2009.2030792. Xia, Y., Kaufmann, H., Guo, X., 2002. Differential SAR interferometry using corner reflectors. IEEE International Geoscience and Remote Sensing Symposium 2, 1243
1246. Available from: https://doi.org/10.1109/IGARSS.2002.1025902. Zerbini, S., Richter, B., Rocca, F., van Dam, T., Matonti, F., 2007. A combination of space and terrestrial geodetic techniques to monitor land subsidence: case study, the Southeastern Po Plain, Italy. Journal of Geophysical Research: Solid Earth 112 (B5), 1978
2012. Available from: https://doi.org/10.1029/2006JB004338. Zhu, M., Wan, X., Fei, B., Qiao, Z., Ge, C., Minati, F., et al., 2018. Detection of building and infrastructure instabilities by automatic spatiotemporal analysis of satellite SAR interferometry measurements. Remote Sensing 10 (11), 1816. Available from: https://doi. org/10.3390/rs10111816.

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C H A P T E R

3 InSAR technical aspects An effective exploitation of Interferometric SAR (InSAR) data requires a correct understanding of the key InSAR technical aspects. This chapter describes the main characteristics of SAR data, their processing and analysis, and the characteristics of InSAR products.

3.1 SAR data acquisition The availability of at least one spaceborne SAR sensor that regularly acquires InSAR images is a sine qua non for InSAR. To have InSAR acquisitions, the sensor needs to be coherent (i.e., able measuring the SAR phase), and the acquisitions over the same area must have an interferometric geometry (i.e., be acquired from approximately the same viewpoint; Rosen et al., 2000). If this is not the case, for example, when the orbits of an image pair are separated by several kilometers, InSAR cannot be performed.1 Several active SAR missions are currently underway (see Fig. 3.1). The abundance of ongoing and new planned missions guarantees SAR data acquisition in the short and midterms. In this section, only the SAR sensors that could potentially impact European Ground Motion Service (EGMS) exploitation are addressed. They can be divided into two classes: the SAR data used to derive the EGMS products and those that can complement such products. The EGMS products are based on the data of the Sentinel-1A and Sentinel-1B sensors.2 In the history of SAR, Sentinel-1 made for a paradigm shift. It provides a short revisiting time, global coverage, an innovative SAR acquisition mode, and a free and open data policy (Torres et al., 2012). As of 2016, the two Sentinel-1 satellites have been providing 1 When consulting a SAR image archive for InSAR purposes, remember to restrict the search to InSAR acquisitions. 2 https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar

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FIGURE 3.1 Main past and present SAR missions. The resolution corresponds to the maximum achievable spatial resolution.

at least two acquisitions (one in ascending and another in descending geometry) of any place in Europe every 6 days. Outside Europe, most landmasses are imaged at regular intervals of 6, 12, or 24 days. Due to an anomaly that occurred in December 2021, the mission of Sentinel-1B was terminated prematurely.3 Since then, the SAR acquisitions are made any place in Europe every 12 days. The so-called EGMS baseline product, which covers the timeframe between February 2015 and December 2020, was not affected by the end of the Sentinel-1B mission. The same holds for the first annual update, from January to December 2021, where only a few acquisitions were missing at the end of the time series. Updates using data after December 2021 can still be processed 3 https://sentinels.copernicus.eu/web/sentinel/-/end-of-mission-of-the-copernicus-sentinel-1b-satellite/1.5

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with a lower repetition rate, accounting for a lower coherence between the acquisitions. However, the quantification of the effect on the measurement quality must be addressed. It is worth mentioning that two other spacecrafts (Sentinel-1C and Sentinel-1D) are planned to replace the first two satellites: the continuity of the Sentinel-1 mission is confirmed for the next decade (Geudtner and Tossaint, 2021). Sentinel-1C is scheduled to be launched in the first half of 2023 and will be fully operational a few months later.4 What SAR data are likely to be used to complement EGMS? First of all, the most recent Sentinel-1 data can be used to update the available products and fill the gap between the date of the most recent EGMS update (e.g., December 2020 for the baseline product) and the present time. Additionally, interesting SAR data can come from the very highresolution X-band sensors: TerraSAR-X, TanDEM-X, PAZ, and CosmoSkyMed. They can complement the EGMS products with their higher measurement point (MP) density. There are, however, two important differences between Sentinel-1 and the X-band sensors. The former performs regular acquisitions, and its data are distributed free of charge. By contrast, the latter mainly acquire the data on demand, and fees are charged for their images.

3.2 Nature of the measurement points InSAR MPs are distributed spatially in an opportunistic way (Hanssen, 2001): there are MPs where different scattering mechanisms have a persistent phase response over time. The exact location of such a persistent phase response is usually unpredictable before the processing is done. There are numerous phenomena in which the phase response changes over time. They include forested and vegetated areas, works to modify buildings and other structures, repavement of streets, earthworks, soil erosion, coastal erosion, rock falls, etc. The areas affected by such phenomena during the observation period usually lack MPs. The pixel footprint over flat terrain of the Sentinel-1 SAR data is approximately 4 m in range by 14 m in azimuth. To be more precise, the dimension in range varies from 3.2 m, at an incidence angle of 46 degrees, and 4.7 m, at 29 degrees. Considering such a pixel footprint, what do the MPs represent? This depends on the specific scattering mechanism, which involves the coherent sum of all the physical scatterers included within a pixel footprint or within neighboring pixel footprints. Three main cases are considered below (see Fig. 3.2). 4 https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1/Ride_ into_orbit_secured_for_Sentinel-1C

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FIGURE 3.2 MP types over a ground grid of approximately 14 by 4 m. There are three main types of scatterers: (1) persistent scatterers (light blue cells); (2) DSs within the same resolution cell (green cells); (3) DSs over different resolution cells (yellow cells). The gray cells contain no MPs. MPs, Measurement points.

• A single-dominant Point-like Scatterer (PS) (Ferretti et al., 2000, 2001). Even though the corresponding pixel has a footprint of 14 by 4 m, the PS response can be due to a single object located in any position within the footprint (see Fig. 3.2). In some cases, especially by using the geocoded data, the nature of the PS can be guessed. There are techniques to locate such scatterers at the subpixel level (Eineder et al., 2010). However, they are not used in the EGMS. The InSAR parameters derived for this type of pixel, for example, deformation and Topo res, refer to the single dominating object. • A set of distributed scatterers (DSs) located within the same pixel footprint. These are weak scatterers, the responses of which are summed up to make one MP for a given area. In this case the response is distributed within the pixel footprint: it is usually difficult to identify the nature of the objects originating such a response. The InSAR parameters derived for this type of pixel refer collectively to the set of such objects. • A set of DSs (Ferretti et al., 2011) over several pixel footprints. In this case, the response of the individual scatterers is very weak. To get a useful MP, the responses of neighboring pixels with similar properties can be added together. In this case, the responding objects are distributed and the InSAR parameters derived for this type of pixel refer collectively to them. It is worth observing that Northern Europe and high-altitude areas are affected by seasonal snow cover, which results in a loss of deformation measurements. In the EGMS the winter SAR scenes covering North Europe are removed from the processing. This affects the quality of the retrieved deformation velocity and time series even though it is still possible to measure different types of ground motion phenomena.

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3.3 Measurement point density The uneven distribution of the MPs is a typical property of InSAR, and a significant limitation of its exploitation (Crosetto et al., 2016). Apart from the loss of MPs due to very fast deformation phenomena, which is discussed in Section 3.7, the MP density is mainly driven by the land cover type. It is worth underlining that the absence of MPs in a given area has to be interpreted as “no data,” that is, providing no information about the presence or lack of deformation phenomena in such an area. Some typical examples of MP density are described in the following paragraphs (see Ferretti et al., 2021). The vegetated areas usually have low MP density, which typically vary between 0 and 1000 MP/km2. In such areas, there are usually no or very few DSs. Low values also characterize agricultural areas. Higher densities can be expected over nonvegetated bare soil: 1000
8000 MP/km2. In this type of land cover, there is a strong benefit to using the DSs. Over industrial areas the density is usually high: 1000
5000 MP/km2. Similar densities can be found in peri-urban areas, while the highest densities are usually found in urban areas: 5000
10000 MP/km2. In industrial, peri-urban, and urban areas, most of the targets are PSs: DSs do not usually improve the MP density. An example of heterogeneous density is shown in Fig. 3.3. The density varies from 33 MP/km2 in a densely vegetated marsh area (blue polygon), to 933 MP/km2 in an agricultural area (red polygon), and up to 21,200 MP/km2 over Terminal 1 of Barcelona’s El Prat Airport (green polygon). Such differences definitely have an impact on the InSAR data exploitation. However, it is worth noting that the sampling density must always be compared with the size of the deformation phenomenon at hand. The density is much less critical over wide deformation phenomena, for example, large uplifts, large subsidences, or landslides. In such cases, the

FIGURE 3.3 Examples of MP density over three different types of land cover. MP, Measurement point. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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MP redundancy is key to interpret the given phenomenon and, if modeling is performed, to achieve a good precision in the estimation of the model parameters. By contrast, the analysis of very small phenomena, for example, small landslides, or single buildings, must often rely on very few MPs. In these cases, careful interpretation is necessary, including consideration of the potential errors associated with InSAR measurements.

3.4 SAR geometric effects When working with SAR images of hilly and mountainous areas, one faces the geometric effects attributable to the ranging nature of such data (Ferretti et al., 2007b). Such effects mainly depend on the radar off-nadir angle, see Fig. 3.4. The dimension of the SAR pixel

Scheme illustrating the foreshortening (AA0 ) and elongation (BB0 ) effects (above). Layover (CD) and shadow (DE) effects (below). Θ is the radar off-nadir angle.

FIGURE 3.4

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footprint in range direction is not constant: it depends on the local topography. The geometric effects are illustrated in Fig. 3.4. The terrain slopes that face the sensor (AA0 in Fig. 3.4) are compressed: they have a pixel footprint dimension RA, which is larger than the corresponding dimension RF in flat terrain. This effect, called foreshortening, is visible in the amplitude images. These images appear brighter because there is more terrain that backscatters the SAR signal than flat terrain. The opposite effect, elongation, occurs in the opposite slopes (BB0 in Fig. 3.4). These slopes are sampled with a higher frequency: RB is smaller than RF. These slopes usually appear darker in the amplitude image. There is a third effect, called layover, which is visible in steep mountainous areas or in areas where tall buildings and structures are present, occurring if the pulse reaches the top of a mountain/building (point D in Fig. 3.4) before reaching the bottom of the mountain/building (point C). In this case, the top of the mountain/building is displaced toward the radar (point D in Fig. 3.4 appears closer to the radar than C). The layover areas cannot be exploited for deformation measurement. A fourth effect is called shadow. It occurs when the slope bent away from the radar is steeper than the incidence ray (see the black segments from D to E in Fig. 3.4). Note that this always occurs in images featuring buildings. There is no information in the corresponding areas in the SAR image: these areas cannot be exploited for deformation measurement. In images of mountainous regions, foreshortening areas are undersampled, and the layover and shadow areas cannot be exploited (van Natijne et al., 2022). Such limitations can be at least partially overcome by using both ascending and descending geometries, effectively increasing the spatial sampling. Note that this is only true if the slope is not in shadow. An example of geometric effects is shown in Fig. 3.5.

3.5 LOS measurement As previously stated, the InSAR deformation measurements are mono-dimensional and refer to the radar line-of-sight (LOS). The InSAR acquisition geometry influences the sensitivity of InSAR to different types of deformation. The most important aspects of the geometry are (1) the polar orbit of the SAR satellites, which make the orbits approximately parallel to the south-to-north direction; (2) the ascending or descending geometry; and (3) the local incidence angle of the

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FIGURE 3.5 Example of foreshortening, layover, and elongation effects in a mountainous area. This figure shows ascending data (the blue line indicates the orbit direction). The area with the red perimeter has almost no MP because it is in foreshortening and layover. The opposite side, with the yellow perimeter, is densely sampled because of the elongation effect. MP, Measurement point. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

radar, see Larsen et al. (2020). Considering the S-1 data, with an incidence angle of 37 degrees, calling the displacement in the LOS DLOS: • 1-cm vertical displacement corresponds to a DLOS of 0.8 cm; • 1-cm horizontal eastward displacement corresponds to a DLOS of 0.59 cm; • 1-cm horizontal northward displacement corresponds to a DLOS of 0.13 cm. The least sensitivity is found in the south-to-north displacements. It is worth observing that a high DLOS value certainly indicates the presence of a deformation process. By contrast, a small or negligible DLOS value does not imply the absence of a deformation process. In fact, the process could be mainly horizontal and south- or northward. The interpretation of DLOS results is not always straightforward. If the deformation is vertical, then negative DLOS values indicate displacements away from the sensor (downwards), while positive values correspond to displacements toward the sensor (upwards). The interpretation is more complex with dominant horizontal deformation, as typically occurs in some landslides (see Fig. 3.6). In this figure the slope that is bent away

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3.5 LOS measurement

S

S

LOS

LOS

D D D LOS

D LOS

FIGURE 3.6 DLOS corresponding to two displacements D of two slopes, one bent away from the ascending satellite S and the other facing S. In the first case, DLOS is negative (in red), while in the second case, it is positive (in blue).

FIGURE 3.7 Displacement Dtot observed by an ascending (Dlos_a) and descending geometry (Dlos_d), which can be combined to derive the vertical (Du) and horizontal east
west (De) components.

from the satellite S (Fig. 3.6, left) corresponds to a landslide displacement away from the sensor. By contrast, in the slope that is facing the satellite (Fig. 3.6, right), the displacement is toward the sensor. By using ascending and descending SAR data, it is possible to retrieve the vertical and east-to-west horizontal components of deformation. This is illustrated considering a simplified acquisition geometry from (Manzo et al., 2006), (see Fig. 3.7). In this geometry the satellite positions, the LOS displacements (Dlos_a and Dlos_d), the total deformation Dtot, and the deformation components belong to the same plane. In addition, ascending and descending data have the same look angle: θ.

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The vertical (Du) and horizontal east
west (De) components can be computed this way: De 5

Dlos

2 Dlos 2 sinθ



a

(3.1)

Du 5

Dlos

d 1 Dlos 2 cosθ



a

(3.2)

d

Note that in Fig. 3.7, Dtot, Dlos_a, Dlos_d, and Du are negative, while De is positive. More complex formulation can be used to derive the two components, for example, see Notti et al. (2014). Other authors have proposed methods to derive three components using three or more geometries (see Wright et al., 2004; Dalla Via et al., 2012; and Morishita and Kobayashi, 2022). In case there is just one acquisition geometry and a reasonable assumption about the direction of the total deformation Dtot can be made, the Dlos can be projected in the direction of the Dtot (see Notti et al., 2014). An example of this is when deformation can be assumed to be vertical. A second example concerns some types of landslides, where Dtot can be assumed to be parallel to the maximum slope direction.

3.6 Reference point By using InSAR, accurate information about the relative ground motion of MPs can be derived. In fact, the InSAR measurements are relative to a reference point located in the processing area (Ferretti, et al., 2000). Such a point has a fixed velocity and deformation time series, which are typically set to zero. If the reference point, together with the entire processing area, undergo an absolute displacement, it will not be visible in the InSAR data. Let us consider the example of an island located far from any landmass. InSAR will provide measurements relative to a reference point located within the island. If the entire island moves, for example, due to tectonic activity, this will not be captured by InSAR alone. It is worth noting that the InSAR deformations do not always refer to a single reference point. For instance, in the EGMS, the deformations of the Basic product refer to the average deformation value computed over an area (see Section 4.2.2). The reference, which can be over a single MP or an area, that is, a set of MPs, is fixed during the InSAR processing. If needed, the reference point can be changed during the interpretation. Let us call Vref and Dref

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the velocity and the time series of the new reference point. This change requires the following operations: Vnew 5 Vold 2 Vref

(3.3)

Dnew 5 Dold 2 Dref

(3.4)

The first operation is repeated for all the points of the velocity map, while the second is done for the entirety of dates from all of the deformation time series of the dataset. Note that the reference point must be of high quality, with very little noise. Otherwise, as indicated in the above formulas, it will affect the measurements of all MPs in a given dataset. In addition, as it is a relative technique, the precision of both deformation velocities and time series decreases with the distance from the reference point (Colesanti et al., 2003a). What about the absolute motion, for example, relative to an Earthcentered reference system? External data are needed to reference the ground motion InSAR estimates to an absolute reference frame. This can typically be achieved by using Global Navigation Satellite System (GNSS) data. This is what it is done in the EGMS, where the GNSS data are used to calibrate all InSAR data, obtaining the Calibrated and Ortho products.

3.7 Nonlinear and fast deformation The InSAR results can have problems related to two types of deformation phenomena: nonlinear deformation and fast deformation. Let us assume that a linear deformation model is used (see Section 2.3). In this case, problems can occur in the time series of MPs that have a nonlinear behavior. An example is shown in Fig. 3.8. In this case the linear model would fit well in the last three-quarters of the time series, which has a constant velocity (i.e., from image number 73). However, the velocity value does not accurately represent the actual behavior of the deformation. For this reason the corresponding MP can be lost because it has a low temporal coherence (see Section 2.3). This is an important consequence related to nonlinear deformation. If deformation is nonlinear and a linear model is used, the temporal coherence is low and therefore the corresponding point is not selected as an MP. For instance, this effect can be clearly visible over mining areas, in which InSAR can typically measure the border of the underground mine, where smaller deformation occurs, while the maximum

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FIGURE 3.8 Example of nonlinear deformation time series. The linear model would fit the last three-quarters of the time series, but not in the first quarter.

FIGURE 3.9 Example of MP loss due to high deformation values in mining areas. MP, Measurement point. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

deformation area may not have any MP (see Fig. 3.9). It is worth noting that the above effect can be partially mitigated by using more complex deformation models, for example, piecewise linear and polynomials. This is the case of some of the processing chains used in the EGMS. The second limitation concerns fast deformation. It is worth noting that in this context the term fast is strictly related to the InSAR

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processing viewpoint, and in particular to the condition assumed to perform the phase unwrapping: the absolute difference between unwrapped phases over neighboring MPs must be less than π, or equivalently in terms of absolute deformation less than λ/4. The phase unwrapping condition corresponds to approximately 2.3 mm/day for 6-day Sentinel-1 acquisitions. The phase unwrapping consists of estimating the number of cycles N to be added to the wrapped phases:

 

(3.5)



(3.6)

ϕunw 5 ϕwrap 1 2 π N In terms of displacements: Dunw 5 Dwrap 1 λ=2 N

where λ is the radar wavelength. The phase unwrapping condition entails an important limitation, discussed in the points next. • The above condition concerns differential phases or differential deformations, that is, differences computed over MP pairs. Therefore the actual capability to measure deformation over a given MP depends on the spatial pattern of the deformation phenomenon at hand (the smoother this pattern is, the better the phase unwrapping) and the available MP density over this phenomenon (the higher the density, the better the phase unwrapping). • Considering the 6-day revisiting time of Sentinel, the limit of λ/4 corresponds to a maximum measurable differential deformation rate of 85.2 cm/year. The above limit is smaller in practice, because other terms like the atmospheric component and noise play a role. • The above discussion concerns the phase unwrapping of single interferogram, that is, bidimensional phase unwrapping. The interferograms can be analyzed jointly (3D phase unwrapping), for example, see Hooper and Zebker (2007); Pepe and Lanari (2006). • When the phase unwrapping condition is not met, the phase cannot be correctly reconstructed (see the example in Fig. 3.10). The black line indicates the ground truth coming from leveling data. The color time series show the InSAR results obtained by different teams participating in the PSIC4 validation exercise (Raucoules et al., 2009). It is noticeable that all of the teams provided biased results due to phase unwrapping problems. Their solutions only properly fit the residual deformation occurring after the main deformation. • A similar example is examined, where the deformation cannot be correctly reconstructed. Let us assume an MP that is moving autonomously, surrounded by stable MPs. The phase unwrapping is performed assuming that the absolute differential deformations between all MPs are below λ/4. If the given MP undergoes a sudden

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FIGURE 3.10 Example of aliasing due to a fast deformation over a coal mining site. Source: This image was derived by TNO (https://www.tno.nl) in the frame of the PSIC4 project (earth.esa.int/psic4).

and large displacement (greater than λ/4), in the corresponding time series, a jump would probably be seen, representing the wrapped version of the actual displacement. For instance, using Sentinel-1 data, if the displacement is 236.6 mm, there will be a jump in the time series of 29.4 mm, which corresponds to 236.6 1 λ/2 mm (see Fig. 3.11), where λ/2 corresponds to 27.2 mm. Note that any solution corresponding to 29.4 1 N 27.2 mm, where N is an integer number, is compatible with the observations. This occurs in the green and blue solutions in Fig. 3.11. In this example a “big” displacement (236.6 mm) corresponds to a much smaller displacement in the time series (29.4 mm). However, it could happen that an even bigger displacement (e.g., 254.4 mm) is completely invisible in the corresponding time series. In fact, the jump would be zero: 0 5 254.4 1 2 λ/2 mm. • In the period covered by the baseline EGMS products (2015
20), a number of significant earthquakes occurred in Italy and Greece. For the interested areas, for example, central Italy, August 2016 and some Greek islands, considering the challenge related to phase unwrapping, a specific processing was carried out during EGMS production. Section 7.2 shows an example of time series in an area affected by a strong earthquake.





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3.8 Deformation time series

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FIGURE 3.11 Example of an ambiguous deformation in case of a sudden big displacement. The three solutions (green, red, and blue), which are shifted by half a cycle (27.2 mm), are compatible with the observed phases.

3.8 Deformation time series The deformation time series represent the most advanced and detailed InSAR product. Their patterns are essential to interpret the underlying deformation phenomena. When interpreting the time series, it is always useful to keep in mind Eq. 2.6. The φNoise plays an important role. This component, which is dominant in the majority of the processed pixels, determines whether a pixel can be an MP. In addition, it is an essential component of the MP deformation time series. It is always present, it cannot be modeled and can only be described stochastically, by using appropriate statistics. In the EGMS, there are different statistics attached to each time series (Kotzerke et al., 2022). Here, it is opportune to mention the root-mean-square error (RMSE), which is evaluated on the time series residuals after applying a regression model of a third-order polynomial plus a seasonal (sinusoidal) component, and the temporal coherence (see Eq. 2.7). These two statistics are treated in greater detail in Section 3.11. These statistics are useful to describe the level of noise of a given time series and can be used to select time series with a given noise level. Fig. 3.12 illustrates four time series of the same area that have negligible deformation velocity. In this figure, three time series have been shifted for visualization purposes. Starting from the top, the blue time series has the least level of noise, with RMSE of 1.5 mm, and a temporal coherence γ t of 0.94. It is an excellent time series. The orange time series has an RMSE of 2.4 mm and a γ t of 0.87. The gray time series has an RMSE of 3.4 mm and a γ t of 0.78, and the yellow one has an RMSE of 4.3 mm and a γ t of 0.64. One may notice the two statistics (RMSE and γ t ) suitably describe the level of noise that is visible in the time series. Note

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FIGURE 3.12 Examples of four time series with different levels of noise.

FIGURE 3.13 Example of spike due to residual atmospheric effects, corresponding to image number 277. The spike is similar in two close MPs separated spatially by approximately 20 m. MPs, Measurement points.

that, depending on the application, the time series can be filtered spatially, for example, by averaging neighboring MPs, or temporally, for example, using a temporal low-pass filter. Another component of the time series is given by φAtm . If this component is properly estimated and removed, its effect on the time series is negligible. If this is not the case, some residual φAtm can be present in the time series. This typically takes the form of spikes that are concentrated on a single date of the time series. Such spikes affect neighboring MPs in a similar way (see Fig. 3.13). If needed, such spikes can be removed or filtered out.

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3.9 Time series and thermal expansion InSAR is sensitive to small displacements. For this reason, it is sometimes able to capture the displacements due to temperature variations, that is, the thermal expansion of materials. This typically occurs over large structures and infrastructures, for example, tall buildings, towers, viaducts, and bridges. While studying a deformation phenomenon with any deformation monitoring technique, the thermal expansion component is often removed to focus on the deformation. To this end, the thermal component can be explicitly modeled and estimated in the InSAR processing (Monserrat et al., 2011). This is not performed in EGMS processing, and hence the thermal expansion component is mixed up with the deformation. An example, concerning a bridge, is shown in Fig. 3.14. The point shows a seasonal thermal pattern. For some types of analysis based on the EGMS data, the thermal expansion needs to be separated from the deformation. The simplest procedure is to fit a periodic function to the time series, remove it from the original data, and analyze the residuals. However, it is worth noting that this is just an approximation of the thermal expansion behavior,

FIGURE 3.14 Example of time series showing a thermal expansion component. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land. copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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which is a function of the actual temperature of the object at hand and its characteristics. For this reason a more accurate procedure to remove the thermal expansion is to consider the geometry, the thermal expansion coefficient of its materials, and the time series of the temperatures at the time of acquisition of the SAR images. This is a detailed scale analysis that is out of the scope of a continental scale deformation map like EGMS.

3.10 Measurement point positioning The 3D positioning of the MPs is fundamental for InSAR interpretation. It is worth noting that the precision of the MP positioning is approximately three orders of magnitude worse than the precision of deformation measurements (meters vs millimeters). The positioning is obtained through geocoding, the last step of the InSAR procedure. It depends on different factors: the orbital data of the reference image; the Digital Elevation Model (DEM) used; the Topo res of the MP; and optionally the subpixel position of the scatterer or scatterers. The positioning precision depends on the temporal coherence of the given MP, the number of images, etc. The specifications of the EGMS prescribe a 3D geolocation precision better than 10 m. Larsen et al. (2020) describe the positioning precision in detail. Without performing subpixel positioning, which is the case of EGMS: • For an image stack of 280 S-1 images, and a temporal coherence of 0.95 (an excellent MP), the standard deviation of the 3D positioning is 4.5 m. • For the same stack and a temporal coherence of 0.6, the standard deviation of the 3D positioning is 6.2 m. These numbers are keys for the InSAR interpretation. They must always be considered, especially in the analyses focused on small or thin objects, like single buildings, and some infrastructures. For instance, they are keys to understand if an MP is inside or outside the perimeter of a given building. In this case, apart from the planimetric positioning, it is key to consider the elevation of the MP: if it comes from the roof, its elevation will be key to discriminate between a building and its surroundings. Sometimes the position of all the MPs in a dataset can be affected by a shift in both planimetric components (see Fig. 3.15). If this is the case, the north
south and east
west errors can be estimated using an orthoimage or other cartographic product, and then removed by shifting (correcting) the coordinates of the entire dataset.

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3.11 Quality of the InSAR estimates

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FIGURE 3.15 Example of a global shift in the geocoding. In this case the geocoding error is approximately 50 m toward east and 50 m toward north.

3.11 Quality of the InSAR estimates The quality of the InSAR result is an important aspect for the acceptability and usability of the InSAR data. The InSAR results are estimates that are affected by unavoidable errors. These errors are described stochastically by quality indices. The quality indices that are described in this section refer to EGMS products. They are derived during data processing and hence they only describe the parameter precision, that is, the parameter dispersion. In fact, to describe the accuracy,5 a ground truth is needed, which is usually characterized by higher precision and accuracy (see next section), which is devoted to InSAR validation. The most important indices, defined for each MP, are described next. • Amplitude Dispersion Index.6 A good estimator of the standard deviation of the interferometric phase. Points with low index values

5 The accuracy can be defined as the “closeness to the true.” 6 It is computed using the stack of amplitude SAR images. It is given, pixel by pixel, by the ratio of the standard deviation of the amplitude values divided by their mean value.

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are characterized by a lower noise level. This index is independent from the deformation model of the given MP. • Precision of the mean velocity.7 Estimated by variance propagation on the model used to derive the mean velocity. It is worth observing that this precision is based on a linear deformation model. Consequently, a low precision can be due to high noise, but also to a nonlinear deformation behavior, for example, the one shown in Fig. 3.8. For this reason, this index cannot be used blindly to select the MPs. In fact, this could cause the loss of valuable MPs that are characterized by nonlinear displacements. In the Basic product of EGMS, precision decreases with the distance from the reference point. For the other two products (Calibrated and Ortho), it depends on the network of available GNSS stations. In addition to the precision of the mean velocity, there are two other similar indices: the precision of the acceleration and the precision of the seasonal amplitude. • Temporal coherence. It measures the similarity between the retrieved time series and a given model (see Eq. 2.7). In EGMS the used model is a third-order polynomial of coefficients a0 , a1 , a2 , and a3 , plus a seasonal component of amplitude A and shift S:



dðtÞ 5 a0 1 a1 t 1 a2 t2 1 a3 t3 1 A cosð2πt 1 SÞ

(3.7)

where t is expressed in years. This polynomial encompasses several different deformation phenomena. The temporal coherence is a global statistic, which concerns all the dates of a given time series. On a single date, there can be large deviations with respect to the other dates, due to noise or residual atmospheric affects. Such deviation can be reduced by spatially averaging the time series of multiple neighboring MPs. • RMSE. It is evaluated on the time series residuals after applying a regression model of a third-order polynomial plus a seasonal component (see Eq. 3.7). To conclude, it is worth mentioning that confidence in the InSAR results can be increased by using both ascending and descending estimates over the same area. This can be systematically done using the EGMS products. If the deformation is vertical, the two geometries offer two independent observations of the same phenomenon.

7 The precision of the mean velocity is derived from the time series after applying a regression model of a first-order polynomial and a sinusoidal component.

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3.12 InSAR validation results

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Otherwise, they can be used to derive two deformation components (see Section 3.5).

3.12 InSAR validation results The validation of the InSAR technique has been a hot topic since its first introduction. The validation has been an important step for the development of the technique and for its acceptability at the scientific, technical, and commercial levels. Most of the validation activities have been based on the comparison of deformation velocities and time series with independent estimates from sources of better quality, mainly leveling or GNSS measurements. Most of the validation results concern deformation velocities. Two main validation exercises were organized by the European Space Agency: PSIC4 (earth.esa.int/psic4; Raucoules et al., 2009) and the Terrafirma Validation Project (Crosetto et al., 2009). A number of interesting results were derived from the latter by the intercomparison of InSAR results coming from different teams and using ERS and Envisat data. The estimated standard deviation of the deformation velocity (σvelo ), the time series (σTS ), and the elevation of MP (σh ) were: σvelo 5 0:4 2 0:5mm=year σTS 5 1:1 2 4mm σh 5 0:9 2 2m:

(3.8)

These values are only representative of areas with characteristics similar to those of the Terrafirma Validation Project, that is, mainly urban areas with zero or moderate deformation velocities: they cannot be generalized to any possible scenario. Some of the most relevant validation results are briefly described as follows: Colesanti et al. (2003b) highlight the millimetric precision of ERS InSAR measurements, especially over a landslide. Musson et al. (2004) describe the validation of deformation velocities, derived by using ERS imagery, with GPS observations. They report differences below 0.5 mm/year. Hooper et al. (2004) describe a volcanic application, where good agreement with leveling and GPS data was achieved. Casu et al. (2006) describe a validation exercise with ERS data, where a σvelo of 1 mm/year and a σTS of 5 mm were found. Heleno et al. (2011) describe the intercomparison of the InSAR results from two independent processing chains, and their validation using leveling and GPS data. This work regards an urban area. Liu et al. (2013) report the

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results of a validation exercise based on TerraSAR-X InSAR results and leveling data. The following research works report the validation of InSAR results from Sentinel-1 data: • Large-scale monitoring. Parizzi et al. (2020) evaluate the InSAR performance over wide areas, considering the German Ground Motion Service.8 • Urban areas. An interesting work, which is based on Sentinel-1 ascending and descending data, is described by Fryksten and Nilfouroushan (2019). Using precise leveling data, they report a root mean square of velocity differences (InSAR vs leveling) of 0.58 mm/ year. A similar validation experiment using ascending and descending data in an urban area is described by Mancini et al. (2021). Another study is described in Hu et al. (2019). • Bridge monitoring. Schlo¨gl et al. (2021) describe the InSAR analysis and validation of a single bridge. • Construction works monitoring. Halicioglu et al. (2021) describe the monitoring and validation of the deformations caused by the construction of metro line stations. Another example related to the construction of a metro line can be found in Gheorghe et al. (2020). Dong et al. (2021) discuss the InSAR monitoring of canal construction works. • Mining monitoring. Pawluszek-Filipiak and Borkowski (2020) address the validation with leveling of an underground coal mine using InSAR monitoring. Sadeghi et al. (2021) evaluate the Sentinel-1 InSAR results coming from four different processing chains. The evaluation is based on the intercomparison of the InSAR results, considering deformation velocity, deformation time series, coverage, and MP density. As an example, the average standard deviation of velocity differences equals 1.1 mm/year. It is worth noting that the issue of consistency of different processing chains is key for the EGMS, which is produced by a team of different companies. Kotzerke (2021) describes in detail an intercomparison exercise performed during the implementation of EGMS, which covered 40,000 km2. The exercise involved four different InSAR companies and addressed the spatial density of MP over different land cover classes, the MP deformation velocity and acceleration, the MP time series displacements, and MP positioning.

8 BBD, BodenBewegungsdienst Deutschland, German Ground Motion Service. WebGIS available at: https://bodenbewegungsdienst.bgr.de.

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3.13 Artificial reflectors The issue of MP spatial density has been discussed in Section 3.3. Over specific areas, structures or infrastructures of high interest, the lack of MP, or a low MP density can be mitigated by deploying artificial corner reflectors (CRs). These devices, installed in situ, provide a strong response in the SAR images resulting in good MP (Sarabandi et al., 1992; Doerry, 2014). By deploying CRs, it is possible to strategically locate the MP in the locations of interest. Therefore this overcomes the opportunistic nature of natural MPs. The drawback is that using CRs requires accessing the area of interest to install the CRs. This deviates from the concept of pure InSAR remote sensing, which can be performed without requiring access to the study area. The CRs are usually installed in situ, and permanently. Their integrity must be guaranteed for the entire monitoring period. This may require certain maintenance activities. There are two main types of reflectors. A passive CR is an object with a simple geometrical shape, designed to be characterized by a high radar reflectivity. Such objects are usually constructed using metal plates, with a size that is large with respect to wavelength, and with faces oriented to maximize the energy reflected toward the radar. Several kinds of passive CR can be installed, one of the most commonly employed is the triangular trihedral (Li et al., 2012; Garthwaite, 2017). An example of passive CR is shown in Fig. 3.16. Designed for use with Sentinel-1 imagery, such a trihedral CR must have a dimension of the

FIGURE 3.16

Example of passive corner reflector used with Sentinel-1 imagery. The edge of the device is highlighted in light blue.

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edge (see Fig. 3.16) of 80
100 cm. For this reason the passive CRs are sometimes heavy and cumbersome to deploy. Their installation can be difficult due to the limited accessibility of the sites, for example, in case of some types of landslides, glaciers, etc. In addition, they can suffer from heavy weather conditions, especially strong winds and snowfalls. An alternative approach is represented by the installation of active reflectors (ARs), which are smaller and lighter (see Fig. 3.17). Their main drawback is the need for a power source, usually a battery and/or solar panel. An AR basically consists of a specific radio frequency device able to provide a radar response seen as a bright pixel in the image and with a stable phase response. Historically, the use of AR for spaceborne SAR started with the first SAR space missions, where they were mainly used for radiometric calibration. Fig. 3.18 shows an example of displacement map derived using a set of CRs and an AR. Some technical aspects of InSAR monitoring using CRs are described in Xia et al. (2002), Crosetto et al. (2013), and Garthwaite (2017), while Luzi et al. (2021) describe the implementation of an AR. CR and AR have been used in different studies: • assessing the InSAR measurement performances (Ferretti et al., 2007a; Marinkovic et al., 2007; Quin and Loreaux, 2012), • subsidence monitoring (Ge et al., 2011; Teatini et al., 2012; Yu et al., 2013; Zhu et al., 2014), • landslide monitoring (Shi et al., 2017; Luzi et al., 2022), • mining-induced deformation monitoring (Wegmu¨ller et al., 2009),

FIGURE 3.17 Example of an active corner reflector with its solar panel.

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3.14 InSAR versus in situ measurements

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FIGURE 3.18 Example of displacement map in a mountain area affected by a landslide, which was derived with a network of CRs and an AR (AC). The displacements are in mm. AR, Active reflector; CRs, corner reflectors.

• monitoring crustal deformation using GNSS and InSAR (Garthwaite et al., 2015), • studying groundwater fluctuations (van Leijen and Hanssen, 2007), and • bridge monitoring (Selvakumaran et al., 2020).

3.14 InSAR versus in situ measurements Often the MPs cover areas where no other types of deformation measurements are available: this is one of InSAR’s great advantages. However, sometimes in situ measurements are available. They can be derived from topographic measurements (e.g., leveling, total station, and GNSS), geotechnical measurements (inclinometers, tiltmeters), structural sensors like crackmeters, etc. To compare or fuse the InSAR and the in situ measurements, some key characteristics of the InSAR measurements are recalled.

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• The MPs are distributed spatially in an opportunistic way. They are usually located where there are scatterers with a persistent response over time. By contrast, the in situ measurements tend to be strategically located with respect to the deformation pattern at hand. An example is illustrated in Fig. 3.19, which concerns a land subsidence due to a lowering of the water table. The three leveling points are strategically located and correctly capture the subsidence. By contrast, the InSAR measurements are clustered on an industrial building, which has a deep foundation and is insensitive to subsidence. Note that in this case the InSAR measurements, which indicate zero deformation, are correct: they simply are not related to the deformation signal of interest, that is, the subsidence. • The above point illustrates a fundamental aspect of InSAR interpretation: it is necessary to consider the location of a given MP.

FIGURE 3.19 Scheme of a subsidence due to lowering of the water table. There are three geodetic measurements (brown triangles) that correctly capture the subsidence, while the three MPs (green points) display no subsidence, because they are related to a building with a deep foundation.

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3.15 Examples of data analysis tools

•

•

•

•

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To this end, it is important to consider the three coordinates of each MP. In particular, the elevation of the MP is often useful to discriminate between an MP on the ground or over other objects, for example, buildings and infrastructures. It is worth recalling that MP positioning suffers from the uncertainty described in Section 3.10. This regards both the planimetric position and the elevation. For the latter component, the values reported in Section 3.12 are recalled: σh 5 0:9 2 2m, which were estimated for ERS and Envisat. The standard deviation σh for Sentinel-1 is slightly worse, due to the characteristics of the Sentinel-1 orbits.9 Temporal sampling is an important characteristic to compare InSAR and in situ measurements. For Sentinel-1, from 2016 to the end of 2021, the sampling rate has been one acquisition every 6 days. In 2022, due to the failure of Sentinel-1B, the sampling rate is one acquisition every 12 days. With such rates, InSAR can typically follow slow movements, while it is not adequate for emergency purposes, which usually rely on in situ measurements. In the comparison with in situ data, it is important to consider the LOS nature of the InSAR measurements (see Section 3.5) and the nature of the in situ measurements. For instance, leveling data are mono-dimensional and refer to the vertical direction. Last, in the comparison or fusion with in situ data, it is necessary to take into account the different sources of InSAR errors, see Sections 3.8, 3.11, and 3.12.

3.15 Examples of data analysis tools In this section are described some examples of data analysis tools based on the InSAR outputs, that is, deformation velocity maps and time series. In particularly are considered: 1. the Active Deformation Areas (ADA) Finder, 2. the ADA Classifier, and 3. the differential deformations.

3.15.1 The Active Deformation Areas Finder The goal of the ADA Finder is to filter high volumes of InSAR output data to facilitate their interpretation and exploitation, especially by nonexpert InSAR users (Barra et al., 2017). The output of the ADA 9 The precision σh depends on the dispersion of the orbits over a given area. A reduced dispersion, which characterizes Sentinel-1, results in a higher σh .

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FIGURE 3.20 Example of deformation velocity map (right) and corresponding ADA map (left). These data are superposed to an orthoimage of the area, located in the French Pyrenees. ADA, Active Deformation Areas.

Finder is the map of ADAs, where each ADA is a cluster of neighbor MPs that have a deformation velocity above a given threshold. The number of MPs per cluster is an input of the procedure. A key output is given by the quality index of each ADA, which measures its reliability. It is a four-value index, which is derived by considering the noise of the deformation time series and the spatial homogeneity of the estimated deformations. An ADA map consists of a set of polygons, one for each cluster, with their main attributes: number of MPs, statistics on the ADA velocities, quality index, etc. It is worth observing that the ADA map is meant to simplify a certain type of InSAR analyses. However, other analyses, for example, those that focus on very localized phenomena, where even a single MP matters, have to consider the original InSAR output dataset. Furthermore, it is worth underscoring that the ADA extraction does not overcome the intrinsic InSAR limitations, for example, the absence of MP due to an unfavorable geometry or low coherence. Fig. 3.20 shows an example of ADA map: the information of a thousand MPs (right side) is concentrated in a limited number of polygons (left side). The ADA Finder tool is available from CTTC.10 This tool has been used to filter the Basic data of EGMS. The result can be displayed using a webmap, available at http://groundmotionADAs.com. 10 CTTC, Centre Tecnolo`gic de Telecomunicacions de Catalunya. Contact person: M. Crosetto, [email protected].

