terrasar-x data in cut slope soil stability monitoring in malaysia

3354 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 50, NO. 9, SEPTEMBER 2012

TerraSAR-X Data in Cut Slope SoilStability Monitoring in Malaysia

Yrjö Rauste, Habibah Bt. Lateh, Jefriza, Muhiyuddin Wan Ibrahim Wan Mohd,Anne Lönnqvist, and Tuomas Häme

Abstract—Landslides in Malaysia, and other tropical countrieswhere heavy monsoon rains are common, form a risk to people,buildings, and roads. In particular, steep slopes that have been cutto accommodate road lines are prone to landslides. The objectiveof the study was to develop algorithms and methods that can beused to alert on increased risk of landslides through detectingsmall signs of slope instability with synthetic aperture radar (SAR)image analysis. A time series of 19 scenes from the TerraSAR-Xsatellite was analyzed in study site Gunung Pass. Amplitude imageanalysis and interferometric techniques were applied to study soilmovements. Two terrace fragments were measured to have slid 2.5and 4.9 m in two amplitude images. The phase of topography-flat-tened coherence images was used to map small movements in aslope-failure area. The highest movement that was detected withinterferometric techniques was 1.71 cm while the correspondingmovement in ground data was 2.65 cm. High soil movements (over16 cm) in the slope failure area prevented the use of phase databecause fringes disappeared.

Index Terms—Landslide, radar, risk management, syntheticaperture radar.

I. INTRODUCTION

LANDSLIDES in mountainous countries like Malaysiaform a serious threat to infrastructure and people. Pre-

ventive measures and damage reduction require monitoring andpredicting landslides. Landslide mapping and prediction withground data and optical satellite images have been studied inMalaysia (e.g., [1] and [2]). In situ monitoring of numerouspotential landslide sites is expensive. Landslides most oftenhappen in periods of heavy rain, when thick clouds preventoptical satellite imaging. Radar sensors are not dependenton weather or daylight; thus, they are well suited for land-slide monitoring. Radar data and interferometric techniques

Manuscript received July 14, 2010; revised February 14, 2011 andSeptember 9, 2011; accepted November 29, 2011. Date of publicationFebruary 3, 2012; date of current version August 22, 2012. This work wascarried out in project EnviStab, which was funded by USM and VTT. InfoTerraGmbH/Germany provided the TerraSAR-X data as part of the cooperationagreement on TerraSAR-X data evaluation between VTT and InfoTerra. Thescientist in charge of the cooperation agreement in InfoTerra GmbH were BerndScheuchl and Felicitas von Poncet. Jefriza’s work was supported by a UniversitiSains Malaysia (USM) Fellowship.

Y. Rauste, A. Lönnqvist, and T. Häme are with the VTT Technical ResearchCentre of Finland, FIN-02044 VTT Espoo, Finland (e-mail: [email protected];[email protected]; [email protected]).

H. B. Lateh, Jefriza, and M. W. I. Wan Mohd are with the School of Dis-tance Education, Universiti Sains Malaysia, 11800 Penang, Malaysia (e-mail:[email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TGRS.2011.2181182

(e.g., [3]) have been applied to landslide mapping since overa decade ago (e.g., [4]–[6]).

The satellite synthetic aperture radar (SAR) systems usedin interferometric studies of landslides include the EuropeanRemote Sensing 1/2 (ERS-1 and ERS-2) (C-band, 6-cm wave-length, orbit repeat cycle 35 or 3 days) [4], [5], [7]–[21],JERS (L-band, 23-cm wavelength, orbit repeat cycle 44 days)[22], [23], [18], Radarsat-1 (C-band, 6-cm wavelength, orbitrepeat cycle 24 days) [16], [24], [25], Envisat/ASAR (C-band,6-cm wavelength, orbit repeat cycle 35 days) [16], [23],ALOS/PALSAR (L-band, 23-cm wavelength, orbit repeat cycle46 days) [25], and TerraSAR-X (X-band, 3-cm wavelength,orbit repeat cycle 11 days) [26]–[30].

The main limitations of interferometric techniques in land-slide monitoring are connected with temporal decorrelation,strong deformation gradients, sensor resolution, and atmo-spheric phase noise. If temporal changes due to vegetationmovements or growth, or changes in surface dielectric char-acteristics (soil and vegetation moisture) change the way theelementary scatterers within a SAR resolution cell sum up,the fringe structure is lost between an image pair. Techniquesemploying so-called persistent scatterers (e.g., [20]) avoid tem-poral decorrelation limitations by restricting to strong stablescatterers. The drawback is that natural persistent scatterers arerare in agricultural and vegetated areas. Deformation gradientscan prevent reliable deformation mapping if the difference indeformation (within an interferometric image pair) betweenneighboring pixels is higher than half the wavelength of theSAR system. For reliable mapping of landslides, the unstablearea should be large (e.g., 10–20 pixels by 50–100 pixels [21])compared to the spatial resolution of the SAR system. In caseof ERS-1 or systems with a similar resolution, this means anunstable area of several hectare. Atmospheric phase noise iscaused by spatial variations of atmospheric refraction index. Insmall areas, atmospheric phase is assumed to be constant.

