Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides

remote sensing

Article Suitability Assessment of X-Band Satellite SAR Data for Geotechnical Monitoring of Site Scale Slow Moving Landslides

Guadalupe Bru 1, Joaquin Escayo 1, José Fernández 1,* ID , Jordi J. Mallorqui 2 ID , Rubén Iglesias 3 ID , Eugenio Sansosti 4 ID , Tamara Abajo 1 and Antonio Morales 5

1 Instituto de Geociencias, CSIC-UCM, Fac. C. Matemáticas, Plaza de Ciencias, 3, 28040 Madrid, Spain; [email protected] (G.B.); [email protected] (J.E.); [email protected] (T.A.) 2 CommSensLab, Departament de Teoria del Senyal i Comunicacions, D3-Campus Nord, Universitat Politècnica de Catalunya, UPC, 08034 Barcelona, Spain; [email protected] 3 Dares Technology, C/Esteve Terradas, 1, Building RDIT, ofﬁce 118, Parc UPC-PMT, 08860 Castelldefels, Spain; [email protected] 4 Istituto per il Rilevamento Elettromagnetico dell’Ambiente, National Research Council (CNR) of Italy, via Diocleziano, 328, 80124 Naples, Italy; [email protected] 5 TPF Getinsa-Euroestudios, C/Ramón de Aguinaga, 8, 28028 Madrid, Spain; antonio.morales@tpﬁngenieria.com * Correspondence: [email protected]; Tel.: +34-91-394-4632

Received: 27 April 2018; Accepted: 11 June 2018; Published: 13 June 2018

Abstract: This work addresses the suitability of using X-band Synthetic Aperture Radar (SAR) data for operational geotechnical monitoring of site scale slow moving landslides, affecting urban areas and infrastructures. The scale of these studies requires high resolution data. We propose a procedure for the practical use of SAR data in geotechnical landslides campaigns, that includes an appropriate dataset selection taking into account the scenario characteristics, a visibility analysis, and considerations when comparing advanced differential SAR interferometry (A-DInSAR) results with other monitoring techniques. We have determined that Sentinel-2 satellite optical images are suited for performing high resolution land cover classiﬁcations, which results in the achievement of qualitative visibility maps. We also concluded that A-DInSAR is a very powerful and versatile tool for detailed scale landslide monitoring, although in combination with other instrumentation techniques.

Keywords: A-DInSAR; geotechnical monitoring; site scale; slow moving landslides; land cover classiﬁcation; Sentinel-2 optical

1. Introduction Landslides are a globally widespread major natural hazard [1,2] that can take place at various temporal and spatial scales. Slow-moving landslides are characterized by motion rates reaching several centimeters per year, being capable to produce a dramatic impact on properties and infrastructures in urbanized areas. In order to design the appropriate engineering solutions if the latter happens, a detailed site scale assessment is needed, making geotechnical monitoring an essential tool to understand the spatial and temporal evolution of the landslide [3,4]. Surface and subsurface time dependent displacements associated with landslides are usually monitored using in situ (e.g., levelling, electronic distance measurement triangulation and trilateration networks, extensometers, inclinometers, piezometers, and Global Navigation Satellite System) and remote sensing techniques (e.g., LIDAR, photogrammetry, satellite- and ground-based synthetic aperture radar). Some of them permit continuous recording, which gives a very useful information for early

Remote Sens. 2018, 10, 936; doi:10.3390/rs10060936 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 936 2 of 29 detection and alert of sudden changes. In situ observation techniques have several limitations: (a) the setting of sparse observation points, which usually cover small areas, thus giving an limited overview of the landslide extent and of the spatial distribution of the displacement field; (b) observations are constrained to accessible areas; (c) instrumentation is usually placed where activity is expected (preventing any opportunity to obtain information on sudden or unknown active areas); (d) reference stations might not be stable, in particular when only small areas are covered with the observation system; (e) displacement information is only available after the first survey is performed, so that there is usually a lack of information about previous epochs that could help us to understand and characterize mass movements; and (f) involvement of staff in field campaigns, direct data collection, and maintenance of continuous recording systems, can be very expensive for the institutions involved in the monitoring. All these considerations affect the sustainability of such a monitoring system, which is a limiting aspect for the availability of long-term studies of unstable slopes [5]. Those limitations can be overcome, at least partially, using the abovementioned remote techniques. Among them, synthetic aperture radar (SAR) satellite observations provide complex reflectivity images that can be processed by advanced differential SAR interferometry (A-DInSAR) algorithms in order to monitor complex deformation episodes. A-DInSAR techniques are capable of measuring subtle ground displacements with sub-centimetric precision [6,7]. More in details, A-DInSAR can measure only the component of the ground displacement along the radar line-of-sight (LOS) direction; however, if SAR acquisitions from both ascending and descending orbits are available, the horizontal (east–west) and vertical components of the displacement can be determined [8,9]. The suitability of A-DInSAR for slow moving landslide investigations at different scales is widely accepted [10–12]. It has the advantage of giving useful information on the spatial and temporal distribution of landslides alongside with their kinematics, at competitively affordable costs for users [13]. It provides spatially extensive information compared to in situ methods and long observation periods at fixed time acquisitions, compared to other remote techniques. A crucial step in performing A-DInSAR monitoring is to select the appropriate SAR dataset suitable for studying a specific area. For the particular case of landslides, the geometry of the slopes is a determinant factor, which can limit the radar visibility for ascending or descending images. The type of land cover of the scene and the wavelength of the satellite radar sensor determine the amount of coherent targets (CTs) at which displacements can be measured by A-DInSAR techniques. To consider a portion of the terrain as a coherent target (CT), the backscattering properties of the surface of that area should remain stable between the dates of SAR image acquisitions. There are freely available land data we can use to assess land coverage in a costless way, but they have a low resolution, making them more adequate for regional analyses than for site scale ones. Sentinel-2 satellite (S2) from the European Space Agency (ESA) provides optical images with 10 m, 20 m and 60 m resolution at different bands. These free images can be used to classify terrain coverage when site scale studies are in order. The purpose of this work is to assess the suitability of X-band A-DInSAR techniques in monitoring site scale landslides affecting urban areas and/or civil infrastructures with the objective to include this tool as a part of the operational geotechnical monitoring. Additionally, we evaluate the performance of supervised land cover classification based on Sentinel-2 satellite high resolution optical images, for the visibility analysis of X-band SAR datasets. We use a site scale landslide located in the Deba valley (Gipuzkoa, Spain) as a test case, in an area where slope movements are frequent. It is a reactivated slow moving paleo landslide whose complex motion has caused moderate to severe structural damage for centuries in Leintz Gatzaga medieval village. Landslide movements have been monitored in recent years by a geotechnical campaign requested by the local council to establish the failure mechanism. First, we describe the landslide and the in situ techniques applied to monitor the landslide motion. Second, we select a priori the most suitable SAR dataset for the test-site and perform a visibility analysis taking into account the land cover and the topographical relief. To obtain the land cover maps at high resolution, we make a supervised classification of different spectral bands combinations of S2 optical images. Afterwards, we carry out an advanced differential interferometric analysis to Remote Sens. 2018, 10, 936 3 of 29 obtainRemote velocities Sens. 2018, 10 and, x FOR displacement PEER REVIEW time series using the coherent pixel technique (CPT), a3 of mature 29 algorithm that has been used in the past to study different kinds of landslides with orbital and analysis to obtain velocities and displacement time series using the coherent pixel technique (CPT), ground-baseda mature algorithm SAR acquisitions that has been [14 used,15]. in We the statisticallypast to study compare different kinds detected of landslides CTs in the with studied orbital area to theand results ground-based of the visibilitySAR acquisitions analysis. [14,15]. Finally, We statistically we examine compare the correlation detected CTs of in displacement the studied area spatial distributionto the results and of temporal the visibility rates analysis. between Finally, X-band we SAR examine results the and correlation other monitoring of displacement data. Ourspatial results showdistribution that the application and temporal of rates X-band between A-DInSAR X-band techniques SAR results in and the other geotechnical monitoring monitoring data. Our results campaign of theshow studied that the site application scale landslide of X-band enhances A-DInSAR the te spatialchniques density in the ofgeotechnical measurements, monitoring permits campaign detecting movementof the studied in previously site scale unknownlandslide enhances active areas, the spatial and complementsdensity of measurements, the information permits given detecting by other monitoringmovement techniques. in previously Moreover, unknown we active observe areas, that and accurate complements high resolution the information land cover given classifications by other for A-DInSARmonitoring applicationstechniques. Moreover, can be achieved we observe from that S2 a opticalccurate images,high resolution allowing land the cover creation classifications of qualitative visibilityfor A-DInSAR maps. applications can be achieved from S2 optical images, allowing the creation of qualitative visibility maps. 2. Leintz Gatzaga Landslide 2. Leintz Gatzaga Landslide 2.1. Geological Context 2.1. Geological Context The study area is located in the upper course of the Deba River, which flows through the province The study area is located in the upper course of the Deba River, which flows through the of Gipuzkoa (Basque Country, Spain). The relief of the Deba valley is quite abrupt and slope movements province of Gipuzkoa (Basque Country, Spain). The relief of the Deba valley is quite abrupt and slope are frequent, mostly triggered by intense rainfall [16]. The region is tectonically complex, formed by movements are frequent, mostly triggered by intense rainfall [16]. The region is tectonically complex, archedformed shaped by arched large-scale shaped folds large-scale of Latter folds Jurassic of Latter and Jurassic Early Cretaceous and Early Cretaceous materials thatmaterials belong that to the Basquebelong Arc to [17 the]. LocalBasque geology Arc [17]. and Local geomorphology geology and isgeomorphology characterized byis weatheredcharacterized cretaceous by weathered materials (argilitescretaceous and siltstones)materials (argilites covered byand quaternary siltstones) colluvialcovered by deposits, quaternary high colluvial topographic deposits, relief high and the presencetopographic of various relief faults and the (Figure presence1). of various faults (Figure 1).

FigureFigure 1. Geomorphological 1. Geomorphological and and geological geological sketch map map of of Leintz Leintz Gatzaga Gatzaga landslide landslide area area with with location location of geotechnicalof geotechnical instrumentation instrumentation (inclinometers, (inclinometers, piezometers, and and co compiledmpiled boreholes boreholes from from previous previous works).works). Blue Blue arrows arrows indicate indicate direction direction ofof movement.movement. Cross Cross section section marked marked in purple in purple is shown is shown below below (modiﬁed(modified from from [18 [18]).]).

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WeWe focusfocus ourour workwork onon aa reactivatedreactivated paleo-landslidepaleo-landslide whosewhose complexcomplex motionmotion hashas causecause moderatemoderate toto severesevere structuralstructural damagedamage forfor centuriescenturies inin LeintzLeintz GatzagaGatzaga medievalmedieval villagevillage [[19],19], whichwhich hashas aroundaround 250250 inhabitantsinhabitants andand preservespreserves anan importantimportant historichistoric heritage.heritage. TheThe villagevillage settlessettles onon anan east-facingeast-facing slopeslope thatthat has an average average gradient gradient of of 25%. 25%. On On the the slope, slope, we wedistinguish distinguish different different movements: movements: the themain main landslide landslide affecting affecting the the village, village, secondary secondary lobes lobes that that develop develop within within the the main main landslide bodybody (with(with shallowershallower slipslip surfaces)surfaces) andand smallersmaller surfacessurfaces ofof rupturerupture besidesbesides thethe mainmain landslide.landslide. TheThe widthwidth ofof thethe main landslide landslide is is 300 300 m m and and the the length length is is600 600 m. m.The The main main scarp scarp of the of landslide the landslide coincides coincides with witha thrust a thrust fault fault that thatputs puts in inmechanical mechanical contact contact argillites argillites and and siltstones siltstones of of the the Urgonian andand SupraUrogonianSupraUrogonian complex.complex. The presence of this weak area,area, inin conjunctionconjunction withwith aa thickthick weatheredweathered horizonhorizon andand thethe erosiveerosive actionaction ofof thethe DebaDeba RiverRiver atat thethe toetoe ofof thethe slope,slope, wouldwould havehave actedacted asas mainmain determinantsdeterminants ofof thethe paleo-movementpaleo-movement thatthat generatedgenerated thethe currentcurrent hillhill proﬁleprofile onon whichwhich thethe villagevillage waswas fundedfunded inin 1331,1331, andand afterafter whichwhich thethe landslidelandslide waswas reactivatedreactivated (Figure (Figure2 ).2).

FigureFigure 2.2. Paleo-landslidePaleo-landslide evolutionevolution scheme.scheme. (a)) OriginalOriginal slopeslope proﬁle;profile; ((bb)) CurrentCurrent slopeslope proﬁleprofile [[10].10].

