Historical land movement monitoring in the city of Tunis (Tunisia) with SqueeSAR TM

Transcription

1 Historical land movement monitoring in the city of Tunis (Tunisia) with SqueeSAR TM Service N 6037_LXS_WB_RFQ4 Final Report 09/07/2010 Project funded by European Space Agency (Directorate of Earth Observation Programmes) supporting EO services for Service managed by

2 Table Of Contents Service managed by Executive summary Résumé Introduction ACC MENA Project PSInSAR TM technique PSInSAR TM methodology SqueeSAR TM, the next generation PSInSAR TM Precision and accuracy Strenghts and weaknesses of PSInSAR technology Approach SAR data selection Data Source and Archive Master Image Selection Limitations of the Available Archive Signal Phase and Amplitude Analysis General Radar Amplitude and Multi-Image Reflectivity Interferograms Estimation of the atmospheric effects ALOS data processing Results ERS descending data ERS ascending data ENVISAT descending data Delivery of Data Other deliverables The structure of the database Results precision assessment Sensitivity versors Validation and additional elaborations Discussion and final observation The alluvial plain of the Oued Medjerda, and the mouth area of Ghar el Melh Lake Southern and western sector of the Lac de Tunis and north-eastern part of the Sebkhet el Sijoumi The area of La Manouba-Hippodrome de Ksar Said The alluvial plain of the Oued Meliane Conclusion Appendix 1: The PSInSAR technique Orbit geometry, LOS and vertical and horizontal movements PS velocity Time series of movements Coherence Page 2 of 92

3 Radar coordinates and geocoded PS Atmospheric disturbance on radar images Systematic errors and low frequency components Appendix 2: Horizontal and vertical displacement component Appendix 3: Abbreviations and Acronyms Appendix 4: A Bibliography of References on PSInSAR TM Page 3 of 92

4 1 Executive summary The Climate Change Adaptation & Coastal Cities of North Africa Project (Adaptation au Changement Climatique en Moyen Orient et l Afrique du Nord - ACC MENA) is implemented in the framework of ESA and World Bank (WB) Cooperation funded by ESA. ESA supports the World Bank activities with Earth Observations based services to promote EO technology and develop the market niche. LuxSpace is facilitating the implementation of selected EO services by providing assistance to ESA and WB to draft project specifications, follow-up the EO service implementation, organizing user utility assessment etc LuxSpace Sarl, in the framework of the ACC MENA project, commissioned Tele-Rilevamento Europa TRE s.r.l (TRE) to perform SqueeSAR TM analysis by exploiting the long term archives of ESA radar sensors, in order to retrieve the ground surface motion of the area of Tunis (Tunisia, TN) (Figure 1). TRE has the exclusive licence to use PSInSAR and SqueeSAR TM. These techniques provide high precision measurements of ground deformations by processing satellite radar data acquired over an area of interest, during a specific time spam. The analysis singles out points of measurement on ground that are good point wise radar target (referred to Permanent Scatterers-PS, which are preexisting, as rock outcrops, buildings, structures ecc.) and spatially distribuited scatterers (DS) (for detail see paragraph 3.2 and Appendix 1). In the context of the ACC MENA Project, TRE contributed to this study by generating land deformation velocity fields over the whole city of Tunis (Figure 1): the study area is 1600 km 2 around the city. The study analysed potential movements between and , the periods for which respectively ERS and ENVISAT satellite data were available over this area (see also paragraph 4.1.1). Over the area of Tunis, two different ERS datasets are available, acquired in the same time interval but in different acquisition geometries (ascending and descending). In the context of this project, since the tight time schedule has made impossible to perform a validation against ground truth it was proposed to perform a cross-comparison of the two different ERS datasets available, in order to validate the measurements obtained with this analysis. The SqueeSAR analysis of the Tunis area led to the identification of 781,114 measurement points, with a density of 700 PS/km 2, considering all the ERS and ENVISAT data in the area of interest not covered by sea (1100 km 2 ). The distribution of the points within the study area is not-homogeneous: the density is excellent in correspondence with the city of Tunis and decreases towards the southern and the northern sectors, in correspondence with the decrease in urban infrastructures. It should be noted that PSInSAR and SqueeSAR are differential techniques: they measure movement relative to a reference point that is assumed to be stationary. If, instead, movement does occur at the measurement point, it is important to have the movement history of the measurement point from independent source (e.g. GPS) that will allow the movement signature to be removed and also provide the added benefit of allowing absolute movement rates to be measured. The reference points chosen for each database are located in the southern portion of the Tunis city (see paragraph 6). As detailed in the paragraph 3.2.3, the quality and precision of velocity measurements depends on several factors. Among the principal are the number of images processed, their temporal distribution and the distance from the reference point. SqueeSAR TM results are delivered with errorbars associated to each measurement point, defined by means of the standard deviation value of the Page 4 of 92

5 estimated velocities In Tunis analysis, the precision associated to velocities retrieved from all the processed dataset is always higher than ±2mm/yr (1σ). The SqueeSAR results for each dataset have provided very interesting deformation patterns, even if the low number and irregular temporal distribution of images available for this project, especially from ERS ascending dataset ( ), have still influenced the measurement points density and the overall precision of the displacement measurements. The SqueeSAR results analysis have suggest the follow considerations: 1. The quaternary recent alluvial-mouth and sabkha deposits are affected by strong subsidence. In particular subsidence phenomena involve (Figure 2): the alluvial plain of the Oued Medjerda, and the mouth area (Char el Melh Lake), in the North of Tunis (indicative subsidence rate around 10 mm/year). the southern and western sector of the Lac de Tunis and the north-eastern part of the Sebkhet el Sijoumi (indicative subsidence rate around 28 mm/years for the La de Tunis and around 5 mm/years for the Sebkhet el Sijoumi); the area of La Manouba-Hippodrome de Ksar Said, in the western part of Tunis (indicative subsidence rate around 5 mm/year); the alluvial plain of the Oued Meliane in the South of Tunis (indicative subsidence rate around 20 mm/year). These movements in the first analysis may be attributed either to natural subsidence, due to deposits compaction and/or solution, or to man-made subsidence, induced by water pumping and/or urbanization of alluvial plains. The subsidence phenomena affecting the alluvial plain of Oued Meliane River presents also a clear seasonality. 2. Despite the low number of images processed for ERS ascending (only 20 images in comparison with the 41 images of ERS descending dataset) the comparison permitted to confirm the consistency of the two elaborations, providing also information about vertical and horizontal displacement components. 3. Differential movements affect the area north of Sebket el Sijoumi, in the western part of Tunis. These movements may be in the first analysis related to the structural setting and consequently to the thickness of recent compressible deposits. In particular: the area north of Sebket el Sijoumi is affected by differential surface displacement, across an E-W lineation, in correspondence to the contact between Pleistocene and recent alluvial deposits affected also by subsidence. The combination of ERS ascending and descending data also suggest that these movements are mainly vertical. For the northern sector the indicative uplift rate was estimated around 4.6 mm/year. For the southern sector the indicative subsidence rate was estimated around 5 mm/year. This deformation pattern breaks off in correspondence with the eastern border fault of the Ariana-Sejoumi graben, as indicated in the Egis BCEOM International / IAU-IDF / BRGM report: Évaluation des risques en situation actuelle et à l horizon 2030 pour la ville de Tunis. Some post-processing elaborations aimed at supporting the interpretation of these data are presented in the discussion section (paragraph 12). Page 5 of 92

6 4. The comparison between ERS ( ) and ENVISAT ( ) data suggest a general consistency of results: the area affected by deformation are the same and the rate are comparable. A significant recent decrease of the deformation rate is present only in the western part of the Lac of Tunis and can be attributed to an increase with time in the consolidation of the compressible deposits. Slightly deformation rate decrease in the ENVISAT period is present in the La Manouba-Hippodrome de Ksar Said area. Page 6 of 92

