monitoring and analysis of displacement using insar ...jestec.taylors.edu.my/vol 14 issue 3 june...

Journal of Engineering Science and Technology Vol. 14, No. 3 (2019) 1558 - 1571 © School of Engineering, Taylor’s University

1558

MONITORING AND ANALYSIS OF DISPLACEMENT USING INSAR TECHNIQUES FOR GULABA LANDSLIDE SITE

AMARDEEP SINGH VIRK*, AMANPREET SINGH, S. K. MITTAL

Research Scholar, IKG-Punjab Technical University, Kapurthala, Pb. India

*Corresponding Author: [email protected]

Abstract

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing

technique, which provides high spatial resolution images with continuous

temporal coverage of the Earth surface data for monitoring of long-term

landslide displacement. In the current study over Gulaba Camp in Manali to

Marhi (Himachal Pradesh, India) region, a novel approach called persistent

scatterer interferometry is applied to monitor the landslide using 30 ascending

and 23 descending Sentinel-1 Interferometric Wide Swath image data sets.

Results over a span of 26 months clearly indicate trends of surface subsidence

over abandoned landslide site and the adjoining areas. This study demonstrates

that multi-temporal persistent scatterer interferometry have capability of high

precision monitoring of the landslide displacements.

Keywords: Baseline, DEM, InSAR, Landslide, Multi-temporal, Natural hazard.

Monitoring and Analysis of Displacement using InSAR Techniques for . . . . 1559

Journal of Engineering Science and Technology June 2019, Vol. 14(3)

1. Introduction

A landslide is short lived and suddenly occurring geological hazard in hilly areas

which include all downward or sudden movement of surface material like rocks,

clays, sand and gravel resulting from natural cause such as earth-quakes, heavy

rainfall, cloud burst, snow melting, volumetric change in ground moisture content.

Other manmade disturbances like deforestation, ground water/oil extraction,

blocked drainages, overburden of rock material, Traffic vibrations, lateral toe

cutting and mining are the triggering factors for landslides in study area [1, 2].

Chandel et al. [3] explained that Kullu district in Himachal Pradesh is experiencing

terrain alterations due to large scale development activities related to hydroelectric

power, leisure industry and road networks and due to other impacts on the

environment such as cloud bursts, landslides, rock falls, etc.

There are recent records of massive landslides; rock falls occurred which has

caused damage to the property, human lives, roads and infrastructure. Some of

those are described as follows; Manali-Leh highway was closed for more than eight

hours following landslide uphill of Manali at Marhi on 28th June 2018; 46 people

were killed and 10 missing after a landslide triggered by a cloudburst in village

kotrupi swept away two buses on a busy highway stretch in Himachal Pradesh on

13 August 2017. A major rock fall was reported at the famous Sikh shrine

Gurdwara Manikaran Sahib on 18 August, 2015 which killed 8 persons and ten

others injured after a building adjoining Gurdwara Sahib caved in.; a landslide

triggered by incessant rain blocked the Manali-Palchan road 6 km ahead of Manali.;

and another rock fall near Dawada between Aut and Hanogi temple in the Mandi

district blocked the Chandigarh-Manali National Highway-21 at 9.30 am. (The

Tribune June 29,2018.; Aug 13, 2017; Aug 18, 2015.; April 3, 2015.; The Times of

India December 8, 2015).There have been a number of historical landslides in the

region (e.g Lugharbhati landslide, Hurla landslide, Raninallah landslide, Gulaba

camp Landslide, Urni landslide, Kotrupi Landslide etc.)

To reduce the Landslide induced impacts on the society, monitoring of slope

instabilities is required. It is very tedious to monitor slow moving landslide

deformation of number of different slopes [4], but Satellite Synthetic Aperture Radar

(SAR) interferometry technique has made it possible and is being used within the

scientific community now a days reporting many encouraging results [5, 6]. High

coherence of images achieved in rocky and urban area is required for monitoring

landslide. Here capabilities of Sentinel-1 satellite data are being evaluated [7]. Based

on studies by Colesanti et al. [8, 9], dense vegetation cover and rough terrain features

of landslide prone sites make it challenging to apply InSAR techniques which cause

temporal and spatial decorrelation. Many advanced categories of algorithms have

been devised and used in the past to improve the ability of InSAR. Multi-temporal

Persistent Scatterers InSAR (PS-InSAR) processes multiple acquisitions, gives

solutions based on Persistent Scatterers (PS) which show stable phase behavior, high

long term phase coherence and stable and strong backscattering of SAR acquisitions

[10-12].