TheThe lithostratigraphiclithostratigraphic columncolumn ofof thethe landslidelandslide isis formedformed byby fourfour levels.levels. TheThe upperupper levellevel isis aa lowlow consistentconsistent clayeyclayey gravelgravel earth earth fill fill which which is is constrained constrained under under the the urbanized urbanized area. area. The The second second level level is is a higha high consistent consistent colluvial colluvial deposit deposit composed composed by low by plasticitylow plasticity clay orclay clayey or clayey gravels, gravels, with large with blocks large ofblocks siltstone, of siltstone, argillites, argillites, and limestone and limestone which derive which from derive rock from falls rock of weathered falls of weathered Cretaceous Cretaceous bedrock frombedrock the from upper the cliff. upper Beneath cliff. it,Beneath there isit, a there level ofis a sandy level gravelsof sandy (composed gravels (composed entirely of entirely siltstone), of containingsiltstone), containing gypsum and gypsum pyrite veins.and pyrite In some veins. areas, In so theme fines areas, percentage the fines increasespercentage to formincreases sandy to claysform withsandy gravels. clays Thiswith levelgravels. has beenThis definedlevel has as abeen “tectonic defined mylonite” as a “tectonic [20], produced mylonite” by ductile [20], produced deformation by ofductile the bedrock deformation due to of shear the bedrock strain accumulationdue to shear stra ofin the accumulation inferred fault of below the inferred Deba river,fault below although Deba it couldriver, although just be an it intense could just weathered be an intense product weathe of thered bedrock product [of18 the]. Finally, bedrock the [18]. substrate Finally, isthe formed substrate by siltstonesis formed (Aptian–Albianby siltstones (Aptian–Albian age) that are inage) mechanical that are in contact mechanical with contact calcareous with argillites calcareous (Albian) argillites by a(Albian) thrust faultby a locatedthrust fault at the located landslide at the main landslide scarp (northeast main scarp of (northeast Villaro fault). of Villaro Several fault). engineering Several worksengineering were performedworks were in performed the last decades in the last to decades alleviate to landslide alleviate activitylandslide effects activity on effects Leintz on Gatzaga Leintz buildingsGatzaga buildings and infrastructures. and infrastructure In 1980,s. a In drainage 1980, a systemdrainage was system installed was andinstalled a rock and armour a rock retaining armour wallretaining was constructedwall was constructed at the foot at of the foot slope of (Figure the slope1). This(Figure solution 1). This had solution the objective had the of improvingobjective of theimproving bearing the capacity bearing of capacity the terrain of the in terrain that area, in that in order area, in to order build to a frontonbuild a fronton court and court a newand a town new hall,town as hall, the oldas the one old was one dismantled was dismantled due to structural due to structural failure caused failure by caused landslide by activity.landslide However, activity. cracksHowever, appeared cracks in appeared the crown in of the the crown wall during of the the wall works during and the a strong works rainfall and a periodstrong aggravatedrainfall period the problemaggravated in 1982. the problem After that, in the1982. upper After part that, of thethe walluppe wasr part unloaded of the wall by excavating was unloaded 1700 mby3 ofexcavating material. 3 Between1700 m 1996of material. and 1998, Between micropiles 1996 wereand 1998, drilled micropiles in shallow were foundations drilled in of shallow a singular foundations building andof a thesingular fronton building court. and In 2009, the fronton the local court. council In 2009, requested the local a geotechnical council requested campaign a geotechnical to monitor campaign landslide to monitor landslide movements and establish the failure mechanism. Field observations indicate

Remote Sens. 2018, 10, 936 5 of 29 Remote Sens. 2018, 10, x FOR PEER REVIEW 5 of 29 thatmovements the landslide and establish is still moving, the failure causing mechanism. damage Field to several observations buildings, indicate the fronton that the court landslide area, is and still roads.moving, causing damage to several buildings, the fronton court area, and roads.

2.2.2.2. Monitoring Monitoring Techniques Techniques Applied SinceSince 2010 2010 different different monitoring monitoring techniques techniques have have been been used used to to measure measure the the surface surface and and in-depth in-depth displacementsdisplacements of of Leintz Leintz Gatzaga Gatzaga landslide landslide (Figure (Figure 33a)a).. In this sub-section we will discuss the results obtainedobtained with with the the in in situ situ monitoring monitoring techniques techniques an andd their their relation relation to to accumulate accumulate precipitation precipitation data data (Figure(Figure 33b).b). Remote sensing A-DInSAR techniques will be discussed in Section 3.3 ofof this this manuscript. manuscript.

Figure 3. (a) Measuring dates of the landslide cinematic monitoring campaigns performed with: in Figure 3. (a) Measuring dates of the landslide cinematic monitoring campaigns performed situwith: (EDM, in situ GNSS/HPL, (EDM, GNSS/HPL, crack gauges, crack inclinometers) gauges, inclinometers) and remote sensing and remote(A-DinSAR) sensing techniques. (A-DinSAR) The temporaltechniques. coverage The temporal of the A-DinSAR coverage oftechni theques A-DinSAR is 15 May techniques 2011–1 isAugust 15 May 2013 2011–1 for the August COSMO- 2013 SkyMedfor the COSMO-SkyMed dataset and 10 February dataset 2013–3 and 10 August February 2014 2013–3 for the August TerraSAR-X 2014 for dataset; the TerraSAR-X (b) Time periods dataset; of(b )maximum Time periods velocities of maximum registered velocities by in-situ registered monitoring by in-situcampaigns. monitoring Accumulate campaigns. precipitation Accumulate data (pp)precipitation in 3 days dataand 30 (pp) days in 3is daysalso displayed, and 30 days in isorder also to displayed, compare pp in orderpeaks towith compare maximum pppeaks velocities. with maximum velocities. Three inclinometers were installed in March 2010 (INC-1, INC-2, and INC-3), another one in October 2012 (INC-6) and two more in the beginning of 2013 (INC-4 and INC-5). The last reading Three inclinometers were installed in March 2010 (INC-1, INC-2, and INC-3), another one in available is from March 2015. Readings were performed manually every three months, although there October 2012 (INC-6) and two more in the beginning of 2013 (INC-4 and INC-5). The last reading is a gap of 14 months from October 2013 to December 2014. All inclinometers show displacement available is from March 2015. Readings were performed manually every three months, although there towards the maximum slope direction. For inclinometers INC-1, INC-2, and INC-3 core samples data is a gap of 14 months from October 2013 to December 2014. All inclinometers show displacement are available, showing that the slip surface develops in weathered siltstone with soft to extremely soft towards the maximum slope direction. For inclinometers INC-1, INC-2, and INC-3 core samples data consistence next to the contact with the bedrock. They detected the main slip surface at 16 m depth are available, showing that the slip surface develops in weathered siltstone with soft to extremely at the head of the landslide (INC-3), 28 m at the main body (INC-4), 23 m below the village (INC-2), soft consistence next to the contact with the bedrock. They detected the main slip surface at 16 m and 13.5 m at the foot (INC-1). Inclinometers INC-5 and INC-6 detected shallower surface planes that depth at the head of the landslide (INC-3), 28 m at the main body (INC-4), 23 m below the village develop within the margins of the main landslide body, at 12.5 m and 13.5 m, respectively. Shear (INC-2), and 13.5 m at the foot (INC-1). Inclinometers INC-5 and INC-6 detected shallower surface deformation band concentrates within a narrow zone of about 1 m for all the inclinometers. A planes that develop within the margins of the main landslide body, at 12.5 m and 13.5 m, respectively. maximum accumulated displacement of 30 mm was measured in INC-3, which is located at the head Shear deformation band concentrates within a narrow zone of about 1 m for all the inclinometers. of the landslide (Figure 4). Almost all of the movement was registered in two rainy periods from A maximum accumulated displacement of 30 mm was measured in INC-3, which is located at the December 2012 to March 2013 and December 2014 to March 2015 with velocities around 40 mm/year. head of the landslide (Figure4). Almost all of the movement was registered in two rainy periods from December 2012 to March 2013 and December 2014 to March 2015 with velocities around 40 mm/year.

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Figure 4. Figure 4. DisplacementDisplacement graph graph for for inclinometer inclinometer INC-3, INC-3, located located at the at head the head of the of landslide, the landslide, with with correspondent core sample lithostratigraphic column. The dotted rectangle represents the depth correspondent core sample lithostratigraphic column. The dotted rectangle represents the depth and and thickness of the shear band. thickness of the shear band.

TenTen crackcrack gaugesgauges werewere installedinstalled onon thethe facadesfacades ofof thethe lineline ofof buildingsbuildings locatedlocated toto thethe northnorth ofof the village.village. Seven readings are availableavailable fromfrom DecemberDecember 20122012 toto FebruaryFebruary 2015.2015. A maximum aperture of 44 mmmm waswas measuredmeasured onon oneone ofof thethe crackscracks ofof thethe frontonfronton court.court. TheThe openingopening ofof thethe crackscracks occurredoccurred inin twotwo periods,periods, fromfrom OctoberOctober 20132013 toto JanuaryJanuary 20142014 andand fromfrom SeptemberSeptember 20142014 toto FebruaryFebruary 2015,2015, whilewhile inin thethe restrest ofof thethe timetime thethe openingopening waswas negligible.negligible. SixSix differentialdifferential globalglobal navigationnavigation satellitesatellite systemsystem (GNSS)(GNSS) complementedcomplemented withwith highhigh precisionprecision levellinglevelling (HPL)(HPL) campaignscampaigns werewere performedperformed onon 2020 benchmarksbenchmarks coveringcovering thethe spanspan time from July 2013 toto NovemberNovember 20142014 (Figure(Figure5 ).5). GNSS GNSS precision precision in in the the horizontal horizontal plane plane is is 3 3 mm mm± ± 0.5 ppm and HPL has aa typicaltypical deviationdeviation ofof 0.30.3 mmmm inin 11 kmkm ofof doubledouble levelling.levelling. The direction ofof thethe movementmovement isis towardstowards thethe maximummaximum slopeslope direction.direction. Maximum displacements werewere detecteddetected inin thethe villagevillage areaarea closeclose toto thethe retainingretaining wall,wall, upup to to 118 118 mm mm in in the the horizontal horizontal plane. plane. Displacements Displacements measured measured on theon the landslide landslide do notdo follownot follow the samethe same pattern. pattern. However However the benchmarks the benchmarks with with maximum maximum displacement displacement (H17, (H17, H18, andH18, H19) and doH19) all do register all register movement movement since the since ﬁrst the reading. first re Weading. calculated We calculated the arithmetic the arithmetic mean of the mean horizontal of the displacementhorizontal displacement measured inme eachasured of thosein each benchmarks of those benchmarks (inset Figure (inset5), fromFigure which 5), from we which can extract we can a meanextract velocity a mean of velocity 60 mm/year of 60 mm/year from the endfrom of the July end to middleof July to October middle 2013, October followed 2013, by followed an increment by an ofincrement velocity of (170 velocity mm/year) (170 untilmm/year) January until 2014 January and followed 2014 and by followed a decrease by a of decrease velocity of (55 velocity mm/year) (55 untilmm/year) July 2014.until AnJuly additional 2014. An measurementadditional measurem was madeent in was April made 2015 in just April on these2015 threejust on benchmarks. these three benchmarks.

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FigureFigure 5. 5.Location Location ofof GNSSGNSS andand prismprism reﬂectorreflector control control points. points. E1 E1 refers refers to toelectronic electronic distance distance measurementmeasurement (EDM) (EDM) robotic robotic station.station. Arrows Arrows indica indicatete the the direction direction of ofthe the movement movement detected detected by by GNSSGNSS on on the the horizontal horizontal plane plane andand areare scaledscaled with re respectspect to to the the total total accumulated accumulated displacement displacement of of thethe sixth sixth campaign, campaign, 20 20 November November 2014.2014. Inset Inset graph graph shows shows the the arithmetic arithmetic mean mean of ofthe the horizontal horizontal displacementsdisplacements measured measured in in benchmarks benchmarks H17,H17, H18, and and H19. H19.

