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There are numerous methods used nowadays to monitor landslide movements. Of these methods, Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are the ones that are most commonly used. In this study, the amounts of movements acquired via these two methods were compared and relations between them were analysed. The Koyulhisar landslide region was selected as the field of study. In this study, 10 Envisat images of the region taken between 2006 and 2008 were evaluated using Persistent Scatterers Interferometric Synthetic Aperture Radar (PSInSAR) technique and annual velocity values at the direction of line of slight at PS points were obtained for the region of interest. The velocity values were then obtained from PSInSAR results and compared with those obtained from six periods of GNSS measurements that were performed between April 2007 and November 2008 on Koyulhisar Landslide area after which the relationship between the two was analysed. Two different movement models from GNSS and PSInSAR results were fit to the landslide region. The velocity values estimated from these movement models for the region were compared and correlation between them was determined. As a conclusion, a high correlation of r D 0.84 was determined between the models obtained from nine GNSS points, except one point at the city centre, and PSInSAR.
Geomatics, Natural Hazards and Risk ISSN: 1947-5705 (Print) 1947-5713 (Online) Journal homepage: http://www.tandfonline.com/loi/tgnh20 Comparing the results of PSInSAR and GNSS on slow motion landslides, Koyulhisar, Turkey Kemal Ozgur Hastaoglu To cite this article: Kemal Ozgur Hastaoglu (2016) Comparing the results of PSInSAR and GNSS on slow motion landslides, Koyulhisar, Turkey, Geomatics, Natural Hazards and Risk, 7:2, 786-803, DOI: 10.1080/19475705.2014.978822 To link to this article: http://dx.doi.org/10.1080/19475705.2014.978822 © 2014 Taylor & Francis Published online: 14 Nov 2014 Submit your article to this journal Article views: 110 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tgnh20 Download by: [203.128.244.130] Date: 15 March 2016, At: 00:48 Geomatics, Natural Hazards and Risk, 2016 Vol 7, No 2, 786À803, http://dx.doi.org/10.1080/19475705.2014.978822 Comparing the results of PSInSAR and GNSS on slow motion landslides, Koyulhisar, Turkey KEMAL OZGUR HASTAOGLU* Department of Geomatics Engineering, Faculty of Engineering, Cumhuriyet University, Sivas 58140, Turkey Downloaded by [203.128.244.130] at 00:48 15 March 2016 (Received 11 April 2014; accepted 15 October 2014) There are numerous methods used nowadays to monitor landslide movements Of these methods, Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are the ones that are most commonly used In this study, the amounts of movements acquired via these two methods were compared and relations between them were analysed The Koyulhisar landslide region was selected as the field of study In this study, 10 Envisat images of the region taken between 2006 and 2008 were evaluated using Persistent Scatterers Interferometric Synthetic Aperture Radar (PSInSAR) technique and annual velocity values at the direction of line of slight at PS points were obtained for the region of interest The velocity values were then obtained from PSInSAR results and compared with those obtained from six periods of GNSS measurements that were performed between April 2007 and November 2008 on Koyulhisar Landslide area after which the relationship between the two was analysed Two different movement models from GNSS and PSInSAR results were fit to the landslide region The velocity values estimated from these movement models for the region were compared and correlation between them was determined As a conclusion, a high correlation of r D 0.84 was determined between the models obtained from nine GNSS points, except one point at the city centre, and PSInSAR Introduction Landslides occupy an important part of natural disasters Landslides, particularly occurring near settlement areas, cause loss of life and property Therefore, monitoring landslide movements is very important The Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) methods are most widely used in monitoring landslides The GNSS method has been frequently used to monitor landslides particularly in the last 20 years (Gili et al 2000; Malet et al 2002; Coe et al 2003; Hastaoglu & Sanli 2011) Similarly, the InSAR method has been frequently used in monitoring landslides in the last 20 years as well (Fruneau et al 1996; Singhroy et al 1998; Rott et al 1999; Crosetto et al 2005; Motagh et al 2013) While monitoring landslide