7 2 Résumé Le projet Adaptation au Changement Climatique en Moyen Orient et Afrique du Nord - ACC MENA a été conduit dans le cadre d une coopération entre ESA qui a financé le projet - et la Banque Mondiale. ESA (Agence Spatiale Européenne) soutient les activités de la Banque Mondiale avec service basé d observation en finançant les services d Observation de la Terre dans le but de promouvoir les technologies pour l Observation de la Terre et développer ce secteur du marché. LuxSpace facilite l implémentation des services pour l Observation de la Terre avec son assistance à ESA et à la Banque Mondiale en dressant les détails des projets, en coopérant dans l implémentation de services, en évaluant l utilité pour les utilisateurs, etc. LuxSpace Sarl, dans le cadre du projet Adaptation au Changement Climatique en Moyen Orient et l Afrique du Nord - ACC MENA, a chargé la société Tele-Rilevamento Europa TRE s.r.l. (TRE) de réaliser l analyse SqueeSAR en utilisant les données radar des satellites de l ESA, pour la mesure des déformations de surface sur la zone de Tunis, dans la Tunisie (Figure 1). TRE a licence exclusive pour l utilisation de la technique PSInSAR et SqueeSAR.Ces techniques fournissent des mesures très précises de déformation de surface en élaborant les données radar satellite acquises sur une zone d intérêt, dans une certain intervalle temporel. L analyse repère les cibles (Permanent Scatterers) qui maintiennent inchangés leurs caractéristiques électromagnétiques. Ces cibles sont normalement constituées par des bâtiments, des signaux routiers, des ponts, des roches, etc. et sont appelés Permanents Scatterers (PS). Dans le cadre du projet ACC MENA, TRE a contribué à cette étude en produisant le champ de vitesse de déformation du sol sur la ville de Tunis: la zone d étude est de ~1600 km 2. L étude analyse les déplacements éventuels entre les périodes et , pendant les quelles les données acquises par les satellites ERS et ENVISAT étaient disponibles sur cette zone (voir le paragraphe 4.1.1). Sur la zone de Tunis, deux dataset ERS étaient disponibles, acquises dans la même période temporelle mais dans deux géométries différentes (ascendante et descendante). Pour ce projet, en raison d un délai trop court pour permettre une validation avec des mesures in-situ, on a proposé une comparaison entre les données ERS acquises dans les deux différentes géométries, dans le but de valider les mesures obtenues par l analyse. L analyse SqueeSAR sur la zone de Tunis a permis de repérer 781,114 points de mesure, avec une densité de ~ 700 PS/km 2, en considérant toutes les données ERS et ENVISAT traitées sur la zone d intérêt non couverte par la mer (1100 km 2 ). La distribution des points dans la zone d étude n est pas homogène: la densité est excellente en correspondance de la ville de Tunis et diminue vers les secteurs au Nord et au Sud, où il y a moins d infrastructures urbaines. Il faut remarquer que PSInSAR et SqueeSAR sont des techniques différentielles : elles mesurent les déplacements relatifs à un point de référence considéré stable. Si, au contraire, le point de référence est affecté par mouvement, il est important de pouvoir disposer d une histoire du mouvement du point de mesure - relevée par une source indépendante (i.e. GPS) qui permet de calibrer les résultats de manière à mesurer les valeurs absolus du déplacement. Les points de référence pour chaque dataset sont localisés dans la partie sud de la ville de Tunis (voir le paragraphe 6). Comme on a mis en évidence dans le paragraphe 3.2.3, la qualité et la précision des mesures de vitesse dépends de plusieurs facteurs. Entre les principaux, il y a le nombre des images processés, Page 7 of 92

8 leur distribution temporelle et la distance par le point de référence. Les résultats SqueeSAR sont livrés avec les error bar associé à chaque point de mesure, définis par les valeurs de déviation standard des vitesses estimées. Dans l analyse sur Tunis, la précision associée à la vitesse calculé sur la base de l élaboration de tous les dataset est toujours supérieur à ± 2 mm/année (1σ). Les résultats de l analyse SqueeSAR pour chaque dataset ont fournis des schémas de déformation très intéressants, même si le nombre limité et la distribution irrégulière dans le temps des images disponibles sur la zone de Tunis - surtout pour ce qui concerne le dataset ERS ascendante ( ), ont affecté la densité des PS/DS et la précision générale des mesures de déplacement. L analyse SqueeSAR a fourni les indications suivantes : 1. Les récents bouches alluviales quaternaires et les dépôts de type sabkha sont affectés par un phénomène de subsidence. Plus spécifiquement, elles sont (Figure 2): La plaine alluviale de la Oued Medjerda et la zone des bouches (Ghar el Melh Lake), dans le nord de Tunis (taux indicatif de subsidence de 10 mm/année); Le secteur sud et ouest du Lac de Tunis et la partie nord-est de Sebkhet el Sijoumi (taux indicatif de subsidence de 28 mm/année pour le Lac de Tunis et d environ 5 mm/année pour la zone de Sebkhet el Sijoumi); La zone de La Manouba-Hippodrome de Ksar Said, dans la partie ouest de Tunis (taux indicatif de subsidence d environ 5 mm/année); La plaine alluviale de la Oued Meliane dans le sud de Tunis (taux indicatif de subsidence d environ 20 mm/année). Ces mouvements, en première analyse, peuvent être attribués soit à un phénomène de subsidence naturelle, du au tassement et/ou à la fusion des dépôts, soit à une subsidence anthropique, induite par le pompage d eau et/ou à l urbanisation des plaines alluviales. Les phénomènes de subsidence qui affectent la plaine alluviale de la Oued Meliane aussi montrent une évidente saisonnalité. 2. Malgré le nombre limité d images ERS ascendantes disponibles (20 images, par rapport aux 41 images du dataset ERS descendant), la comparaison a permis d établir une consistance entre les deux élaborations, en fournissant des informations concernantes les composantes verticaux et horizontaux du mouvement. 3. Les données SqueeSAR aussi montrent des mouvements différentiels qui affectent la zone au nord de Sebket et Sijoumi, dans la partie ouest de Tunis. Ces mouvements peuvent être, en première analyse, mises en corrélation avec l aménagement structural et par conséquence à l épaisseur des récents dépôts compressibles. En particulier: La zone nord de Sebket el Sijoumi est affectée par des déplacements de surface différentiels, le long de la direction E-W, en correspondance du contact entre le pléistocène et les récents dépôts alluviaux, aussi affectés par des phénomènes de subsidence. La combinaison des données ERS ascendantes et descendantes fournissent indication que ces mouvements soient surtout verticaux. Pour le secteur au Nord le taux indicatif de soulèvement estimé est d environ 4.6 mm/ année. Pour le secteur au Sud le taux de subsidence estimé est d environ 5 mm/année. Page 8 of 92

9 Ce schéma de déformation s arrête en correspondance du bord de la faille du fossé tectonique Ariana-Sejoumi, comme indiqué par le rapport rédigé par Egis BCEOM International, IAU-IDF et BRGM : «Évaluation des risques en situation actuelle et à l horizon 2030 pour la ville de Tunis». Dans la section relative à la discussion (paragraphe 12) on ira montrer les résultats des élaborations qui supportent ces arguments. 4. La comparaison entre les données ERS ( ) et les données ENVISAT ( ) une consistance entre les deux élaborations donne indication d une consistance entre les résultats : les zones affectées par déformation de surface sont les mêmes et les taux de mouvement sont comparables. Il n y a qu une récente diminution significative du taux de déformation exclusivement dans la zone occidentale du Lac de Tunis qui peut être attribué à une augmentation dans le temps de la consolidation des dépôts compressibles. On note une légère diminution du taux de déformation sur la période ENVISAT dans la zone de La Manouba-Hippodrome de Ksar Said. Page 9 of 92

10 Figure 1: The Tunis AOI (red polygon) overlaid on a satellite image from Microsoft Virtual Earth, (c)2009 Microsoft Corporation. The area is 1600 km 2, 1100 km 2 of which covered by land. Page 10 of 92

11 Figure 2: Velocity field maps obtained from ERS descending and ENVISAT descending datasets with the indication (blue circles) of the main areas affected by deformation: 1) alluvial plain of the Oued Medjerda and mouth area (Char el Melh Lake); 2) Lac de Tunis area; 3) La Manouba-Hippodrome de Ksar Said area; 4) alluvial plain of the Oued Meliane. Page 11 of 92