The spatial resolution of earlier satellites has not been ad-equate for studying landslides with a very small area. There-fore, small roadside landslides in tropical rain forest areashave not been studied with SAR interferometric techniques.TerraSAR-X, which was launched on 15.6.2007, improvedthe spatial resolution of civilian SAR satellites. The objectiveof the current study was to develop methods—using high-resolution TerraSAR-X data—for detecting small signs of slopeinstability in view of using them as an early warning indicatorof larger cut slope failures along roads in mountainous forestedterrain.

0196-2892/$31.00 © 2012 IEEE

RAUSTE et al.: TERRASAR-X DATA IN CUT SLOPE SOIL STABILITY MONITORING IN MALAYSIA 3355

Fig. 1. Location of the Gunung Pass study site in Malaysia.

II. MATERIALS AND METHODS

A. Study Site and Data

The study site (center coordinates 4◦35′49′′.54 north,101◦20′54′′.16 east) was in Gunung Pass in Cameron Highlandsdistrict in Malaysia. Fig. 1 shows the location of the studysite. In the FAO ecological zones [31], the site is classified astropical rainforest. In the Holdridge life zone map produced byIAASA [32], the area is classified as subtropical wet forest, butthis map does not show any area in the world as “tropical rainforest.” The most important physiographic variable explaininglandslides in the area is topographic slope [33].

The site consisted of open or sparsely vegetated slopes nextto the main road. In places where the cut slope is very steep,the slope has been covered with a thin layer of cement, wheresloping surfaces and horizontal terraces cover the slope surface.The purpose of the terraces is to collect rain water from a slopearea to a number of draining channels that take the water downthe slope. The purpose of the cement cover is to prevent loosesoil from sliding down with rain water. In the main study site,the cement layers have broken as a result of a landslide. Fig. 2shows a ground photograph (part of a panorama photograph)from the study site. On the left, loose soil is visible in thelandslide area. The particle size in this soil varied from siltto stone fragments of diameter of about 5 cm. In the centerof the photograph and in the background, there are cement-covered slopes with a typical terrace structure. On these slopes,cemented surfaces are interspersed with patches of grass andsmall bushes. The slope is separated from the road by a barrierconstructed of stones that are kept in place by metal network.Outside the road and cut slope areas, the study site consistedof steep mountain slopes covered with thick and tall tropicalforest. There are no forest-free areas or outcrops on top of themountains, but the mountain tops are also covered with tropicalrain forest.

Fig. 2. Ground photograph from study site Gunung Pass on 6.11.2009.

TerraSAR-X data (Table I) consisted of 19 scenes thatwere acquired in high-resolution spotlight mode. Fig. 4 showsthe study site and locations that are later referred to. The19 TerraSAR-X scenes were used as 18 pairs of consecutivescenes to compute coherence and as 17 triplets of consecutivescenes to compute triherence. Each scene covered the same 5km (along track) by 10 km (cross track) area on the ground.Incidence angle was 26.6◦. Azimuth resolution was 1.1 m [34]and ground range resolution (in flat areas) 1.3 m. All sceneswere acquired around 11:20 UTC (around 19:20 Malaysiantime). As high winds and rain during image acquisition areknown to reduce interferometric coherence, weather data arelisted in Table I. Baseline data (between a scene and the nextscene in the time series) are shown as well. The weather datafrom the weather station at Ipoh airport, which is 33 km fromGunung Pass and 39 m above sea level, are only indicative forthe Gunung Pass site. The rain data are from a closer station,