by GNSS method produces three-dimensional (3D) and high-precision information, the method itself is demanding and time-consuming Moreover, deformation information obtained by GNSS is point-based and presents no information on deformations in wider areas If one desires to obtain information about wider areas by GNSS method, measurements are needed to be done at *Email: khastaoglu@cumhuriyet.edu.tr Ó 2014 Taylor & Francis Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 787 numerous GNSS points This has a negative effect in terms of cost and time Furthermore, no observation could be performed in monitoring the movements in volcanic and landslide areas since access to such regions is very difficult The areal deformation can be obtained by using interpolation methods from Global Positioning System (GPS) results Therefore, it is considered that in studies performed for monitoring landslides, the use of methods such as Persistent Scatterers Interferometric Synthetic Aperture Radar (PSInSAR) that can provide areal movement data along with the high-precision, point-based GNSS measurements is beneficial InSAR methods have been commonly used in recent years to monitor areal deformations in landslide areas InSAR has the potential to detect ground surface motion phenomena with the accuracy of a small fraction of the radar wavelength on large areas with high spatial resolution (Pratti et al 2010) Permanent Scatterers technique called PSI (Persistent Scatterers Interferometric) method was developed by Feretti et al (2001) Then, the method was improved by many researchers (Berardino et al 2002; Hooper et al 2004; Kampes 2005) The PS (Permanent Scatterers) approach is based on a few basic observations There are ground targets that maintain a coherent reflectivity to the radar in time even when observed from different looking angles (the PS) The interferometric phase in correspondence of these targets is not randomized by temporal and geometric decorrelation phenomena (Pratti et al 2010) Decorrelation is caused by contributions from all scatterers within a resolution cell summing differently, due to relative movement of the scatterers and/or a change in the looking direction of the radar platform “If, however, one scatterer returns significantly more energy than other scatterers within the cell, the decorrelation phase is much reduced This is the principle behind a ‘persistent scatterer’ (PS) pixel, also referred to as a ‘permanent scatterer’”(Hooper et al 2012) PSI technique has been used by many researchers in monitoring the landslides (Colesanti & Andwasowski 2006; Farina et al 2006; Meisina et al 2006; Herrera et al 2009; Notti et al 2010; Righini et al 2010; Liu et al 2013) The PSInSAR is an advanced technique in comparison with conventional InSAR technique It has many advantages to overcome the problems of decorrelation for generating a time series of phase changes without atmospheric and DEM (Digital Elevation Model) residual effects, so the PSInSAR method is preferred There are studies in recent years in which GNSS and PSInSAR results were used together (Peyret et al 2008; Yin et al 2010; Catal~ao et al 2011; Cigna et al 2012; Akbarimehr et al 2013; Zhu et al 2014) 3D movement amounts can be determined by GNSS, whereas one-dimensional (1D) movement amounts can be found at the line of slight (LOS) using the SAR method Therefore, 3D GNSS results are converted into 1D results in the LOS direction in order to explain the situation using the two methods together In this study, GNSS results obtained from Koyulhisar landslide area were evaluated together with the PSInSAR results Hastaoglu (2013) conducted six-period GNSS observations at 10 GNSS points in the landslide area between April 2007 and November 2008 and obtained 3D annual velocity values for the points The current study evaluates 10 descending Envisat images taken between 2006 and 2008 by PSInSAR method The main reason that the number of images is limited by 10 was that there are only 10 images belonging to the study area in ESA’s archive where GNSS observations were made between 2007 and 2008 Annual velocity values belonging to the PSI points at the LOS direction were obtained as a result of the evaluation 788 K.O Hastaoglu Then, a section profile was specified which intersects the landslide area through northÀsouth direction The PSI points were determined on this section Both of the results obtained from two methods were compared through the study field by transforming the 3D velocity GNSS values on LOS direction For the comparison process, two separate movement models (equations (4) and (5)) were fit for the velocity values on