Continuous recording of the displacements of seven prism reflectors were performed Continuous recording of the displacements of seven prism reflectors were performed automatically during 352 days, from September 2013 to September 2014, with an electronic distance automatically during 352 days, from September 2013 to September 2014, with an electronic distance measurement (EDM). The robotic station featured a communication system to obtain the data measurementremotely in (EDM).real time. The Five robotic prism stationreflectors featured (P1–P5 a in communication Figure 5) were systemplaced on to the obtain facades the dataof the remotely line in realof buildings time. Five located prism reflectorsto the north (P1–P5 of the in Figurevillage5) and were a placed two more on the housed facades inside of the H13 line ofand buildings H14 locatedbenchmarks to the north(Figure of 5). the The village displacement and a two pattern more is housed similar insidefor all of H13 them, and with H14 the benchmarks exception of (Figure H13. 5). TheA displacementmean velocity patternof 200 mm/year is similar was for recorded all of them, between with 11 the November exception 2013 of and H13. 23 A November mean velocity 2013 of 200(Figure mm/year 3). After was that, recorded there betweenwas not significant 11 November movement 2013 and until 23 the November period between 2013 (Figure 17 January3). After 2014 that, thereto 25 was March not 2014, significant with mean movement velocity of until 65 mm/year. the period In April between 2015, 17 there January is a jump 2014 in to the 25 time March series, 2014, withdue mean to subtraction velocity of 65of mm/year.a reference In prism, April 2015,which there has isbeen a jump corrected in the timefor later series, comparison due to subtraction with of ainterferometric reference prism, displacement which has data been in corrected the Section for 3.3. later4 of comparisonthis manuscript. with From interferometric 20 April 2014 displacement to 1 July data2014, in thea mean Section velocity 3.3.4 of of 60 thismm/year manuscript. was measured. From 20 April 2014 to 1 July 2014, a mean velocity of 60 mm/yearAdditionally, was measured. two vibrating wire piezometers were installed in 2010 at the toe of the landslide (PZ-1)Additionally, and in the twovillage vibrating (PZ-2) to wire measure piezometers pore pressures were installed at three different in 2010at depths the toe positions. of the landslide Until (PZ-1)2014 and readings in the were village made (PZ-2) manually to measure every porethree pressures months. The at three large different discontinuity depths of positions.readings masked Until 2014 short- and medium-term fluctuations. In 2014, datalogger sensors were installed in the piezometers readings were made manually every three months. The large discontinuity of readings masked performing readings two times a day. The piezometric level at PZ-2 was located at 3 m depth from short- and medium-term fluctuations. In 2014, datalogger sensors were installed in the piezometers the surface and reacts immediately to precipitation, ranging between 2.5 m and 3.66 m. performing readings two times a day. The piezometric level at PZ-2 was located at 3 m depth from the surface2.3. Landslide and reacts Classification immediately and Activity to precipitation, ranging between 2.5 m and 3.66 m. 2.3. LandslideThe variety Classification and complexity and Activity of unstable slope phenomena brings the need for a system of classification to characterize the landslide behavior, being crucial to define the state of activity. The currentThe varietyprofile of and the complexityslope is the result of unstable of a mass slope movement phenomena of predominantly brings the fine need material for a (clayey system of classification to characterize the landslide behavior, being crucial to define the state of activity. The current profile of the slope is the result of a mass movement of predominantly fine material Remote Sens. 2018, 10, x FOR PEER REVIEW 8 of 29 Remote Sens. 2018, 10, 936 8 of 29 soil) that occurred before the village was settled. The landslide style is characterize by a main body and secondary lobes of displaced masses formed by repeated movements of the same type. Urban (clayeydamage, soil) geomorphological that occurred beforefeature the inspection, village wasand settled.ground monitoring The landslide data style show is that characterize the landslide by a mainwas active body in and the secondary studied period. lobes Therefore, of displaced we masses classify formed the landslide by repeated as a reactivated movements multiple of the earth same type.slide Urbanaccording damage, to Cruden geomorphological and Varnes feature classifi inspection,cation [21]. and Also, ground according monitoring to datathe measured show that thedisplacements, landslide was we active distinguish in the studied two patterns period. Therefore,that are directly we classify related the landslideto accumulated as a reactivated rainfall multipleinfiltration earth (Figure slide according3b). The firs to Crudent one corresponds and Varnes to classification an extremely [21 ].slow Also, landslide according velocity to the measured and the displacements,second to a very we slow distinguish velocity twoone. patterns The depleted that are volume directly of related the Leintz to accumulated Gatzaga landslide rainfall is infiltration of about (Figure2.6 × 1036b). m3, The which first corresponds one corresponds to a very to an large extremely magnitude slow landslide landslide according velocity andto Fell the classification second to a 6 3 very[22]. slow velocity one. The depleted volume of the Leintz Gatzaga landslide is of about 2.6 × 10 m , which corresponds to a very large magnitude landslide according to Fell classification [22]. 3. Practical Guidelines for Complex Monitoring of Slow Moving Landslides 3. Practical Guidelines for Complex Monitoring of Slow Moving Landslides Our main objective is to propose a procedure which can serve as a practical guidelines of complexOur mainmonitoring objective of issite to proposescale slow a procedure moving whichlandslides can serve including as a practical remote guidelines sensing A-DInSAR of complex monitoringtechniques, ofto sitebe used scale by slow civil moving protection landslides agencies, including local authorities, remote sensing and private A-DInSAR building techniques, companies. to be usedA summary by civil of protection the procedure agencies, is outlined local authorities, in the flow and chart private shown building in Figure companies. 6. Further Adescription summary of of thethe procedureprocedure isis outlineddetailed inin thethe flowfollowing chart shownsub-sectio in Figurens. Section6. Further 3.1 includes description the scenario of the procedure analysis of is detailedthe studied in the area following and the sub-sections.SAR dataset selection. Section 3.1 Sect includesion 3.2 the describes scenario the analysis classification of the studiedof the land area andcover the data SAR (from dataset high selection. resolution Section Sentinel 3.2 2describes optical data) the classificationand geometrical of the visibility, land cover which data will (from be used high resolutionto create the Sentinel visibility 2 optical maps. data)Finally, and Section geometrical 3.3 includes visibility, the whichA-DInSAR will be processing used to create with the visibilityselected maps.category, Finally, the assessment Section 3.3 includesof high resolution the A-DInSAR qualitative processing visibility with map the selected production, category, and analysis the assessment of the ofmonitoring high resolution results. qualitative visibility map production, and analysis of the monitoring results.

FigureFigure 6.6. FlowFlow chart of thethe proposed procedure. * A de detailedtailed description of of the the different different algorithms algorithms categoriescategories of A-DInSAR processing can be be found found in in [23]. [23]. ** ** To To apply apply if if the the movement movement occurs occurs mainly mainly alongalong thethe steepeststeepest slope.

Remote Sens. 2018, 10, 936 9 of 29

3.1. SAR Dataset Selection The selection of the appropriate SAR dataset suitable for studying a speciﬁc area is crucial. The objective is to detect as much reliable targets to be measurable through time in order to obtain dense spatial coverage of surface displacements. This depends on several factors related to both the scene characteristics (such as land coverage, the extent of the area of interest, deformation dynamics) and the sensor speciﬁcations (such as carrier frequency, image resolution, and revisiting time). For the particular case of landslide processes, another determinant factor to take into account is the geometry of the slope where the instability occurs. The last—but not least—consideration is data availability, especially when archived images are needed for historical analysis. The constrains and limitations when using A-DInSAR techniques to monitor landslides have been discussed in literature [11–13,24] and several authors have proposed “pre-processing” guidance and methodologies to study the suitability and reliability of applying these techniques for particular landslides [25–28]. In this sub-section, we will discuss the considerations to select an adequate dataset for the studied area and asses its visibility taking into account the geometrical parameters of the SAR images, the type of land cover, and geometrical distortions.

3.1.1. Particular Characteristics of the Landslide Scene In order to select an appropriate SAR dataset, we take into account the principal characteristics of the scene where the landslide phenomena takes place: • Type of land cover: in our test-case the area of interest is characterized by both small patches of man-made structures (the village, sparse construction, and roads), which are usually coherent, and vegetated zones (pastures and forest), which instead are generally less coherent. • Spatial extent of the phenomena: landslide extent is in the order of 300 × 600 m2. • Deformation dynamics: according to in situ monitoring data, the expected velocity of the landslide varies between extremely slow velocity and very slow velocity according to Cruden and Varnes classification [21], depending on rainfall infiltration. • Geometry of the slope: the landslide is located on a slope that faces east, with an average inclination of 25%. • Period to be monitored: from early 2010 to middle 2015. The type of land cover of the scene and the wavelength of the satellite radar sensor will determine the amount of CTs at which displacements can be measured by A-DinSAR techniques. To consider a portion of the terrain as a CT, the backscattering properties of that area should remain stable between the dates of SAR image acquisitions. The latter occurs in bare surfaces such as rock outcrops and urbanized areas, whereas the contrary takes place in surfaces that rapidly vary their backscattering properties due to ambient factors, such as plant growth and wind, which produces the effect known as temporal decorrelation [6]. Typical values of radar sensor wavelength pulses are 3.1 cm for X-band, 5.6 cm for C-band, and 23.6 cm for L-band. The greater the wavelength, the greater the penetration capability in vegetated areas, and the lower the sensitivity of the differential phase to deformation [29]. For local scale studies as the one at hand, an orthophoto and local cartography can be enough to discern at least between vegetated and urbanized areas, giving a general overview that will help in choosing the most suitable radar frequency band. Figure7a shows the combination of a 5 m resolution orthophoto provided by the Spanish National Geographic Institute (IGN) [30] and cadastral cartography provided by the regional database of Gipuzkoa council [31]. Vegetated zones constitute nearly the 90% of the analyzed area. Dark green corresponds to conifer forest and scrubland (labelled by F) and very pale green to grasslands (G), which are mainly destined for grazing. The village occupies an extent of 0.02 km2, the average area of sparse constructions is 300 m2 and regional roads are 8 m wide. There are also unpaved trails 3 m wide crossing grazing areas, forest, and along the riverside. With this brief information of the land cover of Leintz Gatzaga study area, we maintain that the most suitable choice is X-band. The fine resolution of the satellites that operate in this band will be able to RemoteRemote Sens. Sens. 20182018, 10, ,10 x, FOR 936 PEER REVIEW 10 10 of of 29 29

Moreover, the short temporal sampling of X-band satellites can be crucial when the deformation phenomenondetect the small to be coherent monitored patches is fast that or are shows well distributed an important in the non-linear studied area behavior. [32]. Moreover, Nevertheless, the short the usetemporal of A-DInSAR sampling data ofis limited X-band to satellites landslides, can rang be crucialing from when extremely the deformation slow to very phenomenon slow phenomena, to be monitored is fast or shows an important non-linear behavior. Nevertheless, the use of A-DInSAR data according to the velocity classification of Cruden and Varnes [21], due to phase measurement is limited to landslides, ranging from extremely slow to very slow phenomena, according to the velocity ambiguity [33]. Additionally, shorter wavelength implies a better sensitivity of the differential phase classiﬁcation of Cruden and Varnes [21], due to phase measurement ambiguity [33]. Additionally, to displacement, although at the same time, the interferograms are more affected by temporal and shorter wavelength implies a better sensitivity of the differential phase to displacement, although at the volumetric decorrelation, thus causing strong decorrelation in all types of vegetated areas. These same time, the interferograms are more affected by temporal and volumetric decorrelation, thus causing effects will limit the sampling density of the surface monitoring of the studied area, which is one of strong decorrelation in all types of vegetated areas. These effects will limit the sampling density of the thesurface disadvantages monitoring of ofA-DInSAR the studied techniques, area, which while is one still of thebeing disadvantages a drastic improvement of A-DInSAR over techniques, classical monitoringwhile still methods. being a drastic improvement over classical monitoring methods.