The principle involves in simultaneous analysis of multiple interferogram pairs

considering those pixels with some stable backscattering behavior. The main

procedure of PS-InSAR methods is the co-registration of all the images from the

set of images with reference to a master image. PS-InSAR methods depend upon

the mathematical definition of the PS. PSI analysis detects ground deformation and

1560 A. S. Virk et al.


helps geo-scientists to analyze the results in order to identifying landslide prone

zones. PS identification mostly depends on the analysis of pixel amplitudes in a

series of multiple interferograms [13, 14].

This method is efficient in metropolitan areas where building structures and less

vegetation gives good back scattering of radar waves which helps to identify stable

scatterers. The possibility of finding PS pixels in natural landscapes is much lower,

still a number of successful activities of landslide monitoring have been reported

[15, 16]. Some of the techniques are further refined and new algorithms are being

developed to improve identification of PS in natural terrain of non-urban areas [17].

The paper is structured as follows. Section 2 describes the study site along with

the analysed datasets and the experimental procedure for assessing the proposed

landslide site deformation. Section 3 presents the results of the displacement

analysis, provides a discussion and gives an interpretation of relationships between

landslide kinematics and variations of the seasonal precipitation. Finally

conclusions are drawn in Section 4.

2. Study Area

Study area taken up in this paper is Gulaba landslide site which is located at

32⁰19′3.04′′ N, 77⁰12′12′′ E in Manali shown in a red ellipse in Fig. 1. Gulaba camp

cushioned in the mountains of Himachal Pradesh is in the north of the Manali at an

altitude of 4300m (14,100 feet) up the Beas River Valley. It is popular because of

its unique geographic conditions and tourists picnic attractions. The temperature

of the region remains - 1.7⁰C to 16.3⁰C within the study area throughout the year.

Fig. 1. Zoom Earth NASA satellite image (2017) of Gulaba campsite.

The area is a mountainous region having flat valleys in lower part of the district.

The elevation of the Kullu district varies from 1100 meters to 4000 meters in a

complex system of mountain ranges. Gulaba experiences around 15 cm rain during

monsoon season in the months of July and August each year. Climate remains

enjoyable with the start of snowfall after November. The fragile rocks in the region,

the climate and various man made disturbances like toe cutting, deforestation has

made the site vulnerable to the landslides impulses. Snow or water saturated rocks

starts moving down the slope in the month of April-to-October resulting in another

type of mass movement.



The district has very high absolute difference between the highest and lowest

elevations ranging from 750-6200 meters. The area has glaciers, snow laden areas,

unfertile rock slopes and ridges. Gulaba site is famous for a landslide activity

throughout the year. It is visible in Fig. 1 that this terrain is very sensitive and at high

altitude, which makes it vulnerable for the landslide. The damage caused to road by

landslide and the diversion to avoid traffic reaching below this mount is clearly visible

here and in base map shown in Fig. 2 also. It is observed by the author that slide is

more proactive during the monsoon. Figure 2 shows landslide prone area marked in

a shape file (blue rectangle) and adjoining roads are also shown. An overplayed over

the base map is given and a previous landslide site boundary is shown in dark green.

(a) (b)

Fig. 2. (a) Total study area coverage, (b) Previous landslide site.

3. Data and Methods Used

There are many satellites which circle in orbit of the earth and capture the image data

on regular intervals using different wavelength. Sentinal-1 satellite data is being used

here for this study. Sentinel-1A has been launched on 03 April 2014 by European

Space Agency (ESA). Satellite equipped with C-SAR instrument operates in dual

polarization modes (HH+HV, VV+VH) have 12 days repeat cycle. Interferometric

wide swath data which cover the large swath of 250KM with a very good geometric

resolution of 5 meters by 20 meters (range x azimuth) have been used.

Each sub-swath image is processed as a separate SLC image. Salient features

of the Sentinel-1A (ASF) data being used here are shown in Table 1.

Table 1. Salient features of the Sentinel-1A.