the LOS direction obtained from GNSS and PS methods LOS velocity values at every 50 m were then calculated using the models through the section representing the landslide field Finally, the correlation between these velocity values calculated was determined Downloaded by [203.128.244.130] at 00:48 15 March 2016 Field of Study Koyulhisar is 180 km away from Sivas, Turkey Since the study area lies upon the Northern Anatolian Fault Zone (NAFZ), which is an active fault, the rock masses in the region contain discontinuities and are usually seen to be cracked and crushed Depending on the steep topography in the region, there are many old and new landslides The direction of motion of these landslides usually threatens residential areas (Sendir & Yilmaz 2002) Koyulhisar district centre is located at a region on the NAFZ which is one of the most important seismic belts in the region Landslides constitute a great risk due to both the lithological properties of the rocks existing in the region and the morphology shaped by intense active faulting For this purpose, various observations were made in the region for scientific and technical purposes in different periods (Toprak 1998; Sendir & Yilmaz 2002) Figure presents the general geological conditions of Koyulhisar region Koyulhisar and the landslide regions are located very close to NAFZ which is one of the biggest active earthquake belts One of the most important features of active earthquake zones are specific land forms Along with land forms the mass movements resulting in changes in land forms such as landslide, rock fall, and soil fluction, are natural events frequently occurring on such active belts Old landslide masses are seen in areas close to Koyulhisar district centre and its surrounding where Eocene aged clayey formations, Lower Miocene aged clayey and gypsum formations and Plio-Quaternary aged sediments are observed Most of these landslides have a circular-failure mechanism Koyulhisar district centre is located on a former landslide which has a circular-failure mechanism The former landslide mass has continued its activity over time However, this activity is not mass type but local landslides occurring in the main mass (Tatar et al 2000) As a result of the analysis of the landslides that have occurred on the study area, it was acquired that rainfall and the flora plays an important role Especially, the cracks and fissures filled with water due to high rainfall between the winter and spring seasons of 1998 and 2000 before landslides caused the strength of clay fillings to be reduced, thus contributing to the movement along failure zones In addition, this has also contributed to the increase in unit weight of soil burden covering the rocks and thus increasing the extra burden on the side slopes Dense forests over the side slopes in the study field have slowed down the flow of water through the slope and eased the water to seep into the soil material As a result, forces causing the landslide (failure) on the slope have increased (Sendir & Yilmaz 2002) It was reported in the previous studies (Sendir & Yilmaz 2002) that the potential slope instabilities in the study field were generally towards the south (S, SWÀSE) Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 789 Figure Geologic map of the Koyulhisar Landslide area These are the dominant slope directions in Koyulhisar district and its surrounding It was observed that the former and new landslides determined within the study field between S¸ ıhlar Fault in the south and Dumanlıca Fault in the north were explained by a complex landslide system which is composed of a combination of many landslides through Dumanlica and S¸ ıhlar Fault from north to south The material accumulated in the region after the landslides occurred between 1998 and 2000 is located in G€ onenli stream that is located on the east of Aklan district which is on the north of Koyulhisar The width of this slide reaches up to km The ground water level in the region is very high and small lakes were formed in the sliding mass It is highly probable that sliding mass can move again in a season with high rainfall since it would increase the contact of the mass with water and thus the mass would become saturated 790 K.O Hastaoglu Data-sets and methods Downloaded by [203.128.244.130] at 00:48 15 March 2016 3.1 GNSS velocities A GNSS study was conducted in the region by Hastaoglu (2013) and 3D annual velocities belonging to 10 GNSS points in the region were obtained The velocities obtained are given in table In order to determine the GNSS velocities, six-period GPS campaigns were conducted between April 2007 and November 