TABLE ITERRASAR-X SCENES OF GUNUNG PASS STUDY SITE AND

METEOROLOGICAL DATA. RAIN FALL DATA FROM CAMERON

HIGHLANDS (12.6 km FROM THE SITE), OTHER METEOROLOGICAL

DATA FROM IPOH AIRPORT (33 km FROM THE SITE). DATA SOURCE

OF THE IPOH DATA: WEATHER UNDERGROUND SERVICE ON INTERNET.BASELINE DATA WERE COMPUTED FROM THE AUXILIARY FILES OF

THE TERRASAR-X IMAGE PRODUCTS AND TIE POINTS BETWEEN

IMAGES. RH(%) = RELATIVE HUMIDITY IN PERCENT, DIR. = WIND

DIRECTION. PERP = PERPENDICULAR BASELINE, PAR. = PARALLEL

BASELINE, ALONG = BASELINE COMPONENT IN THE DIRECTION

OF THE ORBIT, H(2π) = ELEVATION OF AMBIGUITY i.e.,ELEVATION THAT CAUSES A CHANGE OF 2π RADIANS

IN THE PHASE OF AN INTERFEROGRAM

which is also 12.6 km from Gunung Pass, but approximatelyat the same altitude (1545 m). Still the rain amount can varyover the 12.6 km distance, so even the rain fall data of Table Iare not fully representative of the conditions in Gunung Pass.In addition, it is possible that in tropical rain forest areas, themoisture level of the top soil layer may vary enough to destroythe coherence between two radar images even without a rainevent.

Ground data on soil movements in the study site were mea-sured by a total station daily. The total station is a combinationof a theodolite (for measuring angles) and an electronic distancemeasuring instrument. By measuring the angle and distancefrom a known base station, the coordinates of a target (an opti-cal prism) can be computed by the total station. The measuredarea lies just to the left of the are shown in Fig. 2. The soilmovement measurements had started before the first scene inthe TerraSAR-X data set 9.7.2008. The measurement data set

extended past the last TerraSAR-X scene of 9.8.2009, but therewas a long break in the measurements between 24.5.2009 and9.8.2009. The measurement data set included 6 prisms scatteredacross a failed cut slope area with bare soil. These six prismsshowed different levels of movement, but the movements werecorrelated from prism to prism. Therefore, only one prism—theone located highest on the slope—was used as reference data.The movements of this prism (prism number 3) were the largestof all prisms measured.

For topographic phase correction of TerraSAR-X coherencedata, University Sains Malaysia produced a digital elevationmodel (DEM) starting with contour lines of Malaysian topo-graphic maps in scale 1:50 000 that was compiled in 1994. Theroad in the Gunung Pass study site was opened for traffic in2003. It is most likely that the mass transport works in the sitehad not started before the topographic map and its contour lineswere made. Therefore, the DEM was replaced by a new versionin the central part of the Gunung Pass site. This new versionwas produced starting with contour line data from large-scaletopographic maps made by the company responsible for theconstruction and maintenance of the road in the Gunung Passsite. Pixel spacing of the large-area DEM was 0.00006◦ (6.6 m),which was then interpolated (by cubic spline interpolation) to1 m. One-meter pixel spacing was also used in the new versionof the central part of the site. The topographic phase correctionwas based on the accurate DEM in the central part of the studysite, where the slopes discussed in this article are located. Inthe margins of the scenes, the topographic phase correction wasbased on the less accurate DEM derived from the 1 : 50 000topographic maps.

The standard error in elevation in the less accurate DEMwas estimated to be about 5 m (assuming 90% of the area tobe within ± half of the contour interval, which was 20 m).The standard error in the more accurate DEM was assumedto be better than 0.5 m (after interpolation) in this type ofmanaged environment with smooth cement surfaces. Theseaccuracies translate to standard error in measured fringe phaseof 2.9 radians and 0.29 radians for the TerraSAR-X sceneswith wavelength of 0.0311 m, slant range of 564.717 km,incidence angel of 26.6◦, and maximum perpendicular baselineof 360 m.

B. Methods

Landslide detection relied on techniques that compare ob-servations of the same area in different images. These tech-niques required accurate coregistration between scenes. TheTerraSAR-X data of the Gunung Pass site were coregistered us-ing manually measured, correlation assisted tie points betweenscenes. An interactive tool was made for the measurement oftie points. The geolocation data of TerraSAR-X scenes wereaccurate to a few pixels. The use of interactive tie points(instead of traditionally used automatic techniques based onimage correlation) provided an additional step for quality con-trol in coregistration. Tie points were chosen from forest-freeareas only along roads to avoid uncertainty in identification offeatures in forest-covered mountains.


Three analysis methods were used: 1) visual analysis ofamplitude images; 2) analysis of coherence; and 3) analysis oftriherence.

When soil movements in a landslide area were so large thatidentifiable objects moved over one resolution cell or morethese movements could be detected in amplitude images.