Features Sentinel-1A

Centre frequency 5.405 GHz

Azimuth spacing 14 m

Band C

Pass Ascending/descending

Range spacing 2.3 m

Incident angle 39

Revisit time 12 days

Geometric resolution 5 m × 20 m



Thirty (30) S-1A images acquired from ascending track (October 2014-

December 2016) and twenty three (23) S-1A images from descending track

(February 2015 - December 2016) are processed. Ground deformation is measured

using ascending and descending pass Sentinel-1A (S-1A) data of the study area for

the period of 26 months. A good temporal baseline gives good temporal coherence

and a good spatial baseline gives good spatial coherence. Spatial and temporal

baseline connection graph for both the stacks used is shown in Fig. 3.

(a)

(b)

Fig. 3. Spatial and temporal baseline

connection graph (a) descending and (b) ascending.



Sentinel-1 IW data acquired as Terrain Observation with Progressive Scans

(TOPS) is a processing chain in which, data is acquired in the form of bursts by

regular switching antenna beam between the sub-swaths. The acquired data have

large swath widths, superior radiometric performance with minimum scalloping

effect resulting in homogeneous image quality. Each data of interferometric wide

swath is consisting of three sub swaths. Each sub-swath covers 84 km x 172km area.

A part of the image measuring approximately 10 km × 10 km is selected and exploited

considering the 19/02/2016 (D) image as master. In DInSAR processing strong

temporal decorrelation occurs due to vegetation hence PS technique is used here.

According to Perissin [18], multi-temporal SAR data has been exploited using the

Sarproz software (www.sarproz.com) in which, spatial relationship of interferogram

phases is used to find low-phase changes in terrain. SARPROZ got success in

reporting good results for Dam/Landslide/Building monitoring both in urban and

rural areas [19-21].

The PS-InSAR methodology in which, at least 20 scenes are recommended can

help in monitoring the linear deformation rate with height change in comparison

with the SRTM DEM or any other external DEM. After importing all the images

in the software environment selecting the appropriate master considering that there

was no extreme weather change such as precipitation on the day of acquisition, an

appropriate subset is selected for further processing. Complete flow chart of the

Sentinel-1 PSInSAR processing steps using Sarproz is given in Fig. 4. The initial

PS processing chain followed the following steps; Reading Data and orbital files,

AOI selection, SLC Data import, co-registration and generation of reflectivity map.

Gulaba site is extracted for further processing. Co-registration of all the images

with respect to the master images has been done using the 1900 tie point. A

complete quality assessment of co-registered images is carried out to ensure no

positional errors.

Fig. 4. PS processing flow chart (SARPROZ-Software).



Reflectivity map generated on SAR co-ordinates displayed in Fig. 5(a) shows

that, this is very undulating terrain and poses several challenges in proper geo-

coding. An optical image of the study area enclosing landslide site in red ellipse is

also shown in the Fig. 5(b). After co-registration, the permanent scatter candidates

(PSC) are selected using the amplitude stability index threshold value equal to 0.7

or more. Using this threshold of 0.7, 1700 PSC were selected and displayed in the

Fig. 5(c) a PS density of about 32 PS km−2 is achieved, which is also visible.

C-band images have not shown good coherence because of dense vegetation so

to improve coherence atmospheric phase screen (APS) is estimated and

compensated. Results are verified on Google earth after geo-coding.

(a) (b)

(c)

Fig. 5. (a) Reflectivity map, (b) Study area extent over

Google earth and (c) Distributed permanent scatters.



4. Results and Discussions

Encouraging results have been achieved with PSI analysis while investigating the

relationship between the ground subsidence over the mountain slope. PS’s points

obtained over the period of study are in very close proximity of subsidence prone

active areas alongside the slope under which, road is crossing Gulaba.

The scatterer distribution plot and displacement results of both descending and

ascending pass datasets are given in Fig. 6. There is enough number of images

available along with very good temporal and spatial baseline, which has helped to

obtain sufficient density of the PS points. All the PSC’s has been distributed very

well over the entire area and sufficient amount of the PSC’s are available in the slide

site (blue circle). The PS’s points are examined and differential trend and cumulative

displacement noted, which also validates with the ground reality of field inspections.

(a)

(b)

Fig. 6. Scatter plot and displacement of

(a) Descending pass data and (b) Ascending pass data.

