THE COHERENCE ANALYSIS FOR DETECTING THE SUBSIDENCE AT PERMANENT FROZEN AREA IN QINGHAI-TIBETAN PLATEAU

Zhen Li, Chou Xie, Xinwu Li State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Application，Chinese

Academy of Sciences, P.O.Box 9718, Datun 3, Beijing, 100101 ,China, Email: zli@irsa.ac.cn

ABSTRACT

The surface displacement by seasonally freezing bulge and thawing subsidence are main hazards for engineering construction in permafrost regions, especially for the Qinghai-Tibet railway. For detecting the distortion at permafrost area, we try to study the interferometric method of monitoring the deformation at permafrost area with time-series EnviSat ASAR data. In this paper, the coherence characteristics are analyzed for different baseline, time interval with or without season change and different classes (rock, bare soil, vegetation, water), used 13 time-series ASAR data from Jan. 2004 to June 2006 at the Beiluhe test area in the Qinghai-Tibetan Plateau. The results showed that the coherence coefficients is lower in summer and fall than in other two seasons as the freezing and thawing phenomenon. For pairs of crossed different season, such as from spring to summer or from summer to fall, the coherence coefficient decrease for bare soil and vegetation cover, little decrease for rock cover.

1. INTRODUCTION

The permafrost and the seasonal frozen ground cover respectively 1,272,709 km2 and 1,146,399km2 in the Qinghai-Tibetan Plateau, and the highway and railway that passes through permafrost areas about 550km. Extensive areas of frozen ground hold large quantities of ice. The seasonally thawing layer is highly sensitive to temperature changes, and thawing and temperature rising has a great influence on railway stability. One of the main problems is how to monitor the frozen ground’s displacement [1].

The traditional methods to monitor deformation of plateau frozen ground is burying settlement meter, such as inclinometer, or constructing time-serial GPS observation station. Using these methods, the acquired information of plateau frozen ground’s deformation can only be gotten at some limited locations due to the limitation of observation condition, especially in the Tibet Plateau, although which is with high precision and continuity.

Differential Synthetic Aperture Radar Interferometry (DInSAR) has also been widely used in recent years for monitoring ground’s deformation [2]. DInSAR analysis the phase information of radar echo signal, and extract dense deformation information in a relative large spatial domain. But temporal and geometrical decorrelation often prevents traditional SAR interferometry from being

an operational tool for surface deformation monitoring in the area of frozen ground. To get long term deformation information, scientists presented the methods of Permanent Scatters (PS) and Small Baseline Subset (SBAS), and have successfully used them for monitoring subsidence in urban area [3, 4]. But in the area of frozen ground, ground surface changes very quickly with time, so the coherence is relative lower in the area of frozen ground than in the area of urban. To use the method of PS or SBAS to analyze surface subsidence history in the area of frozen ground, characteristics of coherence at permanent frozen area need to be firstly analyzed. With the analysis of the characteristics of coherence, it can be recognized what’s the factors that influent object’s coherence, and also be made clear when the factors work and what degree they influent the object’s coherence. Based on the analysis of coherence characteristics, we can determine the SAR data choice for detecting the surface deformation and the choice of stable scatters.

2 ACTIVE CHARACTERISTICS OF FROZEN GROUND IN THE QINHAI-TIBETAN PLATEAU

There are a lot of factors that influence freezing and thawing of the frozen ground, e.g. season, vegetation, topography, snow cover, water, lithology, and moisture. In the zone of meadow in the Qinghai-Tibetan plateau, vegetation reduces surface temperature and influent thawing and freezing of frozen ground. The influence of topography to the frozen ground is mainly dues to elevation, gradient and direction of slopes.

The active process in active layer in the permafrost region of the Qinghai-Tibetan Plateau is composed of main four sections [5]:

(1) The process of active layer’s thawing in summer begins at the end of April and completes at the middle of September. During the process, the frozen ground thaws downwards from the surface until reaching the maximum depth.

(2) The process of freezing begins at the middle of September and the frozen ground freeze slowly upwards from the bottom. From the middle of September to the middle of October, the frozen ground freezes from two directions (i.e. downwards and upwards). At the end of October, the process of freezing ends.