Figure 7. (a) Orthophoto of Leintz Gatzaga landslide area. Dark green zones correspond to forest (F) Figure 7. (a) Orthophoto of Leintz Gatzaga landslide area. Dark green zones correspond to forest (F) and light green yellowish to grasslands (G). Roads are delineated in black and landslide lobes in light and light green yellowish to grasslands (G). Roads are delineated in black and landslide lobes in light blue. Urban fabric is depicted in pink. (b) Multiband image obtained by the combination of bands 2, blue. Urban fabric is depicted in pink. (b) Multiband image obtained by the combination of bands 2, 3, 4, and 8 of a S2 optical image acquired in 20 August 2017 (10 m pixel size). (c) Land cover classes 3, 4,obtained and 8 of by a supervisedS2 optical image classiﬁcation acquired from in multiband20 August S22017 image (10 m shown pixel insize). panel (c) 6b: Land pink cover for urban classes obtainedor bare by ground supervised (coherence classification is expected from to bemultiband very well S2 preserved), image shown light in green panel for 6b: grass pink (coherence for urban is or bareexpected ground to (coherence be bad preserved), is expected dark to green be forvery forest well (coherence preserved), is expectedlight green to be for completely grass (coherence loss). is expected to be bad preserved), dark green for forest (coherence is expected to be completely loss). We discarded C-band data from Envisat and ERS satellites (ESA) to perform a historical We discarded C-band data from Envisat and ERS satellites (ESA) to perform a historical analysis analysis of the landslide due to their low spatial resolution, which is 4 m in azimuth and 20 m of thein ground landslide range due if to working their low at spatial full resolution resolution, and which much is less 4 m if in applying azimuth a and multilook 20 m in with ground a SBAS range if workingcoherence-based at full resolution techniques. and However, much less other if applying techniques a multilook that combine with permanent a SBAS coherence-based scatterers and techniques.distributed However, scatterers—such other as techniques SqueeSAR [34that]—could combine improve permanent the results scatterers of C band dataand in distributed areas like scatterers—suchLeintz Gatzaga. as Just SqueeSAR a few CTs [34]—could would be detected improve in large the areasresults where of C phase band quality data in is preservedareas likealong Leintz Gatzaga.time (village) Just a few and CTs none would in area be covereddetected by in smaller large areas man-made where structuresphase quality (sparse is preserved constructions along and time (village)roads). and However, none in other area satellitecovered products by smaller which man-made operate instructures C-band with(sparse enhanced constructions spatial resolution, and roads). However,such as RADARSAT-2other satellite (Canadianproducts which Space operate Agency, in CSA) C-band or recently with enhanced launched spatial Sentinel-1 resolution, (ESA) satellite such as RADARSAT-2mission, could (Canadian be appropriate Space for Agency, this scenario CSA) inor further recently studies. launched Sentinel-1 Sentinel-1 is a small (ESA) baselines satellite mission,constellation could withbe appropriate a low revisit for time this (six scenario days in in constellation, further studies. 12 days Sentinel per satellite)-1 is a whichsmall ensuresbaselines constellationlow temporal with baselines a low betweenrevisit time acquisitions. (six days ALOS-1 in constellation, PALSAR data 12 days (Japanese per satellite) Spatial Agency, which JAXA),ensures lowwhich temporal have abaselines spatial resolution between ofacquisitions. about 10 m, AL wouldOS-1 be PALSAR also suitable data for (Japanese this application Spatial because Agency, JAXA),the L-band which is have characterized a spatial byresolution a greater of foliage about penetration 10 m, would capability, be also thus suitable extending for this the application coverage becauseto landslides the L-band in relatively is characterized vegetated by areas a greater [35]. However, foliage penetration the longer revisitingcapability, time thus of extending this satellite the coverage to landslides in relatively vegetated areas [35]. However, the longer revisiting time of this satellite (46 days) reduces the quality of both the displacement velocity and the displacement time series [36]. Indeed, only a few images with large baselines were available in the archive for use in this

Remote Sens. 2018, 10, 936 11 of 29

(46 days) reduces the quality of both the displacement velocity and the displacement time series [36]. Indeed, only a few images with large baselines were available in the archive for use in this study, which are not enough to obtain reliable results. A signiﬁcant improvement for L-band sensors is ALOS-2 satellite, which was launched in mid-2014 and has revisiting cycle of 14 days.

3.1.2. Selected SAR Datasets Parameters Second generation satellites [32] working at X-band, such as COSMO-SkyMed (Italian Space Agency, ASI) or TerraSAR-X (German Aerospace Centre, DLR), provide SAR products mainly on demand. However, sometimes historical data are available from previous planning. COSMO-SkyMed (CSK) is a four-spacecraft constellation whose single satellite has a 16 days repeat cycle; the final revisit time can be significantly shorter depending on the number of active satellites and their configuration. A set of 43 ascending Stripmap HIMAGE mode SAR images was available in the CSK archive, covering the period from 15 May 2011 to 1 August 2013. The time interval overlaps inclinometric and crack gauge data (Figure3a) allowing us to compare A-DInSAR data to ground truth one. The minimum time interval between subsequent images of the available CSK dataset is eight days, while the maximum is 48 days. We also acquired, by request, 22 High Resolution Spotlight TerraSAR-X (TSX) images covering the period from 10 February 2013 to 3 August 2014, which overlaps inclinometric, crack gauge, GNSS/HPL, and EDM data (Figure3a). The repeat cycle of the TSX satellite is 11 days and the maximum interval between subsequent images of the available dataset is 121 days. Spotlight mode has the advantage of having a fine ground projected resolution (0.9 × 1.6 m), although in our case ground resolution is lowered due to the SBAS approach that uses multilook (ML). To obtain a ground resolution of 10 × 10 m, the ML factor used for the TSX Spotlight mode (13 × 7) is greater than the one use for the CSK Stripmap mode (5 × 5). The fact that more pixels are averaged allows reducing the coherence threshold of the TSX interferograms for the same phase standard deviation. Datasets acquisition parameters are summarized in Table1.

Table 1. Acquisition parameters of the selected datasets.

Sensor CSK TSX Number of images 43 22 Acquisition orbit Ascending Ascending Acquisition mode Stripmap HIMAGE High Resolution Spotlight (HS) Polarization HH HH Temporal interval 15 May 2011 to 1 August 2013 10 February 2013 to 3 August 2014 Minimum revisit time (days) 7 11 Maximum revisit time (days) 48 121 Incidence angle (◦) 27 35 Tracking angle (◦) 348 350 Ground projected resolution 2.2 m × 2.2 m 0.9 m × 1.6 m (Azimut × Range) Multilook factor applied 5 × 5 13 × 7 (Azimuth × Range) Ground resolution with ML ≈10 m × 10 m ≈10 m × 10 m

3.2. Visibility Analysis

3.2.1. Land Cover Classification International and regional land cover databases are freely available as cartographic products. Some examples are the Coordination of Information on the Environment project from the European Environmental Agency (CORINE) or the Spanish land use information system (SIOSE) from the Spanish National Plan for Territory Observation (PNOT). However, these databases have a medium-low spatial resolution (the minimum mapping unit of SIOSE for urban areas is one hectare Remote Sens. 2018, 10, 936 12 of 29 and for natural and agricultural areas is two hectares), which is not useful to site scale case studies as the one in hand. High resolution land cover information, in order to assess the expected CTs density in small-scale areas of interest, can be achieved with high resolution optical data. In that respect, we exploit optical images acquired by Sentinel-2 (S2) ESA mission, that comprises twin satellites flying in the same orbit but phased at 180◦, with a revisit frequency of five days at the Equator. Sentinel-2A satellite was launched on 23 June 2015 and Sentinel-2B on 07 March 2017. They carry an optical instrument payload that samples 13 spectral bands: four bands at 10 m, six bands at 20 m, and three bands at 60 m spatial resolution, ideal for land classification purposes [37]. Image classification refers to the task of extracting information classes from a multiband raster image, resulting on thematic maps such as land cover types. The two most common approaches are supervised and unsupervised image classification techniques, depending on the interaction between the analyst and the computer during classification [38]. The former uses the spectral signatures obtained from training samples to classify an image. The latter finds spectral classes without the analyst’s intervention. Among the various algorithms geared toward supervised classification, one of the most common used is maximum likelihood classification. We performed land cover classifications in two S2 images acquired at 22 April 2017 and 20 August 2017. The selection criteria was to have a low cloud shadow percentage (0.16 and 0.15 respectively) and to belong to different seasons. The former corresponds to the end of the rainy season (84.4 mm of precipitation accumulated in the previous 30 days) and the latter to summer that is typically dry (16.1 mm of precipitation accumulated in the previous 30 days). We tested different band combinations and resolutions of 10 and 20 m. In order to gain a relative evaluation between the classifications, we calculated statistics using the kappa coefficient of agreement [39]. The first step was to identify the main classes of land cover according to the area of interest characteristics, the preservation of coherence among time and the X radar wavelength. We distinguish three classes of pixels:

• LC-1: Urban (buildings, roads and bare ground). Coherence is expected to be very well preserved through time. • LC-2: Grasslands. Coherence is expected to be poorly preserved. • LC-3: Forest. Coherence is expected to be completely lost.

The second step was to combine spectral bands sampled by S2 at 10 m and 20 m spatial resolution to create the multiband images of the area of interest (that has an extent of 2020 × 1440 m). The spectral bands used for each combination can be found in the third column of Table2. Then we created training samples in order to identify classes. We ensured that training samples were representative for the area using a 0.25 cell resolution orthophoto, Google Earth map service system, and in situ evaluation during field campaigns. The percentage of training samples area with respect to the whole study area clip is 20.6%. We generated 14 land cover classifications (Table2), one for each multiband image, using a maximum likelihood algorithm. The evaluation of the statistical significance of the difference in accuracy between two thematic maps derived from remotely sensed data has often been based on the comparison of the kappa coefficient calculated for each map [40]. The kappa coefficient of agreement for a thematic map is based on the comparison of the predicted and actual class labels for each case in the testing set and may be calculated from [39]

P − P K = 0 C , (1) 1 − PC where P0 is the proportion of cases in agreement (i.e., correctly allocated) and PC is the proportion of agreement that is expected by chance. To compute these proportions, we calculated the error matrix (or confusion matrix) of each classification, which are tables that show correspondence between the predicted class (classification results) and the actual class (ground truth). Error matrices are available in the Supplementary Material (Table S1). The kappa coefficients and the percentage occupied by each Remote Sens. 2018, 10, 936 13 of 29 land cover class is shown in Table2. According to the classification results, the area of interest is mostly covered by LC-3 class (forest) with mean percentage of 51.4%, followed by LC-2 (grass) with mean percentage of 33% and LC-1 (urban) with mean percentage 15.6%. LC-3 is the class whose percentage varies the least among different classifications and LC-1 the one that varies the most. In general, the classifications obtained from the S2 image of April detect more amount of LC-1. The best kappa coefficient is the one that corresponds to the classification obtained from S2 image of 20 August 2017 with a resolution of 10 m and two-, three-, four-, and eight-band combination (Figure7b,c).

3.2.2. Geometrical Distortion (Relief Index) Classification The side look acquisition mode of the SAR satellites antenna produces geometrical distortions of the terrain area that is imaged in each SAR resolution cell when the topography is not flat [41]. This complicates landslide monitoring with A-DInSAR techniques, as they typically develop along slopes, and careful analysis should be made in order to select the best available acquisition configuration. In general terms, the descending and ascending geometries are favorable, respectively, for west and east facing slopes [25] and landslides that present an orientation parallel to the satellite orbit (quasi polar) will have motion orthogonal to the LOS and thus be undetectable. In a SAR image, the features of the scene are projected along the radar line of sight (LOS, also known as the “slant-range”); or, in other words, ordered depending upon its distance from the satellite along its side-looking direction. Consequently, the terrain slopes facing the radar appear compressed in the image, known as foreshortening effect, and if the inclination of the slope exceeds the incidence angle, the scatters are imaged in reverse order and superimposed on the contribution coming from other areas, causing the layover effect [6,42]. On the other hand, scatters located on the opposite side can be affected by two distortions; if terrain slope is equal to the radar incidence angle the cell dimension becomes very large and details are lost and if the terrain slope exceeds the radar incidence angle the resolution cells cannot be imaged as they are in shadow. The inclination and orientation of slopes can be easily derived from a digital elevation model (DEM) using a geographic information system (GIS). National and regional cartographic open databases in Spain, such as the National Geographic Institute (IGN), provide a wide range of topographic data and DEMs with resolutions up to 2 m. The topographic information can eventually be combined with the orbit parameters of the SAR images datasets to obtain a geometrical visibility map [13,25,43,44]. We use the relief index (RI) [26] in order to check if coherent targets will be visible in the Leintz Gatzaga study area using the CSK and TSX datasets. RI equation [26] is defined as the ratio between the slant range and the ground range, taking into account the acquisition geometry of the radar and the orientation (aspect model) and inclination (slope model) of the terrain

RI = − sin{[(S × sin(A)] − sin(i)}, (2) where S is the slope, A is the corrected aspect with respect to the track angle of ascending or descending orbit mode, and i is the incidence or ‘off–nadir’ angle of the satellite antenna. Values of the RI range from −1 (layover or shadow effects) to +1 (slope with very good geometrical visibility). We define four classes according to [45]. For the first class (RI-1), no CTs can be detected due to geometrical distortions such as layover and shadow effects for negative values and foreshortening effect for RI values very close to zero. The second class (RI-2) comprises values between 0 and 0.33 and indicates that the slope geometry is not favorable for the satellite LOS geometry; therefore, it is expected to retrieve a lower number of CTs. RI-3 comprises values between 0.33 and 0.66 and indicates flat areas or slopes with an acceptable geometry with respect to the satellite LOS. In general, when the value of RI is greater than 0.3, the slope is well-oriented and the main factor that influences the CTs distribution is land use [26]. Finally, RI-4 (values from 0.66 to +1) stands for areas where the slope direction is approximately parallel to the satellite LOS direction and therefore are the most suitable areas to detect CTs. Remote Sens. 2018, 10, 936 14 of 29

Table 2. Kappa coefﬁcients of 14 land cover classiﬁcations obtained from multiband images. The distribution, in terms of percentage, of the land cover (LC) classes on the studied scene (extent of 2020 × 1440 m) is also shown. The last columns show the percentage of detected coherent targets (CT) of CSK and TSX datasets for each LC class. Note that the number of pixels of the whole scene is 29,088 for the 10 m resolution images and 7373 for the 20 m resolution ones.