12 3 Introduction 3.1 ACC MENA Project A study on Climate change adaptation and natural disaster preparedness in the coastal cities of North Africa has been launched in 2008, implemented in the framework of ESA and World Bank Cooperation funded by ESA. The ACC MENA project (Adaptation au Changement Climatique en Moyen Orient et l Afrique du Nord) focuses on three North African coastal cities: Alexandria in Egypt, Tunis in Tunisia, Casablanca in Morocco and on the Bouregreg Valley between Rabat and Salé in Morocco, where a major urban development project is being planned and implemented. The choice has fallen on those three cities following an interaction with national authorities, which have expressed a great interest in the study. The four major objectives of this study are: 1. To evaluate, for the year 2030, the vulnerabilities of the four urban areas confronted with climate change and natural disasters (Phase 1 of the project); 2. To develop action plans to improve the cities adaptation to climate change and their natural disaster preparation (Phase 2 of the project); 3. To communicate the results of the study and get the stakeholders involved in the decisionmaking process. The five areas of work are: - Sea level rise, and coastal area erosion and submersion and the cumulative impacts of subsidence phenomena; - Urban flooding; - Water resource availability; - Increase in room temperature; - Earthquakes and tsunamis. ESA supports the World Bank activities with Earth Observations based services to promote EO technology and develop the market niche. LuxSpace is facilitating the implementation of selected EO services by providing assistance to ESA and WB to draft project specifications, follow-up the Eo service implementation, organizing user utility assessment etc Luxspace,, in the framework of the ACC MENA project, is supporting ESA and the World Bank in defining the project specifications, organizing a transparent tender procedure, following-up the project implementation and quality control and assessment. LuxSpace Sarl commissioned Tele-Rilevamento Europa TRE s.r.l (TRE) to perform SqueeSAR TM analysis by exploiting the long term archives of ESA radar sensors, in order to retrieve the ground deformations in the area of Tunis (Tunisia, TN). Page 12 of 92

13 TRE has the exclusive licence to use PSInSAR and SqueeSAR TM. These techniques provide high precision measurements of ground deformations by processing satellite radar data acquired over an area of interest, during a specific time spam. The analysis singles out points of measurement on ground that are good point wise radar target (referred to Permanent Scatterers-PS, which are preexisting, as rock outcrops, buildings, structures ecc.) and spatially distribuited scatterers (DS). In the following, where not specified, the terms PSInSAR TM and PS will be used to refer both to SqueeSAR TM and DS respectively. 3.2 PSInSAR TM technique 1 PSInSAR is the first Persistent Scatterer Interferometry technology, developed at the Politecnico di Milano, Italy (for detail see also Appendix 1 and 2). With respect to Differential InSAR (DInSAR) analysis, which measures change from a single interferogram developed from two radar images, the Persistent Scatterer Interferometry has a multi-interferogram approach that draws on the changes occurring between a series of radar images. DInSAR is limited by atmospheric influences and by a lack of a continuous history of movement. Persistent Scatterer Interferometry, of which PSInSAR is the most well-known type, overcomes the atmospheric constraints and is specifically directed at determining movement histories over periods of several years. PSInSAR searches the image set for targets that consistently reflect radar signals throughout the entire dataset of images. These targets usually consist of rocky outcrops, buildings, street lights, road signs, among a wide variety of possibilities and are referred to as Permanent Scatterers (PS) PSInSAR TM methodology The identification of measurement points (Permanent Scatterers-PS) comprises a sequence of steps (for details see also Appendix 1). First, all radar data archives are screened to determine the most suitable source of raw data for the particular area of interest and to select all the high quality images within the chosen data set. The signal echo from a single point target contains many returning radar pulses and, thereby, appears defocused in a synthetic aperture radar raw image. Thus, the first processing step, referred to as focusing or compressing, is to focus all the received energy from a target in one pixel.the focused data are then co-registered and analysed for their suitability for the application of interferometry. In particular, the normal baseline and temporal distribution of available images emerge from this analysis and provide an indicator of the suitability of the imagery. There then follows a number of statistically-based analyses on the phase and amplitude characteristics of the backscattered signal of the pixels that might behave as Permanent Scatterers. If a concentrated number of signals reflect off a particular feature within a pixel and backscatter to the satellite, the feature is referred to as a scatterer. When the same scatterer appears in all, or most, of a data set of SAR images, then the scatterer is deemed to be permanent. Once these steps have been completed, it is possible to identify a subset of pixels, which are referred to as Permanent Scatterer Candidates (PSC), and these are used in estimating the impact upon signal phase that arises from atmospheric effects, as well as from possible orbit errors. 1 In the following, where not specified, the terms PSInSAR TM and PS will be used to mean also SqueeSAR TM and DS respectively. Page 13 of 92

14 Once the signal phase has been corrected for these effects, any remaining changes in signal phase directly reflect ground movement SqueeSAR TM, the next generation PSInSAR TM SqueeSAR TM is an advanced second generation PSInSAR analysis, exploiting both point wise (permanent scatterers) and spatially distributed scatterers (DS). PSInSAR searches for objects on the ground, known as permanent scatterers (PS), that are excellent reflectors of radar microwaves. These objects, which correspond to buildings, walls, lampposts, transmission towers, crash barriers, rock outcrops, etc, produce a strong signal-to-noise ratio. However, TRE has noticed that many other signals are present in the processed data. These do not produce the same high signal-to-noise ratios of PS but are nonetheless distinguishable from the background noise. Upon further investigation it was found that the signals are reflected from extensive homogeneous areas where the back-scattered energy is less strong, but statistically consistent. These areas have been called distributed scatterers (DS) and correspond to rangeland, pastures, bare earth, scree, debris fields, arid environments, etc (Figure 3). The SqueeSAR algorithm was developed to process the signals reflected from these areas. It is capable of extracting ground displacement information with the same accuracy as for PS and, furthermore, as the SqueeSAR incorporates the PSInSAR algorithm, no information is lost. Another benefit introduced by SqueeSAR is an improvement in the quality of the displacement time series. The homogeneous areas that produce DS are usually quite large and comprise several pixels. A single time series is assigned for each DS and it is estimated by averaging the time series of all pixels that make up the same DS, which effectively reduces noise in the data series. Figure 3. Illustration of the concept of permanent (PS) and distributed scatterers (DS) in SqueeSAR TM algorithm. Page 14 of 92

15 3.2.3 Precision and accuracy Error bars of PS measurements are calculated as the deformation pattern is developed. However, precision of the displacement calculations is an important element in validating PS data. The most important factors impacting on data quality are: Number of images and their temporal distribution Spatial density of the PS (the lower the density, the higher the errorbar). Quality of the radar targets (signal-to-noise ratio levels). Climatic conditions at the time of the acquisitions. Distance between the measurement point and the reference PS. Table 1 is a chart showing precision values obtained from many analyses of data from the ERS, Envisat, and Radarsat-1 satellites. Displacement (LOS) Average displacement rate Single measurement Precision (1s) < 1mm/year 5 mm Position E-W N -S Height Precision (1s) 6 m 2 m 1.5 m Table 1: Typical values of precision for a point less than 1 km from the reference point for a dataset of at least 30 scenes spanning a 2-year period Strenghts and weaknesses of PSInSAR technology PSInSAR TM is a monitoring tool unique for accuracy, spatial density of measurement points and economic competitiveness. It is not a tool to replace all pre-existing methodologies for measuring surface movement. There are elements of the technology that make it unique, even if there are situations where PSInSAR simply won t work or will produce poor results. Here following, some of those strengths and weaknesses. Advantages of PSInSAR : PSInSAR technique allows to carry out multiscale analysis. Single satellite images can cover area of interest ranging from 1 km 2 to more than km 2 PS measurements are obtained by active sensors that are capable of operating in all weather and lighting conditions PSInSAR data complements other sources of information such as GPS, tiltmetres, optical leveling, etc. The possibility to process data archives of ESA historical data allows to carry out PSInSAR analysis as far back as 1992, enabling an historical review of movements Page 15 of 92