(3) When the process of freezing completes thoroughly, the process of temperature falling begins. In this process, temperature falling very quickly. This

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Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

process ends in the next January.

(4) From the end of January, active layer’s temperature begins rising. At the end of March, thawing at day occasionally takes place near the surface of the ground. At the beginning of April, freezing at night and thawing at day take place every day.

3 THE STUDY AREA AND DATASETS

3.1 The study area in Beiluhe

The Beluhe area is located at the center of Qinghai-Tibetan Plateau (34047´N, 92051´E), is typical of permafrost area according to the standard of classification of frozen ground [6]. The climate in this area is cold and dry. In winter, the climate of this area is controlled by high-altitude westerly airstreams, which are fine, dry and windiness. In summer, the warm moist air from over the Indian Ocean permeates along stream valley, and rain-fall is in plentiful in the area. Annual mean temperature is -4.4 , the mean temperature℃ is-16.5 in January and the mean temperature is 7.5℃ ℃in July over the area. The highest temperature is 32.6 ,℃ and the annual temperature range is 24 . Precipitation ℃mainly occurs in June, July and August, and it usually takes place in the form of snow and hail based on the meteorological data of the Tuotuohe meteorological station near study area.

The mean elevation is above 4,300 meters, but the relative height of mountains is not very high, and slopes of the mountains are smooth in the study area as shown in the CBERS image (Fig. 1). The main types of ground objects in the study area are listed as the following [7]:

1) Bare rock: there is a huge area of exposed rock at the top of mountain, whose elevation is above 4,500 meters.

2) Alpine alm: alm mainly appears in the hillside of the mountains in the mid of the study area, and alpine cushion vegetation (i.e. androsace of cushion morphology) is distributed on the alm.

3) Alpine meadow: there is alpine meadow in the gentle wide valley, where stipa purpurea is the constructive specie. The vegetative cycle of stipa purpurea is relative short, and generally stipa purpurea wilt in August every year.

4) Pebbles: the stream valley is wide and shallow, and there are a lot of pebbles in Beiluhe area. The stream spreads over the area, and there are many sand dunes at the bank.

5) Lake: Some lakes locate at the southwest part of the study area, and it appears blue in the CBERS winter image because of lake ice.

Figure 1. The composite image of band 4 (R), 3(G) and

2(B) CCD sensor from the China Brazil Earth Resources Satellite at Beluhe area in Dec. 8, 2004.

3.2 Data sets

Total 13 scenes ENVISAT ASAR imageries are acquired on the dates between Jan. 8, 2004 and June 1, 2006 in this study. All the imageries are descending pass, VV polarization SAR data in SLC format. The temporal and spatial distribution of the 13 scene ASAR data are shown in Fig. 2, where the horizontal axis stands for spatial perpendicular baseline relating to the data acquired on Jan. 27, 2005, and the vertical axis indicate the date of ASAR data acquired. .All ASAR data is acquired at the UTC 03:59, which is in the morning at local time.

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Figure 2. The temporal and spatial distribution of 13 scenes ASAR data acquired in study area

4 DATA PROCESSING

4.1 Coherence image processing

The coherence is one of the important characteristics for repeat pass interferometry, which represents the local

correlation of the radar reflection characteristics of the surface target between the two observations. It can be calculated by ensemble averaging N neighboring pixels of complex SAR data as:

*22

*11

*21

IIII

II=γ (1)

where, γ is the degree of coherence, denotes

ensemble averaging, and are the SAR data acquired at different time in complex format respectively, and stands for the conjugate of a complex. In practice, the estimation of coherence magnitude can be done over a finite size window by coherently averaging the complex values. A window of 3×15 single-look SAR pixels is selected to compute coherence in the process of coherence computation. To avoid the co-correlation of coherence in neighbor area, windows are close each other and have no overlap. Both requirements of high coherence contrast and high spatial resolution for image are satisfied in this work using 3×15 window for computing coherence.