LC Class Distribution Detected CTs (%) for Each LC Class (%) of the Studied Scene CSK (926 CTs) TSX (658 CTs) Cell Bands Kappa S2 Image LC-1 LC-2 LC-3 LC-1 LC-2 LC-3 LC-1 LC-2 LC-3 Resolution (m) Combination Coefﬁcient 22 August 3,4,2 0.69 16.2 33.6 50.2 85.1 12.1 2.8 88.8 7.8 3.5 10 (29,088 2017 2,3,4,8 0.785 16.6 30.8 52.6 85.2 11.4 3.3 89.7 6.8 3.5 pixels) 20 August 3,4,2 0.807 12.6 35.9 51.5 79.6 18.6 1.8 82.8 14.1 3 2017 2,3,4,8 0.813 12.1 34.6 53.3 79.9 17.5 2.6 86.6 10.9 2.4 3,4,2 0.63 16.2 34.4 49.4 83.9 13.3 2.8 86.6 10.6 2.7 2,3,4,8A 0.694 18.9 28.9 52.2 87.7 8.7 3.6 90.1 7.3 2.6 22 April 5,4,2 0.652 16.8 33.5 49.7 84.7 12.3 3 89.1 8.1 2.9 2017 7,4,2 0.666 17.6 29.4 53 84.7 10.7 4.6 88 8.1 4 2,3,4,5,6,7,8A,11,12 0.75 21 28.5 50.5 95.5 3.3 1.2 94.4 4.1 1.5 20 (7373 pixels) 3,4,2 0.697 13.5 35.6 50.9 81.4 16.4 2.2 87.8 10.6 1.5 2,3,4,8A 0.688 12.9 36 51.1 80.1 17.6 2.3 86.5 12.3 1.2 20 August 5,4,2 0.705 13.6 36.1 50.3 79.8 18 2.2 85.4 11.9 2.7 2017 7,4,2 0.681 12.8 35.9 51.3 78 20 2 85.6 13.4 1.1 2,3,4,5,6,7,8A,11,12 0.754 17.8 28.8 53.4 90.9 5.9 3.1 93 4.6 2.4 Ave. 15.6 33.0 51.4 84.0 13.3 2.7 88.2 9.3 2.5 Max. 21.0 36.1 53.4 95.5 20.0 4.6 94.4 14.1 4.0 Min. 12.1 28.5 49.4 78.0 3.3 1.2 82.8 4.1 1.1 Remote Sens. 2018, 10, 936 15 of 29 Remote Sens. 2018, 10, x FOR PEER REVIEW 15 of 29

ToTo calculate calculate thethe slopeslope andand aspectaspect necessarynecessary toto obtainobtain thethe geometricalgeometrical visibilityvisibility maps,maps, wewe usedused aa 55 mm resolutionresolution DEMDEM providedprovided byby IGNIGN [[46],46], whichwhich wewe resampledresampled to to 10 10 mm andand 2020 m.m. AsAs forfor thethe casecase ofof landland covercover classiﬁcations,classifications, thethe 1010 mm resolutionresolution cellcell scenescene givesgives 29,08829,088 pixelspixels andand thethe 2020 mm resolutionresolution cellcell scenescene 73737373 pixels.pixels. TheThe acquisitionacquisition characteristicscharacteristics ofof thethe two SAR datasets are included in Table1 1.. FigureFigure8 8shows shows the the 10 10 m m resolution resolution RI mapsRI maps of the of CSKthe CSK and and TSX TSX datasets datasets of Leintz of Leintz Gatzaga Gatzaga study study area. Forarea. both For datasets, both datasets, RI maps RI indicate maps indicate that most that of the most landslide of the extentlandslide has goodextent to has very good good to geometrical very good visibility.geometrical Note visibility. that the Note opposite that slopethe opposite of the valley slope that of the faces valley west that shows faces low west RI shows values. low In the RI casevalues. of CSKIn the dataset case of geometry, CSK dataset the most geometry, common the classmost is common RI-3, both class for theis RI-3, whole both clip for and the landslide whole clip extent, and whilelandslide for the extent, TSX iswhile RI-4. for CSK the dataset TSX is has RI-4. a worse CSK data geometricalset has a visibility worse geometrica because ofl itsvisibility smaller because incidence of angleits smaller that induces incidence higher angle topographic that induces effects higher [14 topogr]. Theaphic distribution effects of[14]. each The class distribution of the four of RI each maps class is includedof the four in RI Table maps3 in is terms included of percentage. in Table 3 in terms of percentage.

FigureFigure 8.8.RI RI maps maps or geometricalor geometrical visibility visibility maps maps for (a) CSKfor (a and) CSK (b) TSXand ascending (b) TSX datasetsascending (resolution datasets 10(resolution m). Black 10 arrows m). Black indicate arrows right indicate side-looking right side antenna-looking of the antenna ascending of the satellite ascending orbits. satellite orbits.

TableTable 3.3. Distribution,Distribution, in in terms terms of ofpercen percentage,tage, of the of theRI classes RI classes on the on studied the studied scene scene(extent (extent of 2020 of × 20201440× m).1440 Bottom m). rows Bottom show rows the show percentage the percentage of detected of coherent detected targets coherent (CT) targets of CSK (CT) and ofTSX CSK datasets and TSXfor each datasets RI class. for each RI class. RI Class Distribution (%) of the Studied Scene RI Class Distribution (%) of the Studied Scene CSK geometry TSX geometry Cell resolution (m) Cell resolution RI-1CSK RI-2 geometry RI-3 RI-4 RI-1 TSX RI-2 geometry RI-3 RI-4 (m) 10 RI-1 1.5 RI-213.6 RI-356.5 RI-428.4 RI-10.5 RI-2 8.4 RI-340.3 RI-4 50.8 2010 1.5 1.6 13.613.5 56.556.4 28.428.2 0.50.6 8.4 8.7 40.3 40 50.850.7 20 1.6 13.5Detected 56.4 CT 28.2 (%)0.6 for each 8.7 RI 40class 50.7 CSK datasetDetected (926 CT CT) (%) for each TSX RI dataset class (658 CT) RI-1CSK datasetRI-2 (926RI-3 CT) RI-4 TSXRI-1 dataset RI-2 (658RI-3 CT) RI-4 10 RI-1 0 RI-21.1 RI-374.9 RI-424 RI-10 RI-20.3 RI-354.9 RI-4 44.8 20 0 0.6 76.3 23 0 0.2 52.1 47.4 10 0 1.1 74.9 24 0 0.3 54.9 44.8 20 0 0.6 76.3 23 0 0.2 52.1 47.4

Remote Sens. 2018, 10, 936 16 of 29 Remote Sens. 2018, 10, x FOR PEER REVIEW 16 of 29 3.2.3. Land Cover and RI Classes’ Distribution 3.2.3. Land Cover and RI Classes’ Distribution If we combine land cover and relief visibility data we obtain the relative distribution of LC and If we combine land cover and relief visibility data we obtain the relative distribution of LC and RI classes of the cells or pixels that conform the studied scene. Histograms of Figure9 show the LC RI classes of the cells or pixels that conform the studied scene. Histograms of Figure 9 show the LC and RI distribution of the 29,088 pixels (10 m resolution) of the multiband image with the best kappa and RI distribution of the 29,088 pixels (10 m resolution) of the multiband image with the best kappa coefficient (two-, three-, four-, eight-spectral band combination, date 20 August 2017, cell resolution coefficient (two-, three-, four-, eight-spectral band combination, date 20 August 2017, cell resolution 10 m, Table2) and the relief classifications for the CSK and TSX geometries (Table3). The shape of 10 m, Table 2) and the relief classifications for the CSK and TSX geometries (Table 3). The shape of the graphs are the same for any LC classification and cell resolution, although the total number of the graphs are the same for any LC classification and cell resolution, although the total number of pixels for each class varies. For all the computed LC classifications, the scene is mostly composed by pixels for each class varies. For all the computed LC classifications, the scene is mostly composed by pixels categorized as forest, followed by grass and urban fabric. With the geometrical parameters of pixels categorized as forest, followed by grass and urban fabric. With the geometrical parameters of the CSK dataset, there are more pixels categorized as RI-3 and, for the TSX dataset there are more the CSK dataset, there are more pixels categorized as RI-3 and, for the TSX dataset there are more categorized as RI-4. The reason for this is that the incidence angle of the satellites antennas are different. categorized as RI-4. The reason for this is that the incidence angle of the satellites antennas are From this result, we can conclude that given the relief of the studied area, the TSX incidence angle is different. From this result, we can conclude that given the relief of the studied area, the TSX incidence more adequate, as a greater amount of pixels has high geometrical visibility. angle is more adequate, as a greater amount of pixels has high geometrical visibility.

FigureFigure 9. 9. RelativeRelative distribution distribution in in the the studied studied scene scene of of land land cover cover (from (from classification classiﬁcation obtained obtained from from S2-20S2-20 August August 2017, 2017, bands 2348 and 1010 mm resolutionresolution thatthathas has the the better better kappa kappa coefﬁcient) coefficient) and and RI RI (10 (10 m mresolution) resolution) classes classes of ( aof) CSK (a) datasetCSK dataset geometric geometric parameters parameters and (b) TSXand dataset(b) TSX geometric dataset parameters.geometric parameters.Pixel density Pixel of each density class of witheach respectclass with to therespect total to amount the total of amount 29,088 pixelsof 29,088 of thepixels studied of the scene studied are scenelabelled are inlabelled terms ofin percentage.terms of percentage.

3.3.3.3. A-DInSAR A-DInSAR Processing Processing A-DInSARA-DInSAR techniques techniques use use a a stack stack of of SAR SAR images images to to extract extract the the temporal temporal evolution evolution of of the the deformation.deformation. These These techniques techniques can can be be divided divided in intoto two two categories categories depending depending on on how how targets targets are are selectedselected in in order order to to obtain obtain the the time time series series displa displacements:cements: prevalent prevalent phase-coherent phase-coherent radar radar targets targets by by amplitudeamplitude stability stability or or persistent persistent scatter scatter (PS) (PS) methods methods [47] [47] and and distributed distributed targets targets by by spatial spatial coherence coherence oror small small baseline baseline subsets subsets (SBAS) (SBAS) [48–50]. [48–50]. In In genera general,l, the the permanent permanent scatters scatters approach, approach, which which selects selects thethe PS PS candidates candidates using using the the amplitude amplitude stability stability of of pixels pixels along along time, time, is is better better suited suited for for urban urban areas areas duedue to to the the high high reflectivity reﬂectivity and and low low decorrelati decorrelationon during during man-made man-made cons construction,truction, but but a a minimum minimum numbernumber of of about about 25 25 acquisitions acquisitions is is required required [47]. [47]. The The elevated elevated cost cost of of acquiring acquiring such such a a number number of of imagesimages from from commercial commercial X-band X-band satellites, satellites, can can be be a a limiting limiting factor factor both both for for commercial commercial and and scientific scientiﬁc uses.uses. Spatial Spatial coherence coherence techniques require aa reducedreduced setset ofof imagesimages duedue toto theirtheircapability capability to to generate generate a alarger larger number number of of interferograms. interferograms. However,However, resolutionresolution isis loweredlowered duedue toto the multilooking operation. ForFor Leintz Leintz Gatzaga Gatzaga landslide, landslide, we we used used the the SU SUBSIDENCE-GUIBSIDENCE-GUI processor processor which which is is based based on on the the coherentcoherent pixelspixels techniquetechnique (CPT) (CPT) developed developed at the at remote the remote sensing sensing laboratory laboratory (RSLab) of(RSLab) the Universitat of the UniversitatPolitècnica Politècnica de Catalunya de Catalunya (UPC) [51 (UPC)]. This [51]. approach This approach has some has similarities some similarities with the with most-widely the most- widelyused Small used Baselines Small Baselines Subset (SBAS),Subset (SBAS), when we when use multilookedwe use multilooked images andimages the selectionand the selection of pixels of by pixelscoherence by coherence criteria (as criteria in our case),(as in butour differscase), but in the differs method in the used method for velocity used estimation.for velocityThis estimation. method Thishas providedmethod has good provided results good in the results past for in similarthe past test for sitessimilar [14 test] and sites it was[14] chosenand it was because chosen it hasbecause been itproven has been to also proven work to with also awork limited with number a limited of acquisitions, number of acquisitions, such as the set such of TSXas the used set here.of TSX used here.