16 SAR data acquisitions can be scheduled to guarantee regular updates of ground movement behavior PS motion can be measured over areas of thousands of square kilometers at a fraction of the cost of conventional surveys - while achieving much higher spatial densities of measurement data Limitation of SqueeSAR technique: Measurements of displacements, along the sensor-target direction, are limited to a fraction of the wavelength. This means that the technique is not suited to movements > 300mm/year Vegetation prevent to identify radar targets and consequently to estimate ground deformation patterns over heavy vegetated areas PSInSAR is a differential technique: it measures movement relative to a reference point that is assumed to be stationary. The quality and precision of velocity measurements decrease with the increase of the distance from the reference point PSInSAR technique cannot measure deformation along the North-South direction, since these movements are parallel to the satellite Page 16 of 92

17 4 Approach In the following paragraphs the main elaboration steps are reported. Details about the PSInSAR TM and SqueeSAR TM techniques are reported in the previous chapter and in the Appendix 1 and SAR data selection Data Source and Archive The available historic datasets covering the Tunis area consist on : a 20 images ascending archive acquired by the European Space Agency (ESA) ERS satellite. a 47 images descending archive acquired by the European Space Agency (ESA) ERS satellite. a 27 images descending archive acquired by the European Space Agency (ESA) ENVISAT satellite. a 4 images ascending archive acquired by the Japan Aerospace Exploration Agency (JAXA.) ALOS satellite During ascending orbit the satellite platform travels from South to North, while during the descending orbits the platform travels from North to South. The tight time schedule of the project prevented to access any ground truth possibly available, to be used to validate the SAR results obtained. It was then proposed to process the two ERS datasets in order to cross compare the measurements obtained by the SqueeSAR TM analysis. The ERS satellites acquire radar images at a fixed angle of approximately 23 from the vertical. The acquisition mode for ENVISAT is Image mode, in position Swath 2, which corresponds to the same incidence angle of ERS, approximately 23 from the vertical. In this acquisition mode, each ERS and ENVISAT image covers a nominal area of about 100x100 Km and has a nominal ground resolution of 20 x 5 meters. Each image is identified by the date of acquisition, a Track number - corresponding to the satellite orbit, and a Frame number, which specifies the location of the 100x100 Km tile within the Track. Some of the ERS images were discarded because of an high baseline and/or Doppler Centroid, two key parameters in any SAR interferometric application. In Table 2 the number of processed images with respect to those available is listed. The processed images for each dataset are listed in Table 3, Table 4 and Table 5, with the Master Image (see next section) highlighted in red. Unfortunately, it was not possible to generate any interferogram using ALOS data, since the received data were corrupted. Please refer to section 5 for further details Page 17 of 92

18 Figure 4: Location of the ERS and ENVISAT Tracks. Data Period Sensor Geometry Trak N imgs N imgs provider available processed ESA Jan 1993 Aug ERS1/2 Ascending ESA June 1992 Dec ERS1/2 Descending ESA Sept Mar ENVISAT Descending JAXA Oct Apr ALOS/PALSAR Ascending Table 2: Satellite images available and processed to perform the SqueeSAR analysis over the area of interest. Page 18 of 92

19 ERS ascending TRACK 29 Id Date Satellite Bn^ Bt ^^ [days] Table 3: List of ERS ascending processed images, TRACK 29. ^ Fraction of the critical baseline; (M) Master Image; ^^ The Bt is the temporal baseline: the time span, in days, between the image acquisition and the Master Image. ERS descending TRACK 122 Id Date Satellite Bn^ Bt ^^ [days] Page 19 of 92

20 Table 4: List of ERS descending processed images, TRACK 122. ^ Fraction of the critical baseline; (M) Master Image; ^^ The Bt is the temporal baseline: the time span, in days, between the image acquisition and the Master Image. Page 20 of 92

21 ENVISAT TRACK 122 Id Date Satellite Bn^ Bt^^ [days] Table 5: List of ENVISAT processed images, TRACK 122. ^ Fraction of the critical baseline; (M) Master Image; ^^ The Bt is the temporal baseline: the time span, in days, between the image acquisition and the Master Image. Page 21 of 92

22 4.1.2 Master Image Selection PSInSAR and SqueeSAR processing requires that one image in the dataset becomes both a geometric and temporal reference to which all the other images are related. This image is referred to as the master image. The master image should be chosen such that: it minimises the spread of normal baseline values for the slave images; it minimises the temporal baseline values between the master and each slave image and; it minimises the effects of signal noise arising from changes in vegetation cover and/or small changes in the look angle of the satellite from one scene to another Limitations of the Available Archive In any SAR dataset, there are factors that can limit or impede the application of interferometry. They are: extensive and dense vegetation; water and snow coverage; low number and irregular temporal distribution of images; long intervals between successive images; geometric deformation. The characteristics of the site ensured that the number and distribution of the identified PS is high on the Tunis city. In fact towns and cities are areas that usually produce a high density of PS. The nonurbanized areas cover one third of the interest area and is limited to the north and with a lesser extent to the south. SqueeSAR TM ) approach ensures a significantly increased number of ground measurement points identified over the non-urbanized areas. The principal limiting factor for SqueeSAR TM analysis on Tunis was the low number of images available and their irregular temporal distribution, especially for ERS ascending data. This have reduced the density of measurement points and had an impact on the overall precision of the results as well as on the estimation of complex deformation dynamics (for detail see paragraph 3.2.3). Due to the limited number of images, the time series of ERS ascending data resulted not significant to describe the temporal evolution of motion, however, despite the weakness of the available datasets the average velocity map have showed very useful spatial deformation patterns (see paragraph 6). 4.2 Signal Phase and Amplitude Analysis General Each pixel of a SAR image contains information on the amplitude of signals that are backscattered toward the satellite, as well as on the signal phase. The amplitude is a measure of the amount of the radar pulse energy reflected at the point of contact and received at the radar antenna, while the phase is related to the length of the path of the electromagnetic wave, from the platform to the ground and back again. Page 22 of 92

23 Analyses of both amplitude and phase of the SAR image provide an indication of the stability of each pixel, over time, whereby it is possible to identify those pixels that are most likely to behave as Permanent Scatterers. Statistical methods are used extensively in this process. Among the different statistical parameters that can be computed two are of particular interest: the Phase Stability Index (PSI), obtained from the phases of the images within the dataset, and the Multi Image Reflectivity (MIR) map, derived from the amplitude values of the available acquisitions. The Phase Stability Index (PSI) indicates the variability of the signal phase for every pixel in every image of the archive. It ranges from 0 to 1; a pixel having a high PSI value is most likely to be a PS. The MIR is described in the following section. After the statistical analyses of the SAR images have been completed, a set of differential interferograms is generated. This entails subtracting the phase of each slave image from the phase of the master image. In doing so, the difference in signal path length between the two images is calculated. This difference is related to possible ground motion. The differential interferograms represent the starting point for applying the PSInSAR approach Radar Amplitude and Multi-Image Reflectivity The amplitude of a pixel within a SAR image is the aggregate of the backscattered energy toward the satellite from within the pixel s equivalent land area. This equivalent land area is referred to as the radar resolution, and in the case of ERS and ENVISAT Image mode, it measures about 20 x 5 meters in the East-West and North-South directions, respectively. If the area contained within a pixel, also referred to as a target, has experienced significant change in its surface characteristics it will exhibit variation in its reflectivity (electromagnetic response) between two acquisitions. In such circumstances, the possibility of detecting movement by means of SAR interferometry is compromised. The signal phase difference between the two images now contains not only the contribution due to displacement, but also that due to the change in the reflectivity of the target. This prevents, in the worst case, the obtaining of any useful information on ground movement. Accordingly, it is necessary to look into the amplitude values of all the images in the dataset, in order to understand exactly what was seen by the satellite at the time of each acquisition. Another artefact linked to amplitude is known as speckle. Speckle is random noise that appears as a grainy "salt and pepper" texture in an amplitude image. This is caused by random interference from the multiple scattering returns that occur within each resolution cell. Speckle has an adverse impact on the quality and usefulness of SAR images. However, the higher the number of images taken of the same area at different times or from slightly different look angles, the easier it is to reduce speckle. This increases the quality and level of details of the amplitude image enabling it to be used as a background layer for observing the presence of PS results. The Multi Image Reflectivity (MIR) map is the means by which speckle reduction is accomplished. Averaging a number of images tends to negate the random amplitude variability, leaving the uniform amplitude level unchanged. The MIR map derived from the acquired images is shown in Figure 5. In all images, the North direction corresponds to the top of the image. It should be emphasized that the information in the MIR map is the reflectivity of each pixel, i.e. the ability to backscatter the incident wave toward the satellite. Flat surfaces (roads, highway, rivers, lakes) act like a mirror, meaning that if their orientation is not exactly perpendicular to the incident wave (and this is the most common situation) negligible energy is reflected back to the sensor; they Page 23 of 92