The Time series analysis indicates that the landslide site remained active during

monitoring period and high rate of subsidence is also there in wet season during



July-August. Numbers of available PSC’s exported on Google Earth within the

landslide boundary are shown in Figs. 7(a) and 7(c). Figures 7(b) and 7(d) shows

the plot of displacement of the PSCs (PS1 and PS2) with timeline vs. subsidence

along with the parameters values.

(a)

(b)



(c)

(d)

Fig. 7. (a), (c) Available PS1 and PS2 in landslide boundary

and (b), (d) Displacement time series of PS1 and PS2.

Detailed parameter values of PS1 and PS2 found from time series are depicted

in Table 2. Both PSC’s have shown velocity of -26.8 and -23.7 mm/year

respectively indicating subsidence i.e. target has movement away from sensor in

LOS direction. Summary of results taking 44 most prominent PSC’s along the slope

of slide site at higher altitudes is given in Table 3. Plot of other PSC’s showing

uplift due to debris collected down the mount or at lower altitude is skipped here.

These 44 PSC’s have shown maximum cumulative displacement of -63.1 mm

during the study time period of 22 months from January 2015 to October 2016 with

a maximum decline velocity of -30 mm/year along the line of site (LOS). The

average cumulative displacement of the ROI is about -43.025 mm and average

decline velocity -20.6 mm/year.



Table 2. Time series parameter values.

Parameters PS1 PS2

Time series of PS1 velocity [mm/year] -26.8 -23.7

Velocity standard deviation [mm/year] 1.33 1.51

Cumulative displacement [mm] -56.4 -48.5

Temporal coherence 0.42 0.36

Sample 1202 1290

Line 75 70

Standard deviation 6.0 5.9

Table 3. Result summary.

PS

S. No. PS point ID

Velocity

[mm/year]

Cumulative

velocity

[mm]

Sigma

velocity

[mm/year] Coherent

1 706 (PS1) -26.8 -54.6 1.33 0.42 2 657 (PS2) -23.7 -48.5 1.51 0.36 3 549 -30.0 -63.1 1.68 0.36 4 628 -1.8 -3.9 1.52 0.43 5 805 -26.7 -56.2 2.81 0.29 6 877 -25.8 -56.3 1.68 0.36 7 548 -13.3 -27.9 2.11 0.37 8 878 -14.8 -31.1 1.35 0.51 9 1006 -20.6 -43.3 1.72 0.33

10 1040 -29.9 -48.1 1.61 0.30 11 1007 -25.5 -53.6 3.20 .35 12 1130 -29.0 -60.9 1.82 0.41 13 837 -28.7 -60.4 1.68 0.46 14 937 -19.1 -41.8 2.05 0.25 15 1034 -29.2 -61.4 2.49 0.38 16 1107 -29.8 -62.7 1.58 0.43 17 1374 -30.0 -63.1 0.88 0.45 18 1443 -26.2 -55.1 1.31 0.36 19 1305 -16.1 -33.8 1.41 0.38 20 1298 -4.3 -9.0 2.63 0.40 21 1350 -3.0 -6.7 1.55 0.37 22 1234 -17.6 -37.0 1.87 0.37 23 1244 -5.9 -12.4 1.85 0.44 24 1238 -3.5 -7.4 1.57 0.40 25 1450 -1.8 -3.7 1.33 0.41 26 1617 -30.0 -63.1 0.71 0.22 27 1658 -27.4 -57.7 1.49 0.45 28 1349 -15.4 -32.5 1.70 0.30 29 1361 -16.4 -34.5 1.64 0.41 30 1539 -27.4 -57.6 2.25 0.40 31 1410 -24.9 -52.4 1.6 0.41 32 1246 -24.2 -50.9 1.44 0.37 33 1324 -30.0 -63.1 10.58 0.33 34 1480 -26.6 -55.9 1.77 0.36 35 1335 -17.4 -36.6 1.85 0.37 36 527 -24.3 -51.1 1.46 0.42 37 468 -21.9 -46.1 1.41 0.43 38 603 -24.2 -51.0 1.56 0.26 39 511 -28.7 -60.3 2.58 0.35 40 390 -24.2 -50.9 1.33 0.40 41 503 -16.3 -34.4 1.48 0.39 42 380 -16.8 -35.3 1.33 0.45 43 629 -9.4 -19.8 1.77 0.34 44 517 -18.0 -37.9 2.07 0.39



From the PSI analysis, it is quite evident that there is a linear deformation trend

in this area with a continual decline velocity of −1.8 to −30 mm/year indicating

points are moving away from satellite. This downwards trend shown in the time

series of PSC’s clearly indicate active site and shows how magnitude of the

displacement increased. Precision of PS detection depends on the accuracy of APS

estimation, the number of images and the reliability of phase difference. In present

case, an investigation has been done with only 30A/23D available datasets yielding

satisfactory results. Subsidence has occurred on surface near the active PS’s

positions, which also establishes the continuous deformation trend over the slope.