2008, covering about 1.5 years Each GPS campaign was carried out on three consecutive days Observing session duration for static GPS measurements was about 12 h The data-sampling rate and elevation cut-off angle were set to 30 and 15 s, respectively For each day and each rover point, a position was computed for each 12-h session in ITRF 2005 using BERNESE 5.0 relative static baseline processing strategies (Beutler et al 2005) The accuracy of velocities obtained from GNSS measurements were investigated in detail by Hastaoglu and Sanli 2011 The deformation rates from the GNSS time series were extracted using kinematic Kalman filtering method, and estimated GNSS coordinates from BERNESE were the output for kinematic Kalman filtering method The locations of GNSS points are shown in figure The annual velocities of GNSS points are given in table Statistical tests of the expanded model were conducted, and it was decided that the model consisting of velocities were significant (table 1) The velocity values were divided by its root-mean-square error, and test values were computed Statistical tests were conducted as mentioned previously and results are shown in the decision column p of table If parameters have significantly changed in the kinematic model, a “ ” sign is given in table Otherwise, a “¡” sign is given 3.2 SAR data-set and interferometric processing PS algorithms operate on a time series of interferograms all formed with respect to a single “master” SAR image It is ultimately the level of decorrelation noise that defines whether pixels are PS pixels or not, but an initial selection of candidate PS pixels can be made using various proxies, the most common of which is amplitude dispersion (Ferretti et al 2001; Hooper et al 2012) Table Movement parameters determined with a kinematic model between April 2007 and September 2008 (Hastaoglu 2013) Velocity/unknown (mm) Decision Point ve vup T Tve Tvup KH01 KH02 KH03 KH04 KH05 KH06 KH07 KH09 KH10 KH11 2.3 9.9 11.8 ¡3.1 0.4 ¡15.7 ¡64.4 ¡3.5 3.6 1.4 ¡3.6 ¡3.8 ¡3.4 ¡1.5 ¡14.6 1.5 ¡47.8 ¡14.5 ¡3.5 ¡14.9 ¡3.4 ¡0.7 ¡4.2 ¡11.1 0.0 10.1 ¡2.5 2.9 3.1 ¡0.6 0.2(¡) 1.2(¡) p 2.6( ) 0.5(¡) 0.1(¡) p 1.9( ) p 9.7( ) 0.4(¡) 0.5(¡) 0.4(¡) 0.5(¡) 0.7(¡) 0.5(¡) 0.3(¡) p 2.8( ) 0.2(¡) p 5.0( ) p 1.7( ) 0.4(¡) 1.6(¡) 0.3(¡) 0.1(¡) 0.9(¡) 1.0(¡) 0.1(¡) 1.1(¡) 0.2(¡) 0.3(¡) 0.5(¡) 0.7(¡) Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 791 Figure Processing of GNSS baselines: CMYK and IKYK are fixed (Hastaoglu and Sanli 2011) In this study, the data-set of SAR images acquired by the Envisat satellites (descending orbits track 78) was collected from ESA archive For the PSI analyses, 10 archived raw Envisat images were used These images have descending geometry acquired between October 2006 and December 2008 SAR data dated 22 July 2007 was chosen as master and nine interferograms were calculated from this master image In this study, SAR images are gathered in raw format and converted to SLC (Single Look Complex) images with ROI_PAC public software For the interferogram processing is applied with the public domain Delft object-oriented radar interferometric software (DORIS) Interferograms are produced by using Delft precise orbits The topographic effect is reduced by an external DEM as arcsecond SRTM data which has 90 m resolution Geocoding is referred with World Geodetic System 1984 (WGS84) reference system In this study, Stanford Method for Persistent Scatterers (StaMPS) approach was applied for the monitoring of Koyulhisar landslide PS process was applied with nine interferograms with the pairs which were limited with a 494-m perpendicular baseline and 525 days’ temporal baseline For the PSI analyses, amplitude dispersion index was chosen as 0.3 A list of perpendicular baselines and temporal baselines is shown in table The annual velocity values at the LOS direction were obtained for PS points given in figure by PS process Analysing figure 3, it is seen that there is a subsidence on the former landslide mass and there is an uplift on city centre which is located on further south In order to observe the deformation on PS points in detail, a section was specified through the landslide area (figure 3) and the velocity values of PS points along this section were analysed 792 K.O Hastaoglu Table List of Envisat Asar data used in the PSI approach Bn (m) Btemp (day) 15 October 2006 28 January 2007 04 November 2007 23 March 2008 27 April 2008 06 July 2008 14 September 2008 19 October 2008 28 December 2008 ¡292 ¡494 230 259 ¡127 224 ¡449 254 ¡14 ¡280 ¡175 105 245 280 350 420 455 525 Downloaded by [203.128.244.130] at 00:48 15 March 2016 Date of passes Figure Annual