When soil movements were smaller than the resolution of theused SAR sensor interferometric techniques were employed.The complex coherence γ, to measure the stability of a targetover a period between two radar scenes was computed

γ =〈p1p∗2〉√

〈p1p∗1〉〈p2p∗2〉. (1)

Here, p1 and p2 are complex pixel values in the two scenes.The symbols 〈x〉 indicate averaging over a local window. Inthis paper, averaging was done over a sliding window of 5-by-5pixels. Due to the sliding window, the coherence and triherenceimages had the same pixel spacing as the input TerraSAR-Xdata. A generalization of coherence to the case of three scenesis the so-called triherence [23] η

η =〈(p1p∗2) (p2p∗3)∗〉

〈p2p∗2〉√〈p1p∗1〉〈p3p∗3〉

. (2)

The magnitude of complex coherence and triherence wereused to detect small changes in the target. The phase ofcoherence (or the so-called interferogram, which is usuallydefined as the numerator of (1)) was also used to measureelevation changes. The effect of changing distances over imageswath was computed with the help of a DEM. The topographycorrected coherence γc was computed

γc = γe−jφ (3)

where φ is the reference phase computed with the assumedearth surface described by a DEM. This phase correction is of-ten called flattening of an interferogram. An analogous methodwas used to flatten the (complex) triherence.

The flattened phase images were analyzed by visual interpre-tation. For three slope segments chosen for analysis the numberof fringes was counted from the lower part of the slope untilthe top part of the slope. The lower part of the slope segment(the road) was assumed to be stable with no movements.The soil movement at the top part of the slope segment wasestimated by multiplying the number of (topography-flattened)fringes by the line-of-sight movement per 2π-phase change(see [3, eq. (39)])

δρdisp =λ

2. (4)

Coherence was computed over two consecutive scenes in thetime series of TerraSAR-X images. Triherence was computedover three consecutive scenes in the time series. Fig. 3 showsthe flow of SAR and DEM data in the study.

Fig. 3. Flow of data in analysis of soil movements with TerraSAR-X data.

Fig. 4. (Left to right) Amplitude image 3.2.2009 with slope segments su-perposed, coherence magnitude 12.1.–23.1.2009, and triherence magnitude12.1.–23.1.–3.2.2009. Polygon A is bare soil, polygon B a terraced slope withsome grass and bush vegetation, and polygon C a terraced slope with lessvegetation than B. The ellipse and polygon delineated with a dotted line markareas that are discussed in Section III-B. Range coordinate increases to right.TerraSAR-X data © Infoterra GmbH (Germany) 2009.

III. RESULTS AND DISCUSSION

A. Amplitude, Coherence, and Triherence Time Series inTerraSAR-X Data of the Gunung Pass Site

To study the development of TerraSAR-X data with timein a site with a sliding slope, three slope segments werechosen. These segments consisted of (see Fig. 4) 1) a baresoil segment; 2) a partly vegetated terraced slope next to thebare-soil slope; and 3) a terraced slope further from the bare-soil slope (with slightly less vegetation than in segment 2).The leftmost subfigure of Fig. 4 shows the amplitude image of3.2.2009. The middle subfigure shows the coherence magnitudefrom the period in January-February 2009, when the coherenceand triherence magnitudes reached their highest levels. Therightmost subfigure shows the triherence magnitude from thesame period. Coherence magnitude is high along the terrace


Fig. 5. TerraSAR-X amplitude, coherence, and triherence in Gunung Pass site on three selected slope segments. The movement of prism 3 (in meters in theright scale, measured by theodolite) is shown as a dotted line. The observations of coherence are shown at the time of the first scene of the used image pair.The observations of triherence are shown at the time of the first scene of the used image triplet.

lines of the cement-coated cut slope areas. The protectivebarrier (a fence of stones in a metal cage) along the roadalso forms a line with high coherence. The bare-soil landslidearea has also high coherence values in most of the scenesacquired in January–February 2009. The observations on co-herence magnitude apply also to the triherence magnitude, butthe contrast between high-triherence areas and the rest of thescene is higher in triherence magnitude than in the coherencemagnitude.

Outside the bare-soil landslide area, the high-triherencepoints form box-shaped areas along the terraces and the pro-tective barrier along the road. The form and dimensions ofthese box-shaped areas correspond to the computing window(5-by-5 pixels) used in coherence and triherence calculation.Unlike in an earlier study [23], high-triherence points did notoccur as isolated points that would persist from image to imagein the Gunung Pass study site. Therefore, their presence ordisappearance could not be used in this study site as an indicatorof soil movements due to landslides.

Fig. 5 shows the development of amplitude, coherence mag-nitude, and triherence magnitude in the selected three slopesegments. The drop in coherence and triherence magnitudearound the first half of November 2008 coincides with a periodwhen larger than average soil movements can be seen in themovement of the prism in slope segment A.

Fig. 6. Development of the southwestern corner of polygon B (red oval Din Fig. 4). Each image shows the scene 9.7.2008 in green. The other scene isshown in red and blue from left to right 20.7.2008, 7.11.2008, and 10.12.2008,respectively. Color version of this figure is included in the online article.TerraSAR-X data © Infoterra GmbH (Germany) 2008.

