comparison of sar and optical data in deriving glacier velocity with feature tracking

Comparison of SAR and optical data in deriving glacier velocitywith feature tracking

LEI HUANG*†‡ and ZHEN LI†

†Centre for Earth Observation and Digital Earth, Chinese Academy of Sciences,

Beijing 100012, China

‡Graduate University of the Chinese Academy of Sciences, Beijing 100190, China

(Received 11 February 2009; in final form 12 January 2010)

Feature tracking is an efficient way to derive glacier velocity. It is based on a cross-

correlation algorithm that seeks offsets of the maximal correlation windows on

repeated satellite images. In this paper we demonstrate that different window sizes

lead to different velocities. The averaged velocity gradient (AVG) method is

proposed to improve window sizes in feature tracking and to obtain the most

suitable flow field. The AVG method measures velocity variation between adjacent

windows on the whole glacier in the image. Different window sizes lead to different

AVG values, and the best-size window corresponds to the value where the AVG

changes from abrupt to gradual. Using improved feature tracking, two flow fields

of the same glacier are acquired with Advanced Land Observing Satellite (ALOS)

optical and synthetic aperture radar (SAR) data, respectively. The advantages,

application conditions, accuracy and disadvantages of the two kinds of data using

the feature tracking method are discussed.

1. Introduction

Glaciers and ice caps provide some of the most visible indications of the effects of

climate change. The mass balance at the surface of a glacier is determined by theclimate. Changes in glaciers and ice caps reflect climate variations, in many cases

providing information in remote areas (IPCC 2007). Ice movement is an important

factor in glacier changes. According to movement velocity, glaciers generally fall into

two types: fast-moving outlet glaciers, mainly lying in Greenland and in the Antarctic

Ice Sheet, and slow-moving glaciers, commonly lying in alpine areas (Herzfeld et al.

2004). Moreover, glacier motion may be related to some casualties, especially when ice

melting accelerates markedly, as has been observed in recent years throughout the

world (Paul et al. 2007). Glacier hazards in high mountains include glacier-relatedfloods, stable and unstable glacier length changes, glacier fluctuations, and glacier-

related mass movement (Kaab 2002, Kaab et al. 2005). However, the scarcity of

temporal velocity data has made it difficult to explain the nature of the relationships

between thinning, acceleration and retreat in these glaciers, and also to forecast

hazards (Luckman et al. 2006).

Although in-situ observation of glacier velocities with differential global position-

ing systems (DGPS) can be very accurate, it is costly and spatially limited. Remote

sensing provides new choices in the measurement of the surface motion of glaciers.

*Corresponding author. Email: [email protected]

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2011 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431161003720395

International Journal of Remote Sensing

Vol. 32, No. 10, 20 May 2011, 2681–2698

Global Land Ice Measurements from Space (GLIMS) was established to gather

images of the world’s glaciers, analysing them for their extent and changes (Kargel

et al. 2005, Raup et al. 2007). Radar interferometry and feature tracking are two

methods that have been used frequently in previous research. Radar interferometry is

accurate, as proved in several studies (Kwok and Fahnestock 1996, Joughin et al.1998, Eldhuset et al. 2003); however, the successful use of differential synthetic

aperture radar (SAR) interferometry is limited by phase noise, usually characterized

by its coherence. Across glacier surfaces the coherence is affected by both meteor-

ological and flow conditions, and generally diminishes with increasing time intervals

between the acquisitions of the two SAR images used in the interferogram.

Meteorological sources of decorrelation include ice and snow surface melt and

possibly snowfall and wind through the redistribution of snow and ice (Strozzi et al.

2002). Another limiting factor for both interferometric SAR (InSAR) and SARfeature tracking in mountainous terrain arises from the SAR image geometry, leading

to incomplete spatial coverage due to layover and shadowing (Strozzi et al. 2004).