Remote Sens. 2018, 10, 936 17 of 29 Remote Sens. 2018, 10, x FOR PEER REVIEW 17 of 29

ForFor areas areas with with a a high high vegetation vegetation surface surface coverage coverage,, as as the the one one presented presented in in this this paper, paper, the the user user hashas to to test test different different options options to to find find the the best best comp compromiseromise between between pixels’ pixels’ density density and and their their quality. quality. It isIt important is important to toavoid, avoid, or orat atleast least reduce reduce to to the the minimum, minimum, the the number number of of pixels’ pixels’ subsets subsets (groups (groups of of pixelspixels disconnected disconnected from from the the rest). rest). Using Using multilook multilook images images and and a a coherence coherence criteria criteria to to select select those those pixelspixels with with enough enough phase phase quality quality has has a a drawback: drawback: the the chances chances of of detecting detecting isolated isolated coherent coherent scatters scatters surroundedsurrounded by by low low coherence coherence areas areas is isreduced. reduced. Achieving Achieving a good a good atmospheric atmospheric phase phase filtering filtering rely rely on havingon having a well a well temporal temporal distributed distributed dataset dataset and and hi highgh pixel pixel density density (preferably (preferably with with short short spatial spatial connectionsconnections between pixels).pixels). ForFor both both SAR SAR datasets, datasets we, we used used a coherence a coherence selection selection approach approach [50] with[50] witha 5 × a5 5 multi-look× 5 multi-look for the for CSK the CSK and aand 13 ×a 137 multi-look × 7 multi-look for the for TSX, the TSX, obtaining obtaining ground ground resolution resolution pixels pixelsof approximately of approximately 10 × 10 10 m. × 10 For m. each For dataset, each dataset, we cropped we cropped the SAR the images SAR images to an area to an of area about of 18 about km2 18and km co-registered2 and co-registered to a common to a common master geometry. master geometry. We used aWe 5-m used resolution a 5-m resolution DEM [46] toDEM remove [46] theto removetopographic the topographic contribution. contribution. The mean coherences The mean maps coherences presented maps in Figure presented 10a,c showin Figure that coherence10a,c show is thatvery coherence well preserved is very in well the preserved village, highway, in the village, and roads, highway, while, and as roads, expected, while, vegetated as expected, areas vegetated are fully areasdecorrelated. are fully Overall,decorrelated. the density Overall, of the coherent density areas of coherent is large areas enough is large to obtain enough reliable to obtain deformation reliable deformationmaps although maps long although spatial connectionslong spatial betweenconnections pixels between had to pixels be used had (Figure to be 10usedb,d). (Figure The temporal 10b,d). Thefilter temporal is adjusted filter depending is adjusted on the depending dataset characteristics, on the dataset the characteristics, image temporal the sampling, image totemporal have at sampling,least enough to have temporal at least samples enough to temporal average. Thesamp softwareles to average. used automatically The software expandsused automatically the filter to expandsinclude morethe filter images to include in case more of temporal images gaps in case in theof temporal data. gaps in the data.

FigureFigure 10. 10. (a)( aCoherence) Coherence map map of CSK of CSK data. data. Black Blackcolor indicates color indicates 0 value, 0 white value, color white 1. color(b) CT 1. b c connection( ) CT connection graph obtained graph obtainedwith spider with web-like spider triangulation web-like triangulation of CSK data. of ( CSKc) Coherence data. ( )map Coherence of TSX map of TSX data. (d) CT connection graph obtained with spider web-like triangulation of TSX. data. (d) CT connection graph obtained with spider web-like triangulation of TSX. Leintz Gatzaga Leintz Gatzaga village (L.G.) and Vitoria-Éibar highway (H) are located in all ﬁgures. The reference village (L.G.) and Vitoria-Éibar highway (H) are located in all figures. The reference point is depicted point is depicted in red. in red.

FromFrom the the 43 43 CSK CSK SAR images, we we generated 90 90 interferograms, with with a a maximum maximum temporal temporal baselinebaseline of of 250 250 days days and and spatial spatial baselines baselines below below 500 500 m. m. From From the the 22 22 TSX TSX SAR SAR images, images, we we generated generated 7676 interferograms, interferograms, with with the the same same temporal temporal and and ba baselinesselines restrictions. restrictions. Connection Connection diagrams diagrams of the of interferograms of the CSK and TSX acquisitions is available in the Supplementary Material (Figure

Remote Sens. 2018, 10, 936 18 of 29

theRemote interferograms Sens. 2018, 10, x FOR of thePEER CSK REVIEW and TSX acquisitions is available in the Supplementary 18Material of 29 (Figure S1). We placed the reference point on the highway surface (red triangle in Figure 10b,d), whereS1). We displacements placed the reference are known point to beon inexistent.the highway In surface order to(red select triangle the CTsin Figure candidates, 10b,d), the where model coherencedisplacements threshold are known was to set be ininexistent. 0.4 for theIn order CSK to dataset select the and CTs 0.3 candidates, for the TSX. the model The nextcoherence step of thethreshold CPT algorithm was set in consists0.4 for the on CSK carrying dataset out and a 0.3 triangulation for the TSX. The network next step in which of the CPT nodes algorithm (CTs) are interconnected,consists on carrying with theout objectivea triangulation of working network with in phasewhich incrementsnodes (CTs) among are interconnected, CTs instead ofwith absolute the phases,objective which of working reduces with unwrapping phase increments problems. amon Ing ourCTs test-site,instead of some absolute areas phases, have which low CTs reduces density (lessunwrapping 1 × 104 pixels/km problems.2 In), our so wetest-site, employed some areas redundant have low network CTs density strategies. (less 1 × CTs 104 candidatespixels/km2), wereso connectedwe employed uniformly redundant in all network directions strategies. with arcs CTs no candidates longer than were 800 m,connected forming uniformly a spider-web-like in all networkdirections [52 with], which arcs addedno longer redundancy than 800 m, to theforming system a spider-web-like and ﬁve additional network links [52], with which respect added to the Delaunayredundancy triangulation. to the system A total and subsetfive additional of 1794 CTs link fors with the CSK respect dataset to the and Delaunay 1322 for thetriangulation. TSX passed A the total subset of 1794 CTs for the CSK dataset and 1322 for the TSX passed the selection criteria. selection criteria.

3.3.1.3.3.1. A-DInSAR A-DInSAR ResultsResults AsAs expected expected from from the the pre-processing pre-processing analysis, analysis, CTs CT ofs of both both datasets datasets are are restricted restricted to urbanizedto urbanized areas areas and roads. Despite selecting just a few CTs that accomplished coherence criteria between the and roads. Despite selecting just a few CTs that accomplished coherence criteria between the highway highway (were the reference point was placed) and the Leintz Gatzaga village, relatively dense (were the reference point was placed) and the Leintz Gatzaga village, relatively dense connections connections were achieved (Figure 10b,d). However, other clusters of CTs within the cropped were achieved (Figure 10b,d). However, other clusters of CTs within the cropped processed area have processed area have remained totally disconnected or with scarce connections, which compromise remained totally disconnected or with scarce connections, which compromise phase reliability and, phase reliability and, hence, surface displacement rate estimations. Moreover, these clusters are hence, surface displacement rate estimations. Moreover, these clusters are located either out of the located either out of the landslide area of interest, or over the confronting slope of the valley, so we landslidehave discarded area of them interest, in the or results over the and confronting post processi slopeng analysis. of the valley, From so the we total have initial discarded subsets them of 1794 in the resultsCTs of and the postCSK processingdataset and analysis. 1322 of the From TSX, the 926 total and initial 658 CTs, subsets respectively, of 1794 CTs remained of the CSKafter datasetclipping and 1322the ofarea the of TSX, interest 926 and(Figure 658 11), CTs, which respectively, coincides remained with the after extent clipping of the theclip area used of for interest the suitability (Figure 11 ), whichanalysis coincides (2020 × with1440 them). extent of the clip used for the suitability analysis (2020 × 1440 m).

FigureFigure 11. 11.LOS LOS velocityvelocity maps of the area of of interest interest of of (a (a) )CSK CSK and and (b (b) TSX) TSX datasets. datasets. Positive Positive values values indicateindicate movement movement goinggoing away from from satellite. satellite. A A se secondarycondary landslide landslide located located to tothe the South South of ofLeintz Leintz GatzagaGatzaga villagevillage isis labelledlabelled as S.L. Black arrows arrows indicate indicate right right side-looking side-looking antenna antenna of ofthe the ascending ascending satellitesatellite orbits. orbits.

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The standard deviation (s.d.) of mean LOS velocity values considering all coherent targets of the area of interest (2020 × 1440 m) is 5 mm/year for the CSK and 6 mm/year for the TSX datasets. The scene covers a wider area than the active landslide and includes stable zones. To the latter is added the fact that the landslide does not move homogeneously, which explains high s.d. values. We have assessed a stability threshold of 2 s.d. to establish active CT points. A point is considered active if |LOS velocity| > 2 s.d [53], where |LOS velocity| is the absolute LOS velocity value of each CT. Two standard deviation maps of the area of interest, one for each SAR dataset, are available in the Supplementary Material (Figure S2). We observe different active areas in the scene. The first one is Leintz Gatzaga landslide. It exhibits a mean LOS velocity of 6 mm/year for the extent of time monitored with the CSK dataset and 8 mm/year for the extent of time monitored with TSX. The s.d. of mean LOS velocity considering just the CT located within the landslide body, is 3 mm/year for CSK and 2 mm/year for TSX. Both monitoring periods overlap six months. The landslide does not move homogenously, but presents differential movements, also detected with other in situ monitoring techniques such as GNSS (Figure5). These differences are highlighted in the standard deviation maps (Figure S2). Active zones concentrate in the head of the landslide and at the NE border of the village for both SAR datasets. The CSK dataset also presents active zones in the center of the village. The second active area is at the crown of a small secondary and individual landslide located to the South of Leintz Gatzaga village (labelled as S.L. in Figure 11). A small cluster of CT show mean LOS velocities up to 15 mm/year for the CSK dataset and up to 22.5 mm/year for the TSX dataset. We identified tension cracks at the crown of this secondary landslide in field work (Figures S2 and S3). To the east of the secondary landslide, CTs along the GI-3310 road also exhibit displacement, being more pronounced in the period monitored by the TSX. This area is covered by colluviums (Figure1) where creeping and shallow movements are common. The third active area is Vitoria- Éibar highway. Even though is outside the area of influence of Leintz Gatzaga landslide, a brief analysis is unavoidable, as CPT processing has clearly detected movement at both soil cut slopes sides for the CSK dataset and to the east side for the TSX dataset. No measurements were taken with other monitoring techniques in this area, only in situ inspection was carried out. West side cut slope is composed by dark fine graded colluvium deposits and is currently reinforced with bolts. It is very scarcely vegetated and running water scars are present. Detected motion is extremely slow and it could be due to soil adjustment because the maximum load of the bolts was not reached during the SAR monitoring period or due other shallow process like surface washing. The east side cut slope exhibit a much greater rate of LOS movement that reaches up to 44 mm/year in the CSK dataset. Here a very small (50 × 100 m) local failure was identified; the main scarp can be clearly distinguished in the orthophoto. Surface movement detection at the cut slopes is a clear example on how remote sensing A-DInSAR techniques have the advantage of capturing unexpected or not previously registered with other techniques displacements. Other active zone was detected at the NW part of the scene, due to material for construction being quarried.