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3.15.2 The Active Deformation Areas Classifier The goal of this analysis is to identify, through a classification based on a decision tree, the geological or anthropogenic process that causes the presence of ADAs. This is a useful tool to simplify and speed up the interpretation of ADA maps, especially over broad areas, when dealing with numerous ADAs. In the implementation described in Navarro et al. (2020), six different phenomena are tested, that is, landslide, sinkhole, subsidence, building settlement, expansive soil, and thermal expansion. To do so, some extra auxiliary data are required. The inputs of the ADA Classifier include the output of the ADA Finder; a digital terrain model; the inventories for landslides, sinkholes, land subsidence, and infrastructures; and a geologic map. The output of ADA Classifier is another file with ADAs, where the attribute table of each ADA is extended to include additional fields, each of them stating the probability that the ADA belonging to the corresponding deformation process. The ADA Classifier is part of the ADA tools software, freely available from CTTC. Details about the ADA Classifier implementation and use are provided in Navarro et al. (2020). An example of output of the ADA Classifier is shown in Fig. 3.21.

FIGURE 3.21 Example of output of the ADA Classifier. It includes two layers: one devoted to subsidence and one related to landslides. ADA, Active Deformation Areas.

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FIGURE 3.22 Example of BDD over a small village in a mountainous area. Each building with at least two MPs has a colored perimeter. The triangle shows the position of the maximum deformation gradient. BDD, Building differential deformation; MPs, measurement points.

3.15.3 Differential deformations Given a deformation field, the damages to structures and infrastructures depend on the deformation pattern: the most significant damages are associated with high differential deformations or, said in other words, to high spatial deformation gradient values. Shahbazi et al. (2022) describe two types of differential deformations: terrain differential deformations (TDDs) and building differential deformations (BDDs). They compute slope and aspect of the deformation field, focusing the attention on the local maximum deformation slopes. The TDD is computed using all MPs that fall outside buildings, structures, or infrastructures. To save processing time, the TDD can be computed within only the previously identified ADAs. Examples of TDD maps can be found in Shahbazi et al. (2022). The BDD is computed over each building, structure, or infrastructure of the area of interest, which are identified using an external building map11 and over which there are at least two MPs. In Fig. 3.22, an example of BDD is shown. The buildings that contain more than two MPs are highlighted by a color-coded perimeter. The BDD can be a useful 11 A valuable data source can be OpenStreetMap, https://www.openstreetmap.org/

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screening tool to analyze large InSAR datasets. However, it is worth emphasizing that the reliability of BDD locally depends on the number of MPs that fall within a given building polygon. For large buildings, where dozens of MPs are available, the BDD information is certainly of use.

3.16 Open-source InSAR software It is possible to find online resources useful to perform InSAR processing and analysis. Most such resources are open-source. Some of the most important tools are described next (note that this is not an exhaustive list). • ESA Sentinel-1 Toolbox.12 A collection of processing tools to perform a basic InSAR analysis based on a pair of InSAR images. It does not support the advanced InSAR analysis based on a stack of SAR images. The toolbox can process data from several ESA missions (Sentinel-1, ERS-1/2, and ENVISAT), as well as data from ALOS-1, TerraSAR-X, Cosmo-SkyMed, and RADARSAT-2. The Sentinel-1 Toolbox is complemented by the Sentinel Application Platform13 to process and analyze Earth observation data (see Foumelis et al., 2018). All of the information can be accessed through this link: https://step.esa.int/main/. • Doris.14 An InSAR processor that can be downloaded free for noncommercial applications. It can be used to generate DEMs and displacement maps using single pairs of InSAR images but not SAR stacks. It can process several types of SAR data (Kampes and Usai, 1999). • ISCE.15 The InSAR Scientific Computing Environment, open-source modular software framework capable of supporting InSAR processing of SAR stacks. This software includes some code from the ROI_PAC package. • STAMPS.16 A software package to extract ground displacements from time series of SAR images. It is therefore a tool to perform a full InSAR analysis. The package incorporates PS and DS, with an option to combine both approaches.

12 https://step.esa.int/main/toolboxes/sentinel-1-toolbox/ 13 https://step.esa.int/main/toolboxes/snap/ 14 https://step.esa.int/main/toolboxes/sentinel-1-toolbox/ 15 https://github.com/isce-framework/isce2 16 https://homepages.see.leeds.ac.uk/Bearahoo/stamps/

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• GMTSAR.17 An open-source InSAR processing system designed for users familiar with Generic Mapping Tools (http://www.genericmapping-tools.org). It can be used to process stacks of SAR images (see Sandwell et al. (2011)). • OSARIS.18 A framework that facilitates interferometric processing of large stacks of Sentinel-1 SAR in parallelized environments. It can process stacks of SAR images.

References Barra, A., Solari, L., Be´jar-Pizarro, M., Monserrat, O., Bianchini, S., Herrera, G., et al., 2017. A methodology to detect and update active deformation areas based on sentinel-1 SAR images. Remote Sensing 9 (10), 1002. Available from: https://doi.org/10.3390/rs9101002. Casu, F., Manzo, M., Lanari, R., 2006. A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sensing of Environment 102 (3), 195
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89. Available from: https://doi.org/10.1016/j.isprsjprs.2015.10.011. Dalla Via, G., Crosetto, M., Crippa, B., 2012. Resolving vertical and east-west horizontal motion from differential interferometric synthetic aperture radar: the L’Aquila earthquake. Journal of Geophysical Research: Solid Earth 117 (B2). Available from: https:// doi.org/10.1029/2011JB008689. Doerry, A.W., 2014. Reflectors for SAR Performance Testing (No. SAND2014-0882). Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Dong, J., Lai, S., Wang, N., Wang, Y., Zhang, L., Liao, M., 2021. Multi-scale deformation monitoring with Sentinel-1 InSAR analyses along the middle route of the south-north water diversion project in China. International Journal of Applied Earth Observation and Geoinformation 100, 102324. Available from: https://doi.org/10.1016/j.jag.2021.102324. Eineder, M., Minet, C., Steigenberger, P., Cong, X., Fritz, T., 2010. Imaging geodesy— toward centimetre-level ranging accuracy with TerraSAR-X. IEEE Transactions on Geoscience and Remote Sensing 49 (2), 661
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1923. Available from: https://doi.org/10.1109/TGRS.2013.2256428. Luzi, G., Espı´n-Lo´pez, P.F., Mira Pe´rez, F., Monserrat, O., Crosetto, M., 2021. A low-cost active reflector for interferometric monitoring based on Sentinel-1 SAR images. Sensors 21 (6), 2008. Available from: https://doi.org/10.3390/s21062008. Luzi, G., Barra, A., Gao, Q., Espı´n-Lo´pez, P., Palama`, R., Monserrat, O., et al., 2022. A Low-cost active reflector and a passive corner reflector network for assisting landslide monitoring using multi-temporal InSAR. Remote Sensing Letters 13 (11), 1080
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C H A P T E R

4 European Ground Motion Service

4.1 Introduction The goal of the European Ground Motion Service (EGMS) is to provide consistent, updated, standardized, and reliable information regarding natural and anthropogenic ground motion phenomena over Europe, that is harmonized across national borders. This represents the largest interferometric SAR (InSAR) deformation measurement service ever conceived (Crosetto et al., 2020). EGMS is part of the Copernicus1 Land Monitoring Service’s product portfolio2 and is implemented under the responsibility of the European Environment Agency.3 The service was defined in the period 2016
17 by the so-called EGMS Task Force, which outlined its main characteristics in the EGMS White Paper (EGMS, 2017). It is worth noting that EGMS was anticipated by some Ground Motion Services implemented at the national, Italy (Costantini et al., 2017), Norway (Dehls et al., 2019), Germany (Kalia et al., 2017), and regional levels, Tuscany (Italy), see Raspini et al. (2018). In addition, a demonstration of advanced InSAR processing at the continental scale was published in 2020 by Lanari et al (2020). In 2019 a specific working group was commissioned by the European Environment Agency to detail the EGMS technical specifications (Larsen et al., 2020).

1 Copernicus is the European Union’s Earth observation programme (https://www. copernicus.eu/en) 2 https://land.copernicus.eu/ 3 https://www.eea.europa.eu/

Satellite Interferometry Data Interpretation and Exploitation DOI: https://doi.org/10.1016/B978-0-443-13397-8.00003-0

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FIGURE 4.1 Geometry of a Sentinel-1 SAR image, including three swaths. Each swath is made up by 9 bursts. The approximate coverage of one burst is 20 by 80 km.

Some key characteristics of the EGMS are listed next: • It is based on Sentinel-1 data, which are processed at full resolution. • It involves both ascending and descending data, with a revisit time of 6 days. This was true until the end of 2021; since then and until the launch of Sentinel-1C, there will be a 12-day revisiting time. • It involves about 750 SAR scenes and approximately 20,000 bursts or subimages4 (see Fig. 4.1). • It consists of a baseline product, which ranges from February 2015 to the end of 2020. On average, 260 SAR scenes are available for the baseline product. This leads to a total input volume of approximately 1.5 PB of uncompressed SAR images. • The baseline product will be followed by product updates every 12 months. Every year, the data volume will increase by approximately 350 TB. • Due to the huge volume of processed data, the EGMS deformation data are always published with a given delay. The delay of the baseline product at the time of publication (May 2022) was approximately 18 months. 4 A Sentinel-1 SAR image is divided into three swaths, and each swath is divided into nine bursts or subimages.

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• For the regions affected by seasonal snow cover, the processing is restricted to the snow-free scenes. • The geographical coverage of the service includes the Copernicus participating states, that is: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom, Iceland, and Norway. The coverage is illustrated in Fig. 4.2. • The EGMS requires high computational capacity. The production of the baseline product and of the first three annual updates is performed by the ORIGINAL consortium, “OpeRatIonal Ground motion INsar ALliance,” which is composed of four European companies, namely, e-GEOS (the prime contractor), TRE-Altamira, NORCE, GAF, and six subcontractors (NHAZCA, Earth Metrics, NGU, PPO.labs, SGO, and DLR).

FIGURE 4.2 Geographical coverage of the EGMS. The term “DROM” refers to the overseas departments and regions (De´partements et Regions d’Outre-Mer) of France, which share the same status of the regions and departments of Metropolitan France. They are the islands of Mayotte, Reunion, Martinique, Guadalupe, and the French Guyana. EGMS, European Ground Motion Service.

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FIGURE 4.3 Flow chart of the EGMS to generate the three main service products. EGMS, European Ground Motion Service.

• The processing is split between the different companies that operate their own processing chains. The overlaps between products from different companies are used to ensure seamless harmonization between production chains. The Service includes three types of products: Basic, Calibrated, and Ortho. The relations among these products are illustrated in Fig. 4.3.

4.2 European Ground Motion Service Basic product The EGMS Basic product represents the standard InSAR product: a set of MPs, where each measurement point (MP) contains the time series of the line-of-sight (LOS) displacements, the geocoded coordinates, the quality measures, etc. The Basic product is delivered for individual data blocks, which correspond to the burst geometry of the original Sentinel-1 SAR images. The displacements are relative to a single burst (see Section 4.2.2). Differential movements between points located in different bursts cannot be measured. The Basic product is also known as the Level 2a product. This is because the original raw SAR images are named Level 0, while the Single Look Complex (SLC) SAR images are designated Level 1. Level 2a indicates that the product has been derived from Level 1 data. The other two products are designated as Level 2b (Calibrated) and Level 3 (Ortho). The Basic product is a necessary first step to produce other product levels. The production of the Basic product is by far the most demanding processing step: the production of Calibrated and Ortho products consumes only a small fraction of the total resources. At least one ascending and one descending Basic product is available for each location covered by EGMS. To compare the displacements measured from different geometries, the same reference point must be chosen. The change of reference point is discussed in Section 3.6.

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The input data to produce the EGMS Basic product include all available Sentinel-1A and -1B SAR images for a given relative orbit that cover the chosen processing period. The input SAR images are in SLC format. For each SAR image, the corresponding precise orbit is required. In addition, a Digital Elevation Model is needed. The Copernicus DEM 30-m5 is used for this purpose in EGMS. InSAR processing generally follows the procedure described in Section 2.3. However, it is worth observing that this processing is performed by four different companies, each of which uses different InSAR processing chains. The differences between such chains are described in Ferretti et al. (2021). In the following, as examples, the differences that concern two key steps of the InSAR procedure are mentioned. The first one is the selection of the MP candidates. e-GEOS makes use of the persistent scatterer pair (PSP) approach (Costantini et al., 2008, 2014). The PSPs are scatterers that include both persistent scatterers (PS) and distributed scatterers (DS). TRE-Altamira uses the dispersion of the SAR amplitude to select the PS (Ferretti et al., 2000, 2001) and the analysis of the phases to select the DS (Ferretti et al., 2011). NORCE performs an initial preselection by testing all pixels, while the final MP selection is based on the deviation of the observations from a fitted polynomial and a seasonal model. Last, GAF uses two criteria: the so-called signal-to-clutter ratio and the dispersion of the amplitude (Ferretti et al., 2000, 2001). The second is the estimation of Defo lin and Topores (see Section 2.3). None of the four companies make use of a pure linear term. e-GEOS adopts a third-order polynomial model associated with a seasonal component (Costantini et al., 2008). TRE-Altamira and NORCE use a piecewise linear model. Last, GAF performs this step by using a linear model associated with a seasonal deformation component. As already mentioned in Section 3.12, the consistency of the results rendered by the different processing chains is a fundamental goal for the EGMS. This has been addressed in a detailed intercomparison analysis, which is described in Kotzerke (2021).

4.2.1 Characteristics of the Basic product Basic products are delivered on a single burst logic: a single zip archive file for each Sentinel-1 burst. A Basic product consists of a set of MP points, with their attributes. The attributes are common to both ascending and descending geometries. The detailed description (parameter, measure unit, meaning, example, and data format) of the Basic product attributes is provided in Capes and Passera (2022). 5 https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model

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A key characteristic of the Basic product is its resolution. This must be as high as possible, without losing MPs. For the DSs the resolution must be better than 100 m (Capes and Passera, 2022). The MP density is expected to be more than 5000 MP/km2 in “continuous urban fabric” (Corine land cover6 CLC18
1.1.1); above 1000 MP/km2 in “discontinuous urban fabric” and “industrial, commercial, and transport units” (CLC18
1.1.2 and CLC18
1.2); and above 100 MP/km2 in “open spaces with little or no vegetation” (CLC18
3.3). Concerning the specifications of the Basic product, the standard deviation (1σ) of the mean deformation velocity should be lower than 0.7 mm/ year for MP with temporal coherence greater than 0.7. Regarding the displacements, their standard deviation (1σ) should be better than 4 mm. For the geocoding or geolocation, the 3D accuracy must be better than 10 m. A Basic product consists of a set of MP points: despite the raster nature of the original SAR data, it is made up of vector data. For each MP point the geographic coordinates in the WGS84 datum, and the cartographic coordinates are associated. The chosen projection is the ETRS89 Lambert Azimuthal Equal-Area, with a projection center at 52 N and 10 E. The cartographic projection can be changed by using GIS software. The Basic product is designed to be used by expert users. For this reason, it is not distributed through the EGMS viewer. As is explained in the next section, the deformation of MPs belonging to a given burst refers to the same burst. For this reason the Basic product is mainly to be used to study and interpret local deformation phenomena. The MP density can be rather high, mainly in urban areas. If needed, it can be reduced by applying a threshold, for example, on the temporal coherence. This can be achieved using GIS functionalities.

4.2.2 Reference for the deformations of the Basic products The InSAR deformations usually refer in space to an MP reference point, and temporally to the date of the first SAR image used. This approach has two limitations. The noise of the reference MP is added to all of the MPs, while the noise of the first image is added to the other dates of the given time series. To overcome these limitations, in the Basic product the deformations are referenced as follows. See Ferretti et al. (2021) for details: • In the time domain the deformation refers to the modeled deformation of the first date. The procedure involves: • computing the deformations referring to the first image (see Fig. 4.4), • fitting a third-order polynomial plus seasonal component, 6 https://land.copernicus.eu/pan-european/corine-land-cover/clc2018.

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FIGURE 4.4 Temporal reference for the EGMS Basic product. Original time series (above, in blue), which has a step in the second image due to noise in the first image. The model is shown with a green dotted line. Final time series (below) obtained by removing the value of the model in the first image (4.2 mm). EGMS, European Ground Motion Service.

• taking the value of such a model in correspondence to the first date, and • removing this value from the entire deformation time series. In Fig. 4.4, there is a step that is visible in the deformation of the second image, which affects the entire time series. This is due to noise in the first image. This step is removed using the above procedure (Fig. 4.4). The procedure is basically robust to the noise of the first image. • In the spatial domain the MPs are referred to a virtual point, which represents the average of all MPs that belong to a stable area located in the burst.

4.3 European Ground Motion Service Calibrated product The Calibrated product is generated by harmonizing and mosaicking the Basic product data and by integrating them into a standardized

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reference frame using data from permanent GNSS stations.7 It is worth noting that this product relies on both InSAR data and external data coming from GNSS stations spread across Europe. This product, which is also named Level 2b, is considered the main deliverable of EGMS: it is meant to meet the needs of most EGMS users. By fusing the Basic product and GNSS data, the role of the reference point of the Basic product is played by the used set of GNSS stations, that is, the reference is the GNSS network of stations. The InSAR and GNSS fusion procedure is described in Section 4.3.2. As mentioned earlier, the deformation shown by the Basic product is “relative motion,” referred to a virtual reference point. By contrast, the result of the fusion is that the deformation of the Calibrated product reflects “absolute motion,” which is relative to an Earth-centered reference system. With the Calibrated product, it is possible to compare the MP deformations that belong to different bursts. This is a noteworthy advantage over the Basic product. A comparison of Basic and Calibrated products is shown in Figs. 4.5 and 4.6. In the first, there is no evident difference between the two products. This is due to the fact that, in this area, there is a negligible signal coming from the GNSS data. By contrast, in Fig. 4.6, the change in the velocity field is patent. In this case there is a lowspatial frequency deformation (signal) of several mm/year in the GNSS data (this is due to the phenomenon of postglacial rebound8 that affects Fennoscandia), which is reflected in the Calibrated product. It is worth noting that in this specific example, for a local deformation analysis, the Basic product that does not contain the rebound signal is better.

4.3.1 Characteristics of the Calibrated product The EGMS Calibrated product combines the characteristics inherited from the Basic product with the quality (in terms of precision, accuracy, and reliability) provided by a network of GNSS stations. For this reason, the main characteristics of the EGMS Basic product apply to the Calibrated version. The key characteristics are listed as follows: • The temporal sampling of deformation is the same as in the Basic product. 7 A permanent station is a physical point equipped with a GNSS receiver and a structure for acquisition, storage and processing of data from GNSS satellite constellations (e.g., GPS and Galileo). This is done continuously 24/7 throughout the year, providing position services in various forms and for various applications. 8 What is the postglacial rebound? https://www.antarcticglaciers.org/glaciers-and-climate/sea-level-rise-2/recovering-from-an-ice-age/

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FIGURE 4.5 Difference between Basic (above) vs Calibrated (below) in an area where there are negligible differences between the two products. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

• The Calibrated product is generated at full spatial resolution. • It contains LOS data, with no assumptions regarding the true 3D direction of ground motion. • The Calibrated product includes two datasets: one based on ascending and the other based on descending InSAR data. • Each MP point has its geographic coordinates (WGS84), and its cartographic coordinates, in the ETRS89 Lambert Azimuthal EqualArea system, associated. • Some isolated islands and DROMs have no available GNSS stations. In these cases, the Calibrated product is derived by mosaicking the Basic product, setting the mean ground velocity to zero.

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FIGURE 4.6 Difference between Basic (above) vs Calibrated (below). Example with remarkable differences between the two products. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

A Calibrated product consists of a set of MP points with their attributes. See their definition in Capes and Passera (2022). Concerning the product specifications, the standard deviation (1σ) of the mean deformation velocity should be better than 0.7 mm/year with temporal coherence above 0.7. For displacements the precision (1σ) should be better than 8 mm. For geocoding or geolocation the 3D accuracy must be better than 10 m.

4.3.2 Calibration using GNSS data The calibration procedure requires all of the available Basic products and the GNSS data from all the available permanent GNSS stations

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throughout Europe. The rationale of the calibration is that InSAR and GNSS must be fused because they perform well at different but contiguous spatial scales: • InSAR measurements are dense and accurate at the local scale, typically up to several dozen kilometers. • InSAR provides spatially relative measurements. • GNSS measurements are accurate over large spatial scales. • GNSS provides absolute measurements. • GNSS stations are spatially sparse (the average distance between stations is 50
60 km at the European level). The two datasets are clearly complementary. The goal of the calibration is to exploit the synergy between these two types of data. Specifically, it generates an advanced product (the Calibrated product) by keeping the spatial high frequencies of InSAR and the spatial low frequencies of GNSS. For the details of the procedure, refer to Larsen et al. (2021). Schematically, the calibration involves the following steps: • Merging different sources of GNSS data. • Estimating a pan-European deformation model based on GNSS data. The model consists of a 50-km grid of 3D deformation velocities. It is estimated by using the least-squares collocation method (Moritz, 1978).9 • Interpolating the model in the location of each MP of the Basic product. • Projecting the 3D interpolated model components in the LOS. This is done for each MP and separately for ascending and descending data. • Removing the low-frequency trends of the InSAR results, replacing them with the low (interpolated and projected) frequencies coming from the GNSS model. In this way, the InSAR data are anchored to the GNSS model.

4.4 European Ground Motion Service Ortho product The Ortho product represents an advanced InSAR product, which takes advantage of the ascending and descending geometries that are available in any spot of the EGMS-covered area. Starting from these two geometries, it is possible to estimate two main deformation components: the vertical (up and down), and the horizontal (east
west) components. Each component is provided as a separate layer. What about the third component, that is, the north
south horizontal component? Since 9 The GNSS model can be downloaded from the EGMS.

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Sentinel-1 satellites have a near-polar orbit, they are almost blind to displacements in the north
south direction. Therefore such a component is not computed. Compared to the previous products, the Ortho product has a muchreduced resolution. In fact, it is delivered over a 100-m grid. However, many of the 100 by 100 m cells are empty because in every cell data from both ascending and descending geometries are needed. If only one geometry is available, the corresponding cell remains empty (see Fig. 4.7). Examples of empty cells are those where, in at least one of the geometries, there are layover or shadow effects. An important advantage of the Ortho product is that it is easy to interpret without considering the LOS InSAR geometry. It is particularly useful to study deformation phenomena that are characterized by significant horizontal displacements, for example, some types of landslides. Another advantage, inherited by the Calibration product is that the Ortho product displays absolute motion, which is consistent over the entire EGMS coverage. The Ortho product is the one that is first displayed by the EGMS viewer. The two deformation velocity layers at the continental scale are shown in Fig. 4.8. In the vertical up-and-down map, the deformation is dominated by the postglacial rebound of Fennoscandia and the deformation of Iceland, while in the horizontal east
west map the deformation is monopolized by the tectonic activity around Greece and the Aegean Sea

FIGURE 4.7 Scheme illustrating the coverage of the Ortho product. The three grids represent the 100 by 100 m grid geometry of the Ortho product. Available MPs in the ascending Calibrated (above, left), available MPs in the descending Calibrated (above, right), and final grid of the Ortho product (below).

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FIGURE 4.8 Ortho product. Up-and-down vertical (left) and horizontal east
west (right) over Europe.

and the deformation of Iceland. In these areas of strong GNSS signal the interpretation of local phenomena may be difficult or masked out by large-scale deformation. Chapter 6 will discuss how Calibrated and Ortho data for such areas are dealt with.

4.4.1 Characteristics of the Ortho product The EGMS Ortho product derives some of its key characteristics from the Calibrated product. The main characteristics are listed next: • The Ortho product is generated over a 100-m spacing grid organized in 100 by 100 km tiles. The grid is sparse because the generation of the Ortho product requires, over a given cell, the concomitant presence of at least one MP in the ascending and one MP in the descending datasets. • The Ortho product contains two main layers: the vertical up-anddown component and the horizontal east
west component. • Each layer of the Ortho product consists of displacement time series and attributes. The attribute definition is provided in Capes and Passera (2022). • Each cell has associated geographic coordinates (WGS84). Concerning the product specifications, the standard deviation (1σ) of the mean deformation velocity should be better than 0.7 mm/year with temporal coherence above 0.7. Regarding the displacements, the precision (1σ) should be better than 8 mm. For the geocoding or geolocation the 3D accuracy must be better than 10 m.

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The Ortho product is based on a grid. The cell geometry depends on the chosen projection and datum. If a new projection is needed, the grid must be resampled and is therefore susceptible to distortion.

4.4.2 Estimation of the deformation components The procedure to estimate the two components requires the ascending and descending datasets of the Calibrated product as inputs. Additionally, it requires the GNSS velocity model used in the generation of the Calibrated product. The procedure involves the following steps: • Extracting the horizontal north
south deformation component from the GNSS velocity model. • Projecting this component in the LOS of both ascending and descending geometries. The methodology uses all available InSAR measurements. In southern Europe, there are typically one ascending and one descending datasets. However, the number of available InSAR datasets can even exceed six at very high latitudes for both ascending and descending geometries. • Subtracting the projected component from the Calibrated product, both in ascending and descending geometries. • From the Calibrated product of the previous step, averaging the MPs that fall in the same cell of a grid of 100 by 100 m. For DS the time series are referenced to the center of the effective area. The averaging requires at least one MP for each cell. This results in an averaged MP, with averaged deformation time series, which represents the given cell. The resulting time series is referenced to the center of the cell. This is done separately for the ascending and descending data. • Decomposing in vertical up
down and east
west components, assuming zero displacement in the north
south horizontal direction and using the averaged product, for the cells that have an averaged MP in both ascending and descending geometries. For details of the decomposition, refer to Ferretti et al. (2021).

4.5 European Ground Motion Service validation The quality of EGMS products is monitored during production by internal quality control procedures. The production team supervises the production with automated quality checks within each processing phase supported by intermediate quality reports. Additionally, the EGMS includes validation activities to confirm that the EGMS products are consistent with the specifications and with the expected

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range of applications and use. The validation addresses three main aspects: the completeness and consistency of the products, their accuracy, and their usability. The validation is independent and separated from the data production and is intended to certify the InSAR results and the supporting documentation. Validation is carried out based on ancillary datasets that were not previously exploited for data production purposes. Validation is performed on the complete portfolio: Basic, Calibrated, and Ortho products. Validation is of great interest for EGMS end users since it indicates which phenomena the EGMS can capture, and which are the possible fields of application. The list of validation activities includes, from small to detailed scale: 1. point density check; 2. comparison with other national ground motion services, for example, the German (Kalia et al., 2017) or the Norwegian (Dehls et al., 2019) services; 3. comparison with inventories of phenomena, events, damages, or anthropic activities; 4. consistency check with ancillary geo-information, including geological, lithological, geotechnical, geo-mechanical, hydrogeological, and geomorphological data; 5. comparison with GNSS data; 6. comparison with in situ monitoring data, including ground or single building motion monitoring, or groundwater-level measurements; and 7. evaluation of height, location, and displacement at corner reflector locations. As of the writing of this book, the EGMS validation activities are ongoing. Results will be published on the main EGMS landing page.

4.6 European Ground Motion Service applicability This section discusses the applicability of EGMS (see Table 4.1). Only the main InSAR applications are discussed, without any intent to provide an exhaustive list. It is understood that the main InSAR characteristics and limitations described in this book are applicable in any application. For instance, a key limitation is that EGMS products are published with a given delay with respect to the occurrence of a given deformation. However, in Table 4.1, only the main characteristics and limitations that are relevant to the given application are highlighted. As can be seen in Table 4.1, one of the main limitations of EGMS has to do with providing motion monitoring data with a temporal

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TABLE 4.1 Applicability of the European Ground Motion Service (EGMS) products, considering the main InSAR application fields. Applicaon Subsidence/upli: - natural - anthropogenic Landslide:

- detecon - mapping/inventory - monitoring Tectonics: - measure tectonic moon - earthquakes Volcanos: - inflaon/deflaon - crisis monitoring - acve lava flow monitoring Mining: - acve - abandoned Oil and gas: - effects on surface and infrastructure - pipelines Buildings - single buildings - building surroundings - differenal deformaons Engineering works - dewatering effects - tunneling - excavaon effects Infrastructures: - Roads, highways, railways - Surroundings of above - Dikes - Surroundings of dikes - Bridges, viaducts - Surroundings of above - Dams - Surroundings of basin - Ports - Airports Cultural heritage - heritage building - surrounding areas

Suitable Observaons B C O Good suitability in general, provided the phenomenon is of sufficient size Includes consolidaon and sub-erosion (karst, etc.) Groundwater changes, urbanizaon, etc. Limitaons: velocity must be < 2.3mm/day; land cover; SAR geometric effects; size of the landslide; type of landslide

Restricted to past events due to update EGMS policy Basic product only for localized moon Co-seismic. Restricted to past events due to EGMS update policy Limitaons in the Ortho for geometric effects As above Limitaons: SAR temporal sampling and restricted to past events due to update EGMS policy As above Limitaons: surface changes and geometric effects in open pit mines; land cover in underground mines Restricted to past events due to update EGMS policy Restricted to past events due to update EGMS policy As above As above. Ortho: limitaon of resoluon Limitaon: MP availability, precision of the esmaon. Ortho only for large buildings As above Limitaon: MP availability. Ortho only for large buildings Limitaon: MP availability, precision of the esmaon. Restricted to past events due to EGMS update policy As above As above As above Limitaon: MP availability, precision of the esmaon. Restricted to past events due to EGMS update policy Limitaon: MP availability Limitaon: land cover, geometry of the area Limitaon: MP availability Limitaon: MP availability Limitaon: land cover, geometry of the area Limitaon: MP availability depending on geometry Limitaon: land cover

Limitaon: MP availability. Ortho only for large buildings Limitaon: MP availability. Ortho only for large buildings

In the columns “Suitable,” B stands for Basic, C for Calibrated, and O for Ortho product. In the same column, green means suitable, yellow possibly suitable, orange partially suitable, and red unsuitable.

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FIGURE 4.9 Comparison between Sentinel-1 and very high-resolution Cosmo-SkyMed results over an urban area.

delay. This clearly prevents using the EGMS data for monitoring ongoing deformation phenomena. How can the EGMS data be complemented? The first approach is to use the fresh, updated Sentinel-1 data to perform monitoring of phenomena of interest. Using the data currently available, such monitoring can be updated every 12 days for every acquisition geometry. In the future, with the launch of Sentinel-1C, the update could be every 6 days. The second approach is to use very high-resolution X-band data, that is, imagery from TerraSAR-X, TanDEM-X, PAZ, and CosmoSkyMed. Such data complement EGMS from two viewpoints. The first is MP density. A comparison between Sentinel-1 and very highresolution InSAR data is shown in Fig. 4.9. Over the same area, Sentinel-1 provides 556 MPs and Cosmo-SkyMed 3217 MPs: the gain in the MP density is evident. Second, the images must be planned to perform the monitoring.

4.7 European Ground Motion Service Explorer The EGMS Explorer10 is the tool with which to disseminate EGMS products. It includes two main functionalities: (1) an interactive WebGIS that allows the users to visualize and perform some basic 10 The EGMS Explorer is accessible at https://egms.land.copernicus.eu/.

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FIGURE 4.10 Aspect of the EGMS Explorer. Data menu (1), toolbar (2), color bar box (3), and coordinate box (4). EGMS, European Ground Motion Service.

data analysis operations. No user registration is required for the WebGIS; (2) an interface to search and download the EGMS products. This mode can only be accessed by registered users.11 The two functionalities are described in the following two sections. More details are provided in Larsen et al. (2022).

4.7.1 How to use the European Ground Motion Service WebGIS? The WebGIS is used to visualize the Calibrated and Ortho products only. The Basic products can only be accessed using the download option. • Front-end of the EGMS Explorer. Fig. 4.10 shows the aspect of the EGMS Explorer. Its main part, in the center, includes the map viewer. Other key elements are the data menu and the toolbar (see description next), the coordinate box, which shows the position (by default WGS84 coordinated plus height) of the location clicked in the map viewer, and the box to adjust the color bar of the deformation velocity values. • Data menu. The data menu box is shown in Fig. 4.11. It includes the background layers (orthophotos, topographic map, and land cover map) to be displayed with the InSAR products. Aside from the InSAR data of overseas departments and regions of France (DROM), 11 The registration is done using the EU Login authentication service (previously ECAS), which is a user authentication point for access to a wide range of Commission information systems.

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FIGURE 4.11 Data menu box of the EGMS Explorer. EGMS, European Ground Motion Service.

FIGURE 4.12

Toolbar of the EGMS Explorer. EGMS, European Ground Motion

Service.

it includes the two main datasets: the Calibrated and the Ortho products (see next). • Toolbar. The toolbar is shown in Fig. 4.12. From left to right, it includes options to (1) search a location, which works with both toponyms and WGS84 geographic coordinates; (2) activate the help button; (3) add an external layer from a web map service or a web map tile service; (4) display the box to adjust the color bar; (5) set the light direction from the view angle (3D mode); (6) show the location of the user on the map; (7) generate a link to the current map view; (8) switch between 2D and 3D views; and (9) draw a polygon for computing the average InSAR time series. The last three bottoms are described in the following section. • 2D and 3D views. The map viewer can display the data in both 2D or 3D modes. The latter mode is particularly helpful to visualize and analyze mountainous regions. See Fig. 4.13 for an example. • Display of the Calibrated data. The calibrated data can be displayed by stripes of data that are identified by a number that increases from

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FIGURE 4.13 Example of 3D view of the Ortho product. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA). Ortho product, vertical.

FIGURE 4.14 Numbering of the tracks (stripes) of the Calibrated data.

west to east (see Fig. 4.14). Note that this numbering does not follow the original track numbering of the Sentinel-1 data. • Display of the Ortho data. The display of the Ortho product is simpler than that of the Calibrated product. In fact, due to its reduced resolution, there are only two maps to be displayed: the vertical up-and-down and the horizontal east
west (see Fig. 4.8). • Time series. A key element in the analysis of the EGMS products is the deformation time series. Several examples of time series are shown in this book. A detailed description of the time series is provided in Larsen et al. (2022).

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4.7.2 How to download European Ground Motion Service products? The EGMS Explorer provides an interface to search and download EGMS products in a format that can be handled using GIS software or other types of spatial analysis tools. The download is performed using the toolbar (see Fig. 4.12). The second bottom from the right is used to activate a geographical search mode. This is performed by drawing a polygon, which represents the area of interest. Once the polygon is defined, it is possible to search the products associated with it, which can be Basic, Calibrated, or Ortho, and download them. The Basic and the Calibrated products can be downloaded burst by burst. The Ortho product can be downloaded by tiles. The example in Fig. 4.15 (right) shows the bursts that correspond to a given area of interest (left). Fig. 4.16 shows the window used to download the data,

FIGURE 4.15 Basic data download example: the area of interest over the metropolitan area of Barcelona (left) corresponds to a set of bursts (right).

FIGURE 4.16 Window to download the data, in this case Basic data, which can be downloaded burst-wise.

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FIGURE 4.17

Window to download the data. This case concerns Basic data. The green circle indicates the option to download all of the burst addresses together.

which in this case displays the data burst by burst. Fig. 4.17 shows a more advanced option to directly download the links to download all the selected bursts falling within a search radius. These links can be used to automatically download the selected bursts or tiles.

4.8 European Ground Motion Service dissemination The EGMS is the first Copernicus service aimed at a wide distribution of satellite interferometric products at the European level. The EGMS products are made available on a full, open, and free-access principle. Many different users are involved, ranging from the scientific and industrial sectors to rank and file European citizens. The success of EGMS is important not only for the long-term continuity of the project, but also for the increase of the interferometric data usage, and for stimulating user uptake activities across Europe. A successful service must reach a large number and wide range of possible end users. In addition, it must be actively used for scientific, public, and private purposes by different entities at different levels. The success of the EGMS will trigger downstream activities, leading the industry to perform further product development and generate tailored services.

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To this end, the EGMS data need to be findable, accessible, usable, and understandable. The first three aspects are addressed in the previous two sections. The last term is addressed in this section. The entire EGMS must be well-documented. The documentation must be addressed to both new and advanced users. The key EGMS documentation is available here: https://land.copernicus.eu/pan-european/european-ground-motion-service (see Fig. 4.18). It features: • basic information about EGMS; • information on how to access the data; • a collection of key EGMS documentation, for example,: the Algorithm Theoretical Basis Document; the Product User Manual; the End User Interface Manual; the Product Description Document; the Quality Assurance and Control Report; the End User Requirements; the GNSS Calibration Report; and the EGMS Factsheet; • background and concept; • scientific papers about EGMS; • news articles about EGMS; and • information related to past events. This should be visited periodically to download the updated EGMS documentation, and to be informed on the latest EGMS-related activities. An example is the report on the EGMS activities that, as of the writing of this book, are ongoing.

FIGURE 4.18 Landing page of the EGMS, which collects key EGMS information. EGMS, European Ground Motion Service.

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References Capes, R., Passera, E., 2022. Product description and format specification. In: EGMS Documentation. Available from: https://land.copernicus.eu/user-corner/technicallibrary/egms-product-description-document. Costantini, M., Falco, S., Malvarosa, F., Minati, F., 2008. A new method for identification and analysis of persistent scatterers in series of SAR images. In: Proceedings of IGARSS 2008, Boston. Available from: https://doi.org/10.1109/IGARSS.2008.4779025. Costantini, M., Falco, S., Malvarosa, F., Minati, F., Trillo, F., Vecchioli, F., 2014. Persistent scatterer pair interferometry: approach and application to COSMO-SkyMed SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (7), 2869
2879. Available from: https://doi.org/10.1109/JSTARS.2014.2343915. Costantini, M., Ferretti, A., Minati, F., Falco, S., Trillo, F., Colombo, D., et al., 2017. Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data. Remote Sensing of Environment 202, 250
275. Available from: https://doi.org/10.1016/j.rse.2017.07.017. Crosetto, M., Solari, L., Mro´z, M., Balasis-Levinsen, J., Casagli, N., Frei, M., et al., 2020. The evolution of wide-area DInSAR: from regional and national services to the European Ground Motion Service. Remote Sensing 12 (12), 2043. Available from: https://doi. org/10.3390/rs12122043. Dehls, J.F., Larsen, Y., Marinkovic, P., Lauknes, T.R., Stødle, D., Moldestad, D.A., 2019. INSAR No: a National InSAR Deformation Mapping/Monitoring Service in Norway— from concept to operations. International Geoscience and Remote Sensing Symposium, 5461
5464. Available from: https://doi.org/10.1109/IGARSS.2019.8898614. Ferretti, A., Prati, C., Rocca, F., 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE TGRS 38 (5), 2202
2212. Available from: https://doi.org/10.1109/36.868878. Ferretti, A., Prati, C., Rocca, F., 2001. Permanent scatterers in SAR interferometry. IEEE TGRS 39 (1), 8
20. Available from: https://doi.org/10.1109/36.898661. Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., Rucci, A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE TGRS 49 (9), 3460
3470. Available from: https://doi.org/10.1109/TGRS.2011.2124465. EGMS, 2017. EGMS White Paper. , https://land.copernicus.eu/user-corner/technicallibrary/egms-white-paper . . Ferretti, A., Passera, E., Capes, R., 2021. Algorithm theoretical basis document. In: EGMS Documentation. Available from: https://land.copernicus.eu/user-corner/technicallibrary/egms-algorithm-theoretical-basis-document. Kalia, A.C., Frei, M., Lege, T., 2017. A Copernicus downstream-service for the nationwide monitoring of surface displacements in Germany. Remote Sensing of Environment 202, 234
249. Available from: https://doi.org/10.1016/j.rse.2017.05.015. Kotzerke, P. 2021. Quality assurance and control report
Harmonisation tests. In: EGMS Documentation. Available from: https://land.copernicus.eu/user-corner/technicallibrary/quality-assurance-and-control-report-2013-harmonisation-test. Lanari, R., Bonano, M., Casu, F., Luca, C.D., Manunta, M., Manzo, M., et al., 2020. Automatic generation of sentinel-1 continental scale DInSAR deformation time series through an extended P-SBAS processing pipeline in a cloud computing environment. Remote Sensing 12 (18), 2961. Available from: https://doi.org/10.3390/rs12182961. Larsen, Y., Dehls, J., Marinkovic, P., Rouyet, L., Stødle, D., 2022. End user interface manual. In: EGMS Documentation. Available at: https://land.copernicus.eu/user-corner/ technical-library/egms-end-user-interface-manual. Larsen, Y., Marinkovic, P., Dehls, J.F., Bredal, M., Bishop, C., Jøkulsson, G., et al., 2020. European Ground Motion Service: Service Implementation. Copernicus Land

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References

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Monitoring Service report. Available at https://land.copernicus.eu/user-corner/technical-library/egms-specification-and-implementation-plan. Larsen, Y., Marinkovic, P., Kenyeres, A., To´th, S., 2021. GNSS calibration report. In: EGMS documentation. Available from: https://land.copernicus.eu/user-corner/technicallibrary/egms-gnss-calibration-report. Moritz, H., 1978. Least-squares collocation. Reviews of Geophysics 16 (3), 421
430. Available from: https://doi.org/10.1029/RG016i003p00421. Raspini, F., Bianchini, S., Ciampalini, A., Del Soldato, M., Solari, L., Novali, F., et al., 2018. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites. Scientific Reports 8 (1), 1
11. Available from: https://doi.org/10.1038/s41598018-25369-w.