1I 2I

∗

4.2 Decorrelation factors analyzing

Since the signal itself consists of both correlated and de correlated components. The degree of coherence γ that is calculated from a complex SAR image pair can be considered as the product of different correlating factors as long as the sources of correlation are statistically independent [8]:

temporalonregistratibaselinesystemSNR γγγγγ = (2)

In this work, the first three terms on the right hand side of Eq. 2 are factors that one would desire to minimize, so that the measured coherence in an area is main corresponding to the temporal correlation caused by the ground surface change. The influence of system noise on the interferometric phase can be derived theoretically by determining the signal-to-noise ratio of a specific system [9]. This factor will contribute very little to the overall decorrelation when using SAR data that are processed with a high-performance processor. The influence of decorrelation from the baseline and the error of co-registration are reduced by common spectral filtering and fine co-registration of two images. Finally, the temporal decorrelation is the main factor affecting the total coherence in this study.

temporalγ

5 CHARACTERISTICS OF COHERENCE ANALYZING

Total 16 interferograms are produced by the method of data processing presented in the section 4.1. The

parameters of all the pairs, including ID number, the orbits reference number, the spatial perpendicular baseline, and the dates of two images acquired, are list in Tab. 1. In these image pairs, there are different season change pairs from 35 to 346 days, different spatial perpendicular baseline pairs from 14.4 to 1035.8 meters.

Table 1. The parameters of 16 interferograms in this study area.

ID Master time Slave time Orbit ⊥B (m)

A 2004-11-18 2004-12-23 14215/14716 66 B 2004-12-23 2005-01-27 14716/15217 -49.8 C 2005-01-27 2005-03-03 15217/15718 132 D 2005-01-27 2005-04-07 15217/16219 -185.4E 2006-02-16 2006-03-23 20728/21229 544.8 F 2006-04-27 2006-06-01 21730/22231 1035.8G 2006-03-23 2006-04-27 21229/21730 -81.5 H 2004-01-08 2004-05-27 9706/11710 -14.4 I 2005-04-07 2005-09-29 16219/18724 136 J 2005-09-29 2006-02-16 18724/20728 -181.9K 2004-05-27 2004-10-14 11710/13714 18.6 L 2005-01-27 2005-09-29 15217/18724 -49.3 M 2004-01-08 2004-10-14 9706/13714 -37.7 N 2004-12-23 2005-09-29 14716/18724 -64 O 2005-04-07 2006-02-16 16219/20728 53.8 P 2004-11-18 2005-09-29 14215/18724 48.9

5.1 Coherence characteristics at different season interval for different surface

5.1.1 Coherence characteristics for different ground objects

In the time interval of the two images acquired, the change are taken place in scattering geometry, physical property of scattering mechanism for different ground objects, which leads to the coherence difference of ground surface. We chooses 7 interferograms, which have relative short temporal baseline, for comparing characteristics of coherence of different ground surface, and the result is showed in Fig. 3.

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Figure 3 Comparison of different ground objects’ coherence with temporal baseline.

Comparing the change for different ground surface, it can be found that ground objects’ coherence reduces with the elevation drop of ground objects position, which is bare rock, grassland, pebbles and water body from high elevation to low. Bare rock is stable scatter and change of physical property is slow with time, so it has the highest coherence. The alpine alm and the alpine meadow in Beiluhe area appear some conglomerate on ground surface due to thawing and freezing circle, so they have relative high coherence because of the conglomerate’s stable character. At same time, the alm and meadow vegetation is sparse and its growth cycle is relative short， their coherence show relative big change for different time interval. The pebbles are mainly located in the stream valley, and always appear petty gains. They show relative low coherence, because their pattern is easy to change. The water body is easy to be influenced by wind and its surface change very quickly, so it shows the lowest coherence. In summary, there are widely various rang of coherence for different ground surface in Beiluhe area, the bare rock has the highest coherence and the water body has the lowest, and the coherence reduces in turn according to their elevation from high to low order.

5.1.2 Influence of thawing and freezing in the region of permafrost

The microwave backscatter signature of a landscape is controlled by the landscape's structure and dielectric properties. The interaction of an electric field with a dielectric material has its origin in the response of charged particles to the applied field. Liquid water exhibits a dielectric constant that dominates the microwave response of natural landscapes. As water freezes, the molecules become bound in a crystalline lattice, and the dielectric constant decreases substantially. For vegetated landscapes that undergo thawing/freezing transitions, this drop in dielectric constant results in a large backscatter shift. During this time period of thawing/freezing cycle, temperatures of permafrost surface ranged from warm to well below freezing, it change the ground objects’ dielectric constants, where liquid water in the permafrost froze, resulting in a change of ground objects’ coherence.