SAR images used for interferometry usually require short-term intervals, which are

frequently impossible to obtain for fast-moving glaciers (Lange et al. 2007). Optical

data are useful sources, as they enable the tracking of visible features, but because the

method relies on illumination by the Sun, it is severely limited by cloud cover. Optical

and SAR data are complementary in glacier monitoring, and a comparison of the two

kinds of data is useful for estimating the accuracy of the velocity results.Feature tracking in SAR imagery is similar to optical satellite imagery. In the case

of SAR images, either intensity or coherence of complex data can be used (Strozzi

et al. 2002), and in this paper SAR intensity images are used. Compared to InSAR, the

tracking technique is more useful for measuring flow velocities over longer periods

(Scherler et al. 2008). This method has its own requirements (Kaab 2005): (1) surface

features have to be detectable in at least two images; (2) the multi-temporal datasets

have to be accurately co-registered; and (3) the spatial resolution of the images has to

be finer than the displacements.

2. Study area and data

The Keqikaer Baxi glacier, a large dendritic mountain valley glacier lying on the western

Tienshan Mountain in China, as shown in figure 1(a), was chosen as the study area. One

distinctive characteristic of this glacier is the presence of debris covering a large portion

of the ablation zone, as shown in figures 1(b) and 1(c). At an altitude above 3800 m asl,

the glacier is covered by snow and ice; from 3020 to 3800 m asl it is covered by debris.The debris thickness extends from several centimetres at 3800 m asl to about 2 m at the

terminus of the glacier, and in general the thickness increases with a decrease in altitude.

The average thickness of the ice under the debris is 63 m, and 78 m along two profiles on

the glacier as measured in 2004 (Xie et al. 2007). The debris, composed of rocks and grey

soils, is used on optical images for visualizing the glacier. Thus, a snow-covered glacier

is not suitable because of its intense reflectivity. The period from May to October is the

melting season for the glacier (Zhang et al. 2004).

The data used in this paper were all obtained from the Advanced Land ObservingSatellite (ALOS), which was launched in January 2006. ALOS carries two optical instru-

ments, the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and

the Advanced Visible and Near-Infrared Radiometer type 2 (AVNIR-2), and also a SAR

instrument, the Phased-Array L-band SAR (PALSAR; Rosenqvist et al. 2007). We used

2682 L. Huang and Z. Li

the PRISM data with a resolution of 2.5 m, and Single-Look Complex (SLC) data in the

Fine Beam Single Polarization (FBS) mode of PALSAR with about 3.1 m pixel spacing in

the azimuth direction and 4.7 m pixel spacing in the slant-range direction. All available

images for this study are listed in table 1.

ALOS PALSAR is considered to be the successor of the Japanese Earth Resources

Satellite (JERS), which also operates in the L-band. It has been demonstrated thatoffset tracking of L-band SAR images is a robust and direct technique to estimate

(a)

(b) (c)

Figure 1. (a) Map and location of the Keqikaer Baxi glacier. (b) and (c) Photographs takenfrom the debris-covered Keqikaer Baxi glacier.

Deriving glacier velocity from ALOS data 2683

glacier motion (Nakamura et al. 2007). Furthermore, the L-band of the radar signals

has better penetration into snow as compared to the C-band, and it adds correlation

between two long-term interval images, which is preferable for velocity mapping

(Rignot et al. 2001, Strozzi et al. 2006, 2008).

Digital elevation model (DEM) data are necessary for terrain correction and three-dimensional (3-D) velocity correction. Shuttle Radar Topography Mission (SRTM)

DEM data were used in this study. The spatial posting of the SRTM DEM is

approximately 90 m, and the 90% height error for Eurasia is given as 8.7 m

(Rodrıguez et al. 2006). Glaciers in the study area have a low slope, and therefore

while SRTM data in the region might be imprecise in general, the glacier elevation

data used directly in the derivation of surface velocities are likely to be of good quality

(Luckman et al. 2007).

3. Methods

In the feature tracking method, two co-registered satellite images taken at differenttimes are used. The first image (referred to as the master image) is divided by grids,

and each window searches for its most similar counterpart in the second image (called

the slave image), as shown in figure 2. The correlation coefficient determines similar-

ity; coordinate displacement and time interval determine flow velocity.

Table 1. List of available ALOS data. Two image pairs (6 January 2007 and 24 February2008, 9 May 2007 and 9 June 2008) were used to measure the displacement of the glacier with

feature tracking.