3.3.2. CT Detection Suitability Assessment As described in the previous sub-section, we identified three main classes or categories of land cover (LC) according to the preservation of coherence along time and four classes of relief index (RI) according to geometrical distortions due to topography. To discern which was the best band combination to perform an automated land cover classification, we tested different options (Table2) and computed the kappa coefficient for each of them. The higher value was obtained for image S2—20 August 2017, bands 2348, and 10 m resolution. Once both SAR datasets with the CPT software, were processed we overlapped the A-DInSAR results with the LC and RI maps in order to obtain the number of CTs that fail within each class. Results are shown in right columns of Table2 (LC) and bottom rows of Table3 (RI) in terms of percentage. Regarding the land cover, an average of 84% and 88.2% of the CT were detected in LC-1 class (urban), 13.3% and 9.3% in LC-2 (grasslands) and 2.7% and 2.5% in LC-3 (forest) for CSK and TXS datasets, respectively. This result is as expected; CTs will be Remote Sens. 2018, 10, 936 20 of 29 detected at surfaces where coherence is preserved, as it the case of urban fabric. In general, the land coverRemote maps Sens. with 2018,20 10, mx FOR resolution PEER REVIEW cell detect more CTs at urban areas; the reason is because 20CTs of 29 have a resolution of 10 m after applying multilook so there are more chance that a CT fail within an area have a resolution of 10 m after applying multilook so there are more chance that a CT fail within an classifiedarea classified as urban. as Note urban. that Note georeferentation that georeferenta errorstion have errors not have been not taken been into taken account. into account. Regarding the RI,Regarding most of the the RI, CTs most were of the detected CTs were in areasdetected classified in areas as classified RI-3, for as both RI-3, datasets.for both datasets. Table4 showsTable 4 the averageshows proportion the average (from proportion all the classifications)(from all the classifications) of the CTs detected of the CTs in detected each class in witheach respectclass with to the totalrespect amount to ofthe pixels total amount of the studied of pixels scene. of the Again,studied percentage scene. Again, values percentage of 20m values resolution of 20m cells resolution are higher becausecells theare totalhigher amount because of the pixels total of amount the studied of pixels scene of the are studied less (7373 scene pixels) are less than (7373 the pixels) finer resolution than the of 10 mfiner (29,088 resolution pixels). ofAs 10 form (29,088 previous pixels). works As [for26], previous the influence works of[26], R indexthe influence on CT densityof R index is strongon CT for valuedensity under is 0.3. strong We for expected value under that the 0.3. highest We expected percentage that the was highest achieved percentage by LC-1 was and achieved RI-4 combination. by LC-1 However,and RI-4 it wascombination. for RI-3. ThisHowever, might it bewas due for to RI-3. two Th reasons:is might the be presencedue to two of reasons: disconnected the presence patches of and lossdisconnected of coherence patches of bare and ground loss of classified coherenc ase of LC-1. bare ground classified as LC-1.

Table 4. Average values of (from all the classifications) of the CTs distribution for each class with Table 4. Average values of (from all the classifications) of the CTs distribution for each class with respect to the total amount of pixels of the studied scene. respect to the total amount of pixels of the studied scene. CSK CT Distribution (%) TSX CT Distribution (%) Studied Scene Cell Resolution CSK CT Distribution (%) TSX CT Distribution (%) Studied Scene Cell Resolution RI-1 RI-2 RI-3 RI-4 RI-1 RI-2 RI-3 RI-4 RI-1 RI-2 RI-3 RI-4 RI-1 RI-2 RI-3 RI-4 LC-1 0.0 2.8 21.1 16.0 0.0 1.6 17.2 11.5 LC-1 0.0 2.8 21.1 16.0 0.0 1.6 17.2 11.5 10 m LC-2 0.0 0.3 1.4 1.7 0.0 0.1 0.5 0.8 10 m LC-2 0.0 0.3 1.4 1.7 0.0 0.1 0.5 0.8 LC-3LC-3 0.00.0 0.0 0.0 0.2 0.2 0.2 0.00.0 0.00.0 0.30.3 0.40.4 RI-1RI-1 RI-2 RI-2 RI-3 RI-3 RI-4 RI-1RI-1 RI-2RI-2 RI-3RI-3 RI-4RI-4 LC-1LC-1 0.00.0 6.6 6.6 77.7 77.7 57.3 0.00.0 3.2 3.2 60.360.3 44.144.1 20 m LC-2 0.0 0.0 5.6 4.4 0.0 0.0 1.6 3.2 20 m LC-2LC-3 0.00.0 0.0 0.1 5.6 0.7 1.04.4 0.00.0 0.00.0 0.41.6 0.53.2 LC-3 0.0 0.1 0.7 1.0 0.0 0.0 0.4 0.5 From the information presented in Table4, we have performed a qualitative classification of the X-band SARFrom visibility the information on the studiedpresented area in Table taking 4, intowe have account performed LC and a qualitative RI. We distinguish classification three of classes,the highX-band visibility SAR (HV), visibility low on visibility the studied (LV), area and taking no in visibilityto account (NV) LC and as itRI. is We shown distinguish in Table three5. classes, Figure 12 high visibility (HV), low visibility (LV), and no visibility (NV) as it is shown in Table 5. Figure 12 shows a visibility classification map for both datasets using the LC classification obtained with the shows a visibility classification map for both datasets using the LC classification obtained with the multiband image with better kappa coefficient (S2 image of 20 August 2017 with a resolution of 10 m multiband image with better kappa coefficient (S2 image of 20 August 2017 with a resolution of 10 m andand two-, two-, three-, three-, four-, four-, and and eight-band eight-band combination). combination).

FigureFigure 12. 12.Visibility Visibility classiﬁcation classification mapmap for ( a)) CSK CSK and and (b (b) TSX) TSX datasets. datasets.

Remote Sens. 2018, 10, 936 21 of 29

Table 5. Qualitative visibility classiﬁcation. NV: no visible; LV: low visibility; HV: high visibility.

RI-1 RI-2 RI-3 RI-4 LC-1 NV LV HV HV LC-2 NV NV LV LV LC-3 NV NV NV NV

3.3.3. Post Processing Downslope Projection A limitation of A-DInSAR is that movement is measured only in the LOS component. However, landslides typically address two movement components depending on the failure mechanism: the downslope direction in case of movements subparallel to the slope and the vertical direction in case of predominantly rotational movements [11]. In case of no availability of ascending and descending SAR images, which allow the separation of the displacement in the east–west horizontal and vertical components [8,9] we can apply the downslope projection method [11,36,54] provided that the movement is assumed to occur mainly towards the steepest slope. This procedure reduce geometrical distortions generated by the combination of the topography of mountainous environment and the satellite acquisition parameters [43]. Based on ground truth information and mechanical characterization of Leintz Gatzaga landslide, we assess that surface motion is mainly subparallel to the slope. Therefore, to fully exploit A-DInSAR displacement data, LOS velocity is projected along the steepest slope to obtain SLOPE velocity. We have applied this post-processing method only to the CTs located at the Leintz Gatzaga landslide and surroundings, discarding the highway area. We calculated the orientation (or aspect) and inclination of the slope needed to perform the downslope projection with a 5 m resolution DEM [46]. Figure 13 shows the SLOPE velocity maps of the two different time periods calculated with the geometrical parameters of each SAR dataset (Table1). After performing the downslope projection, CT density has a reduction of about 35%, as we dismissed “uphill” direction values [36]. Average SLOPE velocity of the landslide calculated with the CSK dataset is 12 mm/year (6 mm/year of s.d.) and with the TSX dataset is 14 mm/year (5 mm/year of s.d.). Clearly, SLOPE velocity values are greater than their respective values in LOS. More importantly, differential movements are highlighted within the landslide. Maximum velocities are detected at the head of the landslide (close to INC-1), at the fronton court whose walls exhibit several cracks, and at the center and to the northwest of the village (Figure S3).

3.3.4. A-DInSAR Results Comparison to Ground Measurements In this sub-section, we will compare overlapping satellite radar observations with ground truth data (Figure3a) in terms of space distribution of movement and displacement time series. CSK dataset covers the time span 15 May 2011 to 1 August 2013 and TSX 10 February 2013 to 3 August 2014, overlapping almost six months. Both datasets present a similar space distribution of the landslide movement; higher rates are located at the head of the landslide, to the north of the village (fronton court and town hall), at the crown of a secondary slope failure (labelled S.L. in Figures 10 and 12) and at the colluvium mass waste area around the GI-3310 road. Inclinometers register the cumulated displacement along the line of maximum movement and they are referred to a stable surface under the sliding surface, while A-DinSAR represent the displacements measured at the surface in LOS direction with respect to a stable zone. This fact should be taken into account when comparing time series of both measurements. Inclinometric data overlaps both CSK and TSX datasets. Figure 14a show the average value of SLOPE time series displacements of the CTs located within a radius of 80 m to INC-1 at the head of the landslide. Figure 14b shows the average values of SLOPE displacement of all the CTs located within the village, where INC-2 is located. As discussed in the monitoring section, inclinometers do not register displacement until the rainy period of 20 November 2012–6 March 2013 (Figures3b and 13). However, surface displacements were measured with A-DInSAR since the ﬁrst acquisition, being more pronounced at the head of Remote Sens. 2018, 10, 936 22 of 29

Remote Sens. 2018, 10, x FOR PEER REVIEW 22 of 29 the landslide than at the main body where the village settles, as it also happens in the inclinometers. Thegreater greater SLOPE SLOPE displacement displacement rates rates are registered are registered between between July 2011–November July 2011–November 2011 and 2011 since and sinceApril April2013. 2013.Note that Note the that stitching the stitching of CSK of and CSK TSX and datasets TSX datasets time series timeseries of Figure of Figure 14 is based14 is based on the on clear the clearcommon common trend trend between between the end the of end May of May2013 2013and begi andnning beginning of August of August 2013. 2013. We have We have not applied not applied any anyfilter ﬁlter to the to time the time series, series, so the so thefirst ﬁrst measures measures of TSX of TSX could could be affected be affected by noise, by noise, which which might might be the be thereason reason for fordifferences differences in intime time series series displacement displacementss during during the the time time period period from from December December 2012 toto MayMay 2013.2013.

FigureFigure 13.13. SLOPESLOPE velocityvelocity maps.maps. (aa)) ObtainedObtained withwith CSKCSK dataset:dataset: general view of thethe landslidelandslide (left)(left) andand zoomzoom ofof thethe villagevillage (right).(right). ((bb)) ObtainedObtained withwith TSXTSX dataset:dataset: generalgeneral viewview ofof thethe landslidelandslide (left)(left) andand zoomzoom ofof thethe villagevillage withwith prismsprisms reﬂectorsreflectors locationslocations (right).(right).

TenTen crack gauges gauges were were installed installed on on the the facades facades of the of theline line of buildings of buildings located located to the to north the northof the ofvillage. the village. The maximum The maximum aperture aperturewas measured was measured on one of the on cracks one of of the the cracks fronton of court, the fronton which is court, also whichthe site is were also themaximum site were displacements maximum displacements rates were meas ratesured were by measuredthe CSK dataset by the in CSK the datasetvillage, inup the to village,32.5–35 upmm/year to 32.5–35 in downslope mm/year inprojection downslope (Figur projectione 13a). (FigureRegarding 13a). the Regarding TSX dataset, the TSX downslope dataset, downslopevelocities in velocities the fronton in thecourt fronton are between court are 17.5–12.5 between mm/year, 17.5–12.5 which mm/year, are also which higher are than also higherthe average than theof the average village, of although the village, the although lower CTs the dens lowerity CTs might density mask might higher mask rates higher of movement. rates of movement.

Remote Sens. 2018, 10, 936 23 of 29 Remote Sens. 2018, 10, x FOR PEER REVIEW 23 of 29 Remote Sens. 2018, 10, x FOR PEER REVIEW 23 of 29

Figure 14. (a) Average SLOPE displacements of the CSK and TSX CTs that are closer to INC-1 within a Figure 14. (a) Average SLOPE displacements of the CSK and TSX CTs that are closer to INC-1 within radiusaFigure radius of 14. 80of m.80(a) m.Average (b )(b Average) Average SLOPE SLOPE SLOPE displacements displacements displacements of the ofofCSK the and CS CSKK TSX and and CTs TSX TSX that CTs CTs are within within closer the to the village INC-1 village wherewithin where INC-2INC-2a radius is is located. located.of 80 m. (b) Average SLOPE displacements of the CSK and TSX CTs within the village where INC-2 is located. GNSS/HPLGNSS/HPL and and EDMEDM monitoringmonitoring campaigns overlap overlap wi withth TSX TSX dataset. dataset. Figure Figure 15 15 shows shows the the time time GNSS/HPL and EDM monitoring campaigns overlap with TSX dataset. Figure 15 shows the time seriesseries displacement displacement of offive five prismsprisms locatedlocated to th thee north north of of the the village village (Figure (Figure 5)5 )and and the the downslope downslope series displacement of five prisms located to the north of the village (Figure 5) and the downslope projectionprojection of of the the CTs CTs that that are are closer closer toto eacheach ofof them (Figure 13).13). Displacements Displacements measured measured by byGNSS GNSS projection of the CTs that are closer to each of them (Figure 13). Displacements measured by GNSS complementedcomplemented with with HPL HPL atat benchmarkbenchmark H14 (which houses houses prism prism HP14) HP14) are are also also shown. shown. H14 H14 and and prismscomplemented displacements with HPL are expressed at benchmark as the H14 modulus (which of houses the three prism directions, HP14) arewhich also in shown. all cases H14 points and prismsprisms displacements displacements are are expressed expressed asas thethe modulusmodulus of the three three directions, directions, which which in in all all cases cases points points downslope.downslope. Although Although timetime seriesseries are noisy (no filt filteringering has has been been applied), applied), the the trend trend and and amount amount of of movementdownslope. fits Although quite well time between series areEDM noisy and (no A-DI filtnSARering measurements.has been applied), GNSS/HPL the trend measurement and amount atof movement fits quite well between EDM and A-DInSAR measurements. GNSS/HPL measurement H14movement also captures fits quite the well increment between EDM of movement and A-DInSAR from measurements.January 2014. GNSS/HPLHowever, measurementthe amount ofat at H14 also captures the increment of movement from January 2014. However, the amount of displacementH14 also captures is less thanthe theincrement other techniques. of movement In general, from detectedJanuary average2014. However, velocity by the GNSS/HPL amount isof displacement is less than the other techniques. In general, detected average velocity by GNSS/HPL greaterdisplacement at the ishead less andthan atthe the othe village,r techniques. same as In A-DInSAR.general, detected Maximum average velocities velocity were by GNSS/HPL detected at is is greater at the head and at the village, same as A-DInSAR. Maximum velocities were detected at benchmarkgreater at the H17 head (Figure and 5). at Athe total village, displacement same as ofA-DInSAR. 86 mm was Maximum measured velocities for the period were betweendetected 20at benchmarkOctoberbenchmark 2013 H17 H17 and (Figure (Figure 11 January5 ).5). AA 2014. totaltotal However displacement CTs of of 86th 86e mm TSX mm was dataset was measured measured do not for detect forthe the periodmovement period between betweenat that 20 20siteOctober October probably 2013 2013 dueandand to1111 aliasing January January [12], 2014. 2014 as However. there However are CTs four CTs of acquisitions ofth thee TSX TSX dataset dataset in that do doperiod not not detect detectwith movement a movementgap of 45 at days that at that sitebetweensite probably probably two due of due them. to to aliasing aliasing The measured [ 12[12],], asas values therethere are areare fourfourinclud acquisitionsed in a 15–20 in in mmthat that wide period period strip, with with in aagreement gap a gap of of45 45withdays days betweenthebetween expected two two of precision of them. them. The The of measuredDInSAR measured measurements valuesvalues areare includedinclud [7]. ed in in a a 15–20 15–20 mm mm wide wide strip, strip, in inagreement agreement with with thethe expected expected precision precision of of DInSAR DInSAR measurements measurements [[7].7].