The cycle of permafrost’s thawing and freezing are controlled by soil, climatic and surface conditions, is related to the surface energy and mass balance, which includes solar and long-wave radiation exchange, evaporation, and sensible and latent heat transfer. There are obvious differences in the thawing and freezing time for different object because of different conditions. We compare coherence of 4 pairs of interferomety, which are made up of two images acquired at the process of temperature falling in winter and the process of thawing in summer. The result is showed in Fig. 4.

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Interferogram A Interferogram C Interferogram D Interferogram H

Figure 4 Influence on coherence of part area firstly season thawing. The temporal baseline correspond with A—Nov.18-Dec.23, C—Jan.27-Mar.3, D—Jan.27-Apr.7,

H—Jan.8-May.27

Comparing different profile change, it can be found that the difference of alpine alm’s and alpine meadow’s coherence is more obvious, the coherence of alpine alm is higher than of alpine meadow in the pair A and C, is lower in the pair C and D.

The two images, composing pair A, are all acquired at the process of temperature falling in winter, when the frozen ground is stable. At that time, the coherence is mainly determined by ground objects’ texture characteristics with little influenced from water (ice). The vegetation on the alpine alm is denser than on the alpine meadow, and conglomerates on the alpine alm have higher density, so the coherence of alpine alm is higher than of alpine meadow.

The images, composing pair C or D, are obtained at the time between the end of January and the beginning of March and April respectively, and this period belongs to the process of temperature rising in spring. In the pair C, when the temperature is rising in March and April, alpine alm firstly begins to thaw because of more density vegetation and energy exchange, and the scattering characteristics correspondingly take place change that leading to decorrelation. However, alpine meadow still keeps in lower temperature, so the coherence of ground objects on alpine meadow varies little. The phenomena presented above results that alpine alm has little lower coherence than alpine meadow. In April, with the temperature rising further, frozen ground in the region of alpine alm thaw strongly and the coherence decrease continually. However, frozen ground in the region of alpine meadow has not began to thaw and the coherence in alpine meadow still keep high value relatively. So, there is a big difference on conherence between alpine alm and alpine meadow in the pair D.

The two images, composing the pair H, are acquired at

the beginning of January and the end of May. At the end of May, all near-surface of permafrost in Beiluhe area enter upon the process of thawing with the temperature rising, and the change from thawing phenomenon is same to all the ground objects, so ground object characteristic becomes the main factor that influences the coherence again in Beiluhe area.

To analyze the coherence characteristics for different thawing/freezing process at all permafrost area, we select 6 interferograms to compare the coherence coefficients. Tab. 2 list the coherence coefficients and perpendicular baseline for the 5 pairs.

Table 2 Comparison of coherence for different process of frozen earth changing

ID Date ⊥B

Whole Railway

0.431 0.340 A 041118-041223 66 0.206 0.157 0.335 0.335 D 050127-050407 -185.4 0.175 0.133 0.283 0.176 H 040108-040527 -14.4 0.152 0.091 0.174 0.176 K 040527-041014 18.6 0.112 0.097 0.198 0.143 O 050407-060216 53.8 0.115 0.090

Comparing the pair A and H, it can be found that the pair H has shorter spatial baseline but lower coherence than the pair A for all kinds of ground objects. The two images, composing the pair A, are both acquired in winter. On the contrary, the two images, composing the pair H, are not acquired in the same season. One is acquired in the stable winter, and the other is acquired at the active process on April. In other words, the pair H experiences the process of frozen ground thawing, so the ground objects’ dielectric characteristics have an evident change and coherence falling correspondingly. This means that the coherence is affected by the thawing process.

With regard to the pair H and the pair K, the value of two pairs’ spatial baseline is very close, but their coherence has a great difference and the coherence of the pair H is much higher than that of the pair K. Comparing the time of image acquired for two pair, we can find that the pair H is from a stable statue to an active statue and the pair K goes through two active process which are thawing and freezing, so in the pair K ground surface change a lot and the effect of decorrelation is more strong.