Image Sensor Path Frame Ascending (A)/descending (D)

6 January 2007 PALSAR 514 820 A24 February 2008 PALSAR 514 820 A9 May 2007 PRISM 183 2760 D9 June 2008 PRISM 182 2760 D

Master image

Slave image

Maximalcorrelation

SearchareaP′

P

Window

x

y

Figure 2. Sketch map of feature tracking.


In the following sections, the basic processes of feature tracking are introduced and

improvements are made to optimize the velocity results. These processes are pro-

grammed and fulfilled with Interactive Data Language (IDL).

3.1 Co-registration

The distortion and offset between two satellite images of the same area include many

factors, such as misregistration, topography, orbits and altitude as well as the glacier-

dynamic signal (Berthier et al. 2005). To obtain a valid measurement, all the con-

tributions except the glacier flow should be removed.

The optical images used in our experiment are from different path numbers, so the

datasets should be accurately co-registered and terrain corrected. SAR images in steep

mountainous terrain require very accurate DEM to be correctly orthorectified(Trouve et al. 2007). The two SAR images come from the same path and frame

numbers, so their image centre coordinates are extremely close. In addition, despite

being surrounded by mountains, the glacier tongue itself in this study is not a very

rugged area, so the stereo distortion can be ignored. To keep the surface features

intact and avoid errors caused by image resampling and inaccurate terrain correction,

single-look SAR intensity images are co-registered without terrain correction. Flow

field from a single-look image is resampled and projected onto a georeferenced SAR

base image. For both optical and SAR images, the points on the surface of the glacierare not allowed to be selected as ground control points in the process of

co-registration because they are considered to be unstable. Statistical analysis of

noise and previous accuracy assessments predict a co-registration error of 0–1

image pixel size (Kaab 2005), that is 0–2.5 m for the ALOS optical image.

3.2 Correlation coefficient

The correlation coefficient is a crucial factor in the process of searching for matchingwindows between master image and slave image. There are three popular matching

functions: the cross-correlation coefficient (CCC), the sum of squared difference

(SSD), and sum of the absolute value of difference (SAVD). Although it takes more

time, the CCC function has better performance (Wu et al. 1997, Evans 2000). In our

experiment we used the CCC function:

CCCðu; vÞ ¼

PMy¼1

PNx¼1

ðf ðx; yÞ � �f Þðgðxþ u; yþ vÞ � �gðu; vÞÞ

PMy¼1

PNx¼1

ðf ðx; yÞ � �f Þ2 !1=2 PM

y¼1

PNx¼1

ðgðxþ u; yþ vÞ � �gðu; vÞÞ2 !1=2

(1)

Corresponding to figure 2, f(x,y) is the pixel value in window P of the master image;g(x,y) is the pixel value in the counterpart window P0 of the slave image; u; v are

offsets between P and P0; �f ; �gðu; vÞ are average pixel values of P and P0; and N;M are

the lengths of the two sides of a window that is measured in pixels. The search area

should be large enough to ensure that the largest possible displacement is included.

The correlation coefficient calculated from equation (1) ranges from 0 to 1. In the

search area, the window with the highest correlation coefficient is taken as the most


similar window to calculate flow velocity. The windows are oversampled to subpixel

level before calculation, and the offset accuracy is expected to be 0.1 pixel.

3.3 Velocity calculation

The centre coordinates of window P in the co-registered master image are ðx; yÞ, while

in the slave image the centre coordinates of the maximal correlated window P0 are

ðxþ�x; yþ�yÞ. T is the time interval between acquisitions of two images. The

velocity Vh can be calculated from the displacement �x;�y as follows:

Vh ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðRx�xÞ2 þ ðRy�yÞ2

qT

(2)

Rx and Ry are the pixel spacings in the x and y directions, respectively. They are equal

for optical images, and they represent the pixel spacing in the azimuth and slant rangedirections for SAR data.

3.4 Window size

Window size is a significant parameter in calculating offsets of images. An excessively

small size causes instability in a single window, thus it is unable to find its counterpart

in the other image. However, an oversized window reduces plane resolution of the

flow field. In our experiments we tried to determine a moderate window size to

provide glacier velocity with the best precision.