velocities of PS points Geomatics, Natural Hazards and Risk 793 Downloaded by [203.128.244.130] at 00:48 15 March 2016 As shown in figure 3, apart from the given profile zone, there is a subsidence up to ¡12 mm located at the forested area in the north of study area and also there is an uplift up to 11 mm located to the west of centre, presenting the maximum subsidence and uplift values As you see in figure 4, in these areas standard deviations are the highest of all and these areas are out of the profile zones which is chosen as study area The reason of high standard deviations may be the number of interferograms used in the PS process Unfortunately, there are only 10 images belonging to the study area in ESA’s archive where GNSS observations were made between 2007 and 2008 In the literature, it is suggested to use at least 15 images for PSI processes It is also mentioned in Colesanti and Andwasowski (2006) and Notti et al (2010) Hooper et al (2007) showed that using 12 interferograms are usually sufficient when using StaMPS Analysing figure 4, it is seen that standard deviations are quite low Figure Standard deviation of PS points 794 K.O Hastaoglu for the profile zone in study area This situation shows us that the velocity values of PS points on profile zones are accurate values On the other hand, the time series of some PS points in the area including KH07 and KH10 are investigated (figure 5) As a result of this investigation, it is showed that closer PS points usually show similar deformation trend As the deformation trend is similar, it can be concluded that there is no atmospheric or unwrapping error in the processes Finally, although 10 interferograms are used for PS processing, especially the results of PS process in the landslide area and the city centre are quite safe Downloaded by [203.128.244.130] at 00:48 15 March 2016 Comparing the results of GNSS and PSInSAR In order to compare the GNSS and PSInSAR results obtained, a section line composed of three parts (northÀsouth direction) was specified through the top to the toe of the landslide area (figure 6) Then, the velocities associated with GNSS and PS points along this section line PS points which are not located 200 m away from the section line which is used to determine the points within the study field were excluded from the data-set The section graph of the velocities associated with the PS points within the landslide area is given in figure GNSS velocities obtained are three-dimensional In order to compare them to PSInSAR results, 3D GNSS velocities were transformed into 1D velocities in the LOS direction by using the formulae given in equation (1) Then, LOS values associated with GNSS points given in figure were calculated through the section line specified on northÀsouth direction within the landslide area The 3D (in the east, north, and vertical (up) directions) orthogonal components of the surface displacement of a point on the Earth’s surface is stated as D ¼ ðdx ; dy ; dz ÞT The formula of projection of the surface displacement vector D to the line of sight can be written as dLOS ¼ sT D s ¼ ð¡ cos ah sin u sin ah sin u (1) cos uÞT (2) where dLOS , s, and D and denote the LOS displacement, the satellite unit vector, and the surface displacement vector, respectively For a detailed description of the satellite unit vector and its parameters, see equation (1) (Arıkan et al 2009) I use master acquisition parameters for heading, ah and u incidence angle, of an Envisat I2 descending pass Using those common GNSS points of measurements in equations (1) and (2), velocities in the radar LOS were computed The results of GNSS are illustrated in figure Figure shows the velocities associated with GNSS and PS points along the section line As the figure is analysed, it is seen that the velocity value of the GNSS point at 4700 m of the section is way too different than the others This is the GNSS point denoted by KH07 When the GNSS velocities of the point KH07 is analysed, a horizontal movement different than the other points in the landslide area is observed In Hastaoglu’s (2013) study, it was stated that this movement might be due to a local movement independent from the landslide movement in the region They also indicated the instantaneous change of water level in the well located within the Police Department and emphasized that the velocities found for the point KH07 showed that it is a local movement different than the main landslide movement On the other Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk Figure Time series of some PS points 795 Downloaded by [203.128.244.130] at 00:48 15 March 2016 796 K.O Hastaoglu