24 appear dark in the image. On the other hand, because of their irregular physical shape, metal structures or buildings reflect a significant portion of the incident signal back to the radar, resulting in very bright pixels in the MIR map. Figure 5: The Multi-Image Reflectivity (MIR) image of the Tunis area. The yellow polygon defines the approximate location of the area of interest Interferograms With signal phase and reflectivity analysis completed, the differential interferograms can now be generated. They are obtained by subtracting the phase values of each slave image from those of the master image. In any SAR image, there are embedded topographic distortions that arise during image acquisition. These are removed using a reference Digital Elevation Model (DEM) from SRTM (NASA Shuttle Radar Topography Mission version 2, sampled at 3 arc-seconds), leaving ground movement and the signal phase distortions arising from atmospheric effects as the only embedded variables. Page 24 of 92

25 4.3 Estimation of the atmospheric effects When a radar signal enters and exits a moisture-bearing layer in the atmosphere, its wavelength can be affected, introducing potential errors into the signal path length. So, once all the differential interferograms have been created, the next step is to estimate and remove this atmospheric impact. For this purpose, a sub-set of pixels is chosen from among those that have high PSI values. They usually correspond to buildings, lampposts, antennas, small structures and exposed rocks. The pixels in the sub-set are referred to as PS Candidates (PSC). PSC density is, of course, higher in villages and cities rather than in forests and vegetated areas. However, as the present project demonstrates, it is often possible to obtain good PSC density in rural an mountain areas. For each image, the atmospheric impacts are estimated at each PSC location. The process is statistically based and benefits in accuracy by the greater the number of available images for the analysis. By comparing the atmospheric contribution on neighbouring pixels that would be experiencing the same atmospheric conditions, the atmospheric contribution can be reconstructed over the whole image. Page 25 of 92

26 5 ALOS data processing As mentioned in section a 4-images ALOS dataset was found browsing the archive. The number of data was too low to perform SqueeSAR analysis, so in the proposal it was foreseen the processing of these scenes limited to the generation of differential interferograms to complement the deformation map obtained from the other datasets, taking advantage of ALOS-PALSAR longer wavelength (L-Band). 2 out of 4 images received were corrupted, and TRE requested their reprocessing to the ESA helpdesk. Unfortunately, these data were found to be unusable due to calibration problems. In the following the received from the ESA help desk is reported: [ With reference to the communication below, we have been informed by the QC team that both the delivered products contain Calibration Mode data which would explain the problems you have encountered with the processing this data. Products which only contain portions of Calibration Mode data, may still contain data which can be of use to a user. However within both of these products, 100% of the data is of Calibration Mode. We will consequently accept the rejection of these two products which also will be removed from the catalogue. At this point we can only advise to order alternative scenes on a different orbit. The rejected products will obviously not be deducted from the allocated quota. We apologize for the inconvenience this may cause.] No other images acquired in Image Mode were available over the city of Tunis, so it was not possible to generate any interferogram. The images received that was not corrupted were acquired on the same day over 2 different portion of the AOI, and were focused anyway as a single image just to give an idea on how the area was seen by ALOS. The focused image is shown in Figure 6. In agreement with the client, the budget allocated for ALOS data processing was redirected to purchase a high resolution background layer acquired by the optical satellite GeoEye-1, covering an area of about 350 sqkm. This system provides images at nadir with 0.5-meter panchromatic (black & white) and a geolocation accuracy in the order of 5 m. The purchased images were acquired on 3 different dates, namely on 30 th May 2009, 4 th June 2009 and 2 nd July 2009 Page 26 of 92

27 Figure 6: Focused ALOS images acquired over the Tunis area. This image was obtained by merging 2 different frames acquired on the same day Page 27 of 92

28 6 Results SqueeSAR analysis of the Tunis area led to the identification of total measurement points, with a point density of 700 PS/ km 2, considering all the three datasets in the area of interest notcovered by sea (1100 km 2 ). The distribution of PS/DS within the study area is not-homogeneous: the point density is excellent in correspondence with the city of Tunis and decreases towards the southern and northern section of the area, corresponding to the decrease in urban infrastructure. In particular: SqueeSAR analysis of ERS descending data identified measurement points; with a density of 320 PS/km 2. For each point, the average rate of deformation and time history of movements were estimated. SqueeSAR analysis of ERS ascending data identified measurement points, with a density of 70 PS/km 2. For each point, the average rate of deformation and time history of movements were estimated. SqueeSAR analysis of ENVISAT descending data have identified measurement points, with a density of 310 PS/km 2. For each point, the average rate of deformation and time history of movements were estimated. The following paragraphs show the results obtained for each dataset: velocity map, point elevation map and other properties. Regarding the velocity measurements it s important to remind that SqueeSAR is a differential technique. The velocities of all PS/DS are relative to the reference point, which is shown in the velocity field figure and is assumed to be motionless. The reference points was selected considering the geological information on deformation phenomena affecting the area, as reported in the Egis BCEOM International / IAU-IDF / BRGM report ( Évaluation des risques en situation actuelle et à l horizon 2030 pour la ville de Tunis ) and such that: The point has a low phase noise in all the available data; The data shows a homogeneous behaviour of the neighbours measurement points. The assumption of stability of the reference points should be confirmed and, if it is found to be unstable, the data can be reprocessed using a different reference point. The coloured markers, superimposed on the satellite image from Microsoft Virtual Earth, (c)2009 Microsoft Corporation, in each velocity field maps correspond to the measurement points identified in the AOI: positive values of the measured displacements indicate movement towards the satellite along its line of sight, while negative values indicate movement away from the sensor. For each point precise differential measurements have been carried out and displacement time series have been produced (see also Appendix 1: The PSInSAR ). Knowing the elevation of a PS helps to identify whether the PS/DS is on the top of a building or at ground level and can help to clarify topographic anomalies where ground movement is significant. Page 28 of 92

29 All the following figures have as background the satellite image from Microsoft Virtual Earth, (c)2009 Microsoft Corporation. 6.1 ERS descending data Figure 7 shows the estimated average velocity map for the area of interest from ERS descending data. Figure 8 shows the distribution of measurement points elevation values referenced to the WGS84 Ellipsoid. Table 6, below, provides a summary of other properties of the study and its results. Georeferencing Results have been aligned on Microsoft Visual Earth Projection system used / datum (ESRI) WGS84_UTM_32N Assumption on reference point motion Motionless Area of interest ~ 1100 km 2 Number of measurement points identified points Number of Time Series estimated (for all the measurement points) Table 6: Summary of the output from the ERS descending dataset. Please note that all results are generated in GCS WGS84 and then re-projected in the coordinates system WGS 1984 UTM zone 32N using the algorithms embedded in ESRI ArcMAP. The data produced in this project can be loaded and interrogated in ESRI ArcMAP using the software extension provided by TRE (TreCustomerToolbarSetup). Page 29 of 92