5. Conclusion

Investigation using PSI techniques has shown a continuous linear trend in ground

deformation along the Gulaba site. PSI has shown the capability of achieving

results more conveniently than conducting surveys and using instrumentation on

site. It demonstrates that it is a competent tool to locate slope instabilities and

monitor their displacements. An average cumulative displacement about -43.025

mm and average decline velocity ranging of -20.6 mm/year is observed during the

study time period. As this region is landslide prone, there is an urgent need of

government to adopt suitable measures to identify various adjoining areas of slope

instabilities and to put landslide mitigation plans, which can minimize the impact

of this natural hazard. The future of SAR technology using PS technique is

prominent for monitoring surface displacements due to natural and manmade

hazards in India.

In future, more investigations are needed with recent and high-resolution

images with shorter revisit time like TerraSAR-X and ALOS L-Band image data

over the same area to collect denser PS points for multi-temporal analysis so that

the subsidence prone areas can be demarcated more precisely. L-band data are

capable of penetrating vegetation cover thus somewhat reaching the ground surface

is especially suitable for mapping the surface deformations.

Acknowledgements

Sentinel-1A data provided by European Space Agency under free, full and open

data policy by Copernicus programme and visualized in Google Earth. Author

would like to thank Daniele Perissin for providing access to the software

SARPROZ (2016), http://www.sarproz.com, Guides for their dedication and

grateful to Kapil Malik of Radar Systems and Services, for his continuous

constructive comments and solutions provided during data acquisition and

processing. http://radarsystems.in

Abbreviations

ALOS Advanced Land Observing Satellite

APS Atmospheric Phase Screen

ASF Alaska Satellite Facility

DEM Digital Elevation Model

ESA European Space Agency

HH Horizontal-Horizontal

HV Horizontal-Vertical

InSAR Interferometric SAR

http://radarsystems.in/



IW Interferometric Wide-Swath

LOS Line of Sight

NASA National Aeronautics and Space Administration

PS Persistent Scatterers (Permanent Scatterers)

PSC Persistent Scatterer Candidate

PS-InSAR Persistent Scatterer InSAR

ROI Region of Interest

SAR Synthetic Aperture Radar

SLC Single Look Complex

SRTM Shuttle Radar Thematic Map

TOPS Terrain Observation with Progressive Scans

References

1. Kanwar, R. (2014). Kullu District Disaster Management Plan. Kullu,

Himachal Pradesh, India: District Administration.

2. Peyret, M.; Djamour,Y.; Rizza, M.; Ritz, J.-F.; Hurtrez, J.-E.; Goudarzi, M.A.;

Nankali, H.; Chery, J.; Le Dortz, K.; and Uri, F. (2008). Monitoring of the large

slow Kahrod landslide in Alborz mountain range (Iran) by GPS and SAR

interferometry. Engineering Geology, 100(3-4), 131-141.

3. Chandel, V.B.S.; Brar, K.K.; and Chauhan, Y. (2011). RS & GIS Based

landslide hazard zonation of mountainous terrains a study from Middle

Himalayan Kullu District, Himachal Pradesh, India. International Journal of

Geometics and Geosciences, 2(1), 121-132.

4. Sun, Q.; Zhang, L.; Ding, X.L.; Hu, J.; Li, Z.W.; and Zhu, J.J. (2015). Slope

deformation prior to Zhouqn China landslide from InSAR time series analysis.

Remote Sensing of Environment, 156, 45-57.

5. Piersccini, M.; Casagli, N.; Luzi, G.; Tarchi, D.; Noferini, L.; and Atzeni, C.

(2003). Landslide monitoring by ground-based radar interferometry: A field test

in Valdarno (Italy). International Journal of Remote Sensing. 24(6), 1385-1391.