Figure Annual velocities of PS points and profile hand, Tatar et al (2000) suggested that some deformations are observed around KH07 and these deformations are due to local landslides in the main mass The main cause of local landslides is the water that saturates the material by infiltrating from the crack systems Analysing the section given in figure 7, it was observed that there is a disconformity between GNSS and PSInSAR results obtained for the point KH07 It was stated that the movement in the point KH07 is local Having a small amount of movements in the point KH07 located 250 m away from the point KH10 is a proof for the argument that the movement in the point KH07 is local The main reason why the movement in the point KH07 was not determined by PSInSAR method was that there were no PS points around the point KH07 within the 50-m-radius area shown in green in figure In summary, it is considered that PS results did not reflect the local movement since the movement around the point KH07 is local and there were no PS points in this area From this argument, this point was excluded in the comparison of GNSS and PSInSAR results and the results given in figure were obtained Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 797 Figure LOS velocities of GNSS and PSInSAR points along the profile Analysing figure 9, it is seen that the results obtained from the GNSS and PS results are in good conformity In order to both mathematically express this conformity and obtain the movement model from the methods, movement curves were fit to associated velocity values This process was carried out separately for GNSS and PS results by using equations (3) and (4), respectively, for LOS velocities associated with GNSS and PS points and obtained along the profile The estimated models are given in figure VPS ¼ 5:9 £ 10 ¡ 17 x5 ¡ 1:0 £ 10 ¡ 12 x4 þ 6:4 £ 10 ¡ x3 ¡ 1:7 £ 10 ¡ x2 þ 0:0179x ¡ 7:8905 (3) VGPS ¼ ¡ 4:6 £ 10 ¡ 13 x4 þ 5:4 £ 10 ¡ x3 ¡ 1:9 £ 10 ¡ x2 þ 0:0234 x ¡ 7:8638 (4) where VPS , VGPS, and x denote the annual GNSS velocities (direction of LOS), annual PS velocities (direction of LOS), and the distance from profile, respectively In order to acquire the reliability of the estimated models in equations (3) and (4) R-square values for two models were calculated Coefficient of determination (Rsquared) indicates the proportionate amount of variation in the response variable y explained by the independent variables x in the model The GNSS model R-squared and PSInSAR R-squared values are 0.43 and 0.49, respectively Models explain about 50% of the variability in the response variable Downloaded by [203.128.244.130] at 00:48 15 March 2016 798 K.O Hastaoglu Figure PS points of around the KH07 Two different movement models obtained for GNSS and PSInSAR are given in figure 10 As both of the models are carefully examined, it is observed that there is a subsidence in the initial 1-km part of the section (top of the landslide), uplift in between and km (city centre), and subsidence in the last 1-km part (toe of the landslide) As a conclusion, movement trends in two models are in conformity with each other In order to determine the relationship between two models, velocity values at every other 50 m were calculated Then, correlation value was found from equation (5) for the velocities calculated from two models P ðVPS ¡ VPS ÞðVGPS ¡ VGPS Þ ffi RðVPS ; VGPS Þ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2P VGPS ¡ VGPS Þ ðVPS ¡ VPS Þ (5) where VPS and VGPS denote mean values for the annual GNSS velocities (direction of LOS) and annual PS velocities (direction of LOS), respectively The correlation value R was found as 0.84 As acquired from these results, the velocities obtained from two models justify each other Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 799 Figure LOS velocities of GNSS and PSInSAR points along the profile without of KH07 Figure 10 PSInSAR and GNSS fitting models by using GNSS and PSInSAR velocities Downloaded by [203.128.244.130] at 00:48 15 March 2016 800 K.O Hastaoglu Figure 11 The zones along to profile Conclusions In this study, that is different from previous studies in which PS and GNSS data are processed together, movement models obtained from two methods for LOS velocities are fitted Then, the success of the methods (PS and GNSS) is investigated by calculating the correlation between two models In this study, the velocities obtained from six-period GNSS measurements done for the Koyulhisar landslide region between 2007 and 2008 and the one obtained from the 10 Envisat archive radar image during the same period were compared and landslide movement models were