30 Figure 7: ERS descending velocity field map. Page 30 of 92

31 Figure 8: ERS descending elevation values. Page 31 of 92

32 6.2 ERS ascending data Figure 9 shows the estimated average velocity map for the area of interest from ERS ascending data. Figure 10 shows the distribution of PS elevation values referenced to the WGS84 Ellipsoid. Table 7, below, provides a summary of other properties of the study and its results. Georeferencing Results have been aligned Microsoft Visual Earth Projection system used / datum (ESRI) WGS84_UTM_32N Assumption on reference point motion Motionless Area of interest ~ 1100 km 2 Number of measurement points identified points Number of Time Series estimated (for all the measurement points) Table 7: Summary of the output from the ERS ascending dataset. Please note that all results are generated in GCS WGS84 and then re-projected in the coordinates system WGS 1984 UTM zone 32N using the algorithms embedded in ESRI ArcMAP. The data produced in this project can be loaded and interrogated in ESRI ArcMAP using the software extension provided by TRE (TreCustomerToolbarSetup). Page 32 of 92

33 Figure 9 ERS ascending velocity field map. Page 33 of 92

34 Figure 10 ERS ascending elevation values. Page 34 of 92

35 6.3 ENVISAT descending data Figure 11 shows the estimated average velocity map for the area of interest from ENVISAT descending data. Figure 12 shows the distribution of PS elevation values referenced to the WGS84 Ellipsoid. Table 8 below, provides a summary of other properties of the study and its results. Georeferencing Results have been aligned on Microsoft Visual Earth Projection system used / datum (ESRI) WGS84_UTM_32N Assumption on reference point motion Motionless Area of interest ~ 1100 km 2 Number of measurement points identified points Number of Time Series estimated (for all the measurement points) Table 8: Summary of the output from the ENVISAT descending dataset. Please note that all results are generated in GCS WGS84 and then re-projected in the coordinates system WGS 1984 UTM zone 32N using the algorithms embedded in ESRI ArcMAP. The data produced in this project can be loaded and interrogated in ESRI ArcMAP using the software extension provided by TRE (TreCustomerToolbarSetup). Page 35 of 92

36 Figure 11: ENVISAT descending velocity field map. Page 36 of 92

37 Figure 12: ENVISAT descending elevation values Page 37 of 92

38 7 Delivery of Data Data are delivered in shapefile format. The list of files delivered is shown in Table 9. The associated database file (.dbf) contains all the information described in Table 11. File name TUNISI_ERS_D_T122_LUXSPACE_UTM32N-TSR.shp TUNISI_ERS_A_T29_LUXSPACE_UTM32N-TSR.shp TUNISI_ENVS2_V_D_T122_LUXSPACE_UTM32N- TSR.shp Description Shape file containing the PS code, height, height standard deviation, velocity, velocity standard deviation, coherence, effective area and time series of all the measurement points identified in the AOI. TUNISI_ERS_D_T122_LUXSPACE_UTM32N-REF.shp TUNISI_ERS_A_T29_LUXSPACE_UTM32N-REF.shp Reference points. TUNISI_ENVS2_V_D_T122_LUXSPACE_UTM32N- REF.shp 7.1 Other deliverables Table 9: List of files delivered. The list of additional file delivered is shown in Table 10 File name Description Envisat_Geocoded_MIR.png ERS_Ascending_Geocoded_MIR.png Multi-Image Reflectivity (MIR) map, geocoded (GeoPNG format) ERS_Descending_Geocoded_MIR.png TUNISI_ENVS2_V_D_T122_LUXSPACE-TSR TUNISI_ERS_A_T29_LUXSPACE-TSR TUNISI_ERS_D_T122_LUXSPACE-TSR dem_aster_aoi TUNISI_ENVS2_V_D_T122_LUXSPACE-TSR TUNISI_ERS_A_T29_LUXSPACE-TSR Raster image of interpolated average annual displacement rates (Grid format). ASTER DEM (Grid format) with a 30- meters horizontal posting Raster image of interpolated average annual displacement rates (Grid format). Page 38 of 92

39 TUNISI_ERS_D_T122_LUXSPACE-TSR TUNISI_ENVS2_V_D_T122_LUXSPACE-TSR.kmz TUNISI_ERS_A_T29_LUXSPACE-TSR.kmz TUNISI_ERS_D_T122_LUXSPACE-TSR.kmz High_Quality_Pictures_A3 Historical_Land_Motion_Monitoring_of_the_City_of_Tunis_ Main_Results.ppt Project_Brochure Historical_Land_Motion_Monitoring_of_the_City_of_Tunis_F inal_report.docx TUNISI_NE.tif TUNISI_NO.tif TUNISI_SE.tif TUNISI_SO.tif tunis_orthomosaic.ecw KMZ files to show the average displacement rates of PS and DS identified over the area of interest on GoogleEarth. Folder containing a set of high quality ready-to-print files (.TIF format, size A3) of the most important pictures related to SqueeSAR analysis. A PowerPoint presentation (.PPT file) summarizing the main results of the project. A ready-to-print file (.PDF and.doc) of the project brochure Project final report Geoeye high-resolution (0.5m resolution) satellite images, covering an area of 350 km 2 around Tunis (.TIF format). Detail about Geoeye imagery on Orthomosaic of the four geoeye images (.ecw format) Table 10: List of additional files delivered Page 39 of 92

40 8 The structure of the database Table 11, below, describes the information associated with each point of measurements within the database files (dbf). Field CODE HEIGHT H_STDEV VEL V_STDEV COHERENCE EFF_AREA Dyyyymmdd PS/DS identification code [m] Topographic Elevation [m] Height standard deviation Description [mm/yr] PS/DS movement rate. Positive values correspond to motion toward the satellite; negative values correspond to motion away from the satellite [mm/yr] Velocity standard deviation (paragraph 9) Index varying between 0 and 1, related to the PS phase noise and to the capability of the motion model adopted to cope with the actual PS behaviour. See Appendix 1 for further details [m 2 ] This parameter represents the effective extension of the area covered by a distributed scatterers. For a PS, its value is set to 0 Following the S_PHS_STD column are a series of fields that contain the displacement values of successive acquisitions relative to the first acquisition available. Displacement values are expressed in [mm] Table 11: Description of the fields contained in the shapefile. Figure 13 provides an example of the records contained in the shapefiles. Figure 13: Example of records contained in a shapefile with displacement time series. Page 40 of 92

41 9 Results precision assessment An overview of factors impacting on SqueeSAR(TM) precision, together with general precision values obtained from many analyses of data from the ERS, Envisat, and Radarsat-1 satellites, have been presented in paragraph SqueeSAR TM results are delivered with errorbars associated to each measurement point, defined by means of the standard deviation value of the estimated velocities (V_STDEV in the delivered files). The standard deviation (usually indicated as σ) is a measure of the dispersion, mainly due to the atmospheric noise, of an estimated velocity value. In Tunis analysis, the precision associated to velocities retrieved from all the processed dataset is always higher than ±2mm/yr (1σ). The resulting velocity standard deviation values for each dataset are shown in Figure 14, Figure 15 and Figure 16. A more detailed discussion of the estimated average velocities and the corresponding precision will be presented for some areas of particular interest in paragraph 12. Page 41 of 92

42 Figure 14: ERS descending velocity standard deviation field. Page 42 of 92

43 Figure 15: ERS ascending velocity standard deviation field. Page 43 of 92

44 Figure 16: ENVISAT descending velocity standard deviation field Page 44 of 92

45 10 Sensitivity versors All displacement measurements are carried out along the satellite Line of Sight (LOS). The versor properties relating to the viewing angles for the satellite dataset used in this project are shown in Table 12. Direction ERS descending ERS ascending ENVISAT descending North East Vertical Table 12: Components of the LOS versor for the datasets used in this study. Figure 17 illustrates the satellite viewing geometry. The corresponding angles, referenced to the nadir (vertical) are shown in Table 13. Angle Θ Angle δ Line Of Sight Figure 17: These images illustrate the satellite descending acquisition geometry. See Table 13 for the actual values of the angles. Page 45 of 92

46 Θ Angle δ Angle Line Of Sight Direction Figure 18: These images illustrate the satellite ascending acquisition geometry. See Table 13 for the actual values of the angles Sensor and orbit geometry δ θ ERS descending ERS ascending ENVISAT descending Table 13: Satellite viewing angles for the dataset used in this project. Page 46 of 92