B. Landslide Detection in TerraSAR-X Amplitude Data

In cases where a distinct part of the slope structure has slidas one piece over a distance longer than the spatial resolution ofthe SAR system signs of landslides can be seen in amplitude im-ages. Fig. 6 shows such an example at the south-western cornerof polygon B (the ellipse in Fig. 4). The scene 9.7.2008 is shownin green and another scene in red and blue. In the color compos-ite with the scene 20.7.2008, all terraces including the ends ofthe two uppermost terraces in the yellow circle are coregistered.In the composite with the scene 7.11.2008, the red-blue dotsrepresenting the two uppermost terraces have slid about 2.5 m


Fig. 7. Location of the sliding terrace ends (oval D) of Fig. 6. The photographwas taken on 6.11.2009.

Fig. 8. Small slope failure in a terraced slope in polygon B (Fig. 4). In thescene of 20.7.2008, all terraces show as distinct continuous lines. In the scenesafter the soil movement period of October–November 2008, terraces are brokencross an elongated area. Missing terraces can also be seen in the triherencemagnitude image 7.11.2008 + 18.11.2008 + 29.11.2008. TerraSAR-X data ©Infoterra GmbH (Germany) 2008.

Fig. 9. Location of the small slope failure of Fig. 8. Bare soil of the slopefailure is seen as a brighter area in the center of the image. The photograph wastaken on June 11, 2009.

downslope. In the last composite with the scene 10.12.2008,these terraces have slid about 4.9 m downslope. Fig. 7 showsthe location of the sliding terrace fragments on the ground.

Fig. 8 shows another example of slope failure detectable inTerraSAR-X high-resolution spotlight data. The breaking ofseveral terraces during the earth movement period of October-November 2008 can be seen in a time series of images in Fig. 8.A ground photograph of this smaller slope failure is shownin Fig. 9.

TABLE IIRESULTS FROM VISUAL INTERPRETATION OF TOPOGRAPHY-FLATTENED

PHASE OF COHERENCE. WHEN THE PHASE CONTAINED ONLY NOISE IN

A POLYGON, “NO COH.” IS LISTED. WHEN THE PHASE WAS ABOUT

CONSTANT OVER THE WHOLE POLYGON, ZERO FRINGES IS LISTED.THE COLUMN PRISM 3 INCLUDES THE MOVEMENT OF PRISM NUMBER 3

(FROM GROUND MEASUREMENTS) OVER THE TIME SPAN FROM

THE FIRST SCENE OF A PAIR UNTIL THE SECOND SCENE

C. Soil Movement Detection With Coherence andTriherence Phase

Coherence and triherence images were made for all 18image pairs between two consecutive scenes and all 17 tripletsof three consecutive scenes. Phase of these coherence andtriherence images was flattened for terrain topography usingthe accurate DEM. Table II summarizes the results of thevisual analysis of coherence phase data. Columns “# Fringes”give the number of counted fringes across each of the threeslope segments studied. When no fringes could be seen, “nocoh.” is listed in Table II. Automatic methods for elevation andmovement measurements exist (e.g., [12], [20], [35], and [36]),but visual counting of fringes was adopted for its simplicity.When no fringes could be seen, but the phase was (almost)constant over the whole slope segment zero fringes is listedin Table II. Columns “Movement (cm)” give the estimatedmovement at top part of the slope segment as estimatedby (4). Column “Prism 3 (cm)” gives the ground-measuredmovement of prism number 3 (see Section II-A) at the top partof the bare-soil slope segment A. This movement is the totalmovement (Euclidean distance between the positions in thebeginning and end of the timeframe). As these movements aredue to the landslide development the movement direction canbe assumed to be down the slope. The ground measurement


Fig. 10. Phase of topography-flattened coherence for three scene pairs. Thephase angles between −π and +π are shown between black and white.TerraSAR-X data © Infoterra GmbH (Germany) 2009.

database did not include movements projected on the radarline of sight. In some cases, the time period over which themovement of the prism was computed was not exactly the sameas the time period of the image pair, but measurements fromthe previous or next day were used when the measurement ofthe TerraSAR-X acquisition day was missing. For the end partof the TerraSAR-X time series, ground measurements were notmade (the measurements were started again in August 2009).

Fig. 10 shows an example of topography-flattened phaseof coherence for three image pairs. In the first interferogram12.1.2009 + 23.1.2009, the bare-soil slope (polygon A) showsone fringe. The terraced slope next to it (polygon B) also showsone fringe, while the other terraced slope (polygon C) shows nofringes (a constant phase). These were estimated to representmovements of 1.55 cm, 1.55 cm, and 0 cm as shown in Table II.The next pair (23.1.2009 + 3.2.2009) shows a constant phasein polygons A and B and half a fringe (gray levels spanninghalf the range from black to white) in polygon C (even thoughthis fringe only affects the middle part of the polygon). In thelast pair (3.2.2009 + 14.2.2009), the bare-soil slope A showsno fringes but a random phase pattern across the slope. Theterraced slope B next to it shows a constant phase and the otherterraced slope C again half a fringe in the middle-lower part ofthe polygon.