Figure 15. Displacements time series of: prisms (P1, P2, P3, P4, P5, HP14) measured by EDM, Figure 15. Displacements time series of: prisms (P1, P2, P3, P4, P5, HP14) measured by EDM, downslopeFigure 15. projectionDisplacements of the time CTs thatseries are of: closer prisms to each (P1, prismP2, P3, obtain P4, edP5, by HP14) DInSAR measured processing by ofEDM, the downslope projection of the CTs that are closer to each prism obtained by DInSAR processing of TSXdownslope dataset projectionand benchmark of the H14CTs (thatthat are houses closer pris to meach HP14) prism measured obtained by by GNSS DInSAR complemented processing of with the the TSX dataset and benchmark H14 (that houses prism HP14) measured by GNSS complemented with HPL.TSX datasetSee points and location benchmark in Figure H14 13b.(that Note houses the pris firstm measures HP14) measured of the prisms by GNSS have beencomplemented set at the value with HPL. See points location in Figure 13b. Note the ﬁrst measures of the prisms have been set at the value ofHPL. displacement See points oflocation the correspondent in Figure 13b. CT. Note the first measures of the prisms have been set at the value of displacement of the correspondent CT. of displacement of the correspondent CT.

Remote Sens. 2018, 10, 936 24 of 29

4. Discussion In this work, we propose a procedure that applies SAR data to obtain precise geotechnical monitoring results of site scale landslides affecting urban areas and/or civil infrastructures. The procedure is based on adequate SAR data selection, detailed site specification, high resolution visibility analysis, velocity projection along the steepest slope, and complemented with other monitoring data. We determined that X-band SAR images were the best choice attending to the characteristics of the test landslide, the period wanted to be monitored and data availability. We acquired two datasets of CSK (Stripmap HIMAGE mode) and TSX (high resolution spotlight mode) images, covering the period from 15 May 2011 to 1 August 2013 and from 10 February 2013 to 3 August 2014, respectively. We carried out an advanced differential interferometric analysis to obtain velocities and displacement time series using the coherent pixel technique (CPT) and compare these results with data obtained from other monitoring instrumentation. We also applied the downslope projection method, as the data from other monitoring instrumentation indicates that the Leintz Gatzaga landslide moves towards the steepest slope. Both X-band datasets have shown capability to produce reliable mean LOS velocities and time series displacement maps for this area, thanks to its improved temporal and spatial resolution. The dimension of cropped SAR image, broader than the landslide area, has allowed us to detect movements in unexpected zones, as is the case of the Vitoria–Eibar highway cut slopes. Average SLOPE velocity of the landslide calculated with the CSK dataset is 12 mm/year and with the TSX dataset is 14 mm/year, being both values greater than their respective in the LOS. Differential movements can be noticed within the landslide. Stable areas surrounding the landslide have also been detected. This information is very useful to geologist to make a more detailed landslide cartography. The most critical areas are the fronton court and the head of the secondary landslide. It can be noticed how movement increased along the GI-3310 road at the SW of the scene between CSK and TSK SLOPE results (Figure 12). Comparison between A-DInSAR results and other instrumentation data in terms of space distribution of movement and displacement time series shows good agreement. Based on the results of this work, the main advantage of introducing A-DInSAR techniques in geotechnical monitoring campaigns is the dramatic enhancement of the density of surface measure points, allowing to identify differential movements of the instable zone and to detect previously unknown moving areas. The main disadvantages we found are the limitation of velocity measurability and the masked movements in the time series due to aliasing when there are long time gaps between SAR acquisitions. The enhancement of surface measuring point density is very useful to validate and adjust future stability analysis of the landslide [54]. Additionally to the proposed procedure, in this work we test new Sentinel-2 optical data to create high resolution land cover maps, which are useful for analyzing coherent targets’ detection of short wave length bands. The land coverage information, which is needed along with the topography for the visibility analysis, can be obtained from geographical databases at a low resolution, which is not useful to detailed site scale case studies. Therefore, we performed a supervised land use classification of 10 m and 20 m resolution S2 optical data, based upon three classes of coherence preservation. In order to assess which was the best choice of spectral band combination and epoch, we calculated the kappa coefficient to find the most accurate classification. Our results (Table2) show that the highest kappa coefficients are achieved with 10 m resolution and that the more the spectral bands used, kappa coefficients are higher. Regarding the two epochs tested, we obtained higher kappa coefficient with the optical S2 image acquired in 20 August 2017. We also analyzed what was the relative distribution of the CTs detected in each of the three land cover (LC) classes and the four relief index (RI) classes (Table4). We observed that for values of RI that are less than 0.3, the topography is the main factor that rules CT detection, while for RI values higher than 0.3 is the type the land cover. This result is in concordance with previous works [26]. We created a qualitative visibility classification of the Leintz Gatzaga study area based upon our results (Table5 and Remote Sens. 2018, 10, 936 25 of 29

Figure 12) that can be used in other areas of the Deba Valley, where landslides of similar characteristics and occurring in similar scenarios (small urban patterns surrounded by vegetation), are common. However, there is an important point to take into account, which is CT connections. Even if an area has very good visibility, it can remain isolated if there are scarce connections between CTs, which can lead to detecting anomalous CTs due to unwrapping errors or not detecting them at all.

5. Conclusions Slow moving landslides are a widespread geological hazard that can damage exposed urban elements. Engineering solutions are design either to reduce the vulnerability of those elements and/or to minimize the intensity of the hazard phenomena. For both cases, the mechanical behavior of the landslide must be understood and quantiﬁed in order to adopt the optimal solution. Surface and subsurface geotechnical monitoring of landslides can obtain valuable information of the spatial extension of the sliding body, the deepness of the sliding plane, the velocity, and the behavior in time. The surface extension of a landslide will help us to determine which urban elements are at risk and, in conjunction with the deepness of the sliding plane, the total volume of the displaced material. Both the volume of the mobilized material and the velocity will determine the intensity of the landslide phenomena and hence the potential severity of the damages to exposed vulnerable urban elements and/or civil infrastructures. Moreover, monitoring the movement allows us to analyze the landslide behavior in time and link velocity changes with physical processes, such as variation in water pore pressure due to rainfall or dynamic loads, softening of the soil, etc. This is fundamental to adjust physical-based models, be able to forecast the landslide behavior, and ultimately obtain future hazard scenarios and adopt appropriate solutions to reduce the risk. The suitability of A-DInSAR for monitoring surface displacements of slow moving landslides at different scales is widely accepted, although its use has been restricted to academia, civil protection agencies, local authorities, and private companies are starting to include this tool of the operational monitoring of landslides. The scale at which we perform landslide assessments (regional or site scale) determines the minimum spatial resolution that is required to make a reliable analysis. In this work, we present a practical guideline that applies A-DInSAR techniques to the operational geotechnical monitoring of site scale landslides affecting urban areas and/or civil infrastructures. We summarize this procedure as follows:

• A priori selection of the dataset according to the following aspects of the scenario:

Type of land cover: will determine the radar sensor wavelength pulse selection. # Spatial extent of the phenomena: will determine the minimum spatial resolution. # Deformation dynamics: will determine the expected velocity and hence the wavelength # pulse selection and minimum time between acquisitions. Geometry of the slope: will determine the acquisition mode of the SAR images. # Period to be monitored: will determine if historical archives are needed (data availability) # and to plan satellite data acquisition requests. • Perform a qualitative visibility analysis of the selected SAR dataset with a GIS software, according to:

Land cover: Use freely available S-2 optical image data to make supervised land cover # classification. First identify the main classes of land cover according to the area of interest characteristics, the preservation of coherence among time or the amplitude reflectance (depending if SABS of PS approach are used) and the radar wavelength. We recommend using the higher resolution, closer to the ground resolution of the SAR data, and generating the multiband image (from which the supervised classification will be performed) with the maximum number of multispectral bands available. Remote Sens. 2018, 10, 936 26 of 29

Relief geometrical distortion: Perform the relief index RI to detect geometrical distortions, # using a DEM and the geometrical parameters of the SAR dataset • Process the stack of SAR images with A-DInSAR: We have shown that performing a SBAS approach has the advantage of requiring less number of SAR images, which reduces the costs, although multilooking compromises the spatial resolution. • Analyze the spatial distribution mean LOS velocity that will give information of the extension of the landslide, previously undetected unstable areas and differential movements. • Perform the slope projection method if the movement occurs mainly along the steepest slope. • When comparing and complementing A-DInSAR with other displacement monitoring data, take into account that A-DInSAR is measuring surface displacements; meanwhile other techniques, such as inclinometers, measure subsurface displacement at the rupture plane. If quantitative time series analysis is performed, all the data should be in the same projection. We must not forget that movements can be also masked by aliasing due to the sampling frequency. In short, we have shown that A-DInSAR is a very powerful and versatile tool for geotechnical monitoring of landslides. However, we have to keep in mind that when detailed analysis is needed, a single monitoring technique is not enough. It has been demonstrate that high resolution LC classiﬁcations can be achieved with S-2 data. Future lines include the exploitation of current active C-band satellite Sentinel-1, of the European Space Agency (ESA), whose enhanced spatial resolution will allow to detect small coherent patches as the ones present in Leintz Gatzaga landslide. ESA products, such as Sentinel-1 and Sentinel-2 data, have the advantage of having an open access data policy.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/10/6/936/s1. Table S1: Error matrices; Figure S1: Connection diagrams of the interferograms; Figure S2: Standard deviation maps of the studied area; Figure S3: Urban damages; Figure S4: Location of the pictures shown in Figure S1 over the SLOPE velocity map of the CSK SAR dataset. Author Contributions: G.B. and J.F. defined the work. A.M. coordinated the GNSS/HPL and EDM campaigns. J.F. and E.S. coordinated the proposals to obtain the SAR data. J.E. processed TSX and CSK data. J.J.M. and R.I. provided the interferometric software and technical support. T.A. prepared precipitation data. G.B. prepared the figures and wrote the manuscript with input from all team members. Funding: This work was financially supported by the Spanish Ministerio de Economía y Competitividad (MINECO) research projects IPT-2011-1234-310000, ESP2013-47780-C2-1-R, and RTC-2014-1922-5. Research by RI and JJM has been also supported by the Big Risk project (contract number BIA2008-06614) and by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Research Agency (AEI) and the European Funds for Regional Development (EFRD) under project TEC2017-85244-C2-1-P. CommSensLab is Unidad de Excelencia Maria de Maeztu MDM-2016-0600 financed by the Agencia Estatal de Investigación, Spain. Acknowledgments: The authors would like to thank ASI and DLR for providing the COSMO-SkyMed data (delivered under an ASI license to use) and the TerraSAR-X data (project GEO1409), respectively. Sentinel-2 data were provided by the European Space Agency (ESA). It is a contribution for the Moncloa Campus of International Excellence. Conflicts of Interest: The authors declare no conflict of interest.

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