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C H A P T E R

5 Subsidence and uplift Subsidence normally refers to “a surface point sinking to a lower level” (Whittaker and Reddish, 1989) and mainly involves a vertical displacement with a horizontal component that sometimes accompanies the downward motion. The predisposing factors of subsidence are linked to the geotechnical characteristics of soil that govern the consolidation process according to Terzaghi’s theory (Terzaghi and Peck, 1967). This is a time-dependent process that evolves depending on the local stratigraphic asset. Human activities may accelerate the consolidation process; recent urbanization, (over)exploitation of water, extraction of oil and gas, and mining activity are among the most important causes of anthropogenic subsidence. Subsidence is not only a matter of ground lowering; in fact, it can have socioeconomic
environmental impacts and cause damage to buildings and infrastructures. Moreover, it has a direct link with the effects of climate change, especially in low-lying coastal areas (Dinar et al., 2021). Herrera-Garcı´a et al. (2021) estimate that “subsidence due to groundwater depletion occurred at 200 locations in 34 countries” and that “19% of the global population may face a high probability of subsidence.” This makes evident the need for measurements of ground motion over large areas but also at the scale of single buildings. Subsidence was one of the first fields of application of satellite interferometry. In 1999 Amelung et al. (1999) were among the first researchers to adopt this technique to measure the “ups and downs” of ground surface in Las Vegas. Subsidence is probably the best target for satellite interferometry since (1) it often affects urban or peri-urban areas, where the measurement point (MP) density is maximized; (2) the ground motion rates are generally low and within the detectable limits of the technique (see Section 3.7); and (3) the motion is often linear over time, or with some seasonal variations. Points (2) and (3) are not valid for subsidence induced by mining, where both surface changes and/or ground motion rates may sometimes induce interferometric coherence loss, thus preventing MP detection.

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Table 5.1 features a short and nonexhaustive list of recent interferometric SAR (InSAR)-based examples of subsidence monitoring and mapping. The following sections present the reader with two relevant case studies of subsidence monitoring and mapping with the European Ground Motion Service (EGMS) products. In addition, other examples coming from EGMS are used to describe specific applications and issues. The first comes from central Italy, specifically from the alluvial plain that includes the cities of Firenze, Prato, and Pistoia, in Tuscany. There, ground subsidence is linked to groundwater withdrawal.

TABLE 5.1 List of scientific papers the reader can reference to understand what can be done with interferometric data for subsidence mapping and monitoring. References

Location

Target and type of activity

Osmano˘glu et al. (2011)

Mexico City, Mexico

• Groundwater exploitation • Deformation determined and controlled by the geological asset • Comparison with groundwater level and GPS data

Przyłucka et al. (2015)

Bytom, Poland

• Mining-induced subsidence in an urban area • Fast ground motion rates required ad hoc processing solutions based on differential and multitemporal interferometry

Be´jar-Pizarro et al. (2016)

Alto Guadalentı´n, Spain

• Groundwater exploitation • Interpolation of geological data (thickness of compressible soil) and GPS data assisted by interferometric products

Solari et al. (2018)

Italy

• Review of 20 years of interferometric applications for land subsidence in Italy • Discussion of pros and cons of the technique • Collection of reference bibliography

Smith et al. (2019)

Groningen, The Netherlands

• Subsidence due to gas production in Europe’s largest gas reservoir • Comparison between interferometric and GPS data with a reservoir compaction model • Study of the relationship between reservoir pore pressure and deformation

Guo et al. (2020)

Beijing, China

• Quantitative estimation of the driving forces that lead to ground subsidence in a megacity • Combination of interferometric and geophysical data and borehole information • Deformation induced by groundwater exploitation and controlled by the fault position

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The second presents EGMS data from the Upper Silesian Coal Basin (USCB), one of the largest European coal mining areas, which extends between Poland and Czech Republic. There, mining activity causes several environmental effects, including subsidence.

5.1 Subsidence related to groundwater exploitation in the Firenze
Prato
Pistoia basin The Firenze
Prato
Pistoia basin is located in central Italy, in the Tuscany Region. As the name suggests, the basin extends through the provinces of Firenze, Prato, and Pistoia; Firenze is the most populated city (360,000 inhabitants) and the regional capital. The basin hosts almost 40% of the entire Tuscan population.

5.1.1 Geographical, geological, and hydrogeological context The basin is an intermontane alluvial plain elongated in the NW
SE direction; it is approximately 10-km wide and 45-km long (Fig. 5.1). It is a result of the Neogene extensional tectonics, which are in turn related to the opening of the Tyrrhenian Sea and developed along the axis of the Northern Apennines (Boccaletti et al., 2001). The basin hosts a sedimentary sequence of fluvio-lacustrine unconsolidated deposits that reach a maximum thickness of 500 m. The nonsedimentary part of the basin is made of metamorphic rocks of the Ligurian units (flyschoid formations), which overlie the Tuscan Nappe represented here by the Macigno Formation (Fig. 5.1). The Ligurian units are subdivided into internal and external units and are generally made of peridotites, basalts, ophicalcites, and ophiolitic breccias with shales, calcilutites, flysches, and siltstones. The Macigno Formation is a sandstone flysch.1 The basin is bordered by normal faults subparallel to its axes with the master fault aligned with the north-eastern border; this gives the basin an asymmetric shape with the area, with the maximum deposition shifted to the NE and with the consequent tilting of the sedimentary sequence (Boccaletti et al., 2001). The stratigraphical sequence is formed by three horizons, from the oldest to the youngest: (1) a fluvio-lacustrine succession of sands, pebbles, and clays2 (silts and clays are abundant in the central part of the basin, whereas pebbles and gravels

1 Refer to https://geology.com/rocks/ for a basic explanation, accompanied by photographs, of some of the rock types mentioned here. 2 See https://environment.uwe.ac.uk/geocal/SoilMech/classification/default.htm for an overview of soil classification.

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FIGURE 5.1 Geological map of the Firenze
Prato
Pistoia basin. The black contours are the municipalities of Firenze, Prato, and Pistoia. The inset in the lower left corner shows the location of the basin with respect to the entire Italian territory. Source: The map has been redrawn from the geological map of the Tuscany Regional map at 1:250,000, which is available online (https://www.regione.toscana.it/-/banche-dati-cartografia-geologica). The background image is a 20 cm orthophoto (AGEA flight 2019).

appear in correspondence of paleo alluvial fans3); (2) after a phase of uplift and erosion, the paleo Arno (the main river in the basin which nowadays flows across the city of Firenze) is captured by the basin and coarse-grained sediments are deposited on top of the previous horizon; (3) the actual sedimentation of the Arno river and the deposits of historical flooding events (Boccaletti et al., 2001). The sedimentary sequence of the Firenze
Prato
Pistoia hosts a multilayered aquifer with hydrogeological characteristics that are directly linked to the position within the basin and to the stratigraphical composition of the aquifers.4 In general, a phreatic aquifer is located in the northwestern part of the basin, where the Ombrone River deposited an alluvial fan that is 30- to 40-m thick. This is the main water resource for the city of Pistoia. Moving toward the south, the phreatic aquifer loses importance due to the increased presence of clays and silts. Between

3 A brief geomorphological description of an alluvial fan is provided in https://education. nationalgeographic.org/resource/alluvial-fan 4 An intuitive visual description of aquifers is available through the website of the United States Geological Survey: (https://www.usgs.gov/special-topics/water-scienceschool/science/aquifers-and-groundwater).

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Pistoia and Firenze the water pumping activities focus on multiple confined aquifers situated at different depths formed by gravels and sands lenses. This is where the demand for water is the highest due to agricultural, plant nursery, and industrial activities. The exploitation of water has been and is still triggering ground subsidence with different patterns and ground motion rates around the basin.

5.1.2 Satellite-derived deformation history The Firenze
Prato
Pistoia basin was the target of InSAR investigations due to its economical relevance. As early as 2000, this area was one of the first InSAR applications for subsidence detection in Italy. Colombo et al. (2003) and Canuti et al. (2005) presented the results of a multitemporal interferometric analysis based on C-band ERS 1/2 images acquired over the basin between 1992 and 2001. Subsidence rates of up to 20
30 mm/year were detected in the agricultural area between Pistoia and Firenze (hereinafter referred to as “Bottegone”) and in the cities of Prato and Calenzano (Fig. 5.2). At that time the city center of Pistoia was experiencing a slight uplift at an average velocity of B3 mm/year. For the first time, these results allowed regional authorities to understand and measure the impact of water exploitation in the Firenze
Prato
Pistoia basin. Some years later, Rosi et al. (2016) presented the results of a multitemporal interferometric processing performed on a stack of C-band Envisat images, which covered the time span of 2003
09. In general, the deformation pattern over the basin was the same: the Bottegone area again revealed the highest subsidence rates, up to 30 mm/year; subsidence was detected in Calenzano, and Pistoia was stable. The most evident variation in the deformation pattern was recorded in the city of Prato; in fact, where ERS 1/2 had recorded subsidence rates of B10 mm/year, the Envisat data showed the opposite signal. According to Rosi et al. (2016), the southern border of Prato registered an uplift of B4 mm/year; this has been interpreted as a result of the change in the industrial activities of the area during the economic recession of the early 2000s that generated lower demand for groundwater (Fig. 5.3). Recently, Del Soldato et al. (2018) investigated ground deformation in the basin with Sentinel-1 data from the Tuscany Regional5 satellite monitoring service. This service provides ground motion information

5 The visualization platform for the interferometric data of the Tuscany Region monitoring system: https://geoportale.lamma.rete.toscana.it/difesa_suolo/#/viewer/ openlayers/326

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FIGURE 5.2 Deformation map obtained from ERS 1/2 descending images (1992
2000). Data were processed in the framework of the Italian project “Piano Straordinario di Telerilevamento” and part of the national ground motion service based on ERS 1/2, Envisat, and COSMO-SkyMed data (Costantini et al., 2017). A large subsidence bowl envelops the area of Bottegone, Montemurlo, Prato, and Calenzano. The city center of Pistoia shows a slight uplift, and the urban area of Firenze is stable. Source: The background image is a 20 cm orthophoto (AGEA flight 2019, http://www502.regione.toscana.it/geoscopio/servizi/wms/OFC_2016_2019_AGEA.htm).

with a 12-day update time over the entire region; the deformation maps are coupled with an innovative time series data mining approach, the goal of which is to detect early signs of ground motion accelerations (Raspini et al., 2018). Sentinel-1 data confirmed the presence of a large subsidence bowl in the area of Bottegone, although subsidence rates are on average lower than the Envisat period (maximum subsidence rate of B20 mm/year). The area of Prato is now stable as well as the city of Firenze; in the latter case, only local deformation involving single buildings is recorded. The new discovery in the Sentinel-1 deformation map is the presence of a subsidence bowl in correspondence to the historic city center of Pistoia. As said, this area

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FIGURE 5.3 Deformation map obtained from Envisat ascending images (2003
10). Data were processed in the framework of the Italian project “Piano Straordinario di Telerilevamento” and part of the national ground motion service based on ERS 1/2, Envisat, and COSMO-SkyMed data (Costantini et al., 2017). The Bottegone subsidence bowl is evident as well as the great decrease of LOS velocity in Prato. The Pistoia and Firenze city centers are mostly stable. Local deformation is observed in Calenzano. LOS, line-of-sight. Source: The background image is a 20 cm orthophoto (AGEA flight 2019, http:// www502.regione.toscana.it/geoscopio/servizi/wms/OFC_2016_2019_AGEA.htm).

has not recorded any relevant ground motion in the last 20 years, whereas, from 2015, subsidence rates increased to 10 mm/year. The cause of this unexpected phenomenon has not yet been found, although a link to groundwater extraction in the outer parts of the city has been hypothesized (Ceccatelli et al., 2021). Finally, Sentinel-1 data also revealed a localized ground motion area with very high subsidence rates reaching B40 mm/year in the industrial area of the city of Montemurlo, a few kilometers south of Prato. The phenomenon is linked to unauthorized overpumping of water for textile production (Del Soldato et al., 2019).

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5.1.3 European Ground Motion Service data (Calibrated and Ortho products) at basin scale

BOX 5.1 To view the EGMS data over the Firenze
Prato
Pistoia basin, refer to Footnote 6.

Figs. 5.4 and 5.5 show the EGMS Calibrated and Ortho data in the Firenze
Prato
Pistoia basin, respectively. The deformation maps confirm the presence of known subsidence areas, show some changes with respect to the past, and highlight new moving areas. Specifically, the deformation map in Fig. 5.4 tell the reader that: • The area of Bottegone is still moving 30 years after the first interferometric investigation. Deformation rates are on average equal to 210 mm/year, with minimum values of 222 mm/year. For more information, refer to Section 5.1.3.1. • The historic city center of Pistoia records up to 210 mm/year (26 mm/year on average). Previous data showed stable behavior (see ERS 1/2 and Envisat data earlier). For more information, refer to Section 5.1.3.2. • The area of Montemurlo records the appearance of a small subsidence bowl which experiences the highest subsidence rates in the entire alluvial basin. Subsidence rates reach 240 mm/year in an area previously found to be stable. For more information, refer to Section 5.1.3.3. • The cities of Prato and Firenze are stable. Only localized deformation is recorded. Localized means that ground motion affects only a small area, that is, one or a few buildings. • The area of Calenzano records a general lowering of subsidence rates with respect to the past.

6 https://egms.land.copernicus.eu/#llh=11.00256914,43.91458982,16234.35167286&look= -0.13748520,-0.69358521,-0.70713321&right=0.97829583,0.01665544,-0.20654265&up= -0.15503254,0.72018202,-0.67624165&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent.

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FIGURE 5.4 EGMS Calibrated (2015
20) in the Firenze
Prato
Pistoia basin. The Bottegone subsidence bowl is evident as well as the decrease of LOS velocity in the area of Calenzano. Pistoia’s city center now registers ground motion. A new subsidence bowl has appeared in Montemurlo. The Prato and Firenze city centers are mainly stable. The three blue dashed-line squares refer to the zooms in Figs. 5.6
5.8. EGMS, European Ground Motion Service. LOS, line-of-sight. Source: The background image is a 20 cm orthophoto (AGEA flight 2019, http://www502.regione.toscana.it/geoscopio/servizi/wms/OFC_2016_2019_ AGEA.htm).

Looking at Ortho components (Fig. 5.5): • The vertical component mimics the line-of-sight (LOS) velocity map. This is because subsidence in this case is predominantly vertical motion. Bottegone registers the highest vertical rates, up to 220 mm/ year. For more information, refer to Section 5.1.3.1. • The east/west horizontal component does not present relevant deformation except for Montemurlo, where both eastward and westward motions are registered. There, the horizontal motion reaches B 6 10 mm/year. For more information, refer to Section 5.1.3.3.

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FIGURE 5.5 EGMS Ortho (2015
20) in the Firenze
Prato
Pistoia basin. Left, vertical component; right, east/west horizontal component. The vertical component shows, as expected, the greatest amount of motion; all the subsidence areas of Fig. 5.4 are present. The only area where the east/west component is not negligible is Montemurlo. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

5.1.3.1 European Ground Motion Service data over the Bottegone area—data analysis and interpretation

BOX 5.2 To view the EGMS data over the Bottegone area, refer to Footnote 7.

Fig. 5.6 shows the EGMS Calibrated and Ortho data over the area of Bottegone. The deformation pattern is quite obvious: a large subsidence bowl, indicated by all the points from yellow to dark red, affects this peri-urban and agricultural area. The Ortho products indicate that the deformation is largely vertical with a negligible horizontal component of less than 1 mm/year along the edge of the subsidence bowl. An initial comment can be made regarding MP density. Fig. 5.6 shows the concept of InSAR as an opportunistic technique. In fact, only those pixels with a stable radar signature over time can be used as MPs (Sections 3.2. and 3.3). In this case, the pixels located over agricultural or vegetated surfaces cannot all provide useful information about ground motion. This is why the MP density in this area is uneven. Note: thanks to the 6-day repeat interval of Sentinel-1, some agricultural surfaces may 7 https://egms.land.copernicus.eu/#llh=10.98246253,43.90120433,5783.14047406&look= -0.13794925,-0.69312810,-0.70749094&right50.98116642,0.00188182,-0.19315514&up= -0.13521262,0.72081197,-0.67981443&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent.

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FIGURE 5.6

EGMS Calibrated and Ortho (2015
20) in the Bottegone area. The large subsidence bowl is 7-km wide in the SE
NW direction and 4-km wide in the SW
NE direction. It is located in the agricultural and plant nursery area a few kilometers south of Pistoia. The black circle indicates the localization of the time series. Inset (A) Calibrated data, inset (B) time series, (C) Ortho product—vertical component, (D) Ortho product, east/west horizontal component. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

be selected as potential MPs. The problem is that the degree to which the InSAR phase can be attributed to real ground motion rather than to soil moisture variations is not well understood (De Zan et al., 2014). Yellow to dark red colors represent points with LOS velocities from 22.5 to below 220 mm/year. The velocity sign is negative, as the motion is away from the sensor, indicating a lowering of the ground surface. The spatial distribution of velocities follows the typical concentrical subsidence pattern with an increase of velocities moving toward the center of the main subsidence bowl. In this case, the subsidence pattern mimics the cone of groundwater depression, that is, the surface that identifies the depression of the groundwater level in unconfined aquifers or the reduction of the pressure head in confined aquifers. The time series presented in Fig. 5.6 (inset B) shows that the deformation in Bottegone has a seasonal component. This component is characterized by the maximum displacement at the end of the summer periods, that is, September
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the rainy and cold seasons, that is, February
March. This is clearly related to the groundwater demand for agriculture; when the natural recharge surpasses the water demand at the end of the winter season there is a slight rebound of the ground (i.e., uplift) related to the increase of the groundwater level. To the contrary, the summer water demand causes a strong decrease of the water level that is not balanced by the natural recharge of the aquifer. The disequilibrium between demand and recharge creates the long-lasting subsidence that is detected from satellites. As seen in Figs. 5.2 and 5.3, subsidence due to water overexploitation has always been a problem in the Bottegone area. The problem does not directly affect the population since the constant lowering does not create differential displacements which may cause long-term damage to buildings. On the other hand, the presence of subsidence underscores the need for proper groundwater management policies to avoid the depletion of the water resource. In recent years, plant nursery activities in the area went through a process of use and irrigation management improvement that resulted in a lower demand for groundwater; the effect of this can be seen from space. In fact, the comparison among ERS 1/2 (Fig. 5.2), Envisat (Fig. 5.3), and EGMS (Fig. 5.6) data enables the quantification of the decrease of the subsidence bowl extension and the magnitude of subsidence rates. Another use of EGMS data is for the long-term forecast of land subsidence considering the environmental and anthropic setting in terms of hydrological and hydrogeological conditions and pumping rates. Ceccatelli et al. (2021) estimated that a reduction of 1% per year in pumping rates would be enough to stabilize the groundwater level and consistently reduce the extension of the subsidence bowl in the Bottegone area. Subsidence rates would decrease from over 20 to less than 3 mm/year in a 30-year period spanning from 2020 to 2050. 5.1.3.2 European Ground Motion Service data over the city center of Pistoia—data analysis and interpretation

BOX 5.3 To view the EGMS data over the city of Pistoia, refer to Footnote 8.

8 https://egms.land.copernicus.eu/#llh=10.91524900,43.93438919,1565.06331420&look= -0.13708574,-0.69352154,-0.70727320&right=0.98127706,0.00243951,-0.19258603&up= -0.13528796,0.72043177,-0.68020235&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent.

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FIGURE 5.7 EGMS Calibrated and Ortho (2015
20) in Pistoia. The subsidence bowl includes the whole central portion of the city, where most of the historical buildings and cultural heritage sites are located. The black circle indicates the localization of the time series. Inset (A) Calibrated data, inset (B) time series, (C) Ortho product—vertical component, (D) Ortho product, east/west component. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

Fig. 5.7 presents EGMS Calibrated and Ortho products over the historic city center of Pistoia. The point density is the maximum possible in the urban environment, reaching thousands of MPs per square kilometer. The ground motion area involves the oldest part of the city, where residential buildings and cultural heritage sites are mainly present. LOS velocities range between 25 to 210 mm/year in the area of maximum ground lowering (orange points in Fig. 5.7). The velocities rapidly decrease moving outside the city center. The vertical and east
west components confirm that the main component of motion is vertical, even if the east/west horizontal component shows a small eastward motion in the order of 2.5 mm/year. This motion may indicate the presence of an asymmetric groundwater depression cone with higher gradient along the western border of the subsidence bowl. Time series show a linear displacement that took place until April 2018 when the motion stabilized. No further major trend changes have been recorded since then.

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The interpretation of the interferometric results is rather complex in this case. In fact, as shown in Figs. 5.2 and 5.3, the city center did not record any significant ground motion in the 1993
2016 period. This is because (1) this is an area without any kind of recent urban change or geoengineering activities that could perturb the groundwater table, and (2) there are no industrial or commercial activities with high groundwater demand. It is worth noting that this is not the first time that ground motion is reported in the urban area of Pistoia; in the 1960s
70s the sudden appearance of localized damage (cracks) in some buildings was recorded (Ezquerro et al., 2020). This anomalous deformation event, referred to as “static instabilities” by previous authors, was accompanied by a subsidence of 1.5 cm, as measured with topographical leveling. A clear explanation for the 1960s
70s and 2016
18 events has not been found yet. One hypothesis focuses on oversaturation and the following subsidence of the area already described in the documents of the 1960s (Fancelli et al., 1980). Ongoing studies are obtaining hydrogeological data to improve the knowledge of the processes occurring in the area (Ezquerro et al., 2020). 5.1.3.3 European Ground Motion Service data over Montemurlo— data analysis and interpretation

BOX 5.4 To view the EGMS data over Montemurlo, refer to Footnote 9.

Fig. 5.8 presents EGMS Calibrated and Ortho in the Montemurlo municipality, specifically in the industrial part of the city where several warehouses, mainly used for textile manufacturing, are present. A small subsidence bowl, less than 1-km2 wide, affects some of the buildings. Even if the moving area has a limited spatial extension, it is relevant in terms of recorded LOS velocities. In fact, this is the sector of the Firenze
Prato
Pistoia basin where the highest LOS velocities, of up to 240 mm/ year, are measured. The Calibrated products give a good overview of the extension of the subsidence bowl, but the Ortho products are the most interesting. In the previous examples the east/west horizontal component 9 https://egms.land.copernicus.eu/#llh=11.05743048,43.90828829,1265.28482972&look= -0.13937231,-0.69309991,-0.70723961&right=0.98096621,0.00087980,-0.19417650&up50.13520594,0.72084099,-0.67978498&layers=VHR%20Image%20Mosaic%202012_VHR%20Image %20Mosaic%202012-Image-parent.

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FIGURE 5.8 EGMS Calibrated and Ortho (2015
20) in Montemurlo. An evident subsidence area involves several industrial and commercial warehouses. The black circles indicate the localization of the time series. Inset (A) Calibrated data, inset (B) time series, (C) Ortho product—vertical component, (D) Ortho product, east/west component. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

was always almost negligible. By contrast, here there is a strong horizontal component of motion and a well-defined pattern with eastward (blue MPs in Fig. 5.8, inset D) and westward (red MPs in Fig. 5.8, inset D) movements. The maximum eastward and westward components range between -10 and -15 mm/year. The interpretation of this complex pattern is again related to the shape and gradient of the groundwater depression cone. In fact, the water well that caused the localized subsidence was designed without the proper technical knowledge and the water was extracted without control. It was hypothesized that this created a steep depression cone with turbulent flow (Del Soldato et al., 2019). Time series offer the most interesting outcomes (Fig. 5.8, inset B). Before July 2017, ground motion was almost negligible: the time series is flat with a few minor oscillations. Then, a clear trend change is recorded with LOS velocities that go from almost 0 to more than 240 mm/year. This abrupt change was linked to the start of groundwater pumping

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from an unauthorized well drilled in the vicinity of one of the industrial warehouses. The well, put in place with poor technical solutions and knowledge, created a steep depression cone in the groundwater level. The result is the subsidence bowl shown in Fig. 5.8. It is worth mentioning that the ground lowering damaged surrounding buildings, which recorded cracks and tilting (Del Soldato et al., 2019). Regional authorities reacted swiftly; first, the well responsible for the unauthorized water extraction was identified and closed. Then, tiltmeters10 and crackmeters11 were installed on damaged buildings to monitor their structural state. Moreover, GPS12 measurements have been performed to validate the interferometric results. The visual analysis of Fig. 5.8 may raise a question for the reader: why are some of the buildings in the center of the subsidence bowl not covered by MPs? These are the ideal InSAR targets (see Sections 3.2 and 3.3). What prevents the detection of points here is the type and magnitude of motion. In fact, near the water extraction well (i.e., approximately in the center of the subsidence bowl), subsidence rates are very high, especially after pumping activity began. A nonlinear (exponential) increase of subsidence rates usually occurs. Such deformation cannot be followed, and MPs cannot be identified (see Section 3.7). This creates a lack of points in the center of the subsidence bowl as seen in Fig. 5.8. More advanced deformation models for satellite interferometry exist, but they can only be used at the local scale because of their computational demand (see Yu et al., 2019 for a technical review on this topic). They are not used in EGMS production. In the Ortho product, some MPs fall within the center of the subsidence bowl. This is because such MPs are synthetic, that is, originated on a regular grid and resampled from the Calibrated product. Their position is not connected to a ground target but to the centroid of the resampling cell.

5.2 Mining subsidence in the Upper Silesian Coal Basin The USCB lies between the Polish provinces of Silesia and Małopolska and the Moravian-Silesian region of Czech Republic. The coal basin occupies a total surface of 7500 km2, 75% of the basin is on the Polish side of the border (Ke˛dzior and Dreger, 2019). The USBC is one of the biggest

10 Tiltmeters are sensors sensitive to the inclination of a building with respect to the vertical. 11 Crackmeters are instruments that measure the expansion or contraction of a fracture or crack on a wall. 12 An example video showing how GPS works is available on the UNAVCO’s YouTube channel: https://www.youtube.com/watch?v 5 ucEOAR6U2js.

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FIGURE 5.9 Simplified geological map of the Upper Silesian Coal Basin (Strzałkowski and Szafulera, 2020). Source: Adapted from Strzałkowski, P., Szafulera, K., 2020. Occurrence of linear discontinuous deformations in Upper Silesia (Poland) in conditions of intensive mining extraction—case study. Energies, 13(8), 1897.

coal basins in Europe and today hosts 22 active mining districts. The geographical location of the basin is shown in Fig. 5.9.

5.2.1 Geological and mining context The USCB is the result of a complex geological history. The basin was formed during the Paleozoic in the foreland13 position, parallel to the 13 More about the formation of a foreland basin https://www.youtube.com/watch? v 5 7xtlrh5KlLg

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Moravo-Silesian orogen. The basin hosts an 8500-m deep coal-bearing sedimentary succession characterized by a sequence of sandstone, mudstone, and coal layers that is generally subdivided into two parts: “paralic” and “limnic.” This sedimentary sequence sits on Carboniferous and Devonian carbonate facies and is overlaid by Mesozoic and Cenozoic units in Poland and by Paleogene and Neogene deposits in Czech Republic. The Paralic Series was deposited in a complex system with fluvial, deltaic, and coastal environments; this determined the sedimentary composition of the Series, made of an alternation of marine and continental siliciclastic deposits with coal seams and thin volcaniclastic layers (Jira´sek et al., 2018). The maximum thickness of this stratigraphic unit is 3800 m. The coal seams are usually thin, frequently less than 1.5-m thick (Ke˛dzior et al., 2007). The Paralic Series is known in Czech Republic with the name of Ostrava Formation. The limnic succession is subdivided into three series (from oldest to youngest): the Upper Silesian Sandstone Series, the Mudstone Series, and the Cracow Sandstone Series. This subdivision follows the Polish nomenclature that is based on a longer period of sedimentation. In Czech Republic the Upper Silesian Series and part of the Mudstone Series correspond to the Karvina Formation, whereas the upper series does not have an equivalent due to a stratigraphical hiatus14 in the Czech part of the basin (Jira´sek et al., 2018). The Upper Silesian Sandstone Series is composed of sediments deposited in an alluvial plain with a braided15 to meandering16 fluvial system and consists of sandstones (up to 95%) with coal seams and a limited presence of fine-grained mudstones and siltstones. Here, the coal seams are several meters thick, reaching 25 m (Ke˛dzior et al., 2007). The sediments of the Mudstone Series were deposited in an alluvial plain with a meandering and anastomosing river system. They are mainly fine-grained with less than 30% represented by medium-grained sandstones. This series contains coal seams of economic relevance. The Cracow Sandstone Series is predominantly formed by medium-grained sediments deposited in a braided fluvial system. This unit also hosts economically important coal seams. In general, the coal seams have a reduced lateral continuity, and it is quite rare to find continuous seams longer than 12 km. Thanks to the presence of these coal seams, mining has been an important human activity in the USBC in the last 200 years and triggered rapid urbanization and industrialization. In 1850 there were 14 What is a hiatus? https://glossary.oilfield.slb.com/en/terms/h/hiatus 15 How a braided fluvial system works? https://www.nps.gov/articles/braided-stream. htm 16 How a meandering fluvial system works? https://www.nps.gov/articles/meandering-stream.htm#:B:text 5 A%20meandering%20stream%20has%20a,moving%20faster% 20than%20the%20inner.

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already 71 active coal mines in the USCB, and the annual coal production reached a total of 19 million tons (Dulias, 2016). The mining activity is carried out underground and, at that time, the predominant mining method was the chamber pillars and roof collapse excavation. Coal production saw a boom in the early 20th century; before the First World War, more than 100 mines (many of them small scale) extracted coal from a maximum depth of 100 m. After the Second World War, the number of active mines diminished, but the active mines increased their production rate. In this period, the principal mining system became the longwall exploitation.17 In 2004 there were 142 active mining walls with an average output of 2900 tons per day. The extraction of coal for one reference year (2014) totaled nearly 78 million tons (Dulias, 2016). While this human activity certainly has an enormous economic relevance, it also brings an equally important environmental impact. Specifically, coal mining has caused and still causes mining-induced tremors, ground subsidence in urban and nonurban areas, salinification of underground water, hydrogeological changes to the water table, and accumulation of solid waste (Cabala et al., 2004). These effects do not end when a mine is abandoned, as subsidence can last for decades after the closure of a mine, gas (methane and carbon dioxide) can accumulate in the empty chambers and migrate to the surface, the chambers can be flooded by unregulated groundwater, and different aquifers can mix and change their chemical composition. The following sections are focused on surface motions to see how the EGMS can support ground motion investigations in an active mining environment.

5.2.2 Previous InSAR applications in the area The quantification of ground motion in the USCB has a great importance because of the number of urban areas that suffer from mininginduced ground subsidence. Therefore a number of researchers have already tested the applicability of InSAR in the USBC. The first examples of InSAR applications date back to the early 2000s. Perski (2000a, 2003) tested the capability of ERS-based InSAR to delimit and quantify the subsidence bowls in the urban area of Katowice (northern part of the USCB, see Fig. 5.9 for a geographical reference). This author discovered the presence of several subsidence bowls, evidenced by concentric fringes visible in the interferograms, around the city of Katowice. The maximum displacement rates referred to the 1992
95 period, varied between 5 and 20 mm/month (60
240 mm/year). Two decades later, Sopata et al. (2020) investigated in the same area the correlation between mining-induced tremors and subsidence. 17 About longwall mining: https://en.wikipedia.org/wiki/Longwall_mining

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Przyłucka et al. (2015) applied InSAR techniques to investigate fast subsidence in the urban area of Bytom, some kilometers north of Katowice. The analysis was based on high-resolution X-band TerraSARX images. Differential InSAR was used by these authors to identify 31 subsidence bowls with accumulated deformations that reach values between 100 and 660 mm in a period of 1 year, between July 2011 and June 2012. InSAR was used to delineate a radius around each subsidence bowl, where velocities were lower than -5 mm/year. This threshold was defined to distinguish between areas where building damage is registered and may or may not be expected in the future. Lazecky´ et al. (2020) presented nationwide InSAR results, derived from Sentinel-1 SAR images (2015
19) from over Czech Republic. Data processing was performed with a standard InSAR approach that was refined (i.e., to increase point density) in some areas of interest. One of these areas was the Ostrava
Karvina mining district in the southern portion of the USBC (see Fig. 5.9 for a geographical reference). It was found that LOS velocities in the Ostrava
Karvina mining district could reach 2100 mm/year. The interferometric results were compared and confirmed by leveling.18

5.2.3 European Ground Motion Service data over the Upper Silesian Coal Basin BOX 5.5 To view the EGMS data over the Upper Silesian Coal Basin, refer to Footnote 19.

Figs. 5.10
5.13 show the EGMS Calibrated and Ortho products in the USCB. The deformation maps highlight the existence of several subsidence bowls spread between the Polish and Czech mining districts of the USCB. These examples are also important proof of the relevance of a seamless deformation map to evidence cross-border ground motion. 18 A video introducing the basics of topographic leveling: https://www.youtube.com/ watch?v 5 GuBdpeKfmho 19 https://egms.land.copernicus.eu/#llh=18.94756741,50.05791179,105638.22972506&look= -0.20842098,-0.76658018,-0.60738416&right=0.96541925,-0.06180955,-0.25326912&up= -0.15660894,0.63916696,-0.75295368&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent.

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FIGURE 5.10 Calibrated product/ascending orbit over the USCB. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor. The areas without MPs are due to the presence of unfavorable land cover (forests and crops) or, in urban areas, to the deformation rates that are too high and nonlinear to be detected with the EGMS InSAR approach. EGMS, European Ground Motion Service; USCB, Upper Silesian Coal Basin. InSAR, interferometric SAR. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

A comment must be made on MP density. It is evident that density is uneven, but this is not only related to the land cover. It is true that there are large forests and agricultural parcels where reliable MPs cannot be obtained. However, there are also some urbanized areas and small villages where MPs should be present. This is a problem that was already highlighted, at a much more detailed scale, in the case of Montemurlo, Italy (Section 5.1.3.3) and reported for the same area by Przyłucka et al. (2015). Mining induces fast and high ground motion rates, often with nonlinear behavior over time. As described in Section 3.7, these types of motion prevent MP detection. This is an intrinsic limitation that is difficult to overcome without the use of local-scale processing approaches that are complex to implement in national or continental-scale projects.

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FIGURE 5.11 Calibrated product/descending orbit over the USCB. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor. The areas without MPs are due to the presence of unfavorable land cover (forests and crops) or, in urban areas, to the deformation rates that are too high and nonlinear to be detected with the EGMS InSAR approach. USCB, Upper Silesian Coal Basin. InSAR, interferometric SAR. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

5.2.4 European Ground Motion Service data over the Ostrava
Karvina area BOX 5.6 To view the EGMS data over the Ostrava
Karvina area, refer to Footnote 20.

The Ostrava
Karvina coal basin is the largest hard coal mining area of Czech Republic. Here, mining is carried out following the longwall 20 https://egms.land.copernicus.eu/#llh=18.49579649,49.83938773,23057.96666961&look= -0.02486912,-0.76684211,-0.64135380&right=0.99911281,0.00274561,-0.04202439&up= -0.03398698,0.64182991,-0.76609350&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent.

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FIGURE 5.12 Ortho product/vertical component over the USCB. Yellow to red colors identify subsidence, light blue to blue colors identify uplift. The areas without MPs are due to the presence of unfavorable land cover (forests and crops) or, in urban areas, to the deformation rates that are too high and nonlinear to be detected with the EGMS InSAR approach. The black dashed-line squares are the areas that will be detailed in the next chapters; 1, Ostrava
Karvina area, 2, Katowice area, 3, Tichy area. USCB, Upper Silesian Coal Basin. InSAR, interferometric SAR. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

method and triggers long-term subsidence. The visual confirmation of ground motion is given by the presence of classical surface features such as ruptures, trenches, depressions, and building tilts and cracks. Between 1983 and 2005, the company responsible for the mining activity carried out leveling surveys that estimated the maximum surface displacement over some of the longwall panels to be around 2
4 m (Marschalko et al., 2012). Fig. 5.14 shows the EGMS products in the Ostrava
Karvina area. Both the Calibrated and Ortho products give a good idea of the extension of the subsidence area; considering LOS velocities over

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FIGURE 5.13 Ortho product/east
west component over the USCB. Yellow to red colors identify a motion with westward direction, light blue to blue colors identify a motion with eastward direction. The areas without MPs are due to the presence of unfavorable land cover (forests and crops) or, in urban areas, to the deformation rates that are too high and nonlinear to be detected with the EGMS InSAR approach. The black dashed-line squares are the areas that will be detailed in the next chapters; 1, Ostrava
Karvina area, 2, Katowice area, 3, Tichy area. USCB, Upper Silesian Coal Basin. InSAR, interferometric SAR. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms. land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

25 mm/year, the subsidence area extends for B80 km2 between the cities of Orlova and Karvina to the north and Albrechtice to the south. The area with the highest displacement rates is Stonava, where LOS velocities reach 250 mm/year with vertical and eastward components of motion up to 225 mm/year. Here, time series show a linear trend without remarkable oscillations (Fig. 5.14E). LOS velocities reach 210 mm/year in the urban area of Orlova; here, the motion has a predominant horizontal/eastward component with a magnitude two times higher than the vertical component. This is an interesting finding that may be related to the position of the MPs with respect to the underground mining panel; a lateral location with respect to the

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FIGURE 5.14 EGMS products in the Ostrava
Karvina area. (A) Calibrated data in ascending orbit, (B) Calibrated data in descending orbit, (C) Ortho data—vertical component, (D) Ortho data—east
west component, (E) time series for the east
west and vertical components (the location of the time series is shown by the black circles in insets C and B). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

mining front (and the cavity created) may explain the predominant nonvertical component. Precise information about the location, activity, and mining rates for each mining panel would expedite the interpretation of the interferometric results.

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5.2.5 European Ground Motion Service data from over the Katowice area BOX 5.7 To view the EGMS data over the Katowice area, refer to Footnote 21.

Coal mining in the surroundings of the city of Katowice is carried out at a depth between 300 and 450 m below the Earth’s surface (Perski, 2000b). Fig. 5.15 shows the EGMS Ortho products in the eastern part of

FIGURE 5.15 EGMS products in Katowice. (A) Ortho data—vertical component, (B) Ortho data—east
west component, (C) time series for the east
west and vertical components (the location of the time series is shown by the black circles in insets A and B). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

21 https://egms.land.copernicus.eu/#llh=19.09325275,50.15479364,32961.04352171&look= -0.20962446,-0.76775516,-0.60548295&right=0.94532379,-0.00090600,-0.32613206&up= -0.24984100,0.64074269,-0.72596713&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent,euro_regional.

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the urban area of Katowice. The displacement pattern is similar to the one presented for the Ostrava
Karvina area. The vertical component (Fig. 5.15A) delimits the extension of the subsidence area, but with the uneven point density, it is impossible to distinguish the presence of a single or multiple subsidence bowl. Once again, the lack of points in some urban areas is linked to the high subsidence rates that reach maximum values of 23 mm/year in the village of Halemba. The time series for the vertical component are linear, with a minor trend change in the last year. The east
west horizontal component is more complex to interpret. The highest eastward motion is registered around the city ´ of Konczyce in the east side of the main subsidence bowl; here, horizontal velocities reach values between 6 and 15 mm/year. Westward velocities reach their maximum in correspondence to point 3 in Fig. 5.8B. In this case, the horizontal component is estimated to be around 210 to 213 mm/year. The deformation pattern is coherent with the presence of one large (or multiple) subsidence bowls, with horizontal velocities higher in the external part of the bowl and minimal values in the central sector. This pattern is similar to the others presented in this chapter.

5.2.6 European Ground Motion Service data over the Tychy area

BOX 5.8 To view the EGMS data over the Tychy area, refer to Footnote 22.

Tychy is one of the mining districts of the Polish part of the USCB. Fig. 5.16 shows the EGMS Ortho products in this area. This area was selected for its peculiar ground motion pattern. Here, the vertical component suggests that the main direction of motion is toward the sensor; in other words, the interferometric data highlight the presence of a localized uplift with maximum displacement rates between 10 and 12 mm/year (Fig. 5.16A). The moving area is about 20 km2 and it includes the small towns of Bojszowy, Jedlina, and Mie˛dzyrzecze. The time series for the vertical component shows a constant displacement with linear behavior. The east
west component (Fig. 5.16B) shows a 22 https://egms.land.copernicus.eu/#llh=19.12571410,50.05781460,17603.90806503&look= -0.21035111,-0.76669265,-0.60657629&right=0.94525389,-0.00115935,-0.32633379&up= -0.24949448,0.64201327,-0.72496308&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent,euro_regional.

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FIGURE 5.16 EGMS products in the Tychy area. (A) Ortho data—vertical component, (B) Ortho data—east
west component, (C) time series for the east
west and vertical components (the location of the time series is shown by the black circles in insets A and B). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

westward motion on the left side of the bowl (NW than Mie˛dzyrzecze) and an eastward motion on the right side of the bowl (around Jedlina). The magnitude of the eastward/westward motion reaches a maximum of 5 mm/year in the peripheral portion of the bowl. The pattern is the opposite of what the east
west horizontal component usually shows in case of subsidence. Since no information about the depth and activity of the coal mines in this area is available, only an hypothesis about the triggering mechanism for the detected uplift can be made. Usually, uplift is registered in coal mining areas when the activity stops and underground water floods the chambers created by the mining activity (Zhao et al., 2021). Uplift recovers only a small part of the original ground lowering generated by the mining activity. That said, and without any additional information, it can be assumed that at some time in the past the mining activity stopped, and water started to flow again without control, inducing the ground uplift.