6. Ferretti, A.; Savio, G.; Barzaghi, R.; Borghi, A.; Musazzi, S.; Novali, F.; Prati,

C.; and Rocca, F. (2007). Sub-millimeter accuracy of InSAR time series:

Experimental validation. IEEE Transactions on Geoscience and Remote

Sensing, 45(5), 1142-1153.

7. Barra, A.; Monserrat, O.; Mazzanti, P.; Esposito, C.; Crosetto, M.; and

Mugnozza, G.S. ( 2016). First insights on the potential of Sentinel-1 for

landslides detection. Geomatics, Natural Hazards and Risk, 7(6), 1874-1883.

8. Colesanti, C.; Ferretti, A.; Novali, F.; Prati, C.; and Rocca, F. (2003). SAR

monitoring of progressive and seasonal ground deformation using the

permanent scatterers technique. IEEE Transactions on Geoscience and

Remote Sensing, 41(7), 1685-1701.

9. Colesanti, C.; and Wasowski, J. (2006). Investigating landslides with space-

borne synthetic aperture radar (SAR) interferometry. Engineering Geology,

88(3-4), 173-199.

10. Ferretti A.; Prati C.; and Rocca F. (2001). Permanent scatterers in SAR

interferometry. IEEE Transactions on Geoscience and Remote Sensing,

39(1), 8-20.



11. Hooper, A.; Zebker, H.; Segall, P.; and Kampes, B. (2004). A new method for

measuring deformation on volcanoes and other natural terrains using InSAR

persistent scatteres. Geophysical Research Letters, 31(23), 1-5.

12. Kiseleva, E.; Mikhailov, V.; Smolyaninova, E.; Dmitriev, P.; Golubev, V.;

Timoshkina, E.; Hooper, A.; Samiei-Esfahany, S.; and Hanseen, R.(2014). PS-

InSAR monitoring of landslide activity in the Black Sea Coast of the

Caucasus. Procedia Technology, 16, 404-413.

13. Ferretti, A.; Prati, C.; and Rocca, F. (2000). Nonlinear subsidence rate

estimation using permanent scatterers in differential SAR interferometry.

IEEE Transactions on Geoscience and Remote Sensing, 38(5), 2202-2212.

14. Colesanti, C.; Ferretti, A.; Prati, C.; and Rocca, F. (2003). Monitoring

landslides and tectonic motions with the permanent scatterers technique.

Engineering Geology, 68(1-2), 3-14.

15. Kimura, H.; and Yamaguchi, Y. (2000). Detection of landslide areas using

satellite radar interferometry. Photogrammetric Engineering and Remote

Sensing, 66(3), 337-344.

16. Brugioni M.; Mazzanti B.; Montini G.; and Sulli, L. (2011). Use of SAR

interferometry for landslide analysis in the Arno river basin. Proceedings of

the Second World Landslide Forum. Rome, Italy, 7 pages.

17. Ferretti, A.; Fumagalli, A.; Novali, F.; Prati, C.; Rocca, F.; and Rucci, A.

( 2011). A new algorithm for processing interferometric data-stacks:

SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing, 49(9),

3460-3470.

18. Perissin, D. (2015). SARPROZ software manual. Retrieved November 9,

2015, from https://www.sarproz.com/software-manual/.

19. Bakon, M.; Oliveira, I.; Perissin, D.; Sousa, J.J.; and Papco, J. (2017). A data

mining approach for multivariate outlier detection in post-processing of

multitemporal InSAR results. IEEE Journal of Selected Topics in Applied

Earth Observations and Remote Sensing, 10(6), 2791-2798.

20. Bakon, M.; Perissin, D.; Papco, J.; and Lazecky, M. (2014). Persistent scatterer

InSAR monitoring of Bratislava urban area. Geophysical Research Abstracts,

16, EGU2014-10901, 1 page.

21. Bakon, M.; Papco, J.; Perissin, D.; Lazecky, M.; Sousa, J.J.; Hlavacova, I.;

Batorova, K.; Ondrejka, P.; Liscak, P.; Paudits, P.; and Real, N. (2015).

Monitoring of landslide activity in Slovakia territory using multi-temporal

InSAR techniques. Proceedings of the International FRINGE Workshop.

Frascati, Italy, 5 pages.

https://www.sarproz.com/software-manual/

monitoring and analysis of displacement using insar ...jestec.taylors.edu.my/vol 14 issue 3 june...

Documents