The main slope failure area (A in Table II) shows identifiablefringes in five image pairs out of 18. In the period 9.7.2008to 12.1.2009, the lack of fringes is most likely due to randommovements of the soil in this loose-soil area. In this period, thelowest measured movement of prism 3 was 16.65 cm between7.11.2008 and 18.11.2008. These movements have also kept thecoherence magnitude low (see Fig. 5). The low coherence andlack of fringes in pair 3.2.2009 + 14.2.2009 is not due to soilmovement. Prism 3 was measured to move only 0.88 cm, andthere were identifiable fringes in pair 12.1.2009 + 23.1.2009with a measured movement of 2.65 cm. The reason for lowcoherence and lacking fringes is most likely some changes insoil moisture between the scenes of 3.2.2009 and 14.2.2009(rain fall data at 12.6 km from the site shows 4.4 mm of rain fallfor 3.2.2009). Of the two terraced slopes, polygon C has identi-fiable fringes (or constant phase) in 15 pairs, while slope B nextto the bare-soil slope A has only in seven pairs. The difference

is most likely due to a difference in vegetation cover: slope Bhas more vegetation (grasses and small bushes) than slope C.

Measurement of soil movements in the main slope failurearea A by coherence phase was possible in five pairs out of 18.In three cases, there was a ground-measured movement corre-sponding to the dates of the image pair. In the pair 12.1.2009 +23.1.2009, the SAR-measured movement was 1.55 cm, whilethe ground-measured was 2.65 cm. Part of the difference maybe due to the fact that the SAR-measurement direction (line ofsight) does not exactly coincide with the downhill movement ofthe soil. The slope (in the range direction of the TerraSAR-Xscenes) of polygon A was estimated by 4-by-4 window inthe center of the polygon (38.5◦). Taking into account theterrain slope, the measured line-of-sight movement of 1.55 cmcorresponds to a movement of 1.71 cm along the surface. Themost likely explanation for the difference is the inaccuracy ofphase measurement by visual analysis. The 2.65-cm movementwould correspond to 1.5 fringes (taking into account the dif-ference between the slope direction and the radar line-of-sightdirection), while just one fringe was interpreted from the phaseimage in the leftmost subfigure of Fig. 10. Theoretically, thephase measurement of a 5-by-5 averaged interferogram shouldbe possible down to 45◦ i.e., 1/8 of a full cycle [37]. Visualinterpretation of phase values in a grayscale image may be lessaccurate than the theoretical maximum accuracy. In two othercases (23.1.2009 + 3.2.2009 and 14.2.2009 + 25.2.2009), theSAR-measured movement was considered to be zero, while theground measurements were 0.52 cm and 0.42 cm. As thesemovements correspond to one third of a fringe or less, theymay be below the detection threshold of visual interpretationof phase data, which were analyzed as grayscale images.

Topography-flattened phase of triherence was also analyzedby visual interpretation. Fringes could be identified in fewercases (five cases over the three slope segments) than intopography-flattened coherence (27 cases over the three slopesegments, see Table II). Even in those few cases, the fringeswere noisier than the fringes in the phase of topography-flattened coherence.

D. Discussion on Landslide Detection Methods Applicable inSmall Sliding Areas

Mapping of movements of landslides could be done withaccurately coregistered amplitude data when the movementswere several pixels. This required that the interpreter knewwhere to look for potential landslide areas. It also requiredthat the slope where the landslide occurred contained somedistinct high-contrast features that could be identified both inan image before the landslide and after it. In this type of visualchange detection, the resolution of the SAR system is essential.The mapping of movements of terrace fragments (2.5 m and4.9 m) was possible (as shown in Fig. 6) using TerraSAR-Xhigh-resolution spotlight data with 1.1-m spatial resolution. Thedetection of these movements would not be possible with earliermedium-resolution SAR data with spatial resolutions of 10 mor more.

Breaking of terraces could also be seen in triherence mag-nitude images. This requires a longer time series than an


approach relying on direct comparison of amplitude images.Because gradual breaking of terraces may be difficult to detectreliably in amplitude images, use of triherence magnitude asan additional image layer may facilitate visual interpretation ofimage data. Triherence magnitude seems to be more sensitive tothe presence of terraces than coherence magnitude. Therefore,it can be more effective in detecting the breaking of terraces.