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5.3 Tips and tricks to interpret interferometric data in mining areas Section 5.2 provides a good overview of the complexity of interferometric products in a mining area. In this section, are summarized some of the major characteristics of InSAR for mining applications and given some suggestions that may facilitate data interpretation. InSAR is an indisputably powerful tool to measure ground motion in mining areas, but there are some key issues to keep in mind to avoid overtrusting and misinterpreting the InSAR results, for example: • What is it measured? InSAR does not measure the effects of ongoing mining activity (e.g., the excavation of a new bench in an open pit mine23), but rather, its long-term effect. In case of underground activity, sometimes are measured the surface effects of mining after the excavation, and the residual deformation for a period after the closure of the activity. However, interferometric data are not a tool to precisely locate the mining panel or its depth without any kind of supporting information. In case of surface mining (open pit), are measured the effects induced by mining (e.g., subsidence around the mining area), but it is difficult to measure motion within the mining area because of the frequent surface changes or the steep and irregular topography (Section 3.4). With ad hoc local processing approaches, it is possible to measure motion in those slopes with favorable geometry and with limited surface changes; however, wide-area interferometric products such as EGMS are not tuned to a specific site and will therefore suffer in terms of MP density. A good visual example of this is shown in Fig. 5.17. • Why is point density irregular even in an urban area? The MP density is uneven in urban areas with underground mining activity. Even if buildings are perfect radar targets, there are parts of cities and towns that are not covered by any MP. This is related to the type of motion triggered by mining. Usually, underground mining generates fast displacement rates with nonlinear temporal behavior that pushes the interferometric technique to its limits (Section 3.7). Some MPs are going to be lost if a wide-area interferometric approach is applied. A trade-off between homogeneity of results and ability to solve local scale deformation has to be found, as in the case of EGMS. A continental-scale product can detect the extension of the moving area, but it may fail to gather MPs where the displacement rates are too high. In those areas, additional local processing efforts 23 A video showing one of the largest open pit mines in the world: https://www.youtube.com/watch?v 5 uL5u_9bGUPs

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FIGURE 5.17 Subsidence in the surroundings of the Hambach mine in the North Rhine-Westphalia region of Germany. The Hambach mine is the largest coal extraction site in Germany; here, the EGMS Calibrated products (ascending orbit) detect a large subsidence bowl linked to the need to maintain the water level below the excavation level. The mine is not covered by MPs for the reasons explained previously. A local-scale solution could be adopted to increase point density in the mining area (e.g., Tang et al., 2020). EGMS, European Ground Motion Service. MP, measurement point. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

are needed to increase MP density. One of the goals of the EGMS is to create a reference baseline that can enhance the market potential of the interferometric data, and mining is the kind of application where a continental-scale solution may not be enough for the final users. • How to effectively interpret the data? Mining, like any other InSAR application, requires ancillary data that guide the interpretation process. In the case of underground mining, it would be helpful to gather information about the location of the active mining sectors, and about the start and (eventual) closure of the mining activity. It is also important to know something about the type of mining activity; longwall mining, as in the USCB, creates a deformation pattern that

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is different from salt solution mining, as in Vauvert (Section 5.4). The collection of this kind of ancillary data in mining areas can be challenging because in many cases they have restricted access. It is also important to check not only the single orbit results but also the components of motion (i.e., the Ortho product for the EGMS): mining induces a relevant horizontal component. The estimation of this component can be useful to evaluate the risk potential in urban areas, where buildings may suffer from the presence of differential settlements.24

5.4 Other applications of InSAR for subsidence detection Subsidence is quite common wherever a human activity interferes with the local geological or hydrogeological asset. The two previous chapters presented use cases where subsidence is triggered by water overexploitation and mining. In this chapter, the ground motion effects due to other anthropic activities are briefly presented. The first example comes from The Netherlands, in the plain surrounding the city of Groningen. This area hosts the largest gas field in Europe, operational since 1963. The exploitation of the onshore gas field25 caused the compaction of the deep reservoir, triggered surface subsidence, and induced earthquakes, the largest with a local magnitude26 ML of 3.6 (Spica et al., 2018). The impact of the gas exploitation is directly experienced by the population; due to the ground motion, several buildings recorded critical damage, and, in some cases, people were relocated (Dutch News, 2021). The impact of the gas extraction convinced the Dutch government to rethink the exploitation of the natural resource and the activities will be closed in the next few years, although the Ukraine war led to the postponement of the planned suspension date (Bloomberg, 2022). Subsidence has been measured around Groningen since the 1970s: first, with classical leveling techniques, then in more recent years, with satellite interferometry. Maximum subsidence rates were quantified to be between 7 and 8 mm/year in the period between 1970 and 2015 (Zhang et al., 2022). 24 What is a differential settlement? https://www.geotech.hr/en/differential-settlements/ 25 A brief introduction to onshore gas fields: https://earthresources.vic.gov.au/projects/ victorian-gas-program/onshore-conventional-gas/onshore-gas-types 26 A glossary about magnitude and other earthquake-related terms: https://www.usgs. gov/programs/earthquake-hazards/earthquake-magnitude-energy-release-and-shakingintensity

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BOX 5.9 To view the EGMS data over the Groningen gas field, refer to Footnote 27.

In the EGMS Ortho data, the vertical component clearly shows the extension of the subsidence area surrounding Groningen and extending to the coast (Fig. 5.18A). The subsidence area with vertical velocities lower than 24 mm/year extends for around 20 km in the east
west direction and around 30 km along the north
south direction. In the southern part of the subsidence area, near the towns of Veendam and Winschoten, the vertical velocities are higher than the average, up to 230 mm/year. There, the east
west component is also nonnegligible, with eastward and westward velocities reaching 6 10
15 mm/ year (Fig. 5.18B). The deformation suggests the presence of a localized motion with a strong horizontal component that is not coherent with the gas reservoir compaction that there is in other parts of the area of interest. Here, subsidence is related to underground salt mining (Gee et al., 2019). It is interesting to notice the difference between the time series of the central portion of the gas extraction-related subsidence area (point 1 in Fig. 5.18C) and the area that refers to the subsidence bowl in Veendam (point 2 in Fig. 5.18C). Time series no. 1 shows a linear and gradual ground motion without any major changes, whereas time series no. 2 has a clear break in February 2018, when an acceleration is recorded. It is difficult to say which is the cause of the acceleration without additional ancillary information, but a good hypothesis would include a change in the salt mining activity (e.g., higher mining rates). An example of subsidence induced by a different type of human activity is reported in Fig. 5.19. Here, the factor responsible for subsidence is the urbanization of a delta area along the Tyrrenian coast of Italy. The Tiber Delta is located a few kilometers south of the city of Rome and extends for about 40 km parallel to the coast. The Delta has had a complex evolution with multiple stages, and the stratigraphic context is the reflection of this (Milli et al., 2013). One of the most important evolutionary stages is the creation, between 5000 and 6000 years before the present day, of two marshy coastal ponds that lay on the right and left sides of the Tiber River, a few kilometers from the current coastline. The ponds were reclaimed in the first part of the 1900s to gain arable and

27 https://egms.land.copernicus.eu/#llh=6.67321927,53.24474835,60555.56088606&look= -0.06925516,-0.80112192,-0.59448078&right=0.99339143,-0.00070753,-0.11477355&up= -0.09152699,0.59850077,-0.79587665&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent,euro_regional.

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FIGURE 5.18 EGMS Ortho data and time series in the Groningen area. (A) Vertical component (the numbers refer to the points where the time series have been extracted), (B) east
west component, (C) time series. Ground motion is related to natural gas extraction and, with higher and localized subsidence rates, to salt dissolution mining (area of Veendam). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

buildable terrain. This created two areas of stratigraphical weakness, where buildings and infrastructure were built on unstable, highly compressible, and organic terrains.

BOX 5.10 To view the EGMS data over Rome—Leonardo Da Vinci Airport, refer to Footnote 28.

28 https://egms.land.copernicus.eu/#llh=12.31378035,41.81629812,15956.53357568&look= -0.15901542,-0.66670434,-0.72816167&right=0.97667780,0.00154919,-0.21470463&up= -0.14427258,0.74532068,-0.65090898&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent,euro_regional.

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FIGURE 5.19 EGMS Ortho and Calibrated data and time series in the surroundings of the Leonardo Da Vinci Airport (Rome, Italy). (A) Ortho vertical component, (B) Ortho east
west component, (C) time series for the vertical and E
W components (see the black circles for the location), (D) time series for the ascending and descending orbits (see the black circles in insets E and F for the location), (E) Calibrated data—ascending orbit, (F) Calibrated data—descending orbit. Ground motion is related to the long-lasting consolidation of a reclaimed pond. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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The EGMS products (Calibrated and Ortho) in Fig. 5.19 depict the ground motion situation around the Leonardo da Vinci Airport (also known as Fiumicino), Italy’s largest one. Part of the north runway of the airport lays on the reclaimed terrain of the Maccarese pond, one of the two marshy basins mentioned earlier. The urbanized area of Ponte Galeria, part of the highway that connects the coast with the center of Rome, and some commercial buildings built along the highway are built in this geological weak spot. And InSAR data confirm the presence of compressible soils that trigger long-term subsidence. The most emblematic situation is the N
S runway, which records LOS displacement rates of up to 215 mm/year in both orbits (Fig. 5.19, insets E and F—Calibrated products) for two-thirds of its length, and then a rapid decrease of displacement rates, falling between the stability range (less than 6 2 mm/year). As reported by Del Ventisette et al. (2015), this sharp difference along this linear infrastructure is completely related to the geological asset. In fact, borehole data confirm that the moving part of the runway is built within the boundaries of the reclaimed pond, and therefore on highly compressible and organic layers, whereas the stable part of the runway is located on top of the sand barrier that constituted the border of the pond. The sand barrier has low compressibility, and so this part of the runway is stable. The motion is mainly vertical. As seen in Fig. 5.19 (insets A and B), only the vertical component of Ortho product shows active deformation. The east
west horizontal component does not highlight any clear deformation pattern. This is correlated to the type of subsidence. In fact, the consolidation of compressible terrains is a predominantly vertical phenomenon, different from subsidence related to mining or groundwater exploitation. This kind of subsidence is constant over time and is not linked to any external factor other than the urbanization that accelerates the natural consolidation process. So, time series are linear over time and do not register accelerations or seasonality (Fig. 5.19, insets C and D). Fig. 5.20 provides the reader with an example of subsidence linked to another type of mining activity. The deformation area is located near the city of Vauvert, in the Gard department of southern France. Rock salt (or halite29) mining is carried out in this area; specifically, salt solution mining.30 The concept behind this mining technique is rather simple: first, a dissolving fluid, typically water, is pumped into the depth of interest to dissolve the salt level. Then, the saturated brine 29 What is halite? https://geology.com/minerals/halite.shtml 30 A video explaining how this mining technique works: https://www.youtube.com/ watch?v 5 EHz_iRjWGPU

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FIGURE 5.20 EGMS Ortho data in the Vauvert area. Left inset, vertical component, right inset, east
west component. Ground motion is linked to subsurface mining (salt dissolution mining). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

is pumped back to the surface (Warren, 2016). The process can be summarized in three phases: (1) the water is pumped through the boreholes into the depth of interest and starts to dissolve the salt level, (2) compressed air is used to constrict and contain the fluid within the salt level, and (3) some boreholes are equipped with pumps to extract the semisaturated brines31 that are produced. The brines are then transported by a pipeline to the treatment plant where the sodium chloride is extracted from the brine through chemical processes. This mining activity has a measurable environmental impact. It implies the overexploitation of freshwater, the salinization of streams and aquifers, and induces surface motion (subsidence). When the cavities formed by the dissolving action of circulating water are relatively shallow (,150 m below surface), chimneys and sinkholes may form.

31 What does brine mean? https://www.corrosionpedia.com/definition/189/brine

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BOX 5.11 To view the EGMS data over the Vauvert mining area, refer to Footnote 32.

The mining area of Vauvert has some of the deepest production boreholes in Europe; water is circulated in some halite layers that reach a depth between 1500 and 3000 m (Furst et al., 2021). Only 12 boreholes remain active in Vauvert. This area has a long history of subsidence monitoring projects. The National Institute of Geographic and Forest Information started to perform leveling surveys in 1996 (Furst et al., 2021). Raucoules et al. (2003) carried out the first InSAR analysis based on ERS 1/2 SAR images (1993
99) in Vauvert and estimated the subsidence bowl to be 8km large with a maximum vertical displacement rate of B20 mm/year. As of 2015, the monitoring network relies on four permanent global navigation satellite system stations. Recently, Sentinel-1 SAR images were processed by Furst et al. (2021) by means of a multitemporal InSAR approach that covered the ERS 1/2 (1995
2010), Envisat (2003
11), and Sentinel-1 (2015
19) eras. The maximum subsidence rates were estimated to be 24 mm/year, coherently with the research of Raucoules et al. (2003) completed almost 20 years previously. This attests to the temporal continuity of the motion in this area. The vertical component of the EGMS Ortho product is in total agreement with previous results. In fact, the center of the subsidence bowl, B1-km east of the city of Vauvert, registers vertical velocities between 220 and 224 mm/year (the sign of the velocity values indicates subsidence). The spatial distribution of the moving points is coherent with past InSAR analyses, and the subsidence bowl extends for B8 km in the NE
SW direction and for B5 km in NW
SE direction. The east
west component shows the expected bicolor pattern, with one-half of the subsidence bowl recording eastward motion (blue points in Fig. 5.20) and the other half with westward velocities (orange points in Fig. 5.20). The maximum value of the horizontal component ranges between 6 and 9 mm/year (both directions). Similar patterns are seen in other examples described in this chapter:this it is coherent with the development of a subsidence bowl with high displacement rates.

32 https://egms.land.copernicus.eu/#llh=4.30473826,43.67173512,15104.93871357&look= -0.05429121,-0.69052559,-0.72126755&right=0.99719682,-0.00024881,-0.07482274&up= -0.05148756,0.72330792,-0.68860343&layers=VHR%20Image%20Mosaic%202018_VHR% 20Image%20Mosaic%202018-VHR_2018_WM:None.

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By comparing this example with the one from the USCB (e.g., the results in Section 5.2.3), it can be seen the effect of the type and depth of the mining activity on the density of MPs. Here, the lack of MPs is mostly due to the land cover (crops, high vegetation) that prevent the detection, whereas, in the previous example, the type of mining activity generates displacements with magnitude and temporal behaviors that are not solvable with the InSAR approach used. Another example is taken from the Tuscany Region in Italy, in the geothermal field33 of Larderello. Thermal manifestations have been known in this area since Roman times, when thermal baths were built. In the Middle Ages, sulfuric and boric acids were used for the preparation of pharmaceuticals (Minissale, 1991). Several centuries would pass before a real exploitation of the geothermal field for energy production began. The first power plant that harnessed the natural steam for electricity production was built in 1904 (DiPippo, 1978). This was the first worldwide example of a natural steam-powered power plant. Nowadays, 34 power production plants produce approximately 30% of the electricity demand of the Tuscany region. The geothermal reservoir, hosted by carbonate and metamorphic formations, reaches temperatures between 200 C and 350 C, at a depth between 400 and 3500 m (Bertini et al., 2005).

BOX 5.12 To view the EGMS data over the Larderello geothermal field, refer to Footnote 34.

Rosi and Agostini (2013) analyzed ERS 1/2 and Envisat interferometric products (1993
2010) and discovered the presence of a large subsidence bowl in the geothermal district of Larderello; LOS velocities reached 230 mm/year in the center of the bowl, corresponding to the area where the deepest geothermal well is drilled (Sasso Pisano geothermal field). Fig. 5.21 presents the EGMS Ortho products around Larderello. The results enable tracing the contour of the subsidence area, which extends for roughly 12 km in the NE
SW direction (Larderello
Sasso Pisano axis) and for 10 km in the east
west direction (Lustignano
Castelnuovo Val di 33 More about geothermal energy: https://www.youtube.com/watch?v 5 c7dy0hUZ9xI 34 https://egms.land.copernicus.eu/#llh=10.85761157,43.19409265,21846.38205874&look= -0.13746422,-0.68442694,-0.71600513&right=0.98203261,0.00018095,-0.18871120&up= -0.12928859,0.72908143,-0.67210470&layers=VHR%20Image%20Mosaic%202012_VHR% 20Image%20Mosaic%202012-Image-parent.

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References

-20

mm/yr

-20

20

(A)

2 km

(B)

mm/yr

20

2 km

Point 1, inset A Point 2, inset B

120 100

Displacement mm

80 60 40 20 0 -20 -40 -60 -80 -100 -120

(C)

20160105 20160808 20170312 20171014 20180518 20181220 20190724 20200225 20200928 20160422 20161124 20170628 20180130 20180903 20190407 20191109 20200612

Measurement date

FIGURE 5.21 EGMS Ortho data in the Larderello geothermal field. Left inset, vertical component, right inset, east
west component. Ground motion is linked to the long-term exploitation of a geothermal reservoir used for the generation of energy. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

Cecina axis). Vertical velocity is recorded in the valley between Lustignano and Sasso Pisano, with values of up to 230 mm/year. The vertical component is always smaller than 220 mm/year in the greatest part of the subsidence area (the dark red points in Fig. 5.21A). The time series for the vertical component (point 1 in Fig. 5.21C) is perfectly linear without major seasonal variations. The spatial and temporal deformation pattern is consistent with the observation made about the interferometric data from the previous generation (Rosi and Agostini, 2013).

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Tang, W., Motagh, M., Zhan, W., 2020. Monitoring active open-pit mine stability in the Rhenish coalfields of Germany using a coherence-based SBAS method. International Journal of Applied Earth Observation and Geoinformation 93, 102217. Available from: https://doi.org/10.1016/j.jag.2020.102217. Terzaghi, K., Peck, R.B., 1967. Soil Mechanics in Engineering Practice. John Wiley & Sons, Hoboken, NJ, USA. Warren, J.K., 2016. Solution mining and salt cavern usage. In: Warren, J.K. (Ed.), Evaporites, vol. 1. Springer International Publishing, Cham, Switzerland, pp. 1303
1374. Whittaker, B.N., Reddish, D.J., 1989. Subsidence: Occurrence, Prediction and Control. Elsevier Science Publishers B.V., Amsterdam, The Netherlands. Yu, H., Lan, Y., Yuan, Z., Xu, J., Lee, H., 2019. Phase unwrapping in InSAR: a review. IEEE Geoscience and Remote Sensing Magazine 7, 40
58. Available from: https://doi.org/ 10.1109/MGRS.2018.2873644. Zhang, B., Chang, L., Stein, A., 2022. A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the Groningen gas field using multi-platform SAR imagery. International Journal of Applied Earth Observation and Geoinformation 111, 102847. Available from: https://doi.org/10.1016/j.jag.2022.102847. Zhao, J., Konietzky, H., Herbst, M., Morgenstern, R., 2021. Numerical simulation of flooding induced uplift for abandoned coal mines: simulation schemes and parameter sensitivity. International Journal of Coal Science & Technology 8, 1238
1249. Available from: https://doi.org/10.1007/s40789-021-00465-x.

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C H A P T E R

6 Landslides “Landslide” is a broad term that involves different kinds of slope movements. Generally, a landslide is a downslope movement of rock or soil, or both, which occurs along a surface of rupture, and involves material sliding as a coherent or semicoherent mass with minor internal deformation (Highland and Bobrowsky, 2008). Landslide typology depends on various factors such as the geometry of the rupture surface, the volume and velocity of the moving mass, the material forming the moving mass, the runout1 and many more. One of the most famous classification systems (Varnes, 1978) categorizes landslides in slides (rotational and translational), falls, topples, flows (debris flows/avalanches, earth/debris flows, and creep), lateral spreads, and complex.2 Landslides are caused (triggered) by natural events such as intense rainfall periods, seismic or volcanic events, and by human activities that disturb and destabilize the equilibrium of slopes (e.g., changes to the drainage pattern, clearing of vegetation, and excavation of the slope). Worldwide, landslide events are responsible for human and economic losses that have direct and indirect impacts on the population and the local communities. Haque` et al. (2019) analyzed over 3800 landslide events worldwide in the period 1995 and 2014 and reported a total number of fatalities reaching over 150,000. This number is surely underestimated since events occurring in remote areas or involving a low number of people may be not recorded (Froude and Petley, 2018). These numbers call for actions and interventions to reduce landslide risk. This is not an easy task, as it requires proper planning and the support of data able to inform scientists and risk managers on the status of landslides. 1 The run-out of a landslide defines the distance traveled by the material from the source area to the toe area and depends on the characteristics of the materials involved (e.g., viscosity, water content) and on the morphology of the accumulation zone, that is, where most of the material is deposited. 2 This link provides more information on basic concepts about landslides: https://pubs. usgs.gov/fs/2004/3072/pdf/fs2004-3072.pdf.

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Interferometric SAR (InSAR) is widely accepted and used as a tool for landslide monitoring and mapping. Interferometry is the ideal solution to support on-site surveys and measurements of landslide motion since it provides millimeter-sensitive data with a relatively frequent acquisition over areas that are sometimes unreachable by technicians and surveyors. Moreover, satellite interferometric data can cover an entire valley with a single data processing; for obvious reasons, the same cannot be reached with classic on-site investigations. Solari et al. (2020) classify the InSAR usage for landslide studies in (1) back-analysis, that is, interferometric data acquired after the occurrence of a highly impactful landslide to reconstruct its pre- and postevent behavior; (2) characterization, that is, use of interferometric data coupled with ground information to evaluate the motion of a landslide; (3) input for landslide and susceptibility3 models; (4) landslide inventory4 update (in terms of landslide presence and state of activity); (5) mapping where landslides are moving (at different mapping scales); and (6) monitoring, that is, near-real-time activities focused on the landslide temporal behavior. InSAR suffers some limitations when dealing with landslides, such as: • Landslide typology. Only slow to extremely slow-moving landslides (according to Cruden and Varnes, 1996) can be measured. Furthermore, it is also impossible to measure landslides that take place along vertical or subvertical surfaces (rockfalls, topples). • Land cover. Landslides in vegetated areas cannot be measured because of the lack of measurement points (MPs). This can be improved using long-wavelength SAR data (e.g., L-band data). • Geometrical effects. Slope orientation is a key factor to understand if a landslide will be visible or not in the deformation map, and how much of the real component of motion is measurable. • Type of motion. A landslide is a complex 3D phenomenon which cannot be fully measured with satellite interferometry, which provides 2D deformation when ascending and descending data are used. • Snow cover. The presence of snow limits the availability of displacement values in the time series during winter periods. Perennial snow prevents MP detection. Please refer to Chapter 3 for a detailed technical explanation of the points mentioned previously. 3 It is the likelihood of a landslide occurring in an area, i.e., an estimate of where landslides are more likely to occur. See, for example, https://www.irpi.cnr.it/en/focus/landslide-susceptibility/. 4 Definition of landslide inventory: https://link.springer.com/referenceworkentry/10.1007/ 978-1-4020-4399-4_214.

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Table 6.1 offers a nonexhaustive list of recent examples of landslide studies relying on satellite interferometry. This chapter presents the reader with two case studies using EGMS products addressed to landslides. The first one presents a local-scale analysis of two landslides located on a promontory along the coast of the province of Granada (Spain), in the municipality of Almun˜ecar. The second one shows the capability of wide-area InSAR products for landslide mapping and characterization at the regional scale. The location of this study is Troms og Finnmark county in Norway. TABLE 6.1 List of scientific papers that illustrate what can be done with European Ground Motion Service (EGMS) data for landslide-related activities. References

Location

Target and type of activity

Wasowski and Bovenga (2014)

Various sites

• Review of pros and cons of InSAR for landslide investigation through real case studies • Guidelines on interferometry best practices

Ciampalini et al. (2016)

Messina Province, Sicily Region, Italy

• Susceptibility map refinement supported by InSAR data • Improvement of the susceptibility model by reducing the number of false positives

Mateos et al. (2017)

Granada coast, Spain

• Landslide characterization and state of activity estimation • Integration between InSAR data, unmanned aerial vehicle photogrammetry, and ground surveys

Intrieri et al. (2018)

Maoxian landslide, China

• Postevent analysis of landslide precursors of motion • Time series analysis to evaluate the capability of InSAR as a forecasting tool

Aslan et al. (2020)

French Alps

• Landslide mapping based on the automatic extraction of active deformation areas5 • Moving areas are derived by means of an extraction procedure based on InSAR velocities projected along the slope

Bekaert et al. (2020)

Trishuli River catchment, Nepal

• Landslide mapping and status of activity estimation at valley scale • Postearthquake analysis of time series to evaluate changes to landslide state of activity

5 This term was first proposed by Barra et al. (2017), https://www.mdpi.com/20724292/9/10/1002.

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6.1 Landslide state of activity evaluation on the Granada coast (Spain) The coast of Granada is a striking landscape of rocky cliffs interspersed with beaches and small towns. It is located in southern Spain, in the homonymous province. The focus here is on the Almun˜ecar municipality, located 50-km south of Granada.

6.1.1 Geographical and geological context Punta de la Mona is a promontory located between the towns of Almun˜ecar and La Herradura in the province of Granada (southern Spain). Because of its scenic coastal landscape, this area is mainly devoted to tourism. The majority of the buildings in Punta de la Mona are holiday residences and tourism facilities. Thus the occupancy of the buildings is seasonal to an extent. A large resort that can host up to 2000 people in the summer period is located along the eastward-looking slope of Marina Del Este (Fig. 6.1). The area has a Mediterranean climate, marked by subtropical and semihot temperatures with an annual average around 21 C. Average annual rainfall is around 400 mm. The geology of the area is dominated by the Alpujarride Complex, here represented by two Paleozoic
Triassic units (Manto de la Herradura and Manto de la Salobren˜a). The Alpujarride Complex is a 400-km long region of metamorphic6 units comprising schist, gneiss, migmatite, quartzite, and carbonate (Tubı´a et al., 1992). In the Punta de la Mona promontory, highly faulted and folded graphite schists, quartzites, and blocks of marbles are found. This is the result of the activity of two tectonic lines that cross the promontory from NW to SE. Notti et al. (2015) report that the geological history of this area, together with the presence of active karst processes7 in the marble units, contribute to slope instability. Another key element that can trigger the activation or reactivation of landslides is urbanization. Before the 1980s, the flanks of the promontory were mainly dedicated to agriculture in terraced plots. Notti et al. (2015) report that the presence of an active landslide was clear from the analysis of 1950s orthophotos. For example, the Marina Del Este resort was built precisely on top of an already-unstable slope, further destabilizing the slope equilibrium and changing the drainage pattern. Around 60% of the original landslide area has been urbanized in the last 30 years (Notti et al., 2015). As a consequence, several buildings in the area 6 About metamorphism and metamorphic rocks: https://opengeology.org/textbook/6metamorphic-rocks/. 7 About karst processes: https://www.nps.gov/subjects/caves/karst-landscapes.htm.

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FIGURE 6.1 Geological map of the area of interest. Source: The geological units derive from the geological map of Spain, nominal scale 1:50,000 (MAGNA50, http://info.igme.es/cartografiadigital/geologica/Magna50.aspx). The landslide contour is redrawn from Notti, D., Galve, J. P., Mateos, R. M., Monserrat, O., Lamas-Ferna´ndez, F., Ferna´ndez-Chaco´n, F., et al., 2015. Humaninduced coastal landslide reactivation. Monitoring by PSInSAR techniques and urban damage survey (SE Spain). Landslides, 12, 1007
1014.

recorded different degrees of damage and local authorities have had to take remediation actions to lower landslide risk.

6.1.2 EGMS products BOX 6.1 To view the EGMS data along the coast of Granada, refer to Footnote8.

8 https://egms.land.copernicus.eu/#llh 5 -3.73271236,36.72447814,1939.98176962&look 5 0.05208612,-0.59795129,-0.79983829&right 5 0.99790180,0.00032008,0.06474489&up 5 0.038458 28,0.80153238,-0.59671334&layers=VHR%20Image%20Mosaic%202012_VHR%20Image%20 Mosaic%202012-Image-parent.

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Figs. 6.2 and 6.3 present the EGMS Calibrated products over Punta de la Mona, on the Granada coast. In general, the whole coast looks stable with motion within the so-called stability range (in this case, set to 22.5 to 2.5 mm/year). The most evident deformation areas are in Cerro Gordo and in Punta de la Mona, our areas of interest. Cerro Gordo is a known landslide area, which has an impact on a coastal tourist resort located there (Mateos et al., 2017). The deformation maps in Figs. 6.2 and 6.3 are similar, but let us direct the reader’s attention to a crucial difference: the moving areas in Cerro Gordo and Punta de la Mona are visible in both figures (i.e., the velocities are outside the stability range) but the sign of the velocities

FIGURE 6.2 EGMS Calibrated product along the coast of Granada (upper inset) and over Punta de la Mona promontory (lower inset). EGMS data come from an ascending orbit. EGMS, European Ground Motion Service. Source: The background image is provided by the Spanish National Centre for Geographic Information (PNOA) and available via WMS (https:// datos.gob.es/es/catalogo/e00125901-spaignpnoama).

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FIGURE 6.3 EGMS Calibrated product along the coast of Granada (upper inset) and over Punta de la Mona promontory (lower inset). EGMS data come from a descending orbit. EGMS, European Ground Motion Service. Source: The background image is provided by the Spanish National Centre for Geographic Information (PNOA) and available via WMS (https:// datos.gob.es/es/catalogo/e00125901-spaignpnoama).

and, consequently, the color code for the measurement points are opposing. In particular: • In ascending orbit (Fig. 6.2) the east-looking slope of the promontory records line-of-sight (LOS) velocities between 23.0 and 27.0 mm/year (yellow to orange points), whereas the west-looking slope registers LOS velocities between 4.0 and 8.0 mm/year (light blue to blue points). • In descending orbit (Fig. 6.3) the east-looking slope records LOS velocities between 2.0 and 4.0 mm/year (light blue to blue points), whereas the west-looking slope registers LOS velocities between -8.0 and -13.0 mm/year (yellow to orange points). Two questions can be raised: (1) why are the signs of the velocities different in the same area but in different orbits? and (2) why is the

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magnitude of the measured velocity different in the same place while observed from two orbits? Both questions are linked to the direction of the LOS with respect to the local morphology or, more precisely, to the slope angle and the aspect. (1) In ascending orbit, the satellite travels from south to north and illuminates the area from W to E. Thus, a landslide moving on a westfacing slope will record positive velocity measures since the motion is toward the sensor, that is, the distance between the sensor and the ground target is diminishing at every new acquisition of the sensor (assuming a continuous motion). On the opposite, a slope looking to the east will record negative velocities since the motion is away from the satellite, that is, the distance between the satellite and the ground target is increasing at every new acquisition of the satellite (assuming a continuous motion). The measurements are mirrored in descending orbit, being positive along an east-looking slope and negative along a west-facing landslide. Fig. 6.4 offers a simple scheme to visualize this concept. (2) The geometry of acquisition defines the percentage of the real motion vector that can be measured. It is possible to measure most of the real motion in case of a landslide moving along a direction parallel to the LOS, that is, toward the east for ascending geometry or the west for descending geometry. The capability of InSAR to detect the real motion gradually decreases while the motion vector rotates to the south or to the north. InSAR is almost blind to slope deformations oriented parallel to the orbital path (Section 3.5). All the vector orientations between the best and the worst situation will determine a

FIGURE 6.4 Visual explanation of LOS velocity sign with respect to the orbit direction (black line). The purple lines represent the LOS of the sensor. The blue and red lines represent the movement along the slope. LOS, line-of-sight.

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different degree of “visibility” of the real motion. This concept is visually represented in Fig. 6.5, where the percentage of detectable motion is displayed for the results shown in Figs. 6.2 and 6.3. The map in Fig. 6.5 was derived using a digital elevation model (DEM), the Copernicus GLO-30 DEM (Copernicus Space Component Data Access, 2021), to derive slope angle and aspect, and the direction cosines9 of the LOS.10 For each pixel of the DEM, the expected percentage of ground motion that will be visible considering an alongslope movement is computed. Fig. 6.5 portrays a clear explanation of why the landslide on the western side of the Punta de la Mona promontory registers the highest velocities in descending orbit; in fact, this orbit has the best visibility over this slope and more than 80% of the real motion vector can be measured. In this case, the visibility difference between orbits is relatively small, in the order of 20%
30%. However, there are slope geometries characterized by good visibility on one orbit (e.g., 90%) and very low visibility (e.g., below 20%) in the other. In practical terms, this means that it is possible to record the motion of a landslide in one orbit and that the

FIGURE 6.5 Percentage of detectable motion over the Punta de la Mona promontory. Left image, ascending orbit. Right image, descending orbit. Source: The background image is provided by the Spanish National Centre for Geographic Information (PNOA) and is available via WMS (https://datos.gob.es/es/catalogo/e00125901-spaignpnoama).

9 The direction cosine of the LOS depends on the incidence angle and on the LOS azimuth. To facilitate uptake, EGMS Basic and Calibrated products will contain this information as part of the metadata. 10 The full formulation can be found in Notti et al. (2014).

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same landslide will look almost stable in the other. This highlights the importance of the EGMS Ortho product as a powerful tool for landslide studies. In fact, the values of such products are not subject to the above limitations. Fig. 6.6 presents the EGMS Ortho products in the promontory area. Since these landslides have a predominant along-slope component of motion, the east/west product is the one that offers the best results. Horizontal velocities range between 5 and 13 mm/year (in absolute value); the highest velocities are registered along the western flank of the promontory. The two flanks show opposite signs of velocity, and therefore, opposing MP colors. This is consistent with an eastward or westward movement. The vertical component is negligible; velocities range between 2 and 3 mm/year. Although a landslide model would be needed to fully understand the dynamic of the two landslides, the Ortho products enable us to begin understanding the geometry of the landslide, which is supposed to be roto-translational.11

FIGURE 6.6 Ortho products. (A) Vertical component and (B) east
west component. 1, West landslide, 2, east landslide. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

11 Roto-translational means that the landslide is a hybrid between a purely rotational and a purely translational motion: https://www.bgs.ac.uk/discovering-geology/earthhazards/landslides/how-to-classify-a-landslide/.

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Figs. 6.2
6.6 provide all the information needed to assess ground motion in this area: (1) both flanks of the promontory are moving, (2) the maximum and average velocities are known, (3) velocities vary from ascending and descending results, and (4) there is an estimate of vertical and horizontal components of the moving areas. The last step is to interpret the satellite measurements, cross-check them with ancillary data, and express them in an informative format useful for geohazard management or any other ground motionrelated activity. When dealing with landslides, the radar interpreter must: • Verify the consistency of velocity values and deformation patterns with the local morphology. The interpreter can rely on DEM-derived products such as slope and aspect maps and on topographic maps. In the case of Punta de la Mona, the velocity values are coherent with the expected direction of motion of the landslides and the two moving areas are well defined. • Make an appraisal regarding the landslide’s predisposing and triggering factor. It is important to understand why the landslide is moving to give end users information that is as complete as possible. Geological maps and bibliographic research are starting points for this activity. Landslide susceptibility maps are also key elements. Rainfall data are a useful support for data interpretation, especially when comparing rainfall and displacement time series. In this case study, the geological information provides us with the elements to understand the predisposing factors. As said in Section 6.1.1, tectonic disturbances and the karst processes make this area prone to landslides. Urbanization is an accelerating factor for the entire process. • Compare the moving areas with landslide inventories. Landslide inventories contain information about the areal distribution, the activity, the typology, etc. of landslides. There is not a European database, but some countries have developed and maintained national-scale catalogs, which can be accessed by anyone (Herrera et al., 2018). In the Spanish case study, the eastern landslide (Marina Del Este) was already known; the Spanish land movements database (DB-MOVES, Base de Datos de Movimientos del terreno) includes the landslide contour and categorizes it as a “complex landslide” (see the black contour in Fig. 6.6). Scientific papers are another resource to extract landslide contours; the orange polygon in Fig. 6.6 comes from a previous work in the area (Notti et al., 2015). • Use the interferometric data to update landslide inventories. Knowing the location, spatial distribution, and state of activity of a

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landslide is fundamental for risk management. Satellite interferometry can greatly support this activity. In fact, it is possible to both update existing inventories and trace new landslide contours by analyzing a velocity map. There are examples where interferometric-supported landslide inventories have become an official resource for public authorities and are used in everyday working and research activities (e.g., see Rosi et al., 2018). In this case study, the EGMS products allow us to (1) confirm the activity of the Marina Del Este landslide (black contour in Fig. 6.7), (2) extend its contour southward, and (3) delineate the contour and the state of activity of the landslide’s western flank (purple contours in Fig. 6.7). Note: by relying only on the interferometric data, it is not sure whether the additional moving area is part of the main landslide body or is another landslide connected to the main body but with a different slip surface. • Analyze displacement time series. Time series are powerful tools for landslide investigation. They enable users to detect changes in the behavior of a landslide and make correlations with triggering factors. The detection and mapping of landslides with accelerating behavior is a key element for risk management and civil protection activities. Fig. 6.8 shows an example of time series extracted from the east and west landslides of Fig. 6.7.

FIGURE 6.7 EGMS Calibrated and geomorphological elements. The “MOVES inventory” is the Spanish land movement database (http://info.igme.es/catalogo/resource.aspx? portal 5 1&catalog 5 3&ctt 5 1&lang 5 por&dlang 5 eng&llt 5 dropdown&master 5 infoigme&shdt 5 false&shfo 5 false&resource 5 8308). EGMS, European Ground Motion Service. Source: The background image is provided by the Spanish National Centre for Geographic Information (PNOA) and is available via WMS (https://datos.gob.es/es/catalogo/e00125901spaignpnoama).

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FIGURE 6.8 Time series of displacement and daily rainfall. The time series of displacement are averaged over tens of points in the central portion of the two landslides. Source: Rainfall data are derived from the AEMET database managed by the Spanish Agency of Meteorology (https://datosclima.es/index.htm). The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

The time series are mainly linear with a seasonality that can be connected to rainfall periods; the green bars in Fig. 6.8 are the daily rainfall measures obtained from a pluviometric station 30 km from Punta de la Mona.

6.2 Landslide mapping in Troms og Finnmark county (Norway) The case study of Almun˜ecar is ideal for explaining the usage of interferometric data for landslide studies. The climate and land cover context, density of buildings, geometry, and exposure of the slope make Almun˜ecar a good context to employ InSAR. This section of the book presents the EGMS data in a more challenging environment, with higher relief, different land cover and climate characteristics, and where the valley orientation and geometry can be a determining factor for MP detection. The target of this case study is Troms og Finnmark county in northern Norway. This is one of the areas of Norway with the highest landslide occurrence, where hundreds of landslides have been mapped. The potential risk for the population is not negligible, and research activities were carried out to map the extension of landslides and estimate their state of activity. Interferometry was found to be the right solution for the complex geomorphological context of

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this area. For example, Dehls et al. (2008) presented the first regionalscale interferometric results for Troms og Finnmark region based on ERS and Envisat data. Years later, Rouyet et al. (2021) enriched the investigation over this part of Norway by integrating Sentinel-1derived InSAR results with geomorphological information and field survey results. Henderson et al. (2011) proposed a geomorphological reconstruction supported by InSAR data of the Gamanjunni landslide in the Lyngenfjord, one of the case studies of this chapter.

6.2.1 Geographical and geological context Norway is a peculiar Nordic environment marked by hundreds of long fjords with steep slopes that are frequently affected by small to large landslides. Even if hundreds of kilometers of coastline are inhabited, large rock avalanches can trigger tsunami-like events that, as reported by Dehls et al. (2008), have caused over 170 deaths in the last 100 years. The Lyngen region of Troms og Finnmark county has one of the highest landslide densities in Norway; 80% of all rockslides in the county are found there (Henderson et al., 2011). The Lyngen region hosts one of the largest landslides in the country, the “Gamanjunni 3” rockslide with an estimated volume of 17 Mm 3 (Dehls et al., 2008). Osmundsen et al. (2009) attributed this unusually high density of landslides to the presence of a regional pattern of normal faults.12 From a geological point of view (Fig. 6.9), this area, like most of Norway’s territory, constitutes the remnants of the old and deeplyeroded Scandinavian Caledonides, an orogen13 formed in the Ordovician time (488
443 Ma before present). The orogen is composed of a succession of nappe14 complexes (defined as “allochthonous”) that were stacked up on a rock basement from the Precambrian age (4.6 Ga
500 Ma before present). The allochthonous tectonic units are constituted by metamorphic rocks, both igneous and sedimentary (Corfu et al., 2014), which are bounded by faults. The orogenic succession is represented in this area by the Tromsø Nappe, which consists of schists, gneisses, marbles, calcsilicate rocks, and eclogite and amphibolite (Jana´k et al., 2013). 12 What is a normal fault? https://www.usgs.gov/faqs/what-fault-and-what-are-different-types#:B:text 5 normal%20fault%20%2D%20a%20dip%2Dslip,and%20along%20oceanic%20ridge%20systems. 13 A basic explanation of the structure of an orogen: https://courses.eas.ualberta.ca/ eas421/lecturepages/orogens.html. 14 A nappe is a large body or sheet of rock that has been moved for some kilometers from its original position by faulting or folding. A nappe is exclusively connected to the compressional tectonics that forms mountain chains.