The experiments are based on the hypothesis that the velocity of a glacier changes

gradually on a large scale. The glacier body is assumed to be a whole, so if an

individual window has a much higher or lower velocity than nearby windows itdisrupts that mass. Windows with abnormal velocities that differ distinctively from

nearby ones are considered noise. Special cases may exist in small areas with steep

terrain, but continuity is a basic principle on a large scale. The averaged velocity

gradient (AVG) is introduced as follows:

AVG ¼

PNi¼l

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiR

y2

Rx2þR

y2ðViðx; yÞ � Viðxþ n; yÞÞ2 þ R

x2

Rx2þR

y2ðViðx; yÞ � Viðx; yþ nÞÞ2

� �rN

(3)

It should be stressed that AVG in this equation is about velocity, so the function

operates on velocities of windows rather than on grey values of image pixels. In

equation (3), Rx and Ry are the window spacings in the x and y directions, respectively,

and they are proportional to the pixel spacing;R

x2

Rx2þR

y2and

Ry2

Rx2þR

y2are weights assigned

to different directions. The weights indicate that in the case of the same velocity

differential value, shorter window spacing yields a higher AVG value. In the equationn is the length of a window (n� n pixels contained in a window); Viðx; yÞ is the velocity

of the ith window in the coordinates of ðx; yÞ, while Viðxþ n; yÞ;Viðx; yþ nÞ are

velocities of its adjacent windows; and N is the total number of windows. AVG

expresses the velocity variation of the entire flow field, and high AVG values denote

strong changes.

Figures 3 and 4 reveal that for both optical and SAR images, different window sizes

lead to different velocity fields, and AVG and noise decrease as window size increases.


When a window is 10 � 10 in size, the optical image shows disorderly results and the

SAR image is correspondingly unsatisfactory. As the window size grows, the velocity

tendency becomes clear and stable. When a window is 50 � 50 or 60 � 60 in size, the

velocity results appear to be similar on the optical image; the SAR image corresponds.

To validate whether the offsets of image patches are from glacier dynamics, some

stable non-glacier areas are also taken into the velocity calculation.

Figure 5(a) shows that AVG value changes abruptly when the window size is small

and the changes becomes gradual when the window size increases to a certain extent.When a window is small in size, noise from mismatches plays a key role in raising the

AVG value, and the influence of noise shrinks as the window size grows. When AVG

changes gradually with window size, it is thought that noise is no longer the dominat-

ing factor in AVG changes, although it still exists. However, as the window size

increases, the centre-to-centre spacing between the windows increases as well, losing

velocity details and decreasing the plane resolution of the flow field. Considering the

two sides, we pick the window size corresponding to the turning point where the AVG

curves shift from abrupt to gradual as the optimal size. In figure 5(a) we fitted straightlines with every three continuous points in the curve from beginning to end. While the

30 m year–1

20 m year–1

10 m year–1

0 m year–1

30 m year–1

20 m year–1

10 m year–1

0 m year–1

(c)

(f)

(b)

(e)(d)

(a)

Figure 3. Glacier velocity resulting from different window sizes of optical images. Six out of16 images are shown here. Window sizes are (a) 10 � 10, (b) 20 � 20, (c) 30 � 30, (d) 40 � 40,(e) 50 � 50, and (f) 60 � 60.


slope rate ð¼ j�ðAVGÞ=�W j; �ðAVGÞ and �W are intercepts on the axes) of the

fitted line becomes less than 0.2, it is regarded as a gradual slope, and the middle one

of the three points is defined as the turning point corresponding to the optimal size.When this method is used in the experiment, the optimal window size of the optical

image is 30 � 30 pixels, while the optimal window size of the SAR image is 50 � 50

pixels. In fact, the flow field acquired from the best-sized window is a balance between

30 m year–1

20 m year–1

10 m year–1

0 m year–1

30 m year–1

20 m year–1

10 m year–1

0 m year–1

(c)(b)(a)

(f)(e)(d)

Figure 4. Glacier velocity resulting from different window sizes of single-look SAR images.Six out of 16 images are shown here. Window sizes are (a) 10 � 10, (b) 20 � 20, (c) 30 � 30,(d) 40 � 40, (e) 50 � 50, and (f) 60 � 60.


velocity smoothness and velocity details. The AVG method used to find the best-sized

window can be summarized in the following three steps:

(1) A series of glacier velocity maps are obtained in different window sizes, which

change regularly from small to large. In our experiment flow fields are obtained

every five pixels from 5 to 80.