The interferometric phase of coherence or triherence is notsuitable for mapping large (wavelength of the SAR sensor ormore) soil movements in landslides. The random movementsof pebbles and other soil particles in a landslide cause a lowcoherence (and triherence) magnitude, which in turn makes thefringes disappear as shown in Fig. 5 and Table II. Also, Cole-santi and Wasowski [21] noted that monitoring of incoherentdeformation phenomena by differential SAR interferometry isnot feasible. As changes in soil and vegetation moisture can alsocause low coherence (and triherence), the presence or absenceof fringes is not a good indicator of major movements inlandslides. In the current study, only five image pairs out of 18had clearly defined fringe structure in the main landslide area,which illustrates the difficulty in detecting landslide events.

The interferometric phase of coherence can be used to mapsmall soil movements. However, it is impossible to map (bymeans of phase of coherence) relative movements betweenneighboring pixels that are more than half of the wavelength ofthe radar system. These small movements in turn can be usedfor predicting future landslides or slope failures. Changes insoil moisture and similar meteorological changes can limit oreven prevent this type of mapping.

Detection and measurement of small soil movements requirehigh coherence magnitude between images, which is usuallyachieved in image pairs acquired in dry periods. Since mostslope failures and landslides occur in periods of heavy rain,connecting interferometrically measured soil movements tooccurrence of landslides or slope failures can be a difficult taskthat requires geological expertise.

An alternative method for monitoring small soil movementson cut slopes could use small inexpensive radar reflectors on theslopes susceptible to landslides. These reflectors would behavelike permanent scatters. A technique similar to the methoddescribed by Colesanti et al. [20] could be used to estimate theatmospheric component of measured phase changes. Naturaltargets in tropical rain forest are unlikely to produce three orfour permanent scatterers per square kilometer in X-band radardata. Further research is required to find out if a string ofpermanent scatterers along roads could produce a sufficientlyaccurate and detailed atmospheric phase component for separa-tion of soil movements and atmospheric artefacts. If the slopesto monitor are located on opposite slopes of mountains, care isneeded in planning acquisition of separate radar time series foreach slope direction.

IV. CONCLUSION

Mapping of landslides in nonvegetated cut-slope areas waspossible with TerraSAR-X amplitude data in cases where iden-tifiable blocks of terraces had moved several pixels. Monitoringsmall (of the order of radar wavelength) surface movements (in

nonvegetated cut-slope areas) with interferometric phase waspossible when soil movements were very small and when soilmoisture differences between SAR images did not destroy thefringe structure of an interferometric pair. The identification ofthe reasons for lacking coherence and fringe structure remaineduncertain in the current study because meteorological data werefrom sites that were 12 or 33 km from the study site. In bothamplitude and interferometric applications, the high spatialresolution of TerraSAR-X high-resolution spotlight data wasessential.

No firm outlines for an operational landslide monitoringsystem can be derived from the current experimental case studyonly. Further research is needed before semi-operational sys-tems can be designed. A future hypothetical semi-operationalearly warning system for landslide prediction could utilizehigh-resolution TerraSAR-X data or other SAR data with thesame resolution or better. The setup of the system could be:the data would be analyzed as a time series that is accuratelyco-registered. Each time a new image is acquired, a visualanalysis of amplitude data (in combination with coherence andtriherence magnitudes) is carried out, and coherence phase isused for mapping of movements of the order of radar wave-length. In addition to visual counting of fringes, more elaboratemethods can also be included [12], [20], [35], [36]. Geologicalexperience is used to analyze if the detected movements arelikely to be real or artefacts due to atmospheric phenomena.If real movements are detected, their significance is evaluatedby geological experts and ground surveys. If a newly acquiredradar image is affected by low coherence due to meteorologicalreasons, ground surveys are also used. If a hazardous landslideseems likely in the near future, road authorities are warnedabout the risks.

If the potential landslide sites are readily accessible alongroad lines, terrestrial methods with theodolites, total stations,and inclinometers can be used in landslide monitoring toimprove the reliability and temporal observations. Deployinginexpensive radar reflectors in landslide sites can also improvethe radar-based monitoring of areas prone to landslides. Thisapproach requires further research, particularly in tropical for-est areas where natural persistent scatterers are very rare.

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Yrjö Rauste was born in Espoo, Finland, in 1956.He received the M.S. degree in surveying and map-ping in 1983 and the Licentiate of Technology in1989, and Doctor of Science (Tech.) in 2006, all fromthe Helsinki University of Technology, Espoo.