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FIGURE 6.9 Geological map of part of Troms og Finnmark county, around the regional capital (Tromsø). The legend of the map follows the official nomenclature translated into English and the numbers are ordered following this nomenclature. 102, Granite, 104, tonalite, 111, quartzite, 112, diorite, 113, gabbro, 130, peridotite, 131, dunite, 136, pyroxenite, 150, mafic dyke, 304, metasandstone, 307, conglomerate, 322, dolomite marble, 401, shale, 402, phyllite, 403, mica schist, 404, garnet mica schist, 406, calcareous mica schist, 410, amphibole schist, 411, graphitic schist, 412, chlorite schist, 415, marble, 416, dolomite marble, 420, metasandstone, 421, metagreywacke, 422, metaarkose, 423, metaquartzite, 424, quartz schist, 426, mica gneiss, 427, calc-silicate rock, 428, aluminum silicate gneiss, 429, amphibole gneiss, 430, granitic gneiss, 431, granodioritic gneiss, 432, tonalitic gneiss, 440, migmatite, 441, augen gneiss, 442, banded gneiss, 451, greenstone, 452, amphibolite, 454, metagabbro, 455, eclogite, 470, mylonite. Source: The map is derived from the nationwide bedrock map of Norway, at the nominal scale 1:250,000. The map is available to view and download through the portal of the Norwegian Geological Survey (https://www.ngu.no/en/topic/datasets).

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6.2.2 EGMS products

BOX 6.2 To view the EGMS data over Troms og Finnmark county, refer to Footnote15.

This section presents the Calibrated (Fig. 6.10, ascending orbit, and Fig. 6.11, descending orbit) and Ortho (Fig. 6.12, vertical component, and Fig. 6.13, horizontal component) products for Troms og Finnmark county. The location of the city of Tromsø and the names of the major fjords in the area are indicated in the figures to give the reader some geographical references. The first noticeable detail is the high point density that can be reached in this mountain area, which is in a boreal environment with a subarctic climate. As explained in Section 3.3, the presence of dense vegetation is one of the main factors that can prevent good MP density in mountain areas. Large parts of the steep slopes of the fjords are covered by debris or bare rock, which is an ideal MP detection situation. Here, the two main factors governing MP detection are the presence of perennial snow at high altitudes, as demonstrated by the lack of MPs on some mountain tops, and the geometrical effects that may create image distortions (see Section 3.4). Second, it is possible to appreciate, even at this basin-scale visualization, the presence of dozens of moving areas along the flanks of the fjords that can be associated with active landslides. These moving areas can be viewed in the LOS products (Calibrated ascending and descending, Figs. 6.10 and 6.11) thanks to the changes in MP color, which is connected to the variation in the velocity sign. This gives the reader an immediate confirmation as to the presence of a landslide. Here, it is important to recall that: • The motion of a landslide for dozens of millimeters per year in one orbit does not imply that another orbit will register the same or a similar magnitude of motion. It is not unusual to see a landslide moving more than 20 mm/year in one orbit and a small fraction of this rate in another one. As explained in Section 3.5, the motion recorded is highly sensitive to the angle between the LOS and the direction of motion. • There is not a one-to-one relationship between dimension of the moving area and surface extension of the landslide. The location of 15 https://egms.land.copernicus.eu/#llh=19.64875411,69.47545345,92194.62793118&look= -0.11774121,-0.93649838,-0.33031468&right=0.94529974,-0.00379969,-0.32618089&up=-0.304212 78,0.35065132,-0.88571905&layers=VHR%20Image%20Mosaic%202012_VHR%20Image%20 Mosaic%202012-Image-parent.

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FIGURE 6.10 EGMS Calibrated product, ascending orbit. Yellow to red colors identify LOS movements away from the sensor, light blue to blue colors identify movements toward the sensor. The black dashed-line rectangles indicate some areas of interest detailed next; 1, southern Sørfjorden, Tromsø municipality; 2, Manndalen valley, Ka˚fjord municipality, 3, southern Lyngenfjord, Storfjord municipality. EGMS, European Ground Motion Service.; OS, line-of-sight. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus. eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

the InSAR MPs is opportunistic and the interpreter must consider that the active landslide could be larger (Barra et al., 2017). • The calibration process may mask some of the smaller movements, since the global navigation satellite system (GNSS) signal can embed the motion in areas with strong vertical or horizontal components. In this case, the postglacial rebound of Fennoscandia can visually hinder the detection of landslides with LOS velocity of a positive sign (i.e., moving toward the sensor). Another example of this is to be found in Greece and its westward motion due to tectonics (see an example of this in Section 6.3). Third, the east
west component of EGMS Ortho improves the visualization and identification of landslides. This is even more evident at the basin scale. The availability of a layer that combines the information of two satellite orbits, unbinding the information from the geometry relationship of the LOS, is a powerful aid for landslide mapping. The first screening can be performed over the east
west component at a lower resolution. Then, the landslide inventory can be refined by investigating the signal carried by each LOS product.

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FIGURE 6.11 EGMS Calibrated product, descending orbit. Yellow to red colors identify LOS movements away from the sensor, light blue to blue colors identify movements toward the sensor. The black dashed-line rectangles indicate some areas of interest detailed next; 1, southern Sørfjorden, Tromsø municipality; 2, Manndalen valley, Ka˚fjord municipality, 3, southern Lyngenfjord, Storfjord municipality. EGMS, European Ground Motion Service; LOS, line-of-sight. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus. eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

The next three sections present in greater detail some moving areas of interest in Troms og Finnmark county. 6.2.2.1 Southern Sørfjorden

BOX 6.3 To view the EGMS data along the Sørfjorden, refer to Footnote16.

16 https://egms.land.copernicus.eu/#llh=19.46090769,69.34878096,7952.71391344&look= -0.07539125,-0.55597216,-0.82777480&right=0.99413816,0.02260721,-0.10572719&up=-0.07749 505,0.83089342,-0.55100875&layers=VHR%20Image%20Mosaic%202012_VHR%20Image%20 Mosaic%202012-Image-parent.

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FIGURE 6.12 EGMS Ortho product, vertical component. Yellow to red colors identify downward movements, light blue to blue colors identify upward movements. The black dashed-line rectangles indicate some areas of interest detailed next; 1, southern Sørfjorden, Tromsø municipality; 2, Manndalen valley, Ka˚fjord municipality, 3, southern Lyngenfjord, Storfjord municipality. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

This area is located 50 km south of Tromsø, in the southern part of the Sørfjorden, not far from the fishing village of Laksvatn. From a morphological point of view, this is a classic Norwegian landscape, with very steep mountain flanks that reach 1200 m.a.s.l. on both sides of a narrow fjord, 3-km wide. It is an area highly prone to landslides (Hermanns et al., 2012). In this case, both the west-facing (“Piggtind landslide,” area A in Fig. 6.14) and the east-facing (“Siedi landslide,” area B in Fig. 6.14) flanks of the fjords are affected by two large landslides, the activity of which is revealed by the EGMS data. According to the geological cartography, the slopes are characterized by the presence of widespread moraine and slope deposits.17 The EGMS Calibrated data (insets A and B in Fig. 6.14) correctly define the spatial characteristics of the two moving areas, with LOS 17 Norwegian National Database of Superficial Deposits: http://geo.ngu.no/kart/losmasse_mobil/?lang 5 eng.

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FIGURE 6.13 EGMS Ortho product, east
west component. Yellow to red colors identify westward movements, light blue to blue colors identify eastward movements. The black dashed-line rectangles indicate some areas of interest detailed next; 1, southern Sørfjorden, Tromsø municipality; 2, Manndalen valley, Ka˚fjord municipality, 3, southern Lyngenfjord, Storfjord municipality. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

velocities up to 6 50
70 mm/year for both landslides. The velocities are coherent with a movement along the slope with signs depending on the orbit, for example, the Siedi landslide (area B in Fig. 6.14) in ascending orbit has negative velocity, consistent with a movement away from the sensor. The analysis of the LOS data highlights these facts: • The different point coverage with respect to the geometry of acquisition. Foreshortening and layover effects (Section 3.4) compromise the detection of measurement points. This is the case for the Piggtind1 landslide (area A, inset B, in Fig. 6.14). • Vegetation is a factor. In Fig. 6.14, there is a difference in MP coverage between the lower portion of slopes A and B due to the presence of dense vegetation. The lack of MPs clearly limits our capability to map the real extension of a landslide. Nonetheless, there is no real solution to this problem when using Sentinel-1 data; only the installation of artificial reflectors (Section 3.13) can provide a local solution. The use of data with longer wavelengths (e.g., L-band)

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FIGURE 6.14 EGMS data along the southernmost sector of the Sørfjorden, near the fishing village of Laksvatn. (A) EGMS Calibrated—descending orbit, (B) EGMS Calibrated—ascending orbit, (C) time series for the Calibrated products (refer to insets A and B for the location of the points), (D) time series for the Ortho products (refer to insets E and F for the location of the points), (E) EGMS Ortho—east
west component, (F) EGMS Ortho—vertical component. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

could improve MP density. This should not discourage the application of InSAR for landslide studies, but end users will be made aware of where a landslide can and cannot be measured. • Time series allow the reader to visualize the difference of LOS velocity and displacement between orbits (inset C in Fig. 6.14).

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Looking at the time series for point A, there is a difference of ca. 30 mm/year between the velocities registered in ascending and descending orbits. Because of the geometry of the slope with respect to the LOS, the descending orbit can measure 88% of the along-slope motion, whereas the ascending orbit can estimate only 25% of such components. The percentage of detectable motion affects the spatial pattern of the moving area as well. A low percentage implies a high probability of measurement points falling within the stability range. This can cause misinterpretation of the results, if only one orbit is available. Even if the EGMS Ortho products have a reduced resolution, they can still identify the presence of the moving areas along the flanks of the fjord. The GNSS signal (glacial rebound-uplift of the Fennoscandia) does not mask out the vertical component of the two landslides since the direction of motion is opposite. Nonetheless, the vertical velocity is slightly lowered. It must be pointed out that in case of a vertical component lower than the GNSS signal, the former may be completely embedded by the latter. This has an impact on the local-scale usage of the Ortho product that will be discussed later in this chapter (Section 6.3). The Ortho data can help us understand the dynamics of the two landslides. Insets E and F of Fig. 6.14 present the east
west and vertical Ortho data, respectively. Both components register high displacement rates of up to 6 20 mm/year. This means that the landslides not only have a horizontal-along-slope component of motion but also a relevant vertical component, especially in the upper part of the moving area, where the landslide crowns are supposed to be. From the geomorphological point of view, this means that the phenomena are most likely complex, that is, with both a rotational and translational component. Time series are linear without major oscillations or trend changes. Due to the persistent presence of snow cover, time series can only be obtained for 4
5 months a year, in snow-free periods. The presence of snow has a direct impact on time series. In fact, all the SAR images affected by snow coverage must be removed from the stack to be analyzed, and the time series will contain some gaps with temporal duration dependent on the length of the snow period. In northern Norway, this equals B7 months of no-data gaps. Luckily, it is still possible to process the data, but the general noise level of time series will be slightly higher than a point with a full-year time series. Nonetheless, it is still possible to assess the time series trend and estimate the velocity of the point. Sentinel-1 has been extremely important for these areas since the 6-day-repeat pass time enabled attainment of B30 images per year to process. Unfortunately, the loss of Sentinel-1B reduced the volume of images by half, as of December 2021.

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What does this entail for data interpretation? Potentially, if the landslide has a constant motion without major accelerations, not much, as can be seen in Fig. 6.14. Velocity can be estimated even if the time series is composed of strictly snow-free images and the trend can be appreciated. The loss of images due to seasonal snow cover does not reduce the number of measurement points over a landslide area. However, if the landslide has a seasonal or nonlinear component over the duration of the year, the estimation of the motion will be less accurate, and seasonality may be lost. 6.2.2.2 Manndalen valley

BOX 6.4 To view the EGMS data along the Manndalen valley, refer to Footnote18.

The Manndalen valley is N
S oriented and it is located in the eastern portion of Troms og Finnmark county. The U-shaped valley is the result of its glacial history that oversteepened the mountain flanks, making the valley the optimal location for the development of rock slopes and rockfalls (Molina et al., 2015). Quartz
mica schists and gneisses dominate the lithological composition of mountain flanks. The area hosts some well-known landslides that have been studied for years by local researchers; this is the case of the Gamanjunni 1 and 3 landslides (Fig. 6.15). This case study focuses on the Gamanjunni 3 landslide, considered one of the most active in Norway, with maximum annual velocity of 60 mm/year (Bo¨hme et al., 2018). The landslide consists of a large block of rock that slid downslope as a coherent mass for B150 m and that has a wedge-shaped crown area. According to Dehls et al. (2008), the volume of the block is approximately 17 Mm3. Bo¨hme et al. (2018) estimated that the unstable area stretches for B600 m along the mountain flank, from the altitude of 1200 to 600 m.a.s.l. The toe of the landslide displays the presence of large debris deposits that are formed by the detachment of boulders and blocks from the front of the block. Please refer to Henderson et al. (2011) for a rigorous geomorphological characterization of the landslide. 18 https://egms.land.copernicus.eu/#llh=20.50435996,69.52645690,7069.78192140&look 50.15747327,-0.95945097,0.23378622&right5-0.89488905,-0.03853733,0.44462170&up50. 41758323,0.27922876,0.86467077&layers5VHR%20Image%20Mosaic%202012_VHR%20 Image%20Mosaic%202012-Image-parent,euro_regional.

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FIGURE 6.15 EGMS data along the Manndalen valley. (A) EGMS Calibrated—ascending orbit, (B) EGMS Calibrated—descending orbit, (C) time series for the Calibrated products (refer to insets A and B for the location of the points), (D) time series for the Ortho products (refer to insets E and F for the location of the points), (E) EGMS Ortho—east
west component, (F) EGMS Ortho—vertical component. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https:// egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

The Calibrated product in Fig. 6.15 (insets A and B) spotlights a complex slope situation in which the interferometric data enable the delineation of the contours of multiple moving areas. This is possible thanks to the good

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exposure of the slope with respect to the LOS of both orbits and to the vegetation-free land cover above the forest limit. For this reason, it is possible to rely on more than 700 points within the known contour of the Gamanjunni 3 landslide, which reaches velocity peaks of 240 mm/year in descending orbit and of 65 mm/year in ascending orbit. These values are coherent with the magnitude of motion estimated by previous authors (e.g., Bo¨hme et al., 2018). As a general remark, the northeastern part of the landslide has better visibility in the descending orbit, whereas the southwestern portion of the slope is better illuminated by the ascending orbit. This fact reiterates the importance of double-orbit investigations for landslide studies. Time series for the EGMS Calibrated products do not show major oscillations in the observation periods (inset C, Fig. 6.15). Note: the reader must bear in mind that the Sentinel-1 tracks converge to the Pole at high latitudes. This means that in this area and in all of the northernmost portion of Scandinavia, the overlap between tracks is larger and each ground parcel can be contained in three different Sentinel-1 tracks. On one hand, this means redundancy of interferometric results; on the other, the user should consider that different tracks involve different look angles for the same area. As explained in Section 6.1.2, a change in the incidence angle of the radar signal with respect to the ground surface implies a different “visibility” of the pixel, that is, a different ability to measure the real ground motion component. For example, an MP in the central part of the Gamanjunni 3 landslide will record a variation of B10 degrees between the incidence angles of the three tracks that overlap here (e.g., in track 22 the incidence angle is 35 degrees, while in track 24 it is 45 degrees). This means that the LOS velocities will be different and point density may vary. Higher incidence angles may imply bigger geometrical distortions that prevent MP detection. A visual example of this is given by Fig. 6.16. The same ground area (the white circle in Fig. 6.16, insets A, B, and C) records different LOS velocities, ranging between 244 mm/year for track 23 to 233 mm/year for track 24. It is also interesting to notice the lower point density for track 24 with respect to the other two tracks; this is an effect of the abovementioned higher impact of geometrical effects on MP detection. In Fig. 6.15, insets E and F present EGMS Ortho data for the east
west and vertical components, respectively. As expected, the east
west component shows the largest deformation; all the moving areas identified in the Calibrated products show major horizontal velocities along the slope direction. For example, the body of the Gamanjunni 3 landslide reaches 240 mm/year. Note: the minus sign indicates a westward motion coherent with the slope orientation. The other landslides along the eastern flank of the valley register lower east
west velocities but still remain in the range of 210 to 220 mm/year. Generally speaking, the vertical component magnitude is minor, even if the Gamanjunni 3 landslide registers movements away from the sensor

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FIGURE 6.16 EGMS Calibrated (descending orbit) for the Gamanjunni 3 landslide. (A) Data for track no. 22, (B) data for track no. 23, (C) data for track number 24, (D) time series. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

that register nearly 220 mm/year. This attests to the complex nature of the phenomenon. Note: the vertical component contains the GNSS signal connected to the Fennoscandian uplift; thus small vertical motions may be masked out (here the uplift is around 3 mm/year). Time series for the EGMS Ortho products do not show major oscillations in the observation periods (inset D, Fig. 6.15). 6.2.2.3 Southern Lyngenfjord

BOX 6.5 To view the EGMS data along the Lyngenfjord, refer to Footnote19.

19 https://egms.land.copernicus.eu/#llh=19.97049010,69.25093491,8628.57200517&look50.13658203,-0.35495793,-0.92485146&right50.98970301,-0.08924591,-0.11190673&up50.042817 03,0.93061272,-0.36349232&layers5VHR%20Image%20Mosaic%202012_VHR%20Image%20 Mosaic%202012-Image-parent,euro_regional.

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The Lyngenfjord hosts several active landforms along its mountain flanks, such as complex landslides, rockfalls, fast active moraines, or talus deposits (Rouyet et al., 2021). Landslides have been studied and mapped on the eastern flank of the fjord; one of which is the Jettan landslide (see Fig. 6.17 for the location). The Jettan landslide is a complex rockslide composed of multiple sectors with different dynamics. The reader can refer to the detailed geomorphological reconstruction of the landslide given by Vick et al. (2020). Fig. 6.17 presents the EGMS products along the south-eastern rim of the Lyngenfjord. Insets A and B show the EGMS Calibrated products in ascending and descending orbits, respectively. Insets E and F stand for the two components of the EGMS Ortho data. The Calibrated products once again highlight the presence of multiple active slope movements. Some are of large size, such as the Jettan landslide (area B in Fig. 6.17), or the unnamed active phenomenon in the southern part of the investigated area (area A in Fig. 6.6. 17, hereafter designated as landslide “A”). As in the previous case studies, point density can be relatively high above the forest limit and is greatly influenced by geometrical effects, depending on the orbit direction; the MP density difference between ascending and descending orbit data around the Jettan landslide is an example. LOS velocity for the Jettan landslide reaches 245 mm/year in descending orbit; the velocity value is halved in the ascending orbit. It is interesting to notice that the upper portion of the Jettan landslide has MPs with the same sign in both orbits, meaning that the landslide has a strong vertical component, and this is demonstrated by the EGMS Ortho products (Fig. 6.17 insets E and F). Landslide “A” has similar velocities in both orbits, near 20 mm/year in ascending orbit and B 2 25 mm/year in descending orbit. Time series do not highlight trend variations for landslide “A,” whereas the last period of the Jettan time series presents a minor order acceleration. Vick et al. (2020) reported that the largest rates of deformation are usually expected in the autumn and interpreted as an effect of freezing of groundwater leading to fracture expansion.

6.3 Some considerations on the use of EGMS data for landslide studies This chapter provides an overview of the main advantages and limitations of the EGMS products for landslide monitoring and/or mapping. In this section, the book proposes additional examples to deal with specific issues or applications.

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FIGURE 6.17 EGMS data along the southern portion of the Lyngenfjord. (A) EGMS Calibrated—ascending orbit, (B) EGMS Calibrated—descending orbit, (C) time series for the Calibrated products (refer to insets A and B for the location of the points), (D) time series for the Ortho products (refer to insets E and F for the location of the points), (E) EGMS Ortho—east
west component, (F) EGMS Ortho—vertical component. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus. eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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BOX 6.6 To view the EGMS data over the Peloponnese region, refer to Footnote20.

The first example introduces the problem of distinguishing the east
west motion of a landslide from the east
west component of the GNSS signal, in case this component is not negligible. The previous section already discussed this problem as it exists in Norway, where it is the vertical component that creates interpretation problems. However, the vertical component is usually less relevant and does not bias the interpretation of the Calibrated and Ortho data. In Europe, there is only one region that has east
west GNSS velocities high enough to hinder the interpretation of landslides. This is the case of Greece, and specifically of its southern portion, which includes the Peloponnese and Attica regions, the Cyclades and Dodecanese archipelagos, and the island of Crete. Fig. 6.18 presents an example from this part of Greece. Insets A and B of Fig. 6.18 show the EGMS Calibrated data in ascending and descending orbits, respectively; inset C shows the EGMS Ortho data relative to the east
west component. To interpret the figure the reader should consider (1) the motion of the Aegean21 Plate in the south
west direction, (2) the local motion of the slope phenomena, and (3) the direction of the plate and the landslide motion with respect to the LOS of the sensor. The ascending orbit results serve as visual examples. The black contours indicate probable active landslide locations; the first one (contour 1 in the figure) corresponds to the city of Pyrgos, the second (contour 2 in the figure) to the village of Mesaia Trikala. All the points outside the landslide area record similar velocities, in the order of 9
10 mm/year toward the sensor. This is related to the effect of the GNSS calibration. Once the Basic MPs are referenced to the GNSS velocity model, the calibration process of the LOS data in the ascending orbit will give this kind of result, with points moving toward the sensor following the motion of the Aegean plate in the W
SW direction. A point that is stable in the Basic product will now move toward the sensor in the Calibrated product in ascending orbit (or away from the 20 https://egms.land.copernicus.eu/#llh=22.45317917,38.00122344,14657.71910324&look=0.30088605,-0.61562844,-0.72833317&right=0.92418081,0.00019093,-0.38195522&up=-0.2352 8156,0.78803654,-0.56889454&layers=VHR%20Image%20Mosaic%202012_VHR%20Image% 20Mosaic%202012-Image-parent,euro_regional,D29-007-release. 21 How is the Aegean Plate moving? http://eurasiatectonics.weebly.com/adriatic-andaegean-plates.html.

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FIGURE 6.18 EGMS data in the Peloponnese region. (A) EGMS Calibrated—ascending orbit, (B) EGMS Calibrated—descending orbit, (C) EGMS Ortho—east
west component. Black contours: 1, landslide involving the city of Pyrgos (Πυργoς in Greek); 2, landslide affecting the village of Mesaia Trikala (Mεσαια Τρικαλα in Greek). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

sensor in the other orbit). This can hinder the interpretation and mask out some local motion in case the magnitude of motion is lower than that of the GNSS signal, especially in the opposite direction. However, it is still possible to visualize the motion of landslides with motion rates that fall outside the range of the GNSS signal. This is the case of the Mesaia Trikala landslide in ascending orbit (contour 2 in Fig. 6.18). The landslide records LOS velocities up to 220 mm/year, and the sign is coherent with a motion along the slope moving away from the sensor. Velocities become positive in the upper portion of the slope where the eastward velocity component decreases and is partly absorbed by the GNSS component. In sites where the horizontal component of the GNSS signal is as strong as in southern Greece, the Calibrated product may have a lower applicability. The data can still be interpreted, even if the process is more complex and the GNSS signal can be filtered out knowing the magnitude of the plate component. However, some local motion may be lost, and inexperienced users may fail to extract the information they need. For this reason, the Basic product is used, which is available in the EGMS Explorer for download, to have a clearer estimation of local phenomena in these areas.

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BOX 6.7 To view the EGMS data over the Mirambel municipality, refer to Footnote24. To view the EGMS data along the Portalet mountain pass, refer to Footnote25.

One of the most important applications of InSAR in mountain areas is landslide mapping. Landslide mapping in this context is an InSARsupported activity that can derive a “fresh” database where previous information is not available or can rely on existing landslide catalogs to extract new information layers (e.g., state of activity of known landslides, extension of the contour of known landslides—Rosi et al., 2018). The EGMS Explorer enables the user to import an external web map service (WMS) to visualize data that may help the interpretation or validation of the EGMS products. Not every WMS can be imported in the Explorer and the following requirements must be met: (1) the external WMS must support the necessary CORS headers,22 (2) the external WMS must support secure HTTP, and (3) the external WMS must support delivering imagery in Web Mercator projection (EPSG:3857, EPSG:900913). This is not a problem for many European layers (e.g., the Corine Land Cover), but it may be impossible to load national datasets because they use different reference systems. In the next examples, the Spanish BD-MOVES23 database (Base de Datos de Movimientos del terreno) is used, which is developed and maintained by the Geological and Mining Institute of Spain. The database contains information about the location (areal and punctual) of landslides and subsidence areas. Fig. 6.19 shows the results of a visual comparison between the EGMS products (Calibrated data in ascending and descending orbits) and the

22 What does CORS mean? https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS. 23 Further information about BD-MOVES. The WMS link is available on this page. http://info.igme.es/catalogo/resource.aspx?portal 5 1&catalog 5 3&ctt 5 1&lang 5 spa&dlang 5 eng&llt 5 dropdown&master 5 infoigme&shdt 5 false&shfo 5 false&resource 5 8308. 24 https://egms.land.copernicus.eu/#llh=-0.32626968,40.57214782,6737.35010725&look= 0.00432730,-0.65040224,-0.75957765&right=0.99998307,-0.00014073,0.00581739&up=0.0038 9054,0.75958996,-0.65039062&layers=VHR%20Image%20Mosaic%202018_VHR%20Image% 20Mosaic%202018-VHR_2018_WM:None,A17-030-release. 25 https://egms.land.copernicus.eu/#llh=-0.40256672,42.79074688,8561.04659780&look= 0.00511030,-0.67868026,-0.73441609&right=0.99997475,-0.00015833,0.00710445&up=0.0049 3793,0.73443385,-0.67866231&layers=VHR%20Image%20Mosaic%202018_VHR%20Image% 20Mosaic%202018-VHR_2018_WM:None,euro_regional,A17-030-release.

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FIGURE 6.19 Comparison between EGMS data and the Spanish landslide database (DB-MOVES). (A) EGMS Calibrated—descending orbit in the Mirambel municipality (Aragon region), (B) EGMS Calibrated—ascending orbit along the Portalet mountain pass in the Huesca province (Aragon region). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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DB-MOVES database in two different landslide-prone areas of the Aragon region, in northeastern Spain. Inset A presents the descending data covering a rural area between the municipalities of Mirambel and La Cuba in the Teruel province of Aragon. Here, the DB-MOVES database catalogs some of the mountain flanks south of the village of Mirambel as areas with “diffuse ground motion” related to slope dynamics. As expected in mountain areas, the point density is uneven; therefore some valley flanks will record enough points to map the possible extent of the unstable slopes. Others, due to the intrinsic limitation of the technique, may not be analyzed. However, the EGMS data allow the InSAR interpreter to recognize, even if qualitatively, the presence of several MPs within the “diffuse ground motion” areas that record motion outside the stability threshold. In general, the velocities do not exceed 6 5 mm/year. Inset B shows the Portalet mountain pass, one of the most closely studied landslide areas in Spain. This area features an important commercial route between Spain and France and the stability of its mountain flanks has long been a target of interferometric analyses (e.g., Herrera et al., 2011). The landslide investigated by these authors is indicated by the number 1 in inset B of Fig. 6.19. The DB-MOVES database includes several landslides for this area, of varying typology and spatial extension. The landslides with the best and optimal MP coverage are indicated by the numbers 1, 2, and 3 in inset B of Fig. 6.19. There, the EGMS Calibrated product confirms the activity of the landslides and their spatial extension with respect to the DB-MOVES contours. As stated in the previous example, not all the known landslides will have MPs and the reader must remember that a green point does not always mean no motion, but rather no detectable motion.

References Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., Cakir, Z., 2020. Landslide mapping and monitoring using persistent scatterer interferometry (PSI) technique in the French Alps. Remote Sensing 12, 1305. Available from: https://doi.org/ 10.3390/rs12081305. Barra, A., Solari, L., Be´jar-Pizarro, M., Monserrat, O., Bianchini, S., Herrera, G., et al., 2017. A methodology to detect and update active deformation areas based on sentinel-1 SAR images. Remote sensing 9 (10), 1002. Available from: https://doi.org/10.3390/rs9101002. Bekaert, D.P., Handwerger, A.L., Agram, P., Kirschbaum, D.B., 2020. InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: an application to Nepal. Remote Sensing of Environment 249, 111983. Available from: https://doi.org/10.1016/j.rse.2020.111983Get. Bo¨hme, M., Bunkholt, H.S.S., Oppikofer, T., Dehls, J.F., Hermanns, R.L., Eriksen, H.Ø., et al., 2018. Using 2D InSAR, dGNSS and structural field data to understand the deformation mechanism of the unstable rock slope Gamanjunni 3, northern Norway. Landslides and Engineered Slopes. Experience, Theory and Practice. CRC Press, pp. 443
449.

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Ciampalini, A., Raspini, F., Lagomarsino, D., Catani, F., Casagli, N., 2016. Landslide susceptibility map refinement using PSInSAR data. Remote Sensing of Environment 184, 302
315. Available from: https://doi.org/10.1016/j.rse.2016.07.018. Copernicus Space Component Data Access, 2021. Copernicus DEM: 30 meter dataset now publicly available. ,https://spacedata.copernicus.eu/explore-more/news-archive/-/ asset_publisher/Ye8egYeRPLEs/blog/id/434960. (accessed on 26.08.22). Corfu, F., Andersen, T.B., Gasser, D., 2014. The Scandinavian Caledonides: main features, conceptual advances and critical questions. Geological Society 390, 9
43. Available from: https://doi.org/10.1144/SP390.25. London, Special Publications. Cruden, D.M., Varnes, D.J., 1996. Landslides: investigation and mitigation. Chapter 3-Landslide types and processes. Transportation Research Board Special Report, (247). Transportation Research Board. Dehls, J., Henderson, I., Lauknes, T., Larsen, Y., 2008. Regional landslide mapping and detailed site characterization using InSAR. In: Proceedings of the GeoEdmonton08: 61st Canadian Geotechnical Conference and 9th Joint CGS/IAH-CNC Groundwater Conference, Edmonton, AB, Canada, 21
24 September 2008. Froude, M.J., Petley, D.N., 2018. Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences 18, 2161
2181. Available from: https:// doi.org/10.5194/nhess-18-2161-2018. Haque`, U., Da Silva, P.F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A., et al., 2019. The human cost of global warming: deadly landslides and their triggers (1995
2014). Science of the Total Environment 682, 673
684. Available from: https://doi.org/10.1016/j. scitotenv.2019.03.415. Henderson, I.H.C., Lauknes, T.R., Osmundsen, P.T., Dehls, J., Larsen, Y., Redfield, T.F., 2011. A structural, geomorphological and InSAR study of an active rock slope failure development. Geological Society 351, 185
199. Available from: https://doi.org/ 10.1144/SP351.10. London, Special Publications. Hermanns, R.L., Hansen, L., Sletten, K., Bo¨hme, M., Bunkholt, H., Dehls, J.F., et al., 2012. Systematic geological mapping for landslide understanding in the Norwegian context. In: Eberhardt, E., Froese, C., Turner, K., Leroueil, S. (Eds.), Landslide and Engineered Slopes: Protecting Society through Improved Understanding, Vol. 2. CRC Press, London, UK, pp. 265
271. Herrera, G., Notti, D., Garcı´a-Davalillo, J.C., Mora, O., Cooksley, G., Sa´nchez, M., et al., 2011. Analysis with C-and X-band satellite SAR data of the Portalet landslide area. Landslides 8, 195
206. Available from: https://doi.org/10.1007/s10346-010-0239-3. Herrera, G., Mateos, R.M., Garcı´a-Davalillo, J.C., Grandjean, G., Poyiadji, E., Maftei, R., et al., 2018. Landslide databases in the Geological Surveys of Europe. Landslides 15 (2), 359
379. Available from: https://doi.org/10.1007/978-3-642-31313-4_44. Highland, L., Bobrowsky, P.T., 2008. The landslide Handbook: A Guide to Understanding Landslides. US Geological Survey, Reston. Intrieri, E., Raspini, F., Fumagalli, A., Lu, P., Del Conte, S., Farina, P., et al., 2018. The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data. Landslides 15, 123
133. Available from: https://doi.org/10.1007/s10346-0170915-7. Jana´k, M., Krogh Ravna, E.J., Kullerud, K., Yoshida, K., Milovsky´, R., Hirajima, T., 2013. Discovery of diamond in the Tromsø Nappe, Scandinavian Caledonides (N. Norway). Journal of Metamorphic Geology 31, 691
703. Available from: https://doi.org/ 10.1111/jmg.12040. Mateos, R.M., Azan˜o´n, J.M., Rolda´n, F.J., Notti, D., Pe´rez-Pen˜a, V., Galve, J.P., et al., 2017. The combined use of PSInSAR and UAV photogrammetry techniques for the analysis of the kinematics of a coastal landslide affecting an urban area (SE Spain). Landslides 14 (2), 743
754. Available from: https://doi.org/10.1007/s10346-016-0723-5.

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Molina, F.X.Y., Bunkholt, H.S., Kristensen, L., Dehls, J., Hermanns, R.L., 2015. The use of remote sensing techniques and runout analysis for hazard assessment of an unstable rock slope at Storhaugen, Manndalen, Norway, Engineering Geology for Society and Territory, Vol. 2. Springer, Cham, pp. 329
332. Notti, D., Herrera, G., Bianchini, S., Meisina, C., Garcı´a-Davalillo, J.C., Zucca, F., 2014. A methodology for improving landslide PSI data analysis. International Journal of Remote Sensing 35 (6), 2186
2214. Available from: https://doi.org/10.1080/ 01431161.2014.889864. Notti, D., Galve, J.P., Mateos, R.M., Monserrat, O., Lamas-Ferna´ndez, F., Ferna´ndezChaco´n, F., et al., 2015. Human-induced coastal landslide reactivation. Monitoring by PSInSAR techniques and urban damage survey (SE Spain). Landslides 12, 1007
1014. Available from: https://doi.org/10.1007/s10346-015-0612-3. Rouyet, L., Lilleøren, K.S., Bo¨hme, M., Vick, L.M., Delaloye, R., Etzelmu¨ller, B., et al., 2021. Regional morpho-kinematic inventory of slope movements in Northern Norway. Frontiers in Earth Science 9. Available from: https://doi.org/10.3389/feart.2021.681088. Osmundsen, P.T., Henderson, I., Lauknes, T.R., Larsen, Y., Redfield, T.F., Dehls, J., 2009. Active normal fault control on landscape and rock-slope failure in northern Norway. Geology 37 (2), 135
138. Rosi, A., Tofani, V., Tanteri, L., Tacconi Stefanelli, C., Agostini, A., Catani, F., et al., 2018. The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution. Landslides 15 (1), 5
19. Available from: https://doi.org/10.1007/s10346-017-0861-4. Solari, L., Del Soldato, M., Raspini, F., Barra, A., Bianchini, S., Confuorto, P., et al., 2020. Review of satellite interferometry for landslide detection in Italy. Remote Sensing 12, 1351. Available from: https://doi.org/10.3390/rs12081351. Tubı´a, J., Cuevas, J., Navarro-Vila´, F., Alvarez, F., Aldaya, F., 1992. Tectonic evolution of the Alpuja´rride complex (Betic cordillera, southern Spain). Journal of structural geology 14 (2), 193
203. Available from: https://doi.org/10.1016/0191-8141(92)90056-3. Varnes, D.J., 1978. Slope movement types and processes. In: Schuster, R.L., Krizek, R.J. (Eds.), Landslides, Analysis and Control, Special Report 176: Transportation Research Board. National Academy of Sciences, Washington, DC., pp. 11
33. Vick, L.M., Berg, J.N., Eggers, M., Hormes, A., Skrede, I., Blikra, L.H., 2020. Keynote Lecture: The Jettan rockslide—An engineering geological overview. Workshop on World Landslide Forum. Springer, Cham, pp. 289
315. Wasowski, J., Bovenga, F., 2014. Investigating landslides and unstable slopes with satellite multi temporal interferometry: current issues and future perspectives. Engineering Geology 174, 103
138. Available from: https://doi.org/10.1016/j.enggeo.2014.03.003.

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C H A P T E R

7 Volcanoes and earthquakes

7.1 Volcanoes There are around 1550 active subaerial (i.e., on the land surface) volcanoes in the world that have erupted in the last 10,000 years (Cottrell, 2015). Millions of people live on the flanks of these volcanoes or in areas that can potentially be impacted by the effects of an eruption, that is, by lava or pyroclastic flows. In some cases, the effects of volcanic activity can reach the global scale when ash clouds are involved, sometimes determining short-term climate changes.1 The potential impact of a volcanic event requires continuous monitoring of its state of activity. Monitoring can be performed in many ways: through on-site instrumentation or via remote sensing techniques (Sparks et al., 2012). Considering the class of on-site instruments, seismometers are employed to track the tremors and earthquakes that characterize volcanic activity, tiltmeters and strain gauges are used to measure the inflation or deflation of the volcanic edifice, and geochemical sampling stations are used to evaluate the composition of volcanic gas emissions and correlate the presence of certain chemicals to the status of the magma chamber. Remote sensing data can be used to measure the temperature of the surface of the volcano through thermal sensors, satellite images can be used to track the ash plumes, and SAR images can be analyzed to measure ground deformation. The measure of ground deformation from space is usually performed by means of interferometric SAR (InSAR) techniques that allow the estimation of the motion of the volcanic edifice, its flanks, and the surroundings, and to relate this motion to the state of the volcano (Ebmeier et al., 2018). InSAR data can help answer questions such as: is the 1 How can volcanoes affect climate? https://www.usgs.gov/programs/VHP/volcanoes-canaffect-climate#:B:text 5 Injected%20ash%20falls%20rapidly%20from,potential%20to%20promote %20global%20warming.

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activity of the volcanic system changing? Is the unrest phase finished? Are there flank movements connected to the emplacement of lava flows? And so on. Multitemporal InSAR approaches, such as the one used for the European Ground Motion Service (EGMS), can reliably measure the deformation of a volcanic edifice. The flanks of a volcano can be ideal reflecting surfaces if the vegetation is sparse; thus point density can be high, allowing for a spatially complete reconstruction of the deformation pattern. The time series of the measurement points (MPs) is an optimal tool with which to assess the presence of anomalous trends connected to shifts in volcanic activity. However, all the classical InSAR limitations apply. Further, the type of volcano and the environment where it is located play a role in determining whether InSAR is applicable or not. A volcano in an arid environment will have a completely different MP availability than a volcano in a tropical forest; the latter is the typical environment for a large percentage of active volcanoes in Central and South America (Ebmeier et al., 2013). Stratovolcanoes have steep slopes with vegetation that is densely growing due to the long intervals between eruptions. The combination of geometrical effects and land cover may prevent the detection of reliable MPs. In addition, the elevation gradient may induce large phase components due to atmospheric delay that can be confused with the motion of the volcanic edifice, if not properly treated (Pinel et al., 2011). Classic InSAR approaches based on a pair of SAR images are still a valid solution for volcano monitoring when a high frequency of data update is necessary. Note that such InSAR approaches cannot measure short-term transient deformation (with a duration of a few days) that occurs between two images (Green et al., 2006). There are worldwide volcano-monitoring services entirely based on two-image InSAR products that are automatic or have low levels of human control. These services can measure the deformation of a volcano with a low computational effort. They usually include tools, based on artificial intelligence, aimed at extracting the deformation connected to the potential unrest of a volcano in an automatic way. The system deployed by the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics2 (COMET-LiCSAR, Hooper et al., 2020), or the volcano-monitoring platform MOUNTS3 (Monitoring Unrest from Space, Valade et al., 2019) are examples of the potential of InSAR at the global scale. Both the line-of-sight (LOS) products and the two components of EGMS are usable for volcano monitoring. The Basic product is preferable to the Calibrated product in areas with a strong GNSS signal, to more precisely 2 The volcano monitoring portal developed by COMET: https://comet.nerc.ac.uk/ comet-volcano-portal/. 3 More about this project: http://www.mounts-project.com/home.