47 11 Validation and additional elaborations As already mentioned, due to the tight project schedule it was not feasible to envisage a validation activities based on the use of possible ground truth available in the area. However, as an alternative approach it was proposed to process the two different ERS datasets, acquired in the same time interval over the area of Tunis, but in different acquisition geometries (ascending and descending), in order to cross compare the measurements obtained by the two independent SqueeSAR TM analysis. This comparison allowed to assess the consistency of the results and also to obtain additional information about horizontal and vertical displacement (see Appendix 2). As mentioned above and detailed in Appendix 1 and Appendix 2, radar satellites can only measure movement along their Line-of-Site (LOS), the line along which the sensor views the ground target. Due to the side-viewing geometry, this direction is not vertical but inclined so that what is actually measured by the sensor is the projection of a target s movement onto the LOS. If the movement direction is close to the angle of the LOS then the measured and actual movements will be similar. However, the LOS movement can often differ noticeably from the real value of motion, especially in cases where the ground movement is not purely vertical. However, if an area is viewed with both ascending and descending geometry, it is possible to combine the measured movement information to obtain an accurate estimate of the actual vertical movement and of the East-West component of the movement. For this project, the comparison of the ERS ascending and descending data confirms the consistency of the results (Figure 19). In fact, the two datasets show a similar pattern of ground deformation. Movements that are mainly vertical are expected to have similar value of deformation along the two LOS. On the contrary, in the areas where deformation is mainly horizontal the two value measured could be very different. The estimated actual vertical and East-West component of the movements, obtained combining the SqueeSAR(TM) ascending and descending ERS results, are shown in Figure 20 and Figure 21. Some quantitative comparisons between the results are presented In the next chapter, focusing on the areas affected by deformation. Page 47 of 92

48 Figure 19: The comparison between ERS ascending and descending data confirms the consistency of results. Page 48 of 92

49 Figure 20: Vertical component of movements by combining ERS ascending and descending data. Page 49 of 92

50 Figure 21: East-west horizontal component of movements by combining ERS ascending and descending data. Page 50 of 92

51 12 Discussion and final observation The resultant velocity maps of SqueeSAR TM analysis have shown an interesting deformation pattern, and the availability of two different ERS dataset has allowed to cross-check the measurements and to obtain additional information about the vertical and East-West component of movements. However, SqueeSAR results have been strongly influenced by a low number and irregular temporal distribution of images available for this project, especially for the ERS ascending dataset. A low number of images and also their irregular temporal distribution reduces the overall accuracy of the atmospheric contribution evaluation and consequently the overall precision of the results. Therefore, the number of measurement points fell dramatically in the northern and southern parts of the area of interest, and the time series of ERS ascending data are not very significant because the temporal gaps between successive images can lead to phase unwrapping errors (see Appendix 1) whenever motion dynamic is not linear. The post-processing analysis of SqueeSAR TM ERS and ENVISAT data have highlighted that the quaternary recent alluvial/mouth and sabkha deposits are affected by strong subsidence, according to the data reported by Egis BCEOM International / IAU-IDF / BRGM in the report: Évaluation des risques en situation actuelle et à l horizon 2030 pour la ville de Tunis. The subsidence phenomena affect (Figure 2): the alluvial plain of the Oued Medjerda, and the mouth area (Ghar el Melh Lake), in the North of Tunis. the southern and western sector of the Lac de Tunis and the north-eastern part of the Sebkhet el Sijoumi; the area of La Manouba-Hippodrome de Ksar Said, in the western part of Tunis; the alluvial plain of the Oued Meliane in the South of Tunis. SqueeSAR TM data show also differential movements affecting the area north of Sebket el Sijoumi, in the western part of Tunis, and the Eastern boundary of the alluvial plain of the Oued Meliane. These movements may be in the first analysis related to the structural setting. Some post-processing elaborations aimed at supporting the interpretation of these data are presented in this section. Some quantitative comparison of all the results obtained have been performed, aimed at providing information on motion rate and precision of the estimates for the above mentioned areas The motion rate representative of each area has been extracted from SqueeSAR results following a set of steps required to eliminate possible outliers. The steps are as follow: Delimitation of the four areas affected by main deformations. Selection of data (ERS ascending, ERS descending, ENVISAT descending and ERS vertical component) that are within each area. Page 51 of 92

52 estimation of the mean value and standard deviation 2 of the velocity distribution for outliers 3 elimination Selection of minimum (in case of subsidence phenomena) or maximum (in case of uplift) velocity value as representative of the motion of the aea. Definition of the velocity error bar 4 with a confidence interval 5 of 68% (this is the probability that the representative values lay within the error bar) Evaluation of the indicative maximum cumulative displacement as the product between the maximum (in case of uplift) or minimum (in case of subsidence) velocity and the monitoring period The alluvial plain of the Oued Medjerda, and the mouth area of Ghar el Melh Lake. The absence of urbanization has reduced the density of measurement points in the Oued Medjerda alluvial plain, however subsidence phenomena are detectable in all the monitored period (Figure 22) and may be attributed to natural subsidence, due to deposits compaction and/or solution. In Table 14 are summarized the results of the quantitative analysis for each dataset (the measure is along the Line of Sight - LOS) and for the vertical component in the period covered by ERS ascending and descending data. Figure 23 illustrates the comparison between the datasets and the area where the quantitative analysis was performed. The results show: a general consistency between the subsidence rate calculated from ERS ascending and ERS descending datasets; a general consistency between the subsidence rate calculated in ERS ( ) and ENVISAT monitored period ( ); a maximum total subsidence vertical displacement of 60mm between January 1993 and July The standard deviation (σ) is a measure of the variability or dispersion of a data population. It shows how much variation there is from the "average" (mean, or expected/budgeted value). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data is spread out over a large range of values. 3 Velocities values that in the distribution are outside the confidence interval [Vm-3σ;V m+3 σ], where V m is the average velocity value, are considered outliers. 4 The error bar is defined starting from the standard deviation estimated for each measurement point. (V_STDEV field in the database) ad is obtained as [V ind-v_stdev; V ind+ V_STDEV], where V ind is the indicative velocity of the area. 5 A confidence interval is an interval in which a measurement falls with a given probability. Thus, confidence intervals are used to indicate the reliability of an estimate. 6 This evaluation has as preliminary hypothesis that the deformation is linear in the monitored period, so that the total displacement can be calculated as the product Velocity x Time. Page 52 of 92

53 Figure 22: Subsidence affecting the alluvial plain of the Oued Medjerda, and the mouth area of Ghar el Melh Lake. Dataset Monitored Period Maximum Subsidence rate (mm/year) Error Bar (mm/year) Indicative max subsidence displacement ERS descending 06/ / , mm (LOS) ERS ascending 01/ / , mm (LOS) ENVISAT descending ERS vertical component 09/ / mm (LOS) 01/ / mm (Vertical) Table 14: Indicative values of deformation rate, error bar and cumulative displacement for northern sector of the Oued Medjerda alluvial plain. Page 53 of 92

54 Figure 23: Comparison between the distribution of all the datasets in the Oued Medjerda and indication of the area where the quantitative analysis was performed. Page 54 of 92