He has been with the VTT Technical ResearchCentre of Finland, Espoo, Finland, since 1979, ex-cept for visits to other research centers and hismilitary service in 1983. From 1986 to 1987, hewas a Visiting Scientist with the Institute for ImageProcessing and Computer Graphics, Graz Research

Center, Graz, Austria. From 1997 to 1999, he was a postdoctoral grant holderwith the Joint Research Centre, European Commission, Ispra, Italy. From1994 to 1996, he also served as the Secretary of the Finnish Society ofPhotogrammetry and Remote Sensing. He is currently a Principal Scientist inthe Remote Sensing group of the VTT Technical Research Centre of Finland.His research interests include application of synthetic aperture radar imageanalysis and processing (particularly in forestry applications) and forest firedetection using optical satellite data.

Habibah Bt. Lateh received the B.A (Ed) Hons.degree from the Universiti Sains Malaysia, Penang,Malaysia, in 1984, the M.A. degree from WesternMichigan University, Kalamazoo, in 1986, and thePh.D. degree from the University of Bristol, Bristol,U.K., in 1996. Her Ph.D. research was on landslidesunder Professor M.G. Anderson from the Universityof Bristol.

She has been with Universiti Sains Malaysia,Penang, since 1987 and become a project leader formany projects on landslide research. She had vast

field experience in landslides research and soil erosion study in Malaysia. Ad-ditionally, she had been working with researchers from different countries suchas National Institute of Earth Science and Disaster Prevention (NIED), Japan,VTT Technical Research Centre of Finland, Espoo, University of Rajshahi,Bangladesh and Nepal. Currently, she is working with researchers from theUniversity of Tokyo, Chiba University, and NIED to study landslides with theapplication of remote sensing, GIS, and modeling. She had secured severalnational and international grants on landslide study. Her research interestsinclude landslides, soil erosion, environmental education, and distance learning.She is currently a Dean with the School of Distance Education, Universiti SainsMalaysia, Malaysia.


Jefriza was born in Sigli, Aceh-Indonesia, in 1982.He received the Bachelor degree in civil engineeringfrom Universitas Syiah Kuala, Aceh-Indonesia, in2007 and is currently studying toward the MasterDegree at Environmental Sciences in the UniversitiSains Malaysia, Penang, Malaysia.

From 2007 to 2008, he was a technical staffmember in Aceh Reconstruction and RehabilitationBoard for tsunami disaster project in Aceh and Nias.From 2009 to 2011, he received a Universiti SainsMalaysia scholarship and worked as a fellowship

holder. He had an opportunity from Universiti Sains Malaysia as researchattachment project to visit VTT Technical Research Centre of Finland, Espoo,Finland, to study image processing with TerraSAR-X and ALOS-Palsar andwork as a Visiting Researcher.

Muhiyuddin Wan Ibrahim Wan Mohd receivedthe M.Sc. degree in environmental conservationfrom the Institute for Environment and Development(LESTARI), Universiti Kebangsaan Malaysia, andDoctor of Geographic Information Science and Re-mote Sensing from the University Sains Malaysia,Penang, Malaysia.

He has been with the Geography Department, Uni-versiti Sains Malaysia since 2005. He was involvedwith landslide studies such as landslide spatialmodelling using remote sensing techniques, logistic

regression, and artificial networks. Recently, he was involved with spatial mod-eling for habitat suitability using remote sensing and geographic informationsystems.

Anne Lönnqvist was born in Somero, Finland, in1977. She received the Master of Science (Tech.)(with honors), Licentiate of Science (Tech.), andDoctor of Science (Tech.) degree in electrical engi-neering from the Helsinki University of Technology(TKK), Espoo, Finland, in 2001, 2004, and 2006,respectively.

From 2000 to 2007, she was a Research Engineerat the TKK Radio Laboratory, where she was in-volved with submillimeter-wave antenna testing forthe European Space Agency. Additionally, she was

developing techniques for measuring radar cross section of scaled models oftargets and reflectivity of radar-absorbing materials at submillimeter wave-lengths. Since March 2007, she has been with the Remote Sensing Group, VTTTechnical Research Centre of Finland, Espoo, Finland. Her current researchinterests include fully polarimetric synthetic aperture radar imagery.

Tuomas Häme received the M.S. and Ph.D. degreesfrom the faculty of agriculture and forestry, Univer-sity of Helsinki, Helsinki, Finland, in 1979 and 1992,respectively.

He has been with the VTT Technical ResearchCentre of Finland, Espoo, Finland, since 1979, wherehe is a Manager of Remote Sensing Team. He holds aResearch Professorship on Earth Observation at VTTsince 2005. He was a Visiting Researcher at NorthCarolina State University, Raleigh, in 1989, and inJoint Research Centre, Italy, in 1995–1996. He is an

expert in remote sensing in forestry, and has developed particularly methods forforest change detection, forest area mapping, and biomass estimation from localto continental extents. He has managed several international projects on EarthObservation and is working in close cooperation with value-adding industry.

terrasar-x data in cut slope soil stability monitoring in malaysia

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