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TABLE 7.1 List of scientific papers that illustrate what can be done with interferometric data for volcano-related activities. References

Location

Target and type of activity

Di Traglia et al. Stromboli Island (2018) (Italy)

• COSMO-SkyMED and Sentinel-1 InSAR analysis of the Sciara del Fuoco flank of the volcano • InSAR analysis supported by infrared thermographic4 and bathymetric5 survey data • Correlation between lava emplacement processes and LOS velocities

Ferna´ndez et al. (2021)

Cumbre Vieja—La Palma (Spain)

• InSAR analysis of Envisat and Sentinel-1 images • Integration of the satellite data in a volcanic source model

Liang et al. (2021)

Tatun Volcano (Taiwan)

• InSAR investigation based on ALOS/PALSAR imagery • Dormant-active volcanic system registering subsidence connected to the release of hydrothermal fluids related to tectono-magmatic activity

Papageorgiou et al. (2019)

Santorini Volcano (Greece)

• Multiband and multisensor InSAR investigation (Sentinel-1, Radarsat-2, and TerraSAR-X) • Postunrest investigation of the caldera after an inflation event: steady subsidence is registered • Integration of InSAR results into a data model of the volcanic edifice

Richter and Froger (2020)

Piton de la Fournaise (La Re´union)

• Complete multiband and multisensor approach based on C-, X-, and L-band imagery (Envisat, Radarsat-1 &2, Sentinel-1, ALOS 1 & 2, TerraSAR-X, and COSMO-SkyMED) • InSAR results show subsidence in the caldera/ cone area and the sliding of the flank of the volcano • Validation using GNSS data

Ruch et al. (2012)

Mount Etna (Italy)

• SBAS analysis of C-band ERS and ENVISAT images • Comparison of east
west and vertical components of motion with major tectonic lineaments and faults

quantify the amount of deflation/inflation of the volcanic edifice. The reduced resolution of the Ortho product is seen as a minor issue, since the scale of the motion, in many cases, involves the entire volcanic cone or at 4 What is infrared thermography? https://www.youtube.com/watch?v 5 z2D4-QszvaM. 5 What is a bathymetric survey? https://www.usgs.gov/centers/ohio-kentucky-indianawater-science-center/science/bathymetric-surveys.

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least a large portion of it. Minor movements, such as the compaction of recent lava flows, can be estimated with the LOS products. Note: EGMS is the result of a continental-scale effort with standardized rules for all the processed bursts. However, fine-tuning was performed in areas with strong motion or complex deformation patterns, as in the case of volcanic- and earthquake-related deformation. Still, the results may differ (e.g., estimation of the LOS velocity or time series) from other targeted and more local InSAR data processing. Table 7.1 offers a nonexhaustive list of recent examples of satellite interferometric analysis of volcanoes. The following two sections present the reader with two examples of EGMS data usage. The first addresses ground motion along the flanks of Mount Etna (Sicily, Italy), one of the most active volcanoes in Europe. The second presents the EGMS products in the unique urban environment of the Campi Flegrei caldera in Pozzuoli (Campania, Italy).

7.1.1 Mount Etna (Italy) BOX 7.1 To view the EGMS data over Mount Etna (Italy), refer to Footnote 6.

Mount Etna is the largest active onshore volcano in Europe, and the protagonist of frequent spectacular eruptions that can be admired from the nearby Catania, a city of B310,000 inhabitants located at the foot of the volcano. The vicinity of the city and the potential effects of a major eruption (e.g., closure of flight routes and airports due to the volcanic ash) make Mount Etna one of the most intensely studied and monitored volcanoes worldwide. The monitoring system includes among other equipment: thermal and optical cameras, seismometers, gas monitoring stations, and remote sensing information regarding soil temperature, plumes, and ground motion (e.g., Scollo et al., 2009; D’Agostino et al., 2013; Calvari et al., 2022) (Box 7.1).

6 https://egms.land.copernicus.eu/#llh=15.02887068,37.70366962,47105.65469497&look5% 20-0.22036887,%20-0.60822201,%20-0.76256380&right50.97535682,%20-0.12874695,%200.17917392&up5-0.01%20079976,0.78325616,-0.62160531&layers5VHR%20Image%20Mosaic% 202012_VHR%20Image%20%20Mosaic%202012-Image-parent.

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Mount Etna is a composite stratovolcano,7 the activity of which is predominantly characterized by summit eruptions8 from five craters, and by fissural9 eruptions along three rift zones extending from the summit toward the northeast, south, and west, respectively (Cappello et al., 2013). The fissural eruptions are likely to be created by the propagation of dikes10 from the central conduct of the volcano that sometimes generate flank eruptions (Neri et al., 2011). The activity of the volcano is accompanied by shallow seismicity that can last for hours or days (Patane` et al., 2003). The eastern flank of Mount Etna is unstable, slowly sliding down into the sea because of the continuous spreading of the volcano linked to regional stress, dike-induced rifting, and gravitational force (De Novellis et al., 2019, and references therein). This unstable sector is tectonically controlled by two major faults, the Pernicana fault system (PFS in Fig. 7.1, inset A) and the Ragalna fault system (RFS) (Fig. 7.1, inset A), and by a series of other faults to the south, among which the Fiandaca fault is one of the more active (FF in Fig. 7.1, inset A). During one of the latest major eruptions, in December 2018, the activity of the FF generated an M 4.8 earthquake, one of the strongest that has affected Mount Etna in the last century. This was a flank eruption propagated from a 2.8-km-long fissure opened on the eastern flank of the volcano. The event took place after a 10-year period of very explosive summit eruptions that gave birth to a new eruption cone (Acocella et al., 2016). The volcano has been and continues to be monitored in many ways, one of which is the InSAR technique. Scientists have explored the use of InSAR to measure ground motion over the flanks of Mount Etna since the ERS 1/2 era. The first results derived from the images acquired by this satellite were published by Lundgren et al. (2003). These authors investigated the temporal span between 1993 and 1996, a period of relative quiescence, followed by a reactivation of volcanic activity in 1995. The inversion of the data from the ascending and the descending tracks through the Mogi model, one of the most widely used by scientists in the context of volcanoes (see Taylor et al., 2021 for an overview of volcano models), facilitated the reconstruction of the magmatic source. This research is also interesting for the efforts made to demonstrate that the information carried by single interferograms is real and not an error 7 What is a composite stratovolcano? https://www.nps.gov/articles/000/composite-volcanoes.htm. 8 An atlas of the main types of volcanic eruptions: https://geology.com/volcanoes/ types-of-volcanic-eruptions/. 9 How does a fissural eruption work? https://www.nps.gov/articles/000/fissure-volcanoes. htm. 10 What is a dike? https://volcanoes.usgs.gov/vsc/glossary/dike.html.

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L

generated by the layered atmosphere. As said previously, this is typical for stratovolcanoes such as Mount Etna, which is a high mountain located near a warm sea. This makes the atmospheric contribution to the InSAR phase very important. The work done on ERS imagery was continued by Palano et al. (2008), who reconstructed the behavior of the volcano in the 1993
2000 period by means of single interferograms supported by GPS information, and by Neri et al. (2009). The latter proposed one of the first attempts at the decomposition of multitemporal InSAR deformation maps for Mount Etna. Both ERS and Envisat images were used to generate the InSAR measurements. It was possible to observe, even with an MP density much lower than what could be achievable with Sentinel-1 data, the motion of the east flank of Mount Etna toward the sea and the inflation/deflation of the summit area in the different acquisition periods. Another example of decadal investigation of the volcano’s behavior is given by Solaro et al. (2010). This brief list of research examples demonstrates once again the importance of InSAR for the backward reconstruction of a complex target such as a volcano, making it possible to understand the current activity by learning from the past. More recently, Bonforte et al. (2019) and De Novellis et al. (2019) investigated through single-interferogram InSAR and multitemporal InSAR the deformation induced by the dike intrusion of December 2018. Sentinel-1 images were used to do so. Let us see what the EGMS can tell about Mount Etna. Fig. 7.1 presents the EGMS Calibrated products inset A, for the results in ascending orbit and inset B for the descending orbit. The deformation pattern offered by Mount Etna is one of the most spectacular in Europe. Point density is high; thousands of points are found on the flanks of the volcano thanks to the presence of new and nonvegetated old lava flows that act as perfect radar reflectors. The deformation pattern is certainly eye-catching, but its interpretation is not trivial, as is to be expected for a system as complex as a large stratovolcano. The InSAR measurements are influenced by the inflation and lateral spreading of the volcanic edifice, the motion induced by the December 2018 earthquake and the local tectonics. The LOS products indicate that there is a major east
west component of motion along the flanks of Mount Etna, visualized as a

FIGURE 7.1 EGMS Calibrated data for Mount Etna. (A) EGMS Calibrated data in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (B) EGMS Calibrated in descending orbit. The white star indicates the approximate location of the M 4.8 earthquake of December 26, 2018 (location from De Novellis et al., 2019). EGMS, European Ground Motion Service; PFS, Pernicana fault system, RFS, Ragalna fault system, FF, Fiandaca fault. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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specular pattern of LOS velocities between orbits. For example, the eastern flank moves away from the sensor in the ascending orbit (negative velocities and reddish points) and toward the sensor along the descending track (positive velocities and blueish points). It is also interesting to notice that the area around the epicenter of the December 2018 earthquake has a deformation pattern that seems to be disconnected from the flank-scale motion. The motion of Mount Etna’s flanks is accompanied by a major fault system, the presence of which can be seen in the deformation map; for example, the sharp north border of the east flank, where LOS velocity suddenly falls from 20 to 2 mm/year in the ascending orbit, following the known location of the PFS. Another example is the RFS that seems to be delineated by an area of higher LOS velocities in the descending orbit (Fig. 7.1, inset B). This attests to the potential of InSAR to recognize minor order ground motion elements within large deformation patterns, thanks to the precision of the technique and the possibility to study differential movements in a dense network of MPs. This is a variegated ground motion pattern that requires the support of the Ortho products to be better understood. Fig. 7.2 presents the Ortho products for Mount Etna, inset A shows the vertical component and inset B shows the east
west horizontal component. As hypothesized, there is a strong eastward and westward motion of the east and west flanks. Lundgren et al. (2004) first observed this kind of deformation pattern and interpreted it as the result of “radial spreading and anticline motion linked to recharge of the magmatic system.” On December 26, 2018, an earthquake and the contemporary opening of the volcanic fissure produced a motion that is featured and measured in the time series of deformation. In fact, the time series present a noticeable jump between the last acquisition before the eruption (22/12/2018), between 24 and 27 December, and the first acquisition after the event (28/12/2018). An example of a time series from the east slope of Mount Etna is given in Fig. 7.3. The time series measures the cumulative displacement induced by the event between the two acquisitions of the sensor. The jump does not affect the general trend of the time series but affects the value of velocity of the point, which is calculated from the displacement values.

Satellite Interferometry Data Interpretation and Exploitation

FIGURE 7.2 EGMS Ortho data for Mount Etna. (A) Vertical component, (B) east
west component. The white star indicates the approximate location of the M 4.8 earthquake of December 26, 2018 (location from De Novellis et al., 2019). EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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FIGURE 7.3 Example of time series less than 1 km away from the epicenter of the 26/ 12/2018 earthquake. The jump of B140 mm represents the motion that the MP has experienced between the two acquisitions of the sensor. The trend of the time series does not change after the December event. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

7.1.2 Campi Flegrei (Italy) BOX 7.2 To view the EGMS data over the Campi Flegrei in Pozzuoli (Italy), refer to Footnote 11.

Campi Flegrei is a peculiar and unique example of volcanic-induced deformation in Pozzuoli, a densely urbanized area in the vicinity of Naples, in southern Italy (Box 7.2). Campi Flegrei is considered a supervolcano (Pappalardo & Mastrolorenzo, 2012) which erupted for the last time B400 years ago. Over the last century, it has undergone a series of inflation and deflation episodes (Lima et al., 2009), phenomena known as bradyseism.12 Campi Flegrei is a caldera13 system that erupted 70 times in the last 15,000 years (Isaia et al., 2009), with events of various energy levels (e.g., 11 https://egms.land.copernicus.eu/#llh=14.13909287,40.80203902,24036.03879907&% 20look5%20-0.20040500,-0.65061677,-0.73248594&right50.97947134,-0.11644418,0.16454981&up5-0.02176%20515,0.75042559,-0.66059648&layers5VHR%20Image%20Mosaic% 202012_VHR%20Image%20%20Mosaic%202012-Image-parent. 12 What does the term “bradyseism” mean? https://whc.unesco.org/en/tentativelists/2030/. 13 What is a caldera? https://education.nationalgeographic.org/resource/calderas.

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strombolian, plinian14) that produced different types of magmas (Di Vito et al., 1999). The central portion of the caldera has the highest activity registered in the past B10,000 years with an estimated uplift of at least 100 m (Vilardo et al., 2010). Bradyseism is the predominant volcanic process of the past 2000 years. Two major unrest periods were registered in the last 50 years (1970
72 and 1982
84). In this 14-year period the total uplift is estimated to be 3.5 m. This motion was accompanied by an increase in seismicity (Vilardo et al., 2010). After 1984 the caldera started to subside again, with minor uplift events in the order of some centimeters scale. This stage lasted 20 years. A new unrest phase began in 2005 and is still ongoing (Petrosino et al., 2018). De Martino et al. (2021) reported that the inflation produced a total vertical displacement of 65 cm between 2005 and 2019. The uplift episodes are linked to the migration of magmatic/hydrothermal fluids along the network of faults in the central portion of the caldera (Chiodini et al., 2001), whereas the subsequent subsidence is the result of the decrease in gas pore pressure and the compaction of the pyroclastic layers that form the caldera (Vilardo et al., 2010). These processes are accompanied by distinctive variations in the emissions of volcanic gasses from the fumaroles15 that characterize Campi Flegrei (Box 7.2). Campi Flegrei is a potential direct risk for the population of Pozzuoli and neighboring urban areas. The Italian Department of Civil Protection (DPC) defined a red zone, identifying areas where the risk of pyroclastic flows is high and from which people will be evacuated in the event of a major alert (Ricci et al., 2013). The red zone16 includes entirely four municipalities and part of other neighboring districts, totaling a population of B500,000 inhabitants. The risk is not remote; due to the action of the seismic swarm and ground motion on vulnerable buildings, B30,000 people were evacuated in 1983 from Pozzuoli and relocated elsewhere (Ricci et al., 2013). The DPC evaluates the state of activity of Campi Flegrei on a monthly basis and determines the alert level depending on the outcome of the monitoring system. There are four levels of alert: from green (lowest), through yellow and orange, to red (highest). Currently, the alert level is yellow. The monitoring system is managed by the National Institute of Geophysics and Volcanology and is composed of a permanent seismic network of 26 terrestrial and marine stations, a permanent network of 25 GNSS stations, 10 stations equipped with tiltmeters, and a thermal and geochemical monitoring 14 An overview about the types of volcanic eruptions: https://openpress.usask.ca/physicalgeology/chapter/11-4-types-of-volcanic-eruptions/#:B:text 5 There % 20are % 20four% 20types%20of,%2C%20Vulcanian%2C%20and%20Plinian%20eruptions. 15 What is a fumarole? https://www.usgs.gov/news/earthword-fumarole. 16 The map is available here: https://mappe.protezionecivile.gov.it/it/mappe-rischi/ piano-nazionale-campi-flegrei (in Italian).

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network (Bianco et al., 2022). The monitoring results are collected in monthly bulletins that are available online.17 The presence of an urbanized territory atop the deformation area makes Campi Flegrei a good target for InSAR analysis. In fact, several bibliographic references exist on the topic. For example, Lundgren et al. (2001) proposed the first inversion of the interferometric data to investigate the magmatic source of deformation. The use of interferometric data for the construction of source models that explain the motion of the caldera is a common InSAR application for Campi Flegrei; among many others, some valuable bibliographic examples include Manconi et al. (2010), D’Auria et al. (2015), and Tiampo et al. (2017). Another classic application is the use of InSAR data to investigate specific short-term unrest events. Lanari et al. (2004) analyzed ERS 1/2 images to capture a short-term inflation event that took place between March and August 2000, in a phase of subsidence of the caldera. The 2004
06 uplift episode, the start of the unrest phase still being experienced, was investigated by Trasatti et al. (2008). The source of another unrest event, between 2011 and 2013, was investigated through InSAR by Trasatti et al. (2015). The InSAR data allowed Walter et al. (2014) to explore the potential correlation between the deformation activity of Campi Flegrei and Mount Vesuvius. The EGMS products confirm the uplifting trend of the caldera and provide an opportunity to examine its last 5 years of activity. Fig. 7.4 presents the EGMS Calibrated products, inset A for the results in ascending orbit and inset B for the descending orbit. Campi Flegrei offers another astounding deformation map with a clear pattern in both orbits. The highest subsidence rates are recorded in the central portion of the Gulf of Pozzuoli. The LOS data clearly indicate the uplift of the ground surface. Let us consider an axis that splits Fig. 7.4 into two halves and that ideally defines the center of the uplift dome. In the ascending orbit, the east side of the dome registers negative LOS velocity, since the uplift creates a motion away from the sensor; in turn, the west side will move toward the sensor and LOS velocity is positive. The descending orbit shows precisely the opposite behavior, with negative LOS velocity along the west side of the dome, and negative along its east side. This is a simplification of reality that is useful to understand Fig. 7.4. The data in the two orbits are specular, as regards the pattern and position of the area of maximum ground motion because of the different LOS. In terms of numbers, the maximum uplift is 72.0 mm/year for both orbits. This value is coherent with the continental-scale SBAS test performed by Lanari et al. (2020). Fig. 7.5 shows the EGMS Ortho product, the vertical component is in inset A and the east
west horizontal component is in inset B. The vertical component is simple to interpret. The whole caldera around the gulf is uplifting, with a maximum rate of nearly 80.0 mm/year and average values in a radius 17 The repository for the Campi Flegrei monitoring bulletins: https://www.ov.ingv.it/ index.php/monitoraggio-e-infrastrutture/bollettini-tutti/campi-flegrei (in Italian).

Satellite Interferometry Data Interpretation and Exploitation

FIGURE 7.4 EGMS Calibrated data for the Campi Flegrei in the urban area of Naples (Italy). (A) EGMS Calibrated data in ascending orbit. (B) EGMS Calibrated in descending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor. The white stars in insets A and B indicate the location of the highest uplift rates in the ascending and descending datasets, respectively. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

FIGURE 7.5 EGMS Ortho data for the Campi Flegrei area. (A) Vertical component, (B) east
west component. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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of 3 km around the center of the deformation area of B24.0 mm/year. The east
west component presents a more complex pattern. There is a clear westward and eastward motion of the caldera. The approximate axis of the dome is located along the points that show little or no horizontal motion (materialized by the alignment of green points in the center of Fig. 7.5, inset A). The motion in the eastward direction reaches a maximum of 30.0
35.0 mm/year; in the opposite direction the motion goes down to 230.0 to 225.0 mm/year. Note: by convention, eastward velocity is positive and westward velocity is negative. This pattern confirms the uplift of the central portion of the caldera system. The deformation area occupies a radius of B4 km from the center of the gulf, considering vertical velocities higher than 10 mm/year. De Martino et al. (2021) reconstructed the vertical motion of the caldera from 1905 to 2019 by means of leveling and GPS data. The time series can be split into two components: the general trend (i.e., the deflation or inflation phase) and the inflation events that occur on a shorter temporal period. Thus the use of InSAR time series is of great importance to understand the motion of the caldera. An example of EGMS time series extracted from the area of maximum displacement within the ascending dataset of the Calibrated product is presented in Fig. 7.6. The time series has a general uplift trend, but there are some intervals in which the displacement rates slow down or reaccelerate. From a visual point of view, the time series can be subdivided into five intervals, two of them are representative of a slowdown of the uplift. The first one is between July 2016 and September 2019, the second, smaller interval is between January and June 2020. For example, the velocity extrapolated from interval 2 is B38.0 mm/year, whereas the subsequent reenhancement of the volcanic activity in interval 3 carries a displacement of 190 mm in c. 2 years, corresponding to a velocity of B82 mm/year. The accumulated displacement in a time span of 5 years (2015
20) is 428 mm.

FIGURE 7.6 Example of time series extracted from the Calibrated product in ascending orbit in the area of maximum displacement of the caldera (see the white star in Fig. 7.4). Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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7.2 Earthquakes Earthquakes are a destructive natural hazard that causes immense damage to the population and the economy of a country. Earthquakes can create cascade effects with geohazards that are no less powerful, such as tsunamis, landslides, or fires. The United States Geological Survey estimates that in the period 1990
2020, more than 30 earthquakes with magnitudes higher than 8 were felt on the Earth’s surface. Additionally, over 50,000 earthquakes above magnitude 5 were registered, with a total death toll of 923,463 people (Pal et al., 2023). Seismology is the classical tool to measure the magnitude of an earthquake and to derive its focal mechanism18 (the so-called “beachballs”). The advancement of the InSAR technique made it a valid tool for measuring ground motion induced by an earthquake and for providing an independent measure of fault location (Liu et al., 2021 and references therein). The fault location is achieved by inversion of the coseismic deformation fields. However, the detectability of earthquakes depends on the strength of the ground motion signal to be distinguished from the atmospheric phase component and the noise. In fact, only strong earthquakes that are shallow enough can be detected by InSAR: earthquakes with magnitude between 5 and 6 can be difficult to measure because of their “low” signal (England & Jackson, 2011). Nonetheless, even earthquakes of this magnitude can cause severe damage. Trasatti et al. (2015) subdivide the applications of InSAR into two macro-classes: preearthquake and postearthquake. InSAR contributes to the preearthquake phase to detect the interseismic strain that can indicate the accumulation of energy along one seismogenic fault; this accumulation may or may not lead to an earthquake. Note: this does not mean that earthquake forecasting is presently feasible, and the presence of strain accumulation cannot be flagged as an earthquake precursor. After an earthquake, InSAR measures the static displacement (coseismic displacement) induced by the dislocation of the fault, with a deformation pattern that depends on the geometry of the fault. Then, the InSAR results can be inverted to model the mechanism that generated the earthquake. Single-interferogram InSAR is a technique that is still widely used to measure the coseismic deformation induced by an earthquake. Interferograms are an effective way to visualize the displacement, which is materialized by two lobes of interferometric fringes. This can be done with relatively low computational efforts and only the pre- and postearthquake images are required. Nowadays, open software like SNAP19 allows a broad range of researchers 18 What is the focal mechanism of an earthquake? https://www.usgs.gov/programs/ earthquake-hazards/focal-mechanisms-or-beachballs#:B:text 5 A%20focal % 20mechanism %2C%20or%20%22beachball,of%20the%20fault%20that%20slipped. 19 Know more about SNAP: https://step.esa.int/main/download/snap-download/.

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TABLE 7.2 List of scientific papers that illustrate what can be done with interferometric data to estimate earthquake-induced ground motion. References

Location

Target and type of activity

Longitudinal Valley Fault (Taiwan)

• Interseismic deformation monitoring • PSI processing of ALOS L-band SAR images • Deformation pattern that materializes an SW
NE fault

Preearthquake Champenois et al. (2012)

with velocity offset of 3 cm/y between the two blocks of the fault Jolivet et al. (2013)

Haiyuan fault (China)

• Investigation of the spatial and temporal variations of strain rates along 35 km of the Haiyuan fault

• SBAS analysis of ERS 1/2 and Envisat C-band SAR images

• The InSAR analysis revealed a creep rate increase along the fault Manzo et al. (2012)

San Andreas Fault (United States)

• Interseismic strain accumulation along the San Andreas Fault

• SBAS processing of ERS 1/2 C-band SAR images • Prefiltering of SBAS interferograms with GPS time series Postearthquake Guns et al. (2022)

Ridgecrest earthquakes (United States)

• Postevent (M 6.4 and 7.1) investigation of deformation • PSI analysis of Sentinel-1 C-band SAR images • Time series correction to minimize the effect of earthquake-induced jumps

Wang et al. (2020)

Pishan earthquake (China)

• Postevent (M 6.5) investigation of deformation • SBAS analysis of Sentinel-1 C-band SAR images • Deformation connected to the afterslip20 on the fault and the three-stage creep

Zhu et al. (2022a,b)

Arkalochori earthquake (Greece)

• Postevent (M 5.9) investigation of deformation affecting the urban area of Heraklion and its cultural heritage

• SBAS analysis of Sentinel-1 C-band SAR images • Increased postearthquake deformation registered by the cultural heritage elements in the city In this table are only considered those authors that employed multitemporal InSAR approaches.

to compute a coseismic interferogram after a big event. Interferograms help the scientific community to communicate with the population after an event: they are often found in news items and seen on TV news. Multitemporal InSAR can provide an additional level of information with respect to two-passes InSAR. The analysis of a stack of images enables investigation of the motion sometime before and after the 20 What is post-earthquake motion? https://earthquake.usgs.gov/research/eqproc/posteqmotions.php.

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earthquake. This means that ground motion other than the earthquake can be followed. The earthquake will generate a jump in the time series, but the local deformation will not be lost. This is something that cannot be estimated with a single interferogram. The EGMS can be a resource for investigating the effects of large earthquakes (M . 5.5) that struck Europe in the period covered (February 2015
December 2020, for the baseline). All three EGMS products carry the information regarding the coseismic displacement accumulated after the event, and these data are visible in the deformation map and the time series. Note: earthquakes are complex objects to measure with wide area processing approaches and ad hoc measures had to be taken to estimate ground motion in the best way possible (Section 3.7). The reader must consider that the results may differ slightly from those obtained with other more targeted InSAR data processing. Table 7.2 lists some bibliographic references useful to understand the analysis and interpretation of multitemporal InSAR data related to a strong earthquake. The list only includes papers where multitemporal InSAR analysis was carried out, and not those based on singleinterferogram InSAR. Nowadays, single interferogram can be generated in an automatic and unsupervised way to build databases of coseismic ground motion maps (e.g., Monterroso et al., 2020). The following section presents the ground motion effects of one of the most damaging earthquake events in Italy in recent years: the 2016 Central Italy seismic sequence, as seen by the EGMS.

7.2.1 2016 Central Italy seismic sequence BOX 7.3 To view the EGMS data over the epicenter area of the 2016 seismic sequence, refer to Footnote 21.

In August 2016, one of the most important seismic sequences of recent history struck a portion of the Central Apennines in Italy, split between the Lazio, Abruzzo, Umbria, and Marche regions. The area affected by the 21 https://egms.land.copernicus.eu/#llh=13.31568127,42.78719760,78686.62350830&look5% 20-0.17217874,%20-0.67724866,-0.71532422&right50.98467268,-0.09781702,-0.14440061&% 20up5-0.0278%202424,0.72922294,-0.68371026&layers5VHR%20Image%20Mosaic% 202012_VHR%20Image%20%20Mosaic%202012-Image-parent.

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earthquake is 50-km long in the SE
NW direction and 30-km wide in the SW
NE direction (Luzi et al., 2017). The sequence consisted of three main shocks: (1) the M 6.0 “Amatrice” earthquake on August 24, 2016, (2) the M 5.9 “Ussita” earthquake on October 26, 2016, and (3) the M 6.5 “Norcia” earthquake on October 30, 2016 (Pizzi et al., 2017). See Fig. 7.7 (inset A) for the location of the epicenters. The first shock was followed by B55,000 aftershocks with lower magnitude, 62 of them with magnitude between 4 and 5.5. The earthquake caused B300 deaths, and B41,000 people lost their homes. The town of Amatrice and other villages were severely damaged. The mechanism behind the earthquakes was purely normal faulting,

FIGURE 7.7 EGMS Calibrated and Ortho products for the area of the 2016 Central Italy seismic sequence. (A) EGMS Calibrated data in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (B) EGMS Calibrated in descending orbit, (C) EGMS Ortho—vertical component, (D) EGMS Ortho—east
west component. The black stars indicate the location of the three main shocks of the seismic sequence. EGMS, European Ground Motion Service. Source: The ground motion data have been extracted from the EGMS Explorer: https:// egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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in agreement with the extensional regime of Central Apennines and with a mechanism similar to that of the L’Aquila earthquake in 2009, another damaging event to hit this part of Italy (Box 7.3). The earthquake triggered the immediate response of the national civil protection system to ensure the safety of citizens and adopt recovery actions. This impactful event also aroused the attention of the scientific community, interested in understanding the mechanism that had generated the seismic sequence. InSAR was one of the techniques employed to measure ground motion induced by the earthquake. Cheloni et al. (2017) generated C-band (Sentinel-1), X-band (COSMO-SkyMed), and Lband (ALOS) interferograms and analyzed GPS time series to determine the source parameters for the three main shocks of the seismic sequence. Other representative examples of this kind of analysis can be found for example in Lavecchia et al. (2016), Walters et al. (2018), or Zhu et al. (2022a,b). Earthquakes also trigger other types of geohazards, such as landslides. Polcari et al. (2017) performed an InSAR analysis of C- and X-band images for detecting local deformation connected to the main shocks of the Central Italy seismic sequence. They discovered that several landslides were reactivated and found the motion of secondary faults. A similar approach, targeting earthquake-induced landslides, was presented by Huang et al. (2017). Fig. 7.7 presents the Calibrated and Ortho products over the area affected by the Central Italy seismic sequence. Insets A and B show the Calibrated data in ascending and descending orbit, respectively; insets C and D show the Ortho products for the vertical and east
west components, respectively. All of the EGMS data show a clear deformation area covering an area of approximately 40 by 40 km (considering MPs with LOS velocity higher than 5 mm/year in absolute value). The data in ascending orbit are the best to visually materialize the fault. In inset A of Fig. 7.7, it is relatively easy to identify the track of the fault that generated the three main shocks. An alignment of no displacement/low displacement MPs follows the epicenters of the Amatrice, Norcia, and Ussita earthquakes. The presence of points with no displacement along the fault line does not mean that the ground did not move there, but that the deformation model used to generate the time series was not able to follow the ground movement. The strong motion along the fault has another direct consequence: the loss of MPs due to the decorrelation of the signal (Kobayashi et al., 2012). This is evident in the data in descending orbit. The data show a movement toward the sensor over the east side of the fault and away from the sensor over its west side in ascending orbit, and the opposite deformation pattern in the descending orbit. LOS velocity reaches maximum values of 6 35 mm/year for both orbits. The Ortho products help to better understand the kind of motion induced by the earthquake. The vertical component (inset C of Fig. 7.7) shows uplift

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FIGURE 7.8 Example of time series extracted along the fault line. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

along the west side of the fault and subsidence along its east side. The two sides of the fault register different vertical velocity; the east side shows maximum rates of 255 mm/year, whereas the west side does not exceed 15 mm/year. The east
west component registers higher displacement rates for the west side of the fault, which reaches 250 mm/year; while the east side does not exceed 30 mm/year. What are the components and the LOS products telling us? The deformation pattern is coherent with the hypothesized normal22 geometry of the fault. The west side of the fault is the footwall and the east side the hanging wall. This interpretation is in line with the results of Cheloni et al. (2017) and Huang et al. (2017). As said earlier, the calculation of ground motion velocity is impacted by the earthquake. Fig. 7.8 shows an example of time series of the vertical component of motion (Ortho product). The time series presents two welldefined jumps: the first between 21 and 27 August 2016 and the second between 19 and 31 October 2016. The first jump is B35 mm and corresponds to the first main shock, the Amatrice earthquake. The second jump is B70 mm and corresponds to the second and third shocks, the Ussita and Norcia earthquakes. The velocity for the entire time series is 15.0 mm/ year. This velocity value is clearly impacted by the two jumps. Without the displacement accumulated because of the earthquake, the velocity value would have been much lower, in the order of a few millimeters per year. It is also interesting to notice that the time series has a different behavior before and after the seismic sequence. Before August 2016 a minor order uplift is recorded. After the event a moderate subsidence is documented. This may be interpreted as a postseismic response of the ground to the earthquake, meriting further investigation. 22 What are normal faults? https://www.geologypage.com/2017/10/three-main-typesfaults.html.

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References Acocella, V., Neri, M., Behncke, B., Bonforte, A., Del Negro, C., Ganci, G., 2016. Why does a mature volcano need new vents? The case of the New Southeast Crater at Etna. Frontiers in Earth Science 4, 67. Available from: https://doi.org/10.3389/feart.2016.00067. Bianco, F., Caliro, S., Martino, P.D., Orazi, M., Ricco, C., Vilardo, G., 2022. The permanent monitoring system of the Campi Flegrei Caldera, Italy. Campi Flegrei. Springer, Berlin, Heidelberg, pp. 219
237. Bonforte, A., Guglielmino, F., Puglisi, G., 2019. Large dyke intrusion and small eruption: the December 24, 2018 Mt. Etna eruption imaged by Sentinel-1 data. Terra Nova 31, 405
412. Available from: https://doi.org/10.1111/ter.12403. Calvari, S., Biale, E., Bonaccorso, A., Cannata, A., Carleo, L., Currenti, G., et al., 2022. Explosive paroxysmal events at Etna volcano of different magnitude and intensity explored through a multidisciplinary monitoring system. Remote Sensing 14, 4006. Available from: https://doi.org/10.3390/rs14164006. Cappello, A., Bilotta, G., Neri, M., Negro, C.D., 2013. Probabilistic modeling of future volcanic eruptions at Mount Etna. Journal of Geophysical Research: Solid Earth 118, 1925
1935. Available from: https://doi.org/10.1002/jgrb.50190. Champenois, J., Fruneau, B., Pathier, E., Deffontaines, B., Lin, K.C., Hu, J.C., 2012. Monitoring of active tectonic deformations in the Longitudinal Valley (Eastern Taiwan) using persistent scatterer InSAR method with ALOS PALSAR data. Earth and Planetary Science Letters 337, 144
155. Available from: https://doi.org/10.1016/j. epsl.2012.05.025. Cheloni, D., De Novellis, V., Albano, M., Antonioli, A., Anzidei, M., Atzori, S., et al., 2017. Geodetic model of the 2016 Central Italy earthquake sequence inferred from InSAR and GPS data. Geophysical Research Letters 44, 6778
6787. Available from: https://doi. org/10.1002/2017GL073580. Chiodini, G., Frondini, F., Cardellini, C., Granieri, D., Marini, L., Ventura, G., 2001. CO2 degassing and energy release at Solfatara volcano, Campi Flegrei, Italy. Journal of Geophysical Research: Solid Earth 106, 16213
16221. Available from: https://doi.org/ 10.1029/2001JB000246. Cottrell, E., 2015. Global distribution of active volcanoes. Volcanic Hazards, Risks and Disasters. Elsevier, pp. 1
16. D’Agostino, M., Di Grazia, G., Ferrari, F., Langer, H., Messina, A., Reitano, D., et al., 2013. Volcano monitoring and early warning on MT Etna, Sicily based on volcanic tremor— methods and technical aspects. Complex Monitoring of Volcanic Activity 53
92. D’Auria, L., Pepe, S., Castaldo, R., Giudicepietro, F., Macedonio, G., Ricciolino, P., et al., 2015. Magma injection beneath the urban area of Naples: a new mechanism for the 2012
2013 volcanic unrest at Campi Flegrei caldera. Scientific Reports 5, 1
11. Available from: https://doi.org/10.1038/srep13100. De Martino, P., Dolce, M., Brandi, G., Scarpato, G., Tammaro, U., 2021. The ground deformation history of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma
Vesuvius Volcano, and Ischia Island) from 20 years of continuous GPS observations (2000
2019. Remote Sensing 13, 2725. Available from: https://doi.org/10.3390/rs13142725. De Novellis, V., Atzori, S., De Luca, C., Manzo, M., Valerio, E., Bonano, M., et al., 2019. DInSAR analysis and analytical modeling of Mount Etna displacements: the December 2018 volcano-tectonic crisis. Geophysical Research Letters 46, 5817
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C H A P T E R

8 Urban area: infrastructure, buildings, and cultural heritage The integration of geohazard information in urban planning activities is an increasingly present priority of central and local governments, to increase the awareness and reduce the risk for the population. Unfortunately, some countries still lack the ability or willingness to develop proper legal frameworks and legislations to adopt geohazard risk management methodologies in urban areas (Mateos et al., 2017). This includes the identification and mapping of the structures (buildings, bridges, etc.) that register active motion due to natural or human-induced geohazards. It is fundamental to quantify the magnitude of the motion, especially when a structure is affected by differential settlements1 that can compromise it. Interferometric SAR (InSAR) provides a solution for measuring the motion of single urban elements. Even if classical ground measurement instrumentation such as leveling can reach higher precision, it is indisputable that InSAR can cover an entire city with very dense measurement points (MPs), and that these MPs can reach 1 mm/year precision in velocity estimation. This provides valuable input to map structures affected by deformation and even determine the presence of differential settlements (Barra et al., 2022). For instance, InSAR can detect the motion of structures that are affected by the movement of a landslide or a subsidence. Additionally, InSAR can measure the deformation due to thermal expansion: this is certainly true for high buildings and elongated structures like towers, bridges (Huang et al., 2017), and viaducts. However, it should be noted that the European Ground Motion Service (EGMS) is not meant to forecast the failure of structures. In fact, such a forecast would need, at the least, a much more frequent temporal sampling to capture the failure precursors, if any. 1 How can differential settlements put the stability of a building at risk? https://www. liveabout.com/differential-settlements-844692.

Satellite Interferometry Data Interpretation and Exploitation DOI: https://doi.org/10.1016/B978-0-443-13397-8.00002-9

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TABLE 8.1 List of reference scientific papers that illustrate InSAR applications for infrastructure or buildings monitoring. References

Location

Target and type of activity

Del Soldato et al. (2019)

Patigno, Coloretta (Tuscany region, Italy)

• Building-scale investigation in a landslide-affected village of the northern Apennines • Reconstruction of fragility curves2 for masonry buildings • Possible link between registered InSAR velocities and damage level

Milillo et al. (2016)

Pertusillo dam (Basilicata, Italy)

• Very high-resolution (COSMO-SkyMed and TerraSAR-X) investigation of the dam • Hydrostatic models used to simulate the nonlinear deformation of the dam

Solano-Rojas et al. (2020)

Mexico City (Mexico)

• Ad hoc data processing to unravel very local motion within the high subsidence area of Mexico City • Investigation of local subsidence patterns related to changes in the stratigraphical asset • Application of the approach to linear infrastructure (viaducts and an overpass)

Sawyer et al. (2016)

Rotterdam and Gouda (The Netherlands)

• Report about the usage of InSAR data by gas and water companies to monitor ground deformation that may cause problems in the urban pipeline network • Data representation as heat maps of motion along the pipeline network • Link between research, industry, and urban planning

Tang et al. (2016)

Summer Palace (Beijing, China)

• InSAR investigation of an urban UNESCO heritage site3 • Quantification of the effects of urban expansion around the heritage site in terms of ground motion

Wu et al. (2020)

Hong Kong international airport (Hong Kong)

• Twenty-year reconstruction of the settlement of the airport area, the largest part of which is built on reclaimed land • Multisensor reconstruction (ERS, Envisat, COSMOSkyMed, and Sentinel-1) • Comparison between line-of-sight (LOS) velocities and characteristics of the filling material and GPS validation

The main drawbacks of InSAR and EGMS data for buildings/infrastructure monitoring are as follows: • The geolocation accuracy of C-band data. EGMS Basic and Calibrated products have geolocation precision better than 10 m, which is three 2 A fragility curve describes the probability of reaching or exceeding a specific damage level as a function of a parameter that defines the ground displacement (in this case the average LOS velocity value for each building). 3 https://whc.unesco.org/en/list/.

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8.1 Rules Dam and Reservoir

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orders of magnitude worse than what is obtainable for the deformation estimation (Section 3.10). The user must be aware of the potential difficulty of associating a single MP with a specific part of the structure of interest. • The 2D geometry of ascending/descending orbit data can be insufficient to estimate the complex 3D motion of a building or infrastructure. • The opportunistic nature of InSAR may limit an exhaustive spatial sampling of buildings or infrastructures. Additional considerations valid for the EGMS include: • The GNSS signal that is part of the Calibrated product may envelop the local signal related to a building. Thus, it is recommended that the Basic product is used as an additional resource when a very local-scale analysis is the goal. • The reduced resolution of the Ortho product limits its usage for single-structure investigations. These data represent an average of several points within the sampling cell. Therefore they do not fit well with the needs of a single-structure analysis aimed at, for example, the recognition of differential settlements. Table 8.1 features some recent representative papers that deal with infrastructure or building monitoring. The following sections discuss a series of examples of different local-scale applications of the EGMS products to measure the motion of infrastructure, buildings, and cultural heritage.

8.1 Rules Dam and Reservoir BOX 8.1 To view the EGMS data over the area of the Rules Reservoir, refer to Footnote4.

4 https://egms.land.copernicus.eu/#llh 5 -3.48256296,36.87129549,5000.00000001& look 5 0.04859538,-0.60001926,-0.79850822&right 5 0.99815237,-0.00002038,0.06076058& up 5 0.03647380,0.79998556,-0.59890965&layers 5 VHR%20Image%20Mosaic%202012_ VHR%20Image%20Mosaic%202012-Image-parent,A15-001-release.