estimated As a conclusion, both the GNSS and PSInSAR results (except one GNSS point) along the section on the landslide area had a correlation of 0.84 It is considered that the main reason why the GNSS and PSInSAR velocity values at the point KH07 not conform to each other was that there are no PS points in the area within 50-m radius around the point In addition, as stated before in previous studies, it is considered that the movement at the point KH07 is local and it may cover a small area Thus, the movement at the point could not be determined by PS Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 801 As GNSS velocity values extracted from the data-set, subsidence and uplifts in LOS direction cover the same regions along the section according to movement models obtained from both of the data-set As seen in Figure 11, the LOS values of both models on the landslide area divide the area into four regions According to these movement models, an uplift is observed at the initial 1000-m part of the section (Zone I), i.e top of the landslide area, whereas a subsidence is observed at Zone II (1000À2500-m part along the section) where it includes former landslide mass By the effect of this subsidence, an uplift is observed at Zone III (2500À5000 m) where city centre is located and finally a subsidence is also observed at Zone IV In order to understand the up field and downfield movements well, geophysical and geological studies must be done in the field In summary, both GNSS and PSInSAR results justify each other and present general information about the landslide mass movement It can be said that the weakness of PS method is that the deformation at point KH07 could not be determined by PS method since the deformation at that point is local and in horizontal direction The GNSS method has also a weakness since the movement at the point KH07 was determined by GNSS, but it cannot be found if the movement is areal or not by only using the GNSS measurements As a conclusion, in this study, GNSS method was effective in determining the local point deformations in particular whereas PS method is powerful in determining the areal movement trend of the whole landslide area However, it is considered that better results would be obtained by the utilization of both the methods in landslide regions such as Koyulhisar landslide region where there exist local movement independent from the movement of the main mass Acknowledgements The author would like to thank Sivas Cumhuriyet University and The Scıentıfıc and Technologıcal Research Councıl of Turkey for supporting the studies under CUBAP project M 468 and TUBITAK Project 111Y111 Interferometric data were processed using the public domain SAR processor DORIS, StaMPS and satellite orbits used are from Delft University of Technology The author is grateful to the European Space Agency (ESA) for providing Envisat data under TRHK001 Fatih Poyraz is acknowledged for his support with the PSInSAR process References Akbarimehr M, Motagh M, Haghshenas-Haghighi M 2013 Slope stability assessment of the Sarcheshmeh Landslide, northeast Iran, investigated using InSAR and GNSS observations Remote Sens 5:3681À3700 doi:10.3390/rs5083681 Arikan M, Hooper A, Hanssen R 2009 Radar time series analysis over West Anatolia Fringe 2009 Workshop; Frascati 30 NovemberÀ4 December (ESA SP-677, March 2010) Berardino P, Fornaro G, Lanari R, Sansosti E 2002 A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms IEEE Trans Geosci Remote Sens 40:2375À2383 Beutler G, Bock H, Brockmann E, Dach R, Fridez P, Gurtner W, Hugentobler U, Ineichen D, Johnson J, Meindl M, et al 2005 Bernese Gps 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geomorphological and geomechanical aspects of the Koyulhisar landslides in the North Anatolian Fault Zone (Sivas-Turkey) Environ Geol 42:52À60 Singhroy V, Mattar KE, Gray AL 1998 Landslide characteristics in Canada using interferometric SAR and combined SAR and TM images Adv Space Res 21:465À476 Tatar O, Aykanat O, Kocbulut F, Yilmaz I, Sendir H, K€ ur¸c er A, Sa glam B 2000 Landslide Investigation and Evaluation Report of Koyulhisar Town Centre and Services Building of Police Chief Turkish Toprak V 1988 Neotectonic characteristics of the North Anatolian Fault Zone between Koyulhisar and Su¸s ehri (NE Turkey) METU J Pure Appl Sci 21:155À168 Yin Y, Zheng W, Liu Y, Zhang J, Li X 2010 Integration of GNSS with InSAR to monitoring of the Jiaju landslide in Sichuan, China Landslides 7:359À365 Zhu W, Zhang Q, Ding X, Zhao C, Yang C, Qu F, Qu W 2014 Landslide monitoring by combining of CR-InSAR and GNSS techniques Adv Space Res 53:430À439 [...]