55 12.2 Southern and western sector of the Lac de Tunis and north-eastern part of the Sebkhet el Sijoumi In this area, subsidence affects sabka and recent quaternary deposits in all the monitored periods ( and ) (Figure 16). In particular, strong movements away from the satellite involve the eastern urbanized area of Tunis. In accordance with data reported by Egis BCEOM International / IAU-IDF/BRGM ( Évaluation des risques en situation actuelle et à l horizon 2030 pour la ville de Tunis ), these movements in the first analysis may be attributed to natural subsidence, due to deposits compaction and/or solution. In Table 15 and Table 16 are summarized the results of the quantitative analysis for each dataset (the measure is along the Line of Sight - LOS) and for the vertical component in the period covered by ERS ascending and descending data. Figure 25 and Figure 27Figure 23 illustrate the comparison between the datasets and the areas where the quantitative analysis was performed. The results for the western sector of the Lac de Tunis show: a general consistency between the subsidence rate calculated from ERS ascending and ERS descending datasets; a decrease of the subsidence rate in the ENVISAT monitored period ( ) respect to the ERS period ( ; a maximum total subsidence vertical displacement of 188 mm between January 1993 and July The recent decrease of subsidence rate could be attributed to a increase with time in the consolidation of the compressible deposits. It s very interesting to note also that some new buildings show a clear stability, suggesting a good substructure foundation strategy (Figure 26). The results for the north-eastern sector of the Sebkhet el Sijoumi show that: the subsidence rate calculated from ERS ascending is slightly lower than from ERS descending datasets, suggesting the presence of a horizontal component of the deformation; the subsidence rate in the ENVISAT descending monitored period ( ) is comparable with the subsidence rate in the ERS descending period ( ); the maximum total subsidence vertical displacement is 32 mm between January 1993 and July Page 55 of 92

56 Figure 24: Subsidence affecting the southern and western sectors of the Lac de Tunis and the north-eastern part of the Sebkhet el Sijoumi. Page 56 of 92

57 Dataset Monitored Period Maximum Subsidence rate (mm/year) Error Bar (mm/year) Indicative max subsidence displacement ERS descending 06/ / mm (LOS) ERS ascending 01/ / mm (LOS) ENVISAT descending ERS vertical component 09/ / mm (LOS) 01/ / mm (Vertical) Table 15: Indicative values of deformation rate, error bar and cumulative displacement for the weastern sector of the Lac de Tunis. Dataset Monitored Period Maximum Subsidence rate (mm/year) Error Bar (mm/year) Indicative max subsidence displacement ERS descending 06/ / mm (LOS) ERS ascending 01/ / mm (LOS) ENVISAT descending ERS vertical component 09/ / mm (LOS) 01/ / mm (Vertical) Table 16: Indicative values of deformation rate, error bar and cumulative displacement for the north-eastern sector of the Sebkhet el Sijoumi. Page 57 of 92

58 Figure 25: Comparison between the distribution of all the datasets in the western sector of Lac de Tunis and indication of the area where the quantitative analysis was performed. Page 58 of 92

59 Figure 26: Close up aimed to show the presence of stable buildings in the area of the western Lac de Tunis affected by subsidence. Page 59 of 92

60 Figure 27: Comparison between the distribution of all the datasets in the north-estern sector of Sebkhet el Sijoumi and indication of the area where the quantitative analysis was performed 12.3 The area of La Manouba-Hippodrome de Ksar Said This area in the ERS period ( ) is affected by differential movements, across a E-W lineation in correspondence with the contact between Pleistocene and recent alluvial deposits, affected also by subsidence (Figure 28). The southern sector is affected by a strong subsidence, instead the northern sector is affected by an uplift. This deformation pattern breaks off in correspondence with the eastern border fault of the Ariana-Sejoumi graben, as indicated in the Egis BCEOM International / IAU-IDF / BRGM report: Évaluation des risques en situation actuelle et à l horizon 2030 pour la ville de Tunis. In the first analysis the subsidence affecting the recent alluvial deposits in the southern sector may be attributed either to compaction and to man-made subsidence, induced by water pumping and/or Page 60 of 92

61 urbanization of alluvial plains. The comparison of the ERS ( ) and ENVISAT ( ) datasets suggest also a recent decrease in subsidence affecting the area of La Manouba-Hippodrome de Ksar Said, in the western part of Tunis. This subsidence phenomena breaks off sharply in correspondence with the eastern border fault of the Ariana-Sejoumi graben, suggesting a structural control of the recent compressible deposits thickness. According with BRGM geological data in the east sector of the Sejoumi fault, where the data show a general stability, the mio-pliocene substratum is almost outcropping, instead, in the west sector, where the subsidence is strong, the recent quaternary deposits, affected by compression, are thicker. The differential movements across the E-W lineation, with an uplift affecting the northern sector, may be related also to the structural setting and seems to suggest the presence of an active fault. To support these assumptions, some post-processing elaborations were done. The key steps are as listed: Extraction of easting and vertical component of displacement, by combining ascending and descending ERS tracks. Data filtering to reduce noise. Evolution analysis of displacements along representative cross-sections, in order to identify evidence of faults. Calculation of the gradient field of vertical displacements, in order to highlight trends and anomalies in the spatial distribution of data. As previously mentioned, displacements are measured along the satellite line of sight (LOS). The incident angle (the angle drawn between an imaginary line from the centre of the Earth to the satellite, and the direction of propagation of the signal) is approximately 23 for both ERS and ENVISAT satellites. This means that displacement values measured along the LOS are influenced by vertical and horizontal components of displacement. Due to the orbital tracks of the satellites (approximately North South), only East West displacements can be detected. Thanks to the availability in the Tunis area of the two different ERS geometries (two different LOS), the results obtained by ascending and descending dataset can be combined to estimate the vertical and E-W movement. For detail see Appendix 2. Figure 29 shows the vertical and easting components of displacement calculated after re-sampling the original ERS ascending and descending data on a common grid of 50x50m. It can be seen that the movements have a greater vertical component, and a lower left slip horizontal component. To reduce the noise component, the vertical displacement data have been spatially filtered. Corrections were applied to the vertical displacement results using neighbourhood statistics and the computation of the mean values with a search radius of 100m; a raster surface from cumulative displacement data was created. The calculated surfaces can be considered as isochronous displacement surfaces. Figure 31 shows the isochronous surface of the last acquisition, corresponding to the total cumulative displacement. The displacement evolution is calculated by analysing the two cross-sections (Figure 30 and Figure 31) of the displacement raster surfaces obtained by each satellite acquisition. The profiles show some discontinuities, indicated by red arrows. These anomalies can be interpreted as faults, and in particular the differential movement across the E-W lineation suggest a neo-tectonic activity. Figure 32 shows the location of discontinuities along the cross-sections, superimposed on the in progress Carte de Page 61 of 92

62 microzonation sismique, realized by INM (Institut National de la Météorologie) and provided by Egis BCeom. To highlight these linear anomalies Figure 33 shows the gradient field of the vertical displacement raster surface, corresponding to the last acquisition. A verification of all these assumptions makes it necessary to compute a comparison and integration of SqueeSAR TM results with geological, geophysical and/or other ground based surveillance data. These post-processing calculations highlight that SqueeSAR TM data can be a helpful tool in recognising active faults (Tamburini et al. 2010) thanks to millimeter-scale surface displacements measurements over wide area. Finally, in Table 17 and Table 18 are summarized the results of the quantitative analysis for each dataset (the measure is along the Line of Sight - LOS) and for the vertical component in the period covered by ERS ascending and descending data. Figure 25Figure 23 illustrates the comparison between the datasets and the area where the quantitative analysis was performed. The results show: a general consistency between the subsidence and uplift rate calculated from ERS ascending and ERS descending datasets; a slightly decrease of the subsidence and uplift rate in the ENVISAT monitored period ( ) respect to the ERS period ( ); a maximum total subsidence vertical displacement of 33 mm between January 1993 and July 1999, in the southern sector; a maximum total uplift vertical displacement of 30 mm between January 1993 and July 1999, in the northern sector. Page 62 of 92

63 Figure 28: Differential movements affecting the area of La Manouba-Hippodrome de Ksar Said in the ERS datasets. Page 63 of 92

64 Figure 29: Vertical and E-W components calculated after resampling ERS ascending and descending data on a common grid. Page 64 of 92

65 Figure 30: Vertical displacement surface, corresponding to the total cumulative amount of displacement as of the last acquisition. The surface is obtained using neighbourhood statistics and computation of mean values of point vertical displacement data, with a search radius of 100m. AB and CD are the profiles of Figure 31, showing the evolution of vertical displacement over the entire ERS acquisition period ( ). Page 65 of 92

66 Figure 31: Evolution of vertical displacement along AB and CD cross-section, with four acquisition time steps (from t0 to t21). The red arrows highlight the anomalies referred to possible faults. Page 66 of 92