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8. Urban area: infrastructure, buildings, and cultural heritage

Dams are a common target for InSAR investigations. On one hand, InSAR can guarantee an acceptable MP density over these structures considering their size and their characteristics as reflectors. In the optimal viewing scenario, InSAR can provide measurement points (MPs) for the entire length of the dam; this is an advantage over classic ground instrumentation that can give better precision and accuracy for single measure locations (Sousa et al., 2014). The monitoring capability increases with high-resolution X-band InSAR products. However, C-band data can supply an initial screening of the dam. The real added value of InSAR is the capability of mapping not only the motion over the dam structure but also in the surroundings, that is, in the mountain flanks that border a dam reservoir. In many cases, the problem is not the stability of the dam, but rather the presence of flank instabilities that can collapse and create severe consequences. The most famous example of this is the Vajont landslide,5 which occurred in 1963 (Barla and Paronuzzi, 2013). The Rules Reservoir is located in the province of Granada, Spain, at the confluence of the rivers Gualdalfeo and ´Izbor. The reservoir has the important role of supplying water to the coastal area of the province of Granada for domestic and agricultural use (Bergillos and Ortega-Sa´nchez, 2017). The dam is a 188-m-high gravity dam that was completed in 2004. Another important infrastructure located in this area is the Rules Viaduct, which crosses the central portion of the dam lake and has a length of nearly 600 m (see Fig. 8.1 for location). The construction of the viaduct, finally inaugurated in 2015, was beset by numerous problems related to the presence of active slope movements that required the Spanish government to invest tens of millions of euros in the stabilization of the viaduct foundations. Up to the present, this is the most expensive highway track ever built in Spain (Reyes-Carmona et al., 2020). Further, it demonstrates that the flanks of the reservoir are geomorphologically active, and the oscillations of the water basin certainly play an important role in maintaining or changing the behavior of landslides. In fact, it is well known that water-level oscillations in artificial water basins can reactivate dormant landslides or accelerate the motion of active ones. For example, the construction of the Three Gorges Reservoir in China implied a water-level rise of B100 m in the valley, which in turn triggered the development of hundreds of landslides during the first phases of the impoundment (Yin et al., 2016). The flanks of the Rules Reservoir are prone to landslides. The presence of highly folded and faulted phyllites, part of the Alpuja´rride Complex, is considered one of the major predisposing factors for landslide formation (Irigaray et al., 2000). Moreover, the geomorphological context is another important conditioning factor; the high topographical gradient of this area,

5 https://blogs.agu.org/landslideblog/2008/12/11/the-vaiont-vajont-landslide-of-1963/.

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FIGURE 8.1 EGMS data around the Rules Reservoir. (A) Location of known landslides, (B) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (C) Calibrated product in descending orbit, (D) time series for the El Arrecife landslide. Source: Inset A modified from Reyes-Carmona, C., Barra, A., Galve, J.P., Monserrat, O., Pe´rez-Pen˜a, J.V., Mateos, R.M., et al., 2020. Sentinel-1 DInSAR for monitoring active landslides in critical infrastructures: the case of the Rules reservoir (Southern Spain). Remote Sensing (12), 809. The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

with steep fluvial valley incisions, increases the instability of slopes (Reyes-Carmona et al., 2020). Despite the evidence of active landslides, the first interferometric analysis over this area was only performed in 2016 by Lazecky et al. (2016b), who processed Envisat SAR images. Later on, Reyes-Carmona et al. (2020) made a more complete InSAR investigation relying on Sentinel-1 SAR images and geomorphological information collected during field surveys. These authors mapped various active landslides along the flank of the Rules Reservoir, involving linear infrastructures such as the National Road N323 or the southern abutment of the Rules Viaduct. The location of these landslides is reported in inset A of Fig. 8.1.

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The EGMS Calibrated products for the area of the Rules Reservoir are shown in Fig. 8.1, inset B for the ascending track and inset C for the descending track. The density of MPs is high thanks to the semiarid environment that limits the growth of dense vegetation. The motion along the main roads/highways in the area (A44 and N323) can be measured, as well as along the Rules Viaduct and the dam. It is evident from the deformation maps that there are active landslides along the flanks of the artificial basin. One of them involves the southern abutment of the Rules Viaduct as reported by Reyes-Carmona et al. (2020). The landslide that registers the highest line-of-sight (LOS) velocities is El Arrecife, with an average LOS velocity of 215.2 mm/year in the ascending orbit and 4.2 mm/year in the descending orbit. Chapter 6 explained why it is possible to have such a great difference between the data in the two orbits. Velocities are coherent with the work of Reyes-Carmona et al. (2020). However, the local-scale InSAR approach employed by these authors enabled them to discover some MPs in the foot area of the landslide that were not detected in the continental-scale EGMS product and reached 240 to 250 mm/year in the ascending orbit. Landslide activity had a number of effects on the national road (N323) that crosses the entire length of El Arrecife landslide along its median portion. Part of the track of the road had to be diverted downhill and visible cracks and bumps have appeared in the new track of N323 (Reyes-Carmona et al., 2020). This demonstrates the cross-utility of an InSAR product that can map the presence of active motion over large areas and at the same time characterize the single urban element (in this case, a major road). Time series for the El Arrecife landslide (Fig. 8.1, inset C) show a slight seasonality that can be connected to the seasonal rainfall or to the infill/discharge cycles of the dam or to a combination of both. Fig. 8.2 presents a snapshot of the EGMS deformation map over the Rules Dam and Viaduct. The dam does not register any anomalous motion, LOS velocities are in the order of 6 1
1.5 mm/year in both orbits, near the precision of the LOS measurements. It is interesting to notice that the number of MPs above the concrete structure is noticeable for a continental-scale map; both orbits record over 200 MPs along the structure. This facilitates an initial estimation of the stability of the dam and the recognition of any potential differential pattern (not recorded here). The motion pattern over the Rules Viaduct is completely different. The movement is clear in both orbits, with LOS velocities going toward the sensor in ascending orbit and away from the sensor in the descending orbit. Maximum and minimum LOS velocities reach 5 mm/year and 28 mm/year in the ascending and descending orbits, respectively. This is consistent with the research done by Reyes-Carmona et al. (2020). There can be multiple reasons for this motion and there may be a

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FIGURE 8.2 EGMS data over the Rules Dam and Viaduct. (A) Calibrated product in ascending orbit for the dam, (B) Calibrated product in descending orbit for the dam, (C) Calibrated product in ascending orbit for the viaduct, (D) Calibrated product in descending orbit for the viaduct. Source: The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

correlation with the pressure applied to the piles of the bridge by the landslide, which is active along the bridge’s southern abutment. However, these data must be interpreted with caution. It is true that the EGMS products can act as a baseline “warning” layer that can redirect the user to discover potential criticalities to which efforts and funds should be allocated. This would appear to be one such case.

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8.2 Nice Coˆte d’Azur Airport BOX 8.2 To view the EGMS data over the airport of Nice, refer to Footnote6.

Airports are another common target for interferometric investigations. The runways, airport terminals, and hangars can easily be measured by InSAR. Airports require large spaces for their construction, and in some cases, space is created by reclaiming land from the sea. Building large structures such as airports over reclaimed areas constituted by unconsolidated material can trigger long-lasting ground motion that can last for years after the construction. InSAR can be used to detect this kind of deformation that affects airport structures. The technique can provide the best results considering the constant and relatively slow deformation rates and characteristics of the target reflectors (large man-made objects, no vegetation or topographic obstacles). Examples can be found all over the world, in places as diverse as Hong Kong (Sun et al., 2018), Beijing (Gao et al., 2016), Kuala Lumpur (Marshall et al., 2018), or Rome (Gagliardi et al., 2021). This section presents the EGMS products in the coastal area occupied by the Nice Coˆte d’Azur Airport in the Alpes-Maritimes department of southern France. The airport covers an area of 3.7 km2 and in prepandemic years reached traffic levels of 14 million passengers per year. The airport is built at the mouth of the Var River and most of its offshore extension was built in the seventies. The two main runways of the airport are located atop a reclaimed area (see the location in Fig. 8.3). A severe incident marred the airport construction works in 1979 when a submarine landslide of thousands of cubic meters detached from the continental shelf 7 on top of which the airport was being built. The landslide and subsequent 3-m tsunami wave killed 10 people, most of them in the airport construction area. The landslide was not triggered by an earthquake, but by the exceptional loading imposed by the land reclamation operations being carried out at that time on top of the continental shelf (Courboulex et al., 2020). In general, the margin of the continental slope is seen as prone to instability, which is demonstrated by the evidence of other failure events before that of 1979 6 https://egms.land.copernicus.eu/#llh 5 7.21505501,43.65364259,5418.14915883& look 5 -0.09128386,-0.69021232,-0.71782603&right 5 0.99198892,0.00018344,-0.12632475& up 5 -0.08732257,0.72360689,-0.68466623&layers 5 VHR%20Image%20Mosaic%202012_VHR %20Image%20Mosaic%202012-Image-parent,D20-139-release. 7 A representation of the main elements of the continental margin, including the continental shelf: https://www.britannica.com/science/continental-shelf.

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FIGURE 8.3 EGMS data over the Nice Coˆte d’Azur Airport. (A) Simplified geological map, (B) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (C) Ortho product, east
west component, (D) Calibrated product in descending orbit. The approximate location of the 1979 landslide detachment zone derives from Courboulex et al. (2020). Source: Inset A modified from Cavalie` et al. (2015). The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

(Kelner et al., 2014). The development of new slope failures in the continental margin may retrogressively involve the platform on top of which the airport is built, and this creates concern regarding the current stability of this area. InSAR data (Envisat period) were exploited to evaluate the stability of the airport’s runways and structures (Cavalie´ et al., 2015).

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Fig. 8.3 presents the EGMS products over the airport area; insets B and D present the Calibrated products in ascending and descending orbits, respectively, and inset C shows the east
west horizontal component of the Ortho product. As is to be expected in an urban area, the MP density is quite high, with B5000 points over the airport area per orbit. MPs are found on the runways and the airport terminals, as they are optimal reflectors. In general, all the structures of the airport terminal are stable, with average LOS velocities in both orbits that barely exceed 6 1 mm/year. This is also true for the first runway, built closest to the terminal, which records average LOS velocities of 21.5 mm/year in ascending orbit and 21.7 mm/year in descending orbit. LOS velocities increase for the outer runway, which records an average of 22.3 mm/year in ascending orbit and 22.2 mm/year for the descending orbit. Note: this is an area where the GNSS signal is null. LOS velocities increase at the borders of the reclaimed platform, around the outer runway (points 1 and 2 in insets B, C, and D of Fig. 8.3). In area 1 (south
west limit of the reclaimed platform), LOS velocities reach 29.0 to 27.0 mm/year in both orbits, whereas for area 2 the LOS velocities range between 211.0 and 26.0 mm/year. The latter is the most interesting movement. This area is in a retrogressive position with respect to the 1979 landslide and is the only one that records an east
west component of motion, as highlighted by the Ortho product of inset C in Fig. 8.3. However, the westward motion is of limited magnitude, around 1.5 mm/year. The magnitude, location, and direction of the motion are consistent with the results of Cavalie´ et al. (2015) obtained for the time span 2003
2010. From a qualitative point of view, this means that the motion is relatively constant in spatial and temporal terms.

8.3 Blackfriars Railway Bridge, London (United Kingdom) BOX 8.3 To view the EGMS data over the Blackfriars Railway Bridge in London, refer to Footnote8.

Some types of bridges, especially large ones, can be monitored by InSAR. The band used by the SAR sensor is relevant. X-band data 8 https://egms.land.copernicus.eu/#llh=-0.10406780,51.50948453,1262.10010324&look50. 00116062,-0.78269312,-0.62240672&right50.99999831,0.00002022,0.00183930&up50.00142 702,0.62240781,-0.78269182&layers5VHR%20Image%20Mosaic%202012_VHR%20Image %20Mosaic%202012-Image-parent.

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provide the best performance; however, C-band data can allow for a baseline investigation. A detailed scale analysis requires local data processing, aiming at maximizing the quality and density of MPs over the bridge. Nonetheless, wide-area products such as the EGMS can give an initial overview of the presence of anomalous movements to be further investigated. Another point of discussion regarding the usability of InSAR for bridge motion detection is this technique’s level of precision. Even if an InSAR data processing can reach, in the optimal situation, a precision of the LOS velocity estimation around 1 mm/year, this may be considered insufficient for a very detailed analysis of the bridge stability. Several authors had suggested the capability of InSAR to perform this sort of initial screening activity, such as the case of Waterloo Bridge in London (United Kingdom) (Selvakumaran et al., 2020), the Lupu Bridge in Shanghai (China) (Zhao et al., 2017), the Radotı´n Bridge in Prague (Czech Republic) (Lazecky et al., 2016a,b), and the Hong Kong
Zhuhai
Macao Bridge (Xiong et al., 2021). Note: these examples are based on ad hoc local-scale processing approaches targeting the bridges. Blackfriars Railway Bridge (BRB) is a railway bridge crossing the River Thames in London. The bridge was opened in 1886 and is B280m long. It is a covered bridge, the roof of which holds 4400 solar panels installed during partial reconstruction works carried out between 2009 and 20139 (Bischoff et al., 2017). The EGMS Calibrated products for the bridge are presented in Fig. 8.4, with the ascending orbit in inset A and the descending orbit in inset B. The hard roof is an ideal target for InSAR and the optimal point coverage obtained for the structure is a direct result of this: B100 MPs are available for the bridge in each orbit. The first comment regards the localization of the points. As stated in Section 3.10, the geolocation accuracy of the MPs is one of InSAR’s weak points. For the EGMS the geolocation accuracy is better than 10 m, which is enough to correlate the MP location with the building or structure, but not enough to connect the MP to the exact reflective element or level of the structure. In our example, MPs can be related to the hardcover of the bridge, to the reinforcement structure built on its side or to one of the pylons. The above geolocation precision is the reason why some points seem to fall into the water. In addition, the fact that the highest number of MPs is found on the eastern side of the bridge in the descending orbit and on its western side in the ascending orbit is related to the way the radar signal impacts the structure. In fact, the 9 For more on the completion of the hard rooftop of the Blackfriars Bridge: https:// www.theguardian.com/environment/2014/jan/22/worlds-largest-solar-powered-bridgeopens-in-london.

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L

radar wave will hit the eastern side of the bridge first and with a better incidence angle in the descending orbit, making this part “brighter” and with more potential MP candidates. The bridge is arbitrarily divided into three sectors (north, middle, south) to investigate the presence of motion or differential displacement (Fig. 8.4, insets A and B). The bridge records LOS velocities averaged on its length of 22.8 mm/year in ascending orbit and of 23.7 mm/year in descending orbit. The analysis of the three sectors does not reveal any large differential displacements, with velocity deltas that vary between 6 0.2 and 6 0.7 mm/year. Insets C and D of Fig. 8.4 show the time series of displacement for the ascending and descending orbits, respectively. There is not a significant difference in the temporal behavior of the MPs between the orbits. A 12-month seasonality of the time series is detectable in both orbits. The seasonality is added to the trend of the time series and reaches its maximum value during summer and its minimum during winter. This seasonality is related to the thermal expansion of the bridge (most likely of the hardcover). There is an expansion of the materials during the summer periods, and a consequent positive trend is registered in the time series. To the contrary, the materials contract during the winter periods, and a negative trend is registered in the time series. Martin et al. (2022) reported the same type of seasonality for the BRB. This is a signal commonly registered in InSAR time series over bridges or linear structures in general and part of the normal behavior of these structures, which is taken into consideration when a bridge10 is being designed. The thermal component of the motion can be modeled and eventually removed from the time series (Crosetto et al., 2015). The Ortho products were not considered in this example, as the 100-m resolution does not fit the detailed scale dimension of the analysis.

FIGURE 8.4 EGMS data over the Blackfriars Railway Bridge in London (United Kingdom). (A) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (B) Calibrated product in descending orbit, (C) average time series for the ascending orbit referred to the NS, MS, and SS sections of the bridge. The sections were defined arbitrarily, (D) average time series for the descending orbit referred to the NS, MS, and SS sections of the bridge. The sections were defined arbitrarily. MS, Middle; NS, north; SS, south. Source: The ground motion data have been extracted from the EGMS Explorer: https:// egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

10 About the seasonal movement of a bridge: https://practical.engineering/blog/2018/ 8/1/why-do-bridges-move.

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8.4 Railway in Finland BOX 8.4 To view the EGMS data over railway track in Finland, refer to Footnote11.

Railways and other linear infrastructure such as highways or major roads are well suited to InSAR analysis. It is difficult for managing entities to monitor networks made up of thousands of kilometers of railways with classical in situ tools. InSAR tools aid this analysis by providing a dense grid of MPs over the entire network with a single data analysis. The limitations mentioned in Chapter 7 still apply (e.g., resolution vs acquisition band, precision/accuracy requirements), but the InSAR products are being recognized as a potential railway monitoring tool, at least in deferred time. Some national and local railwayholding companies have already chosen and continue to implement InSAR to support their network management operations. One powerful aspect of InSAR is that it can provide information regarding the presence of motion of the infrastructure and its surroundings. This means that it is possible to measure the motion of portions of terrain outside the railway track and prevent the potential impact of phenomena that, starting from this terrain, may end up impacting the track. For example, an InSAR analysis in a mountain valley can detect active landslides (potential threats for the railway) in the slopes surrounding the linear infrastructure. InSAR data can even provide an indication of the stability of terrain before the construction of a new transport infrastructure, helping in the design of a safe route. These characteristics make InSAR suitable for railway monitoring at both the local and wider scales. For example, Chang et al. (2016) performed an InSAR-based analysis of the entire Dutch railway network, consisting of 3000 km of track. Radarsat-2 images were used. The Qinghai
Tibet Railway is a B2000-km-long infrastructure that was investigated by means of InSAR techniques (e.g., Zhang et al., 2019). Other examples come from major railways in Indonesia (Jakarta
Bandung line, Luo et al., 2022), Italy (Campania regional network, Poreh et al., 2016; Lombardy regional network, Polcari et al., 2019), and the United Kingdom (urban networks of Bristol, Bath,

11 https://egms.land.copernicus.eu/#llh=24.55951593,60.17437199,6455.97442291&look= -0.20673041,-0.86751695,-0.45241229&right=0.90952561,0.00002030,-0.41564789&up=-0.3605 9078,0.49740762,-0.78902468&layers=VHR%20Image%20Mosaic%202012_VHR%20Image% 20Mosaic%202012-Image-parent.

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Bournemouth, Grantham, Kings Lynn, and Peterborough, North et al., 2017). A continental-scale service like the EGMS can provide valuable information for railway management activities. The following case study from Finland illustrates this. Finland has a railway network with 6000 km of track, which is quite expensive to maintain and verify due to its extension.12 The network is endangered by the presence of permafrost, parts of which can melt too fast during the summer and create long-term subsidence. This is now exacerbated by global climate change.13 Fig. 8.5 presents the EGMS Calibrated products along part of the railway tracks that runs to the south of Helsinki between the cities of Kauklahti to the north and Masala to the south. The railway track of interest runs in a peri-urban rural area for c. 2.3 km between these two cities. According to the geological cartography, the bedrock of the area of interest (yellow rectangle in Fig. 8.5, inset A) is composed of amphibolite and felsic gneiss, part of the Svecofennian domain (Pajunen et al., 2008). These rocks are highly deformed and have been through a high-pressure/low-temperature metamorphic process.14 The EGMS Calibrated data (Fig. 8.5, insets B and E) offer a complete overview of the ground motion recorded along the railway track, which is an ideal reflector as it is composed of metal (rails) and debris (track ballast). Thus MPs are available for nearly the entirety of the tracks with a density of 10
30 MPs per 100 m. The data are consistent between the two orbits and show the presence of sectors with evident velocity changes. The track was subdivided into five sectors (S1, S2, S3, S4, and S5 in the insets B and E of Fig. 8.5) based on the visual inspection of the deformation maps. The average value of LOS velocity is extracted for every sector and for each orbit. The resulting values are included as tables in insets C and D of Fig. 8.5. Subdividing a linear infrastructure in sectors of different velocities is an effective way to downstream the interferometric data and convert them into an easy-to-digest output for the end users. In this case, it is evident that the two sectors with the highest LOS velocities are S1 and S3, the southernmost and northernmost in the area, respectively. S1 registers average LOS velocities of 211.6 mm/year in ascending orbit and of 29.6 mm/year in descending 12 An example of InSAR usage by the Va¨yla¨virasto, the Finnish Transport Infrastructure Agency: https://www.advian.fi/en/blog/railway-infrastructure-maintenance-analysis-usingsatellite-data. 13 How is climate change threatening linear infrastructure in the Nordic countries? https://www.helsinkitimes.fi/world-int/17990-if-climate-change-proceeds-potential-ecosystems-disasters-will-be-more-common-university-of-oulu-finland.html. 14 Some information about the metamorphism processes in rocks: https://csmgeo.csm. jmu.edu/geollab/fichter/metarx/MetaKind.html.

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FIGURE 8.5 EGMS data along one section of the Helsinki commuter track between Masala and Kauklahti. (A) Geological map of the area of interest. The yellow rectangle indicates the section of the railway that records ground motion. (B) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (C) average time series for the ascending orbit referred to the five sectors in which the railway track was subdivided, (D) average time series for the descending orbit referred to the five sectors in which the railway track was subdivided, (E) Calibrated product in descending orbit. Source: Inset A modified from Pajunen, M., Airo, M.L., Elminen, T., Niemela¨, R., Salmelainen, J., Vaarma, M., et al., 2008. Construction suitability of bedrock in the Helsinki area based on the tectonic structure of the Svecofennian crust of southern Finland. Special Paper
Geological Survey of Finland (47), 309
326. The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

orbit, while peak negative values reach 225.0 mm/year. S3 registers average LOS velocities of 28.9 mm/year in ascending orbit and of 25.2 mm/year in descending orbit, where peak negative values reach 213.0 mm/year. On the contrary, sections S2 and S4 register the lowest

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average velocities, below 6 2.0 mm/year. Note: the values contain the vertical motion of the Fennoscandia, which is around 2.0 mm/year at this site. This means that negative velocities would actually be slightly lower and positive velocities slightly higher. Time series, which show gaps due to snow cover, are linear without major accelerations, even if section S1 recorded a minor trend change (acceleration) in the summer of 2019 (Fig. 8.5, insets C and D). The Ortho products were not considered in this example, as the 100-m resolution does not fit the detailed scale dimension of the analysis.

8.5 Levees in Bregenz (Austria) BOX 8.5 To view the EGMS data along the levees of the Rhine river in Bregenz (Austria), refer to Footnote15.

Levees are linear elements that are fundamental for flood defense. They can have different geometries, but they are essentially earth-filled embankments with an impermeable core.16 The failure of a levee can have severe consequences, and the capability to recognize early signs of failure is essential. The maintenance of a levee includes its visual inspection, aimed at recognizing the presence of cracks, burrows, or irregular vegetation (Sharp et al., 2013). Another more advanced level of maintenance consists of the installation of onsite monitoring instrumentation (e.g., strain meters) or a detailed-scale remote sensing investigation (e.g., light detection and ranging, LIDAR). Levee maintenance is a time and money-consuming activity that must be performed over many kilometers of these linear elements. Levees can experience two types of deformation: long-term deformation such as subsidence, linked to the compaction of the material constituting the levee, and seasonal deformation related to shrinkage and ¨ zer swelling processes, related mainly to changes in soil moisture (O 15 https://egms.land.copernicus.eu/#llh=9.67003202,47.49774986,5932.98838579&% 20look5-0.11330447,-0.73718150,-0.66612726&right50.98571432,0.00068679,-0.16842447&% 20up%205-0.12461689,0.67569443,-0.72657255&layers5VHR%20Image%20Mosaic%202012_ %20VHR%20Image%20Mosaic%202012-Image-parent. 16 Some information about the constitutive elements of a levee: https://www.leveesafety.com/levee-components/.

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et al., 2019). This behavior can be monitored from space, and the InSAR analysis provides valuable data that can aid levee maintenance activity. The thin dimension of levees with respect to the SAR satellite resolution and the presence of vegetation on the levee slopes are negative factors to consider. Usually, the reflectors come from the paved or dirt roads that run atop the levees, or from any kind of other anthropic reflectors. However, the application of InSAR for the detection of levee deformation is feasible. As said in previous sections, widearea products, such as the EGMS, allow users to get an initial idea (a screening) of the presence of active deformation, whereas the outputs of a local-scale analysis will be used to refine the analysis. Examples of InSAR applications for levee monitoring come from the Netherlands ¨ zer et al., 2019), the United States of America (canal levees in Delft, O (New Orleans, Nguyen et al., 2019), China (Lingang New City— Shangai, Yin et al., 2019), and Japan (Urayasu City, Aimaiti et al., 2018). Fig. 8.6 presents the EGMS Calibrated products in the city of Bregenz (Vorarlberg Land, Austria) where the Rhine River enters the lake of Constance. The urban area is protected from the floods of the Rhine by levees on its right and left banks. The river has its peak flow during the thawing of the winter snow in the Alps; in this period the flow can be 10 times higher than in the dry period (Muller, 1966). The Rhine contributes to B64% of the annual inflow in the Constance Lake and its annual discharge17 (around 7.3 km3) strongly contributes to the distribution and transportation of sediments in the lake. The current position of the Rhine mouth is the result of a 12-km diversion to the east of the river in 1900 to avoid flooding in the Alpine Rhine Valley (Wessels et al., 2015). The target of this example is a 2-km-long section of the Rhine levees, at the border with Switzerland. The Calibrated products in ascending and descending orbits (insets A and B of Fig. 8.6, respectively) have a good coverage of MPs over the left and right bank levees. However, the coverage is discontinuous, and some sections of the levees do not record the presence of any MP. This is due to the nature of the target. A levee is not a perfect reflector as a railway and its backscattering characteristics can vary a lot, being an almost natural surface. The EGMS data show that there is a clear increase of LOS velocities along the levees from the south to the north. The first kilometer of the levees can be considered stable in both orbits. LOS velocities do not exceed 6 1.5
2.0 mm/year along both left and right banks. The situation changes as the river mouth is approached. The area of maximum displacement is indicated by the white dashed rectangle in the insets A and B of Fig. 8.6. On average, LOS velocities in this area reach 17 What is meant by “river discharge”: https://climate.copernicus.eu/ESOTC/2019/ river-discharge.

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FIGURE 8.6 EGMS data along the Rhine River levees in the city of Bregenz (Austria), where the river enters the Constance Lake. (A) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (B) Calibrated product in descending orbit, (C) average time series for the sector of the levee with the highest displacement rates (white rectangle). Source: The ground motion data have been extracted from the EGMS Explorer: https:// egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

211.0 mm/year in descending orbit and 214.0 mm/year in ascending orbit. The minimum LOS velocity reaches 218.0 mm/year in the ascending orbit. Time series do not show seasonality (inset C of Fig. 8.6). It is difficult to interpret these data without any additional information regarding the constitutive elements of the levees, the composition of the landfill, or the local geological asset. The deformation can be

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related to the general subsidence of the delta area of the Rhine or be part of a process of compaction of the levees or a combination of the previous phenomena. The first hypothesis is supported by two pieces of evidence: the time series do not show any seasonality that can be linked ¨ zer et al., 2019), and to shrinkage/swelling processes (as reported by O there are other MPs with similar LOS velocities along the coast of the Constance Lake in the eastern portion of Fig. 8.6 (unfortunately just a few MPs are available due to the pond-like environment). The second hypothesis would require field data to be verified, but these are not available to the authors of this book. Nonetheless, EGMS products indicate the presence of a motion that requires further investigation. Note: this does not mean that there is a real risk for the population and the presence of a moving point does not imply the structural instability of the levees. All of the cautions regarding the use of wide-area data for the structural investigation of man-made objects are still valid.

8.6 Port of Antwerp (Belgium) BOX 8.6 To view the EGMS data of Antwerp, refer to Footnote18.

Ports and harbors are good targets for interferometric analysis since the docks and warehouses are ideal persistent scatterers. The recognition of active movements has an economic relevance, as long-term ground motion may damage the structure and slow down or block port operations, causing greater economic setbacks. The presence of ground motion in harbor areas is usually linked to the compaction of recent landfill, where the docks were extended, or to the settlement of artificial protection structures such as the breakwaters. In general, ports are expanded on new terrain reclaimed from the sea, and such areas are intrinsically prone to record vertical motion due to the compaction of the infill materials. Satellite interferometry has been used in the past to detect active motion involving port structures, as in the case of the Busan New Port (South Korea, Ramirez and Kwon, 2022), the Al-Faw Grand Port breakwater (Iraq, Alshammari and Mohammed, 2022), the 18 https://egms.land.copernicus.eu/#llh=4.30451088,51.28898133,25500.11420459&look50.04694078,-0.78030954,-0.62362937&right50.99710409,0.00079310,-0.07604471&up5-0.0598 3301,0.62539299,-0.77801261&layers5VHR%20Image%20Mosaic%202012_VHR%20Image% 20Mosaic%202012-Image-parent.

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Port of Gela (Italy, Bruno et al., 2016), and various coastal cities of China (Tang et al., 2022). This example of EGMS usage is taken from Belgium, specifically the city of Antwerp and its harbor, which is built along the Scheldt River. The port of Antwerp is ranked second in terms of annual shipping volume at the European level (B200 million tons per year), behind the port of Rotterdam. The estuary of the Scheldt River is highly urbanized; in the 19th century the need for space to devote to shipping and the industrial expansion led to the construction of numerous docks, which substituted many polders.19 Meire et al. (2005) report that about 16% of the surface of the estuary was lost this way. The Port of Antwerp is located within the delta of the river, some kilometers inland from the river’s mouth. The urban area is built on a low-lying coastal area where the landfills on which the docks are built reach 10 m a.s.l. From a geological point of view, this area hosts the classic Holocene deltaic succession, represented by a succession of intercalated fine-grained and sand layers, peats, and gravels representing the various evolutionary stages of the Scheldt River. The distribution of the Holocene deposit with respect to the structure of the harbor is presented in the inset A of Fig. 8.7. A more detailed description of the stratigraphical asset can be found in Declercq et al. (2021). Deltas are intrinsically prone to subsidence because of the presence of unconsolidated fine-grained soil. This area is no exception. The EGMS Calibrated products highlight the presence of ground motion affecting some of the port structures, which are mainly located on the left bank of the Scheldt River. The EGMS data are shown in the insets B (ascending orbit) and D (descending orbit) of Fig. 8.7. As expected for an urban environment, the density of MPs is very high and allows a detailed reconstruction of the deformation pattern affecting the docks and the structures related to port activity. The highest LOS velocity is reported for both orbits on the left bank (i.e., the west side) of the Scheldt River, where 15,000 ha are dedicated to port docks and interchange platforms. This is where the newest docks were built in the 2000s (Fig. 8.7, inset A). LOS velocity for areas 1 and 2 in Fig. 8.7 (inset B, EGMS Calibrated—ascending orbit) is on average below 210.0 mm/year, with negative peaks of around 220.0 mm/year. The descending data show the same spatial and temporal pattern (inset D, Fig. 8.7). The reason for these high displacement rates near the new constructions is linked to the compaction of the landfill material (with thicknesses of B4
8 m) composed of unconsolidated material such as sand, silt, and clay dredged from the estuary (Declercq et al., 2021). Time series of the MPs with the highest LOS velocity is linear, without 19 What is a polder? https://www.britannica.com/science/polder.

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FIGURE 8.7 EGMS data along the river Scheldt in the harbor of Antwerp (Belgium). (A) Distribution of Holocene deposit in the area and age of construction of the major docks, (B) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (C) average time series for some harbor areas (black rectangles), (D) Calibrated product in descending orbit. Source: Inset A modified from Declercq, P. Y., Ge´rard, P., Pirard, E., Walstra, J., Devleeschouwer, X., 2021. Long-term subsidence monitoring of the Alluvial plain of the Scheldt River in Antwerp (Belgium) using radar interferometry. Remote Sensing (13), 1160. The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

any sign of deceleration that may suggest a decrement of the compaction rates; this is a time-dependent process that can be visible in InSAR time series (Ciampalini et al., 2019). The oldest parts of the port on the left bank of the Scheldt River have lower LOS velocities; for example, area 3 (inset B, Fig. 8.7) registers an average LOS velocity of around 22 mm/year and only a few scattered MPs have LOS velocities below 25 mm/year. The same average values are registered on the right bank of the Scheldt River, on the south border of the oldest artificial channel (area 4, inset B of Fig. 8.7). The MPs in areas 3 and 4, and in neighboring areas, are measuring the “base” subsidence of the Scheldt River alluvial sequence set in motion during the natural deposition/erosion processes of the river (Declercq et al., 2021).

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8.7 Thyborøn port (Denmark)

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8.7 Thyborøn port (Denmark) BOX 8.7 To view the EGMS data over the port of Thyborøn in Denmark, refer to Footnote20.

Denmark offers a second example of a port affected by ground motion. In this case, the context is not an alluvial plain but the mouth of a fjord (Limfjord), with a landscape that is very different from the one in the landslides of Norway (Section 6.2). In Thyborøn the coast is almost flat and characterized by sandy beaches prone to erosion by the currents of the North Sea. Due to this morphology, this coastal area is strongly impacted by the effects of winter storm surges (Nørgaard et al., 2014). Thyborøn is part of the Lemvig municipality (Midtjylland region of western Denmark). It is a small fishing village of 1890 inhabitants located at the outer end of the Limfjord channel that cuts across the Jutland peninsula for 180 km. Prior to the late 19th century, the channel did not have an inlet from the North Sea (i.e., from west Denmark). However, in 1862 a particularly intense storm cut the sand barrier and connected the sea with the channel. Thyborøn is now located in the southern portion of this coastal breach and large sea dikes protect the village from the impact of storm surges from the North Sea. The western portion of the village is the oldest and is the remnant of the original settlement predating 1862. In the 1960s the village gradually expanded toward the east, reaching its current configuration in the 1990s (Sorensen et al., 2016). The last expansion of the port has taken place over the past 15 years. The entire village is located at an altitude ranging between 1.5 and 2 m a.s.l., except for a road that runs atop an embankment running parallel to the coast in the west-northwest portion of the town (see Fig. 8.8, inset C). The reference stratigraphic cross section of inset C in Fig. 8.8 shows that the upper 2
3 m of the subsurface are made of heterogeneous landfill (also containing organic material). Below that is a layer (of variable thickness) of gyttja, an organic-rich mud generated by the decay of peat levels. As of this layer, the marine

20 https://egms.land.copernicus.eu/#llh=8.21238261,56.69593516,2794.12178284&look50.03753393,-0.83032228,-0.55601810&right50.99221705,0.03514217,-0.11945858&up5-0.1187 2880,0.55617439,-0.82254090&layers5VHR%20Image%20Mosaic%202012_VHR%20Image% 20Mosaic%202012-Image-parent.

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8.7 Thyborøn port (Denmark)

219

L

sand succession begins and is locally intercalated by marine clay (Sorensen et al., 2016). The EGMS Calibrated products are presented in the insets A and B of Fig. 8.8 for ascending and descending orbits, respectively. The deformation maps show a clear west
east pattern of subsidence rate increase (i.e., LOS velocity decrease). The oldest part of Thyborøn, which lies on the sand barrier, is essentially stable (area 1 in Fig. 8.8, inset B), with an average LOS velocity below 6 1 mm/year. Then, LOS velocity gradually increases toward the Thyborøn port, which is the newest urbanized area. LOS velocity doubles between area 2 (Fig. 8.8, inset B), located in the portion of the village built between 1965 and 1978, and area 3 (Fig. 8.8, inset B), located in one of the first docks built between 1981 and 1992. For the ascending orbit, area 2 registers an average of 22.5 mm/year, just outside the stability threshold, whereas LOS velocity goes up to 27.7 mm/year in area 3. The fourth area (Fig. 8.8, inset B) encompasses the newest docks, built after 2002. Here, LOS velocity is, on average, 219.9 mm/year. Ascending and descending data provide consistent and very similar results in the spatial and temporal dimensions. The increase of velocity toward the newest portion of the port is due to the concomitance of two factors: the age of construction and the thickness of the landfill. Note: this part of Denmark registers a small glacio-eustatic uplift estimated at 0.8 mm/year that does not change the interpretation made but should be considered for single-building investigations. Even if the subsidence rates are not significant, especially in the village center, Sorensen et al. (2016) reported that the local water company had to repair and replace pipes and sewer lines due to the presence of differential displacements between the stable and the unstable portions of the village. Another consequence of subsidence is the increased risk of sea flooding. The cooccurrence between subsidence and sea-level increase will threaten the coastal communities here and along the entire coast of the North Sea in the future (Melet et al., 2021).

FIGURE 8.8 EGMS data in the coastal town of Thyborøn (Denmark). (A) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor. Z-Z0 is the reference cross section presented in the third inset, (B) Calibrated product in descending orbit, (C) simplified geological cross section (Z-Z0 ) for the area of interest, (D) average time series for some urban areas (black rectangles). Source: Inset C modified from Sorensen, C., Broge, N.H., Molgaard, M.R., Schow, C.S., Thomsen, P., Vognsen, K., et al., 2016. Assessing future flood hazards for adaptation planning in a northern European coastal community. Frontiers in Marine Science (3) 69. The ground motion data have been extracted from the EGMS Explorer: https:// egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

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8.8 Historic center of Sighi¸soara (Romania) BOX 8.8 To view the EGMS data over the UNESCO heritage site of Sighi¸soara (Romania), refer to Footnote21.

Europe is home to more than 400 UNESCO heritage sites and countless other historic sites of undisputable value. InSAR is applicable to the monitoring of active ground motion affecting cultural heritage in urban areas: monuments, churches, towers, etc. are optimal targets for InSAR analysis. InSAR enables measurement of the motion of the heritage site and its surroundings, guaranteeing a comprehensive view over the area of interest. InSAR data are appreciated by the cultural heritage community, as they provide a large amount of data on the entire heritage site and offer the mapping capability that is required to identify the location of active geohazards. For example, Spizzichino et al. (2016) report that 45% of the Italian UNESCO heritage sites are threatened by landslides, whereas 18% of the heritage sites in the United Kingdom register active subsidence (Cigna et al., 2018). Even if these numbers suggest the need for actions that focus on geohazards, there is still a lack of knowledge about these phenomena with respect to other sources of threat (Pavlova et al., 2017). InSAR was used by some authors as a noninvasive tool for cultural heritage investigation in different parts of the world, such as Poland (the UNESCO Heritage of the Wieliczka Salt Mine—Nitti et al., 2009), Italy (Rome—Tapete et al., 2012), Cyprus (Paphos area—Tzouvaras et al., 2019), and Romania (Alba Iulia—Moise et al., 2021). The following application example presents the EGMS data in the Romanian city of Sighi¸soara, located in the Transylvania region. Sighi¸soara is a small medieval fortified city that was founded by German merchants and craftsmen and played a central role in the commerce of central Europe for several centuries. Sighi¸soara was included in the UNESCO World Heritage List in 1999.22 The reason for the inscription of Sighi¸soara states, “the heritage represents an invaluable testimony to the culture of the Transylvanian Saxons and an outstanding 21 https://egms.land.copernicus.eu/#llh=24.78726199,46.22029978,2595.41520928&look= -0.29010427,-0.72199304,-0.62814455&right=0.90781262,0.00009480,-0.41937601&up=-0.3028 4611,0.69190032,-0.65540689&layers=VHR%20Image%20Mosaic%202012_VHR%20Image% 20Mosaic%202012-Image-parent. 22 Photographs of the historic city center of Sighi¸soara: https://whc.unesco.org/en/list/ 902/gallery/.

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8.8 Historic center of Sighi¸soara (Romania)

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FIGURE 8.9 EGMS data in the UNESCO heritage city of Sighi¸soara (Romania). (A) UNESCO core and buffer zones, (B) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (C) time series for the ascending orbit, and (D) Calibrated product in descending orbit. CT, Clock Tower; sNC, Saint Nicholas Church; TT, Tailors’ Tower. Source: Inset A from https://whc.unesco.org/en/list/902/documents/. The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

example of a small, fortified city in the border region between central and south-eastern Europe” (UNESCO, 1999). The core zone23 of the UNESCO heritage is 33 ha (0.33 km2), whereas its buffer zone24 is 145 ha (1.45 km2, see Fig. 8.9 inset A). The most unique buildings of this UNESCO heritage site are the Clock Tower (CT), the Tailors’ Tower (TT), and the Church of Saint Nicholas (sNC) (see Fig. 8.9 inset B for location). The first two are remnants of the original tower defense system 23 The core zone is the perimeter of the heritage. In this case, the historic center of Sighi¸soara. 24 The buffer zone is a tampon area that surrounds the heritage and that should ensure an additional level of protection to areas recognized as World Heritage sites.

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of the city made up of 14 towers (nine of which are still in place today, Negula and Poenaru, 2015). Sighi¸soara is an important tourist destination for Romania and hosts thousands of travelers interested in the historic center, which is considered an open city museum (Dragulanescu et al., 2014). The fortified city is located on top of a small hill that stands B100 m over the newer parts of Sighi¸soara. From the geological point of view, the hill is composed of an alternating succession of marls, clays, and clayey sands overlaid by an anthropogenic landfill that is 9 m thick (Marunteanu and Coman, 2005). The hill is affected by small and surficial landslides and gully erosion. Landslides are generally linked to the leakage of water from pipelines or the presence of nonregulated surface and subsurface water flow (Marunteanu and Coman, 2005). Fig. 8.9 shows the EGMS products in the historical city center of Sighi¸soara. Insets B and D show the EGMS Calibrated data in ascending and descending orbits, respectively. Inset C contains the time series of displacement and a table reporting the velocity values for some reference areas. Point density is optimal for investigating the presence of motion within the boundaries of the heritage site; the core zone contains B800 MPs and the buffer zone four times this number (B3500 MPs). There is only one vegetated area around the sNC that cannot provide any information regarding ground motion. A small group (less than five) of MPs is used to calculate the LOS velocity for the three reference buildings: the CT, the TT, and the sNC. CT and sNC do not register any evidence of active motion; the LOS velocities fall well within the precision of the velocity estimation. Time series are slightly noisy. LOS velocities are a bit higher in the case of TT ( 6 1.1 mm/year), but still not far from the precision of the LOS estimation. Therefore it is safe to say that the EGMS data do not highlight the presence of active motion within the boundaries of the Sighi¸soara core zone. The low LOS velocity values for the three heritages are in line with the results obtained by Negula et al. (2015) by analyzing TerraSAR-X SAR images. The buffer zone registers some kind of slow deformation, and it is in general less stable than the core zone. Two areas, identified by the black rectangles in the inset D of Fig. 8.9, show LOS velocities on average between 22.0 and 23.0 mm/year, with minimum displacement rates that reach 25 mm/year. From the geomorphological point of view, these two areas are located in the alluvial plain of the Sighi¸soara river that cuts through the city. It is most likely that the local geological asset plays a role in the control of ground deformation. This case study highlights the importance of interferometric data in general, and of the EGMS, as mapping tools for managing cultural heritage over wide areas. Even if the motion here is not very relevant, its presence certainly has to be considered in the management of the UNESCO buffer zone.