(2) AVG values are calculated from velocity maps, and curve diagrams of AVGvalues and window sizes are constructed.

(3) The turning point that switches the AVG curve from abrupt to gradual is

selected, and its corresponding window size is taken as the optimal size.

In figure 5(a) the SAR image gets higher AVG values than the optical image in every

size. The SAR and the optical images have different pixel spacing and texture. To

determine the primary reason for the difference in the AVG curves, we resampled the

optical image with SAR image pixel spacing and resampled the SAR image with

optical image pixel spacing. AVG values of the resampled image were calculated and

the differences were found to be small between the resampled and original images (see

figure 5(b)). The gap of the AVG value between the SAR image and the resampled

24

22

20

18

16

14

1210

8

6

0 10 20 30 40 50 60 70 80Window size (pixels)

(a)A

VG

SAR imageOptical image

0 10 20 30 40 50 60 70 80Window size (pixels)

24

22

20

18

16

14

12

10

8

6

AV

G

SAR image

Resampled SAR imageResampled Optical image

Optical image

(b)

Figure 5. (a) Relationship between window size and AVG for SAR and optical images. (b)The SAR image is resampled with pixel-space of the optical image, and the optical image isresampled with pixel-space of the SAR image. AVG values for these two kinds of images arecalculated.


optical image is still wide despite the same pixel spacing, and similar results occur

between the optical image and the resampled SAR image. This indicates that the

image texture has greater impact on the AVG value than pixel spacing, and image

texture is the main cause of the AVG difference.

Referring back to figures 3 and 4, the flow field calculated from the best-sizedwindow contains three characteristics: (1) it reflects the glacier velocity trend clearly;

(2) mismatched noises are reduced more obviously than in smaller sizes; and (3) it

retains more velocity details than in larger sizes. In brief, the flow field derived from

the best-sized window contains the maximal velocity information.

3.5 Rejection criterion

Besides movement, other variations also exist on glaciers, such as melting ice androlling rocks. Image mismatches resulting from these variations are inevitable. The

signal-to-noise ratio (SNR) has been used as a measure that expresses the correlation

strength of a match to discriminate between good and false matches (Strozzi et al.

2002, Lange et al. 2007). This value expresses the strength of the modelled correlation

maximum relative to the averaged remaining field. However, the SNR values

obtained in this experiment do not differentiate well between correct results and

mistakes when no high-pass filtering has been applied.

According to the hypothesis that velocity variation is gradual on a glacier, isolatedwindows with much higher or lower velocity are regarded as noise caused by mis-

matches. To eliminate this kind of noise, the velocity gradient in a single direction

(VGS) of a single window is used in the following form:

VGS ¼ RyffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiRx2 þ Ry2

p jðViðx; yÞ � Viðxþ n; yÞj (4)

Rx, Ry, Vi (x,y) and Vi (x þ n,y) have the same meaning as in equation (3), and VGS

expresses the velocity gradient in one out of four adjacent directions of a window. By

comparing VGS values of four adjacent directions to a threshold, robust velocity

estimates can be selected, so that the flow field is spatially incomplete but of high

confidence. For windows on the edge with less than four adjacent windows inside the

flow field, comparison in three or two directions is sufficient. In this experiment, ifVGS values in at least two directions do not exceed a threshold, the VGS values will be

saved, or they will be excluded. VGS¼ 5 is set as the threshold and the filtered velocity

is shown in figures 6(a) and 7(a). In addition, figure 6(a) shows the shape changes of

the glacier lakes. Excessive deformation of the lakes makes them difficult to track and

leads to mismatching. As the velocity field is smooth, the incomplete space can be

linearly interpolated with adjacent velocities. The flow fields after linear interpolation

are shown in figures 6(b) and 7(b).

The velocity of the SAR image is also filtered with the VGS criterion. A multilookimage with two-looks in range and four-looks in azimuth is generated and geometric

registration is performed. The flow field acquired from SAR data is projected onto the

georeferenced image as in figure 7, for easier comparison with the optical image.