Both the pair O and the pair D experience the process of thawing and freezing, but the pair O goes through all thawing/freezing circle in almost one year interval. The coherence of the pair O is lower than that of the pair D, which means that ground surface has seriously decorrelated after the process of thawing and freezing,

even the ground surface goes back to the stable status again.

5.2 Coherence analyzing of Qinhai-Tibetan railway and highway

Whenever enough images are available, DInSAR limitations can be overcome by adopting a multi-interferogram framework. The PS technique takes advantage of long temporal series of SAR data, acquired over the area of interest along the same (nominal) satellite orbit, to filter out atmospheric artifacts and to identify a subset of image pixels where high-precision measurements can be carried out. These pixels, almost unaffected by temporal and geometrical decorrelation (usually but not necessarily corresponding to man-made objects) are PS [10, 11].

There are Qinghai-Tibetan railway and highway passing Beiluhe area. We select four typical coherence images (Fig. 6) containing the railway for comparing and analyzing. There are two lines interlacing with each other in these images, the left one is the Qinghai-Tibetan railway, and the right one is the highway. The surfaces around the two ways belong to alpine meadow.

(a) (b)

(c) (d) Figure 6 Coherence of Qinghai-Tibet railway and

highway in different interferograms, (a) coherence at pair A, (b) coherence at pair B, (c) coherence at pair E,

(d) coherence at pair F

Fig. 6(a) and 6(b) show the local coherence in the pair A and B, and Fig. 6(c) and 6(d) correspond to the pair E and F. In Fig. 6(a) and 6(b), the coherence of the railway and highway has not big difference with the coherence of alpine meadow, so the railway and highway is not

obvious in the two figures. However, in Fig. 6(c) and 6(d), the two ways are much higher coherence than the surround objects.

The coherence of the four pairs (A, B, E, and F) for the railway and alpine meadow are calculated, all parameters are listed in Tab. 3. The two images, composed the pair A and B, are acquired in the winter, the frozen ground is stable in this period, so all objects including the railway show high coherence. The images, composed the pair E or F, acquired in the different process with long spatial baselines. The coherence coefficients of all different surfaces are too low to loose their meaning. However, the railway and highway keeps higher coherence than the others (Fig. 6c and 6d).

Table 3 Comparison of coherence of Qinghai-Tibet railway, highway and alpine meadow

ID Date ⊥B meadow Railway

A 041118-041223 66 0.484 0.340

B 041223-050127 -49.8 0.745 0.461

E 060216-060323 544.8 0.102 0.221

F 060427-060601 1035.8 0.097 0.162

The Qinghai-Tibetan railway and highway keep high coherence than other objects under the condition of long spatial baseline. The image pixels, representing the railway and highway, are coherent over almost all the observation. So these pixels can be chosen as permanent scatters for deformation sequence analyzing of frozen ground.

6 CONCLUSIONS

In this paper, we process 13 ASAR images and obtain 16 interfergrams with different temporal and spatial baseline. The characteristics of coherence for different surface objects are analyzed at Beiluhe area in the Qinghai-Tibetan plateau. Due to the difference of their structure and dielectric properties, ground objects show widely variety on their coherence coefficients.

The permafrost’s thawing and freezing influence the ground objects’ pattern and structure, and lead to change of ground objects’ coherence. Between the active periods of frozen ground, coherence reduces obviously and is mush lower than in the stable period. With temperature rising and beginning of process of thawing, alpine alm’s coherence reduces quickly. The ground objects’ coherence pass two active processes are lower than after one active process, and are full decorrelation after whole thawing/fazing circle procedure in the study area.

The Qinghai-Tibetan railway and highway keep high coherence under the condition of long spatial. The image

pixels, representing the railway and highway, are coherent over almost all the observation. So these pixels can be chosen as permanent scatters for deformation sequence analyzing of frozen ground.

ACKNOWLEDGEMENT

The work is funded by NFSC with 40671140. The ASAR SAR data were provided by ESA through the Category-1 ID:1406. The authors wish to thank Dr. Bert Kampes for providing Doris software.

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