Satellite Interferometry Data Interpretation and Exploitation

8.9 Solnitsata-Provadia archeological site (Bulgaria)

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8.9 Solnitsata-Provadia archeological site (Bulgaria) BOX 8.9 To view the EGMS data over the Solnitsata-Provadia archeological site, refer to Footnote25.

A second example of cultural heritage endangered by ground motion comes from the archeological site of Solnitsata-Provadia26 in the Varna Province of Bulgaria. The site, discovered between 2005 and 2014, is located in a flat area, 50 km west of the Black Sea coast. Here, prehistoric activities revolved around the extraction of salt, made possible by the presence of a salt diapir.27 There are also salt springs with high saline concentrations that are associated with the presence of the diapir. Prehistoric activity relied on these brines to extract the salt through a process based on boiling the fluids in pottery (Nikolov, 2016). The remains of the prehistoric settlement are dated 5500
4200 BCE, making Solnitsata-Provadia the oldest salt-production center in Europe and, potentially, the oldest prehistoric urban center in Europe (Nikolov, 2016). The archaeological complex occupies B13 ha and hosts a saltproduction center, a fortified citadel (a “tell,” in the archeology nomenclature), a ritual ground, and a necropolis (see inset A of Fig. 8.10 for the location). The presence of the salt diapir has attracted the attention not only of the prehistoric population, but also modern-day humans. Beginning in the 1950s, the industrial exploitation of the salt diapir has been carried out by injecting pressurized fluids into the diapir, dissolving it and retrieving the oversaturated brines from the subsurface through a system of injection and pumping wells (Paskaleva et al., 2010). Mining is carried out at three depths: 700, 1000, and 1200 m below the surface. The activity has created 43 underground chambers B100-m wide and B50- to 200-m high (Paskaleva et al., 2010). Since the 1970s, mining activity has increased the seismicity in the area and produced relevant ground motion. Such movement was measured by previous authors using GPS and leveling (Botev et al., 2006, Paskaleva et al., 2010) 25 https://egms.land.copernicus.eu/#llh=27.47329393,43.13138639,5000.00000000&look50.33667678,-0.68367365,-0.64748675&right50.88722593,0.00000007,-0.46133518&up5-0.3154 0275,0.72978787,-0.60657297&layers5VHR%20Image%20Mosaic%202012_VHR%20Image% 20Mosaic%202012-Image-parent. 26 Some photographs of the prehistoric site: https://www.provadia-solnitsata.com/en/. 27 The process of formation of a salt diapir, also known as salt dome: https://geology. com/stories/13/salt-domes/.

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FIGURE 8.10 EGMS data in the archeological site of Solnitsata-Provadia in Bulgaria. (A) Location of the prehistoric structures, (B) Calibrated product in ascending orbit. Yellow to red colors identify movements away from the sensor, light blue to blue colors identify movements toward the sensor, (C) Ortho product/east
west component, (D) Calibrated product in descending orbit. Source: Inset A modified after Nikolov, V., 2016. The prehistoric salt-production and urban center of Provadia-Solnitsata, Northeastern Bulgaria. Revue ge´ographique des pays me´diterrane´ens/Journal of Mediterranean geography (126), 71
78. The ground motion data have been extracted from the EGMS Explorer: https://egms.land.copernicus.eu, r European Union, Copernicus Land Monitoring Service 2022, European Environment Agency (EEA).

or by InSAR, based on Sentinel-1 images (Ponco¸s et al., 2022). Maximum subsidence rates were estimated to be between 230.0 and 245.0 mm/year with GPS and leveling and to be equal to 223.0 mm/ year with satellite interferometry. The EGMS products for the Solnitsata-Provadia archeological site are shown in Fig. 8.10; insets B and D show the Calibrated data in ascending and descending orbits, respectively, and inset C presents the Ortho data referred to the east
west horizontal component of motion. The whole archaeological area registers relevant LOS velocities, reaching

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References

TABLE 8.2 Average LOS velocity values calculated for the main archaeological elements of Solnitsata-Provadia, as defined in Nikolov (2016). Archeological element

Avg. vel. asc. (mm/y)

Avg. vel. desc. (mm/y)

Middle Chalcolithic cemetery

25.1 (15 MPs)

211.8 (20 MPs)

Late Chalcolithic cemetery

215.2 (38 MPs)

210.9 (35 MPs)

Prehistoric salt-production site

217.8 (20 MPs)

225.3 (16 MPs)

Ritual site

218.7 (8 MPs)

215.0 (6 MPs)

Tell Provadia-Solnitsata

212.2 (27 MPs)

218.4 (18 MPs)

In parentheses, the number of MPs from which the average is calculated. The Chalcolithic (or Copper Age) is the period between the Neolithic and the Bronze Age and lasts between the mid-5th millennium BC and the late 4th to 3rd millennium BC. What is a tell in archaeology? https://www.britannica.com/science/tell-mound.

235.0 mm/year in the center of the subsidence bowl for both orbits of the Calibrated product. The deformation pattern is interesting. There is a clear east-to-west increase of LOS velocities in the ascending orbit. The descending orbit registers the opposite situation, with LOS velocities increasing toward the east. This indicates that subsidence is taking place not only along the vertical axis but also has a relevant horizontal component. Inset C of Fig. 8.10 confirms this hypothesis; horizontal velocities up to 6 16 mm/year are registered for both the east and the west components of motion. Table 8.2 lists the average LOS velocities for the five main elements of the archeological site: the Middle and Late Chalcolithic cemeteries to the south, the prehistoric salt-production site to the north, the ritual site to the west, and the tell in the middle of the area (inset A of Fig. 8.10). All of the heritage sites register LOS velocities well-above 210 mm/year on average. With respect to the time series of the LOS data, the motion is constant over time without any kind of seasonality or trend change. Once again, these results indicate the usefulness of the EGMS data for mapping ground motion in heritage areas. Even if the constant motion may not directly endanger the archeological elements, the knowledge of such motion is certainly relevant for the authorities responsible for cultural heritage management.

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C H A P T E R

9 Conclusions This book is devoted to the interpretation and exploitation of the data obtained by the interferometric SAR (InSAR) technique. With the advent of the European Ground Motion Service (EGMS), it is now easy to access and visualize InSAR results in Europe. The correct interpretation of such results requires a proper understanding of what is behind them. This is useful to increase awareness and avoid excessive expectations. It also requires a correct understanding of the technique and the products that it generates. For this reason, Chapter 2 sheds light on the InSAR technique. It introduces the basic concepts of InSAR for deformation measurement and monitoring, the characteristics of the InSAR measurement points, and a generic InSAR processor to generate the InSAR products starting from a stack of SAR images. Furthermore, it discusses the pros and cons of the technique and its most important InSAR applications. Chapter 3 takes a further step to understand the InSAR technique. To this end, it discusses the main technical aspects related to InSAR processing and especially, to InSAR products. The book is focused on the data originating from the Copernicus EGMS. For this reason, throughout the work, Sentinel-1 sensors, which are EGMS inputs, are often cited. However, most of the concepts described in Chapters 2 and 3 are valid for any InSAR sensor. EGMS stands for the most relevant initiative in InSAR deformation monitoring ever carried out. It is based on ambitious and innovative product specifications and involves the generation of advanced InSAR products. This book devotes an entire chapter to the description of EGMS. The core of Chapter 4 is the description of EGMS products: Basic, Calibrated, and Ortho. Then, it discusses the validation of EGMS products and their applicability. An important aspect is the dissemination of these products, which is one of the strong points of the Service. The dissemination is realized through an interactive WebGIS to visualize the data and an interface to search and download EGMS data. In the following section, the main characteristics of InSAR and hence of EGMS are recapped, focusing on the limitations. The main advantages of InSAR are listed in Section 2.5. Section 9.2 features some considerations

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about EGMS. Last, in Section 9.3, the lessons learnt from the case studies described in this book are summarized.

9.1 List of main interferometric SAR limitations The main InSAR limitations are briefly recalled next: • The SAR satellite revisiting time determines the type of measurable phenomena. Due to the 6-day revisiting time of Sentinel-1, EGMS can only be used to monitor slow deformation. As of December 2021, the revisiting time has been 12 days. • The EGMS products are published with a delay period from the final date of their time series. In the case of the baseline product, the delay from series end to their publication was approximately 18 months. This influences the applicability of the products for monitoring purposes but does not impair the use of the data in deferred time. • The measurement point (MP) spatial density is uneven and varies considerably as a function of the land cover (Section 3.3). It is worth emphasizing that the lack of MPs simply means “no data,” not “no deformation.” • The densest MP coverage is over built-up areas. • InSAR does not work with objects that change their form and appearance over time, for example, due to elevation of buildings and earthworks. • The least MP density is over vegetated and forested areas. • Snow is a source of noise in InSAR processing. In North Europe the winter scenes are excluded from the data processing. This causes a loss of measurement dates in the time series and makes processing more complex. • InSAR does not work over water surfaces: it cannot be used to monitor the water level of lakes, reservoirs, etc. InSAR does not work over temporally flooded areas, like certain coastal areas. • MP density is influenced by the local topography, due to the geometric effects of the SAR images (Section 3.4). • Particular care is needed to analyze nonlinear and fast motion (Section 3.7). • Nonlinear deformation can cause a loss of MPs. • Fast motion can imply that the deformation has not been correctly reconstructed. • The InSAR deformation measurement is mono-dimensional and along the line-of-sight (LOS) (Section 3.5). • By using both ascending and descending SAR data, one can retrieve two components of the deformation: the vertical and eastto-west horizontal.

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• InSAR with polar orbit satellites is basically blind to south
north horizontal displacements. • InSAR measures relative ground motion (Section 3.6). This applies to the EGMS Basic product. For all other EGMS products, absolute motion relative to an Earth-centered reference system is achieved by fusing InSAR and GNSS data. • MP positioning is key to InSAR interpretation. EGMS specifications prescribe a 3D geolocation precision that must be better than 10 m. Such a value influences the InSAR interpretation and particularly limits the analysis of single small objects, for example, a single building or a small landslide.

9.2 Some considerations about EGMS and its products With respect to the EGMS: • It is a new European geospatial dataset of unique value, from which other products and services can be developed. • The EGMS products offer an unprecedented cross-border capability that will be exploited by national entities as they cooperate to develop protocols for cross-border ground motion management. • It offers a major opportunity to better know a territory, unravel new deformation areas, and monitor known active phenomena. • Numerous and diverse communities and user groups will be interested in the data, while others may not consider the products useful. Nonetheless, the EGMS offers a good opportunity to involve those users who have never had access to this kind of data. • The products are a quality-controlled and validated baseline for potential downstream and uptake activities. • The data are relevant for Copernicus services other than the Copernicus Land Monitoring Service. For example, they can provide useful information for the Risk and Recovery Mapping activations of the Copernicus Emergency Mapping Service. • The EGMS products have a wide variety of applications (see the summarized overview proposed in Section 4.6), but they cannot cover every type of ground motion or provide results for all types of targets. They cannot be oversold outside their field of applicability. For example, they cannot follow movements that are “too fast” or instantaneous (Section 3.7), and they cannot provide information where the land cover is unfavorable (Section 3.3). • The Basic product is the “classic” deformation map that some users are now accustomed to. It provides relative measurements of ground motion (velocity and time series) for single bursts. These data are

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useful to determine the local motion (e.g., a subsidence bowl involving a group of buildings) but are less effective to measure large deformation areas since the measurements between neighboring bursts can vary due to the different deformation reference and look angles. • The Calibrated product is the most relevant EGMS dataset. The GNSS calibration enables attainment of absolute measurements, which are no longer linked to a local reference. In this way, neighboring bursts can be directly compared. However, the quality of this product is linked to the quality of the input GNSS model and the low-frequency GNSS signal that is added to the time series. In areas of low GNSS signal the Basic and Calibrated products are essentially the same thing. Where the GNSS signal is significant (e.g., Fennoscandia and Greece) the two products differ, sometimes in a considerable way. Section 6.3 shows how to deal with this (potential) issue in case of landslide studies. In general, if local analysis is the target, the user should refer to the Basic product—whereas in case of wide area investigations (on multiple bursts), the Calibrated product would be the best choice. • The Ortho product has a lower resolution (100 by 100 m, instead of full resolution) but carries a significant level of information: the components of the motion (vertical and east
west horizontal). The LOS data can be difficult to interpret for non-InSAR experts, as the velocity value (and the color bar of the deformation map) cannot be converted immediately into the direction of the motion. This is something that an expert interpreter can do almost instantly, but that is not necessarily simple for an untrained eye. Ortho is a derived product, meant to facilitate the work of InSAR nonexperts. Since it is derived from the Calibrated product, it carries the same GNSS information. This should be considered for the usability of Ortho in areas of strong tectonic signal.

9.3 Lessons learnt from the case studies In over 30 years of history, InSAR has faced two main challenges: processing the large amount of data collected by the SAR satellite sensors and interpreting and exploiting the InSAR results. The intense development of algorithms and the increase of computational capability have enabled the processing of large InSAR datasets. The EGMS service is a clear example of this. Now, the main challenge that remains is the interpretation of EGMS results. This book attempts to contribute to resolving this challenge. The case studies discussed herein address some of the most important InSAR applications. In this section, the

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lessons learnt by employing EGMS products in various case studies from across Europe are summarized. • Subsidence is probably the best target for an interferometric analysis based on the EGMS products. In fact: • The high point density in urban areas allows proper spatial reconstruction of the phenomena, and the type of motion (in many cases of low magnitude and a constant rate over time) facilitates the temporal reconstruction. • Urban subsidence due to water (over)exploitation or natural/ accelerated consolidation is also an ideal target. The example of the Firenze
Prato
Pistoia (Section 5.1) offers an overview of the capability of EGMS to measure different spatial and temporal patterns linked to the extraction of underground water. • Mining-induced subsidence can be measured, but here the multitemporal InSAR technique is pushed to its limits. If the deformation rates begin to turn patently nonlinear, many MPs will be lost, even in urban areas, and it will be impossible to follow the real motion. This is why the central portion of the moving areas above the coal mines of Poland and Czech Republic does not record any MPs (Section 5.2). This is a limitation that it is difficult to overcome without the use of specific approaches based on more complex deformation models, which are too computationally demanding to adopt at continental scale. • Calibrated data may have limited applications in the Fennoscandia region, where the postglacial rebound generates an uplift of some millimeters per year that may mask local subsidence areas with small magnitude. Here, the use of the Basic product, in addition to the Calibrated product, is recommended. • Landslides are another classic InSAR target and EGMS provides useful information for their study. In particular: • The wide area coverage, without border boundaries, offers great potential for deriving a landslide database or updating existing ones. EGMS can be freely used as a tool for defining the state of activity of known and unknown landslides over an entire basin or a region. As an example, EGMS can be used to discover dozens of moving slopes along the fjords of Troms og Finnmark county in Norway (Section 6.2). • Mountain environments suffer most from land cover and morphological InSAR limitations. The noncontinuous point coverage must be considered when planning to work with the EGMS data in such landscapes.

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• In landslide studies, it is mandatory to consider the double orbit data. One orbit may be insufficient to understand the motion of a landslide, especially when the slope orientation is not favorable, and the real motion is strongly underestimated. An explanation of this concept is given in Section 6.1.2. • The interpretation must be supported by the Ortho products that can describe the motion of the landslide in a twodimensional way. Landslides are complex phenomena, and they usually have motion vectors that differ from one part of the landslide to the other (e.g., the crown area may have a major vertical component). Clearly, the Ortho product can only work with large landslides and where both ascending and descending data are available with a good density of MPs. An example of Ortho data usage is given in Section 6.1.2. • The horizontal component of motion in the Calibrated and Ortho data may be masked by the GNSS signal in case of tectonically active areas such as Greece. In this sense, the Basic product may be a better solution for local-scale applications. An example of this concept is provided in Section 6.3. • In general, the proper interpretation of EGMS (and InSAR) results for landslide applications shall consider: - the verification of the consistency of velocity values and deformation patterns with the local morphology, - the appraisal of the predisposing and triggering factors for the landslide, - comparison of the moving areas with landslide inventories, and - the analysis of deformation time series. • Volcanoes can be monitored from space, and the EGMS offers valuable examples of this capability. • It is possible to measure the deformation of the volcanic edifice, connect the measured ground motion to the evolutional stage of the volcano, and measure the motion of faults connected to the volcanic activity. The results from over Mount Etna (Section 7.1.1) are an example of this. • The MP availability depends to a large degree on the geographical location and the typology of the volcano. A unique volcanic area in an urban environment such as Campi Flegrei is the best setup for an interferometric analysis (Section 7.1.2). • A multitemporal InSAR investigation on its own cannot unravel the state of activity of a volcano. The integration with seismological, geochemical, and other data is needed to perform a complete evaluation of the status of a volcano.

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• Two-pass InSAR is a valid resource for investigating the motion of a volcano during the emplacement of an eruption, when high-frequency updates are necessary. • InSAR products can be integrated into models to properly investigate the magmatic source. • The strong motion generated by an earthquake can be followed by satellite, and some of the major earthquakes that struck Europe between 2015 and 2020 can be seen in the EGMS time series. • Only earthquakes of high magnitude (approximately .6) can be effectively measured from space. Minor events, although they can be destructive, have a signal that is too small to be distinguished from the general noise with InSAR. • Two-pass InSAR is a valuable resource for measuring the effect of an earthquake by analyzing the last image before and the first image after an earthquake. However, multitemporal InSAR can also follow motion long before and after the earthquake. • The multitemporal InSAR processing of earthquakes is particularly challenging, and the continental-scale protocol must be adjusted to adapt to this level of complexity. In EGMS the areas of major earthquakes were processed separately from the other tiles. • The motion of the earthquake will have an impact on the deformation map and on the time series. The deformation map registers the accumulated motion between the two sides of the fault, and the pattern will materialize the motion of the fault depending on its geometry. The deformation time series includes a jump that impacts the estimation of the velocity value. An example of this is the Central Italy seismic sequence presented in Section 7.2.1. • The EGMS is a baseline for infrastructure and building investigations. Point density is sufficient to measure the motion of standard linear infrastructures such as highways and railways, large bridges, or buildings. It is possible to estimate the differential motion of structures if there are enough MPs, and the thermal expansion of tall or elongated structures can be captured by the time series. However, the reader should carefully consider that: • The spatial sampling of InSAR may be insufficient to completely sample buildings or infrastructures. • The geolocation precision must be considered (Section 3.10). • The 2D geometry of ascending/descending orbit data can be insufficient to follow the 3D motion of buildings or infrastructures. • The precision of the velocity and especially of the time series estimations may be insufficient for users interested in the monitoring of single buildings or infrastructure elements.

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9. Conclusions

A recent article published by the Copernicus Observer refers to EGMS using these words “mapping ground motion at European Scale: from dream to reality.”1 This is particularly true since EGMS is the result of a long process that involved a direct request from user communities and followed the advancement of the InSAR research and development world. This book intends to show the virtues of the Service through the presentation of several use cases and provide a balanced discussion in its explanation of the positive and negative aspects of its different applications. At the time of writing this book, the EGMS baseline product is only a few months old, and the downstream impact of the Service has yet to be demonstrated. However, the authors of this book strongly believe in the undisputable value of the Service for many user communities, especially those who have never had access to such data.

1 https://www.copernicus.eu/en/news/news/observer-mapping-ground-motion-europeanscale-dream-reality.

Satellite Interferometry Data Interpretation and Exploitation

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A Absolute motion, 37, 70, 74
75 Active corner reflector, 50f Active Deformation Areas (ADA) Classifier, 53, 55 example deformation velocity map and corresponding ADA map, 54f output of ADA Classifier, 55f Finder, 53
54 Active reflectors (AR), 50
51 ADA. See Active Deformation Areas (ADA) Advanced DInSAR, 1 Aegean Plate, 161
162 Airports, 202 Almun˜ecar, 145 Alpuja´rride Complex, 136, 198
199 Amplitude Dispersion Index, 45
46 Applications, InSAR, 19
20 AR. See Active reflectors (AR) Artificial reflectors, 18, 49
51 example active corner reflector with its solar panel, 50f displacement map in mountain area affected by landslide, 51f passive corner reflector used with Sentinel-1 imagery, 49f Ascending SAR data, 35 Atmospheric component estimation, 13 Average deformation velocity, 14 Azimuth, 14

B Base de Datos de Movimientos del terreno (BD-MOVES), 163 Baseline product, 64, 232 Basic products, 66, 83
84, 233
234 Basin scale, European Ground Motion Service data at, 96
104

BD-MOVES. See Base de Datos de Movimientos del terreno (BD-MOVES) BDDs. See Building differential deformations (BDDs) Beachballs, 184 Blackfriars Railway Bridge (BRB), 204
207 EGMS data over, 207f Bottegone area, European Ground Motion Service data over, 98
100 BRB. See Blackfriars Railway Bridge (BRB) Building differential deformations (BDDs), 56
57, 56f

C C-band (Sentinel-1) interferogram, 188 Calibrated products, 66, 83
84, 96
104, 234 Calibrated product/ascending orbit over USCB, 109f Calibrated product/descending orbit over USCB, 110f Calibration using GNSS data, 72
73 Campi Flegrei, 178
183 EGMS Calibrated data, 181f Ortho data for, 182f time series, 178f, 183f Case studies, 234
238 Central Italy seismic sequence (2016), 186
189 EGMS Calibrated and Ortho products, 187f time series, 189f Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET-LiCSAR), 170 Cerro Gordo, 138
139 Church of Saint Nicholas (sNC), 220
222 Classic InSAR approaches, 170

239

240 Clock Tower (CT), 220
222 Coal mining, 114
115 production, 106
107 COMET-LiCSAR. See Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET-LiCSAR) Copernicus GLO-30 DEM, 140
141 Copernicus Observer, 238 Copernicus1 Land Monitoring Service’s product portfolio, 63 Corner reflectors (CRs), 49
51, 49f, 50f Coseismic deformation, 4, 184
185 Cosmo-SkyMed, 29, 188 Cracow Sandstone Series, 106 CRs. See Corner reflectors (CRs) CT. See Clock Tower (CT)

D Dams, 198 Data analysis tools ADA Classifier, 55 Finder, 53
54 differential deformations, 56
57 examples of, 53
57 Data processing, 108, 204
205 Deflation, 169
175, 183 Deformation estimation of deformation components, 76 estimation procedure, 10
14 flow chart of general InSAR approach, 11f map, 9, 237 obtained from Envisat ascending images, 95f obtained from ERS 1/2 descending images, 94f measurements, 7 phase component, 8 phenomena, 31
32, 43 process, 34 reference for deformations of basic products, 68
69 temporal reference for EGMS basic product, 69f time series, 14
16, 41
42 from Barcelona metropolitan area, 17f example of spike due to residual atmospheric effects, 42f

Index

examples of four time series with different levels of noise, 42f from North Europe, 17f from port of Barcelona, 16f velocity, 14 map of airport and port of Barcelona, 15f map over coast of Barcelona, 15f Departments and regions of France (DROM), 80
81 Descending SAR data, 35 Differential deformations, 53, 56
57 example of BDD over small village in mountainous area, 56f Differential InSAR (DInSAR), 1 deformation measurement, 8f Differential interferometric phase, 8 Digital Elevation Model (DEM), 11
12 DInSAR. See Differential InSAR (DInSAR) Dispersion of the amplitude, 67 Distributed scatterers (DS), 3, 10, 30 Doris (InSAR processor), 57 DPC. See Italian Department of Civil Protection (DPC) DROM. See Departments and regions of France (DROM) DS. See Distributed scatterers (DS)

E e-GEOS, 65, 67 Earthquakes, 184
189, 237 Central Italy seismic sequence (2016), 186
189 scientific papers, 185t EGMS. See European Ground Motion Service (EGMS) Elongation, 32
33, 32f, 34f Envisat interferometric products, 126 Envisat SAR images, 198
199 ERS 1/2 products, 126 ESA. See European Space Agency (ESA) European Environment Agency, 63 European Ground Motion Service (EGMS), 1, 10
14, 63, 90, 170, 231, 237 applicability, 77
79 applicability of European Ground Motion Service products, considering main InSAR application fields, 78t comparison between Sentinel-1 and very high-resolution Cosmo-SkyMed results, 79f

Index

baseline product, 27
29 basic product, 66
69 characteristics of basic product, 67
68 reference for deformations of basic products, 68
69, 69f Calibrated and geomorphological elements, 144f Calibrated product, 69
73, 101 calibration using GNSS data, 72
73 characteristics of, 70
72 difference between Basic vs. Calibrated, 71f, 72f for Campi Flegrei, 181f for Mount Etna, 175f considerations, 197 about EGMS and products, 233
234 data, 100 in archeological site of SolnitsataProvadia in Bulgaria, 224f over Blackfriars Railway Bridge, 207f in coastal town of Thyborøn, 219f over Nice Coˆte d’Azur Airport, 203f along Rhine River levees, 213f over Rules Dam and Viaduct, 201f around Rules Reservoir, 199f along Scheldt River, 216f along section of Helsinki commuter track, 210f in UNESCO heritage city of Sighi¸soara, 221f data at basin scale, 96
104 EGMS Calibrated and Ortho in Bottegone area, 99f EGMS Calibrated and Ortho in Pistoia, 101f EGMS Calibrated in Firenze
Prato
Pistoia basin, 97f EGMS Ortho in Firenze
Prato
Pistoia basin, 98f European Ground Motion Service data over Bottegone area, 98
100 European Ground Motion Service data over city center of Pistoia, 100
102 European Ground Motion Service data over Montemurlo, 102
104 data from the over Katowice area, 114
115 data over Ostrava
Karvina area, 110
113 EGMS products in Katowice, 114f EGMS products in Ostrava
Karvina area, 113f

241

data over Tychy area, 115
116 EGMS products in Tychy area, 116f data over Upper Silesian Coal Basin, 108
109 Calibrated product/ascending orbit over USCB, 109f Calibrated product/descending orbit over USCB, 110f Ortho product/east
west component over USCB, 112f Ortho product/vertical component over USCB, 111f description, 231 dissemination, 84
85 main portal of EGMS, 85f drawbacks, 196
197 Explorer, 79
84, 163 aspect of, 80f data menu box of, 81f download European Ground Motion Service products, 83
84 European Ground Motion Service WebGIS, 80
82 example of 3D view of Ortho product, 82f numbering of tracks of Calibrated data, 82f toolbar of, 81f flow chart of EGMS, 66f geographical coverage of, 65f geometry of Sentinel-1 SAR image, 64f landslide mapping in Troms og Finnmark county, 148
159 Ortho data, 120 and time series in Groningen area, 121f for Campi Flegrei, 182f for Mount Etna, 177f in Larderello geothermal field, 127f in Vauvert area, 124f Ortho product, 73
76, 75f, 101, 125
127 characteristics of, 75
76 estimation of deformation components, 76 scheme illustrating coverage of Ortho product, 74f products, 27
29, 83
84, 123, 137
145 basic data download example, 83f window to download data, 83f window to download data, 84f Task Force, 63 validation, 76
77 European Space Agency (ESA), 1, 47

242

Index

European Space Agency (ESA) (Continued) Sentinel-1 Toolbox, 57 Exploitation of data, 1

F Fast deformation phenomena, 31, 37
40 Firenze
Prato
Pistoia basin, subsidence related to groundwater exploitation in, 91
104 Foreshortening, 32
33, 32f, 34f, 152 Four time series with different levels of noise, 42f Four-value index, 53
54 Fragility curve, 198

G GAF, 65, 67 Gamanjunni 3 landslide, 155, 158f Generic Mapping Tools, 58 Geocoding, 14 example of global shift in, 45f Geohazards, 188, 195 Geological map of Upper Silesian Coal Basin, 105f Geometrical effects, 134 Global Navigation Satellite System (GNSS), 37 calibration, 234 data, calibration using, 72
73 GMTSAR (open-source InSAR processing system), 58 GNSS. See Global Navigation Satellite System (GNSS) Ground motion phenomena, 30 Groundwater depression, 99 management policies, 100 subsidence related to groundwater exploitation in Firenze
Prato
Pistoia basin, 91
104

H High point density in urban areas, 235 High resolution X-band sensors, 29 Hong Kong
Zhuhai
Macao Bridge, 205

I IGME. See Instituto Geologico y Minero de Espan˜a (IGME)

Image coregistration, 11 In situ measurements, 51
53 Inflation, 169
176, 183 Infrastructure monitoring, 196
197 Input data, 10 InSAR. See Interferometric synthetic aperture radar (InSAR) InSAR Scientific Computing Environment (ISCE), 57 Instituto Geologico y Minero de Espan˜a (IGME), 163 Interferograms, 184
185 generation, 11
12 Interferometric data in mining areas, tips and tricks to interpret, 117
119 Interferometric phase, 8 Interferometric synthetic aperture radar (InSAR), 1, 7, 43, 117
119, 134, 195, 231 acquisition geometry, 33
34 applications, 19
20 for subsidence detection, 119
127 in the area, 107
108 approaches, 10 flow chart of general, 11f basics, 7
9 scheme of DInSAR deformation measurement, 8f characteristics, 231
232 data, 169
170 deformations, 68 measurements, 33
34 drawbacks, 196
197 lessons learnt from case studies, 234
238 limitations, 232
233 measurement points, 9
10, 18, 29 observation equation, 9 processing, 17, 67 products, 14
17 deformation velocity map of airport and port of Barcelona, 15f deformation velocity map over coast of Barcelona, 15f example of deformation time series from North Europe, 17f example of deformation time series from port of Barcelona, 16f example of two deformation time series from Barcelona metropolitan area, 17f pros and cons, 18
19 reference scientific papers, 196t

Index

technical aspects ADA Finder, 53
54 artificial reflectors, 49
51 deformation time series, 41
42 example of aliasing due to fast deformation over coal mining site, 40f example of ambiguous deformation in case of sudden big displacement, 41f example of foreshortening, layover, and elongation effects in mountainous area, 34f example of MP loss due to high deformation values in mining areas, 38f example of nonlinear deformation time series, 38f examples of data analysis tools, 53
57 InSAR validation results, 47
48 InSAR vs. in situ measurements, 51
53 LOS measurement, 33
36 measurement point density, 31
32 measurement point positioning, 44 nature of measurement points, 29
30 nonlinear and fast deformation, 37
40 open-source InSAR software, 57
58 quality of InSAR estimates, 45
47 reference point, 36
37 SAR data acquisition, 27
29 SAR geometric effects, 32
33 scheme illustrating foreshortening and elongation effects, 32f scheme of subsidence due to lowering of water table, 52f time series and thermal expansion, 43
44 techniques, 108 Interpretation of data, 1 ISCE. See InSAR Scientific Computing Environment (ISCE) Italian Department of Civil Protection (DPC), 179
180

J Jettan landslide, 158
159

K Katowice area, European Ground Motion Service data from, 114
115

243

L L-band (ALOS) interferogram, 152
153, 188 Land cover, 134 Landslides, 133, 235
236 considerations on use of EGMS data for landslide studies, 159
165 mapping in Troms og Finnmark county, 145
159 risk, 133 scientific papers, 135t state of activity evaluation on Granada coast, 136
145 EGMS Calibrated and geomorphological elements, 144f EGMS Calibrated product, 138f EGMS products, 137
145 geographical and geological context, 136
137 geological map of area of interest, 137f Ortho products, 142f visual explanation of LOS velocity sign, 140f typology, 134 Layover, 32f, 33, 34f, 152 Levees in Bregenz, 211
214 Level 2a product. See Basic products Ligurian units, 91
92 Limnic succession, 106 Line of sight (LOS), 7 measurement, 33
36 Displacement Dtot observed by an ascending and descending geometry, 35f DLOS corresponding to two displacements, 35f Long-term deformation, 211
212 LOS. See Line of sight (LOS) Lupu Bridge in Shanghai, 205

M Macigno Formation, The, 91
92 Manndalen valley, 155
158, 156f Marina Del Este resort, 136
137 Measurement points (MPs), 9, 29, 195, 198 candidates, 12 densification, 13
14 density, 31
32 examples of MP density over three different types of land cover, 31f

244

Index

Measurement points (MPs) (Continued) loss due to high deformation values in mining areas, 38f nature of, 29
30 MP types over ground grid of approximately 14 by 4 m, 30f positioning, 44 example of global shift in geocoding, 45f redundancy, 31
32 Mining areas subsidence in surroundings of Hambach mine, 118f tips and tricks to interpret interferometric data in, 117
119 Mining subsidence in Upper Silesian Coal Basin, 104
116 Mining-induced subsidence, 235 Model estimation, 12 Monitoring Unrest from Space (MOUNTS), 170 Montemurlo EGMS Calibrated and Ortho in Montemurlo, 103f data over, 102
104 Motion, 134 Mount Etna, 172
177 EGMS Calibrated data for, 175f EGMS Ortho data for, 177f Mountain environments, 235 Mountainous regions, 33, 81 MOUNTS. See Monitoring Unrest from Space (MOUNTS) MPs. See Measurement points (MPs) Mudstone Series, 106 Multitemporal InSAR, 1 approaches, 170, 185
186 processing of earthquakes, 237

N National Institute of Geographic and Forest Information, 125 Nice Coˆte d’Azur Airport, 202
204 EGMS data over, 203f Nonlinear deformation, 37
40 time series, 38f NORCE, 65, 67

O Oil and gas, extraction of, 89 Open-source InSAR software, 57
58

OpeRatIonal Ground motion INsar ALliance (ORIGINAL consortium), 65 ORIGINAL consortium. See OpeRatIonal Ground motion INsar ALliance (ORIGINAL consortium) Ortho data, 82 Ortho products, 66, 96
104, 234 Ortho product/east
west component over USCB, 112f Ortho product/vertical component over USCB, 111f OSARIS, 58 Ostrava
Karvina area, European Ground Motion Service data over, 110
113 Ostrava
Karvina coal basin, 110
111

P Paralic Series, 106 Passive CR, 49
50, 49f Past deformation phenomena, 18 PAZ, 29 Pernicana fault system (PFS), 173 Persistent scatterer interferometry, 1 Persistent scatterer pair (PSP), 67 PFS. See Pernicana fault system (PFS) Phase ambiguity, 8
9 Phase difference, 8, 11
12 Phase unwrapping, 12
13, 38
41 Pistoia, European Ground Motion Service data over the city center of, 100
102 Point-like scatterers (PS), 3, 9
10, 30 Port of Antwerp, 214
216 Postearthquake, 184
185, 185t Preearthquake, 184
185, 185t PS. See Point-like scatterers (PS) PSIC4, 47 PSP. See Persistent scatterer pair (PSP) Punta de la Mona, 136 percentage of detectable motion, 141f

Q Qinghai
Tibet Railway, 208
209

R Radotı´n Bridge in Prague, 205 Ragalna fault system (RFS), 173 Railway in Finland, 208
211 EGMS data along section of Helsinki commuter track, 210f

Index

Range, 14 Reference point, 36
37, 70 Reference time series, 99
100 Reflectors, 49
50 Relative motion, 70 Remote sensing data, 169 RFS. See Ragalna fault system (RFS) Rhine River, 212 EGMS data along Rhine River levees, 213f RMSE. See Root-mean-square error (RMSE) Rock salt, 123
124 Root-mean-square error (RMSE), 41, 46 Rules dam, 197
201 Rules Reservoir, 197
201 Rules Viaduct, 198, 200
201

S Salt diapir, 223
224 Satellite interferometry, 214
215 Satellite-derived deformation history, 93
95 deformation map obtained from Envisat ascending images, 95f obtained from ERS 1/2 descending images, 94f Satellites, 27
29 SBAS approach, 13 Scheldt River, 215
216 EGMS data along, 216f Seasonal deformation, 211
212 Sedimentary sequence of Firenze
Prato
Pistoia, 92
93 Seismology, 184 Seismometers, 169 Sentinel Application Platform, 57 Sentinel-1, 27
29, 53 data, 93
95 imagery, 49
50 InSAR, 48 SAR data, 29
30 images, 125 satellites, 27
29 sensors, 7 sentinel-1A sensors, 27
29 sentinel-1B sensors, 27
29 sentinel-1C, 27
29 Toolbox, 57 Service products, 66, 66f

245

Shadow, 33 Sighi¸soara historic center, 220
222 EGMS data in, 221f Signal-to-clutter ratio, 67 Single Look Complex (SLC), 66 Single-dominant point-like scatterer, 30 Single-interferogram InSAR, 184
185 SLC. See Single Look Complex (SLC) SNAP, 184
185 sNC. See Church of Saint Nicholas (sNC) Snow cover, 134, 154 Solnitsata-Provadia archeological site, 223
225 average LOS velocity values, 225t EGMS data in archeological site of, 224f Southern Lyngenfjord, 158
159 EGMS data along southern portion of, 160f Southern Sørfjorden, 150
155, 153f STAMPS (software package), 57 Static instabilities, 102 Stratovolcanoes, 170 Subsidence, 235 applications of InSAR for subsidence detection, 119
127 in surroundings of Hambach mine, 118f and uplift applications of InSAR for subsidence detection, 119
127 EGMS Ortho and Calibrated data and time series in surroundings of Leonardo Da Vinci Airport, 122f EGMS Ortho data and time series in Groningen area, 121f EGMS Ortho data in Larderello geothermal field, 127f EGMS Ortho data in Vauvert area, 124f European Ground Motion Service data at basin scale, 96
104 European Ground Motion Service data from over Katowice area, 114
115 European Ground Motion Service data over Ostrava
Karvina area, 110
113 European Ground Motion Service data over Tychy area, 115
116 European Ground Motion Service data over Upper Silesian Coal Basin, 108
109 geographical, geological, and hydrogeological context, 91
93, 92f

246

Index

Subsidence (Continued) geological and mining context, 105
107 mining subsidence in Upper Silesian Coal basin, 104
116 previous InSAR applications in the area, 107
108 satellite-derived deformation history, 93
95 scientific papers reader can reference, 90t simplified geological map of Upper Silesian Coal Basin, 105f subsidence related to groundwater exploitation in Firenze
Prato
Pistoia basin, 91
104 tips and tricks to interpret interferometric data in mining areas, 117
119 Synthetic aperture radar (SAR). See also Interferometric synthetic aperture radar (InSAR) data, 29 data acquisition, 27
29 main past and present SAR missions, 28f geometric effects, 32
33 interferometry, 7

T Tailors’ Tower (TT), 220
222 TanDEM-X, 29 TDDs. See Terrain differential deformations (TDDs) Temporal coherence, 46 Temporal sampling, 53 of deformation, 70 Terrafirma Validation Project, 47 Terrain differential deformations (TDDs), 56 TerraSAR-X, 29 Terzaghi’s theory, 89 Thermal expansion, 43
44 Three Gorges Reservoir in China, 198 3D positioning of MPs, 44 Thyborøn port, 217
219 EGMS data in coastal town of, 219f Tiber Delta, 120
121 Time series (TS), 13
14, 16, 43
44, 82, 101, 103
104 data mining approach, 93
95

example of time series showing thermal expansion component, 43f InSAR, 1 for vertical component, 115 Toolbar, 81 TRE-Altamira, 65, 67 Troms og Finnmark county EGMS Calibrated product, 149f, 150f EGMS Ortho product, 151f, 152f EGMS products, 148
159 Manndalen valley, 155
158, 156f Southern Lyngenfjord, 158
159 Southern Sørfjorden, 150
155, 153f geographical and geological context, 146
147 geological map, 147f landslide mapping in, 145
159 TS. See Time series (TS) TT. See Tailors’ Tower (TT) Two-pass InSAR, 237 Tychy area, European Ground Motion Service data over, 115
116

U Upper Silesian Coal Basin (USCB), 91, 105
106 European Ground Motion Service data over, 108
109 mining subsidence in, 104
116 Upper Silesian Sandstone Series, 106 Urban areas, 195. See also Active Deformation Areas (ADA) Blackfriars Railway Bridge, 204
207 Levees in Bregenz, 211
214 Nice Coˆte d’Azur Airport, 202
204 Port of Antwerp, 214
216 railway in Finland, 208
211 rules dam and reservoir, 197
201 Sighi¸soara historic center, 220
222 Solnitsata-Provadia archeological site, 223
225 Thyborøn port, 217
219 Urban planning, 195 Urban subsidence, 235 Urbanization, 136
137, 143 USCB. See Upper Silesian Coal Basin (USCB)

V Vajont landslide, 198 Validation exercises, 47
48 Visual analysis, 104

Index

Volcanoes, 169, 236
237 Campi Flegrei, 178
183 Mount Etna, 172
177 scientific papers, 171t

247

Web map service (WMS), 163 WebGIS, 79
83 WMS. See Web map service (WMS)

X W Waterloo Bridge in London, 205

X-band (COSMO-SkyMed) interferogram, 188