... measurements done for the Koyulhisar landslide region between 2007 and 2008 and the one obtained from the 10 Envisat archive radar image during the same period were compared and landslide movement models were estimated As a conclusion, both the GNSS and PSInSAR results (except one GNSS point) along the section on the landslide area had a correlation of 0.84 It is considered that the main reason why the GNSS and. .. excluded in the comparison of GNSS and PSInSAR results and the results given in figure 9 were obtained Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 797 Figure 7 LOS velocities of GNSS and PSInSAR points along the profile Analysing figure 9, it is seen that the results obtained from the GNSS and PS results are in good conformity In order to both mathematically... VPS , VGPS, and x denote the annual GNSS velocities (direction of LOS), annual PS velocities (direction of LOS), and the distance from profile, respectively In order to acquire the reliability of the estimated models in equations (3) and (4) R-square values for two models were calculated Coefficient of determination (Rsquared) indicates the proportionate amount of variation in the response variable... well, geophysical and geological studies must be done in the field In summary, both GNSS and PSInSAR results justify each other and present general information about the landslide mass movement It can be said that the weakness of PS method is that the deformation at point KH07 could not be determined by PS method since the deformation at that point is local and in horizontal direction The GNSS method has... (direction of LOS), respectively The correlation value R was found as 0.84 As acquired from these results, the velocities obtained from two models justify each other Downloaded by [203.128.244.130] at 00:48 15 March 2016 Geomatics, Natural Hazards and Risk 799 Figure 9 LOS velocities of GNSS and PSInSAR points along the profile without of KH07 Figure 10 PSInSAR and GNSS fitting models by using GNSS and PSInSAR. .. Natural Hazards and Risk 801 As GNSS velocity values extracted from the data-set, subsidence and uplifts in LOS direction cover the same regions along the section according to movement models obtained from both of the data-set As seen in Figure 11, the LOS values of both models on the landslide area divide the area into four regions According to these movement models, an uplift is observed at the initial... figure 10 As both of the models are carefully examined, it is observed that there is a subsidence in the initial 1-km part of the section (top of the landslide), uplift in between 2 and 5 km (city centre), and subsidence in the last 1-km part (toe of the landslide) As a conclusion, movement trends in two models are in conformity with each other In order to determine the relationship between two models,... 1000-m part of the section (Zone I), i.e top of the landslide area, whereas a subsidence is observed at Zone II (1000À2500-m part along the section) where it includes former landslide mass By the effect of this subsidence, an uplift is observed at Zone III (2500À5000 m) where city centre is located and finally a subsidence is also observed at Zone IV In order to understand the up field and downfield... from the crack systems Analysing the section given in figure 7, it was observed that there is a disconformity between GNSS and PSInSAR results obtained for the point KH07 It was stated that the movement in the point KH07 is local Having a small amount of movements in the point KH07 located 250 m away from the point KH10 is a proof for the argument that the movement in the point KH07 is local The main... would be obtained by the utilization of both the methods in landslide regions such as Koyulhisar landslide region where there exist local movement independent from the movement of the main mass Acknowledgements The author would like to thank Sivas Cumhuriyet University and The Scıentıfıc and Technologıcal Research Councıl of Turkey for supporting the studies under CUBAP project M 468 and TUBITAK Project ... conclusion, both the GNSS and PSInSAR results (except one GNSS point) along the section on the landslide area had a correlation of 0.84 It is considered that the main reason why the GNSS and PSInSAR. .. contain discontinuities and are usually seen to be cracked and crushed Depending on the steep topography in the region, there are many old and new landslides The direction of motion of these landslides... region on the NAFZ which is one of the most important seismic belts in the region Landslides constitute a great risk due to both the lithological properties of the rocks existing in the region and