Compared to figure 6(a), figure 7(a) contains fewer discontinuous spaces of the flow

field. Due to lower spatial resolution and side-looking character, the SAR images are

not as sensitive to changes in small lakes as are optical images. In addition, the larger

window size used in the SAR image includes more features, which will add stability for


velocity computation. In figure 6, 3-D velocity correction is also performed, as

described in the next section.

3.6 3-D velocity correction

Flow velocity Vh calculated in §3.3 is simply a projection of V onto the horizontalplane. The 3-D velocity V, which is the real velocity of the glacier surface, can be

calculated from Vh and DEM as:

30 m year–1

20 m year–1

10 m year–1

80°05′E80°05′E

80°05′E80°05′E

80°09′E80°09′E

80°09′E80°09′E

41°42′N41°42′N

41°45′N

41°42′N41°42′N

41°45′N41°45′N41°45′N

41°48′N41°48′N41°48′N41°48′N

0 m year–1

(a) (b)

Figure 7. (a) Velocity map after VGS filter of the multiple-look georeferenced SAR image.(b) Complete velocity map after linear interpolation.

30 m year–1

10 m year–1

20 m year–1

0 m year–1

(b)(a)

Figure 6 (a) Velocity map after VGS filter of the optical image, and lake changes on theglacier. (b) Complete velocity map after linear interpolation.


V ¼ Vh

cos �(5)

where � is the angle of the gradient, which can be acquired from the DEM (Florinsky

1998), as shown in figure 8. In figure 8, P is a point on an ortho-image whose ground

coordinates and plane velocity are known. Furthermore, its location on the DEM can

also be obtained.This step is a conversion from image offsets to ground displacements; however, it is

only performed on the optical image in our experiment. As mentioned in §3.1, the

SAR image is difficult to be orthorectified in this area. The glacier is relatively flat, so

the terrain has little influence on the velocity results, and the 2-D velocity is used as the

ultimate result for the SAR image. In §§3.4 and 3.5, the 3-D velocity correction is not

performed and the 2-D velocity is used for both the optical and SAR images. There is

no need to use the 3-D velocity in these two steps because they involve only the relative

velocities between adjacent windows on the image.

4. Results and analyses

The results of the analyses are based on the velocity maps of figures 6(b) and 7(b).

Debris is covered by snow and ice on the upper side of the glacier, so debris features on

the optical images are visible only below 3600 m asl. Above 4000 m asl, the glacier is

overlaid and shadowed by steep mountains in the SAR image.

Flow fields obtained from both optical and SAR images show the same trend thatthe Keqikaer Baxi glacier moves slower at the bottom and faster as the elevation

rises, whereas its velocity reduces again between 3900 and 4000 m asl. The velocity

maps confirm that it is a healthy and dynamic glacier. To better illustrate the

potential for these two kinds of data in monitoring velocities in the glacier, centre-

line profiles of glacier surface elevation (see figure 9) are extracted. Meanwhile,

velocity (see figure 10(a)) and deviation (see figure 10(b)) of two kinds of data along

the centre line are presented. To reduce accidental error rates, along the centre-line,

average velocities within small circles (radius ¼ 100 m on the ground) ratherthan independent points are sampled to make a comparison between the two flow

fields.

Table 1 shows that the optical and SAR data used do not cover exactly the same

period of time. In fact, it is very difficult to get completely synchronous data in the

same area considering the snow, cloud and satellite orbits. This is partly because the

Normalline

P

P

Image

DEM

V

Vh

θ

VhP′

P′

Figure 8. Horizontal velocity and real velocity of the glacier surface.


satellite was launched only 2 years ago. Fortunately, the two pairs of data cover the

same melting season in 2007: from May to October, the most active period of the year

in this glacier (Zhang et al. 2004). Therefore, comparison between the two results from

optical and SAR data is still meaningful.

In figure 1(a), at the elevation of 3800 m asl the glacier is divided into two primary

branches. Velocity and topography results of the right branch are used in figures 9 and10. Figure 10(a) reveals the velocity results acquired from the two kinds of data. The

maximal velocity of the optical image appears at about 3550 m asl, moving at a rate of

13.6 m year–1; the maximal velocity of the SAR image appears at the elevation of

about 3850 m asl, moving at a rate of 17.4 m year–1. It is clear from figure 9 that at

3550 and 3850 m asl the glacier has two descents, which accelerate the flow of the

glacier. Minimum velocity appears at the terminus of the glacier almost at the same

181614121086420

0 2 4 6 8 16141210Distance from top (km)

Vel

ocity

(m

yea

r–1) Optical image

SAR image

(a)

2

0

–2

Dev

iatio

n (m

yea

r–1)

(b)

0 2 4 6 8 16141210

Distance from top (km)

Vopt-Vsar

Figure 10. (a) Velocity of the glacier surface from optical and SAR imagery. (b) Deviation ofthe two flow fields. Vopt is the velocity from the optical images and Vsar is the velocity from theSAR images.

4000

3800

3600

3400

3200

30000 2 4 6 8 10 161412

Distance from top (km)

Ele

vatio

n (m

)

Figure 9. Topographic change of the Keqikaer Baxi glacier along the centre-line.


rate of 1.8 m year–1. According to equation (2), the minimal detectable displacement

of optical images is 2.3 m year–1 (pixel spacing is 2.5 m; time interval is 13/12 years;

minimal displacement is 1 pixel on image). For a single-look SAR image this value is

2.86 m year–1 in the azimuth direction (pixel spacing is 3.1 m) and 4.34 m year–1 in the

slant-range direction (pixel spacing is 4.7 m). An averaged velocity of 1.8 m year–1,

which is slower than the minimally detectable displacement in a year, indicates that

the velocity of some areas inside the sampling circle is zero, that is there is nodetectable displacement in those small areas. Two branches of rivers originate from

the terminus. It is speculated that fast ice melting and flat terrain may be the reason

that the glacier flows slowly at the terminus.

Furthermore, 35 circular areas (radius ¼ 100 m) are evenly sampled to provide a

map (figure 11) of velocity difference for the entire velocity field. The greatest velocity

difference is 5.6 m year–1. The average absolute velocity difference of these circular

areas is 1.37 m year–1.

Until now, no in-situ measuring results or published information with which tocompare velocities on the Keqikaer Baxi glacier have been available. Despite some

differences, the two respectively obtained velocity curves reveal high levels of spatial

consistency. This suggests that velocities obtained from the two kinds of data can be

validated against each other. The average velocity of non-glacier areas is 0.14 m year–1

for the optical image and 0.23 m year–1 for the SAR image. As the non-glacier areas

are stable, the velocities obtained from these areas that come close to zero provide

additional validation of the velocity results. Figure 10(b) shows that flow field can be

divided into three parts: (1) at the elevation between 3400 and 3600 m asl the opticalimage gets a higher velocity than the SAR image; (2) the result reverses between 3400

and 3200 m asl; and (3) between 3200 m asl and the terminus two results correspond

well with each other. By computing the average absolute velocity difference, we obtain

an absolute deviation, 0.71 m year–1, lower than the counterpart value (1.37 m year–1)

of the entire flow field. To explain the difference, several factors are taken into

Figure 11. Velocity differences of the entire flow field in common areas that are covered byoptical and SAR images.


account: first, as mentioned above, image co-registration contains its intrinsic error,

the 0–1 image pixel, which translates to 0–2.5 m for the optical image and 0–3.1 m in

the azimuth or 0–4.7 m in the range direction for the SAR image. By contrast, the

absolute deviation 0.71 m year–1 is within the extent of the co-registration error.

Second, optical and SAR data are not completely synchronous with each other; aminor difference between the two velocities is possible, since the dynamics of the

glacier change with time. Third, it is thought that different types of image features lead

to small deviations in some windows. The major issue with SAR imagery is that basic

textures are generally affected by multiplicative speckle noise. The presence of speckle

reduces the radiometric resolution of the image and detectability of the image features

(Gupta and Gupta 2007). However, the speckle is retained because reduction of

speckle effects also destroys image textures. Moreover, the optical data used have

higher spatial resolution, so it is thought that the flow field obtained from the opticalimage has relatively higher precision, and some deviation between the two flow fields

is inevitable. On the contrary, the SAR image is less influenced by snow and clouds.

This perspective is notable in our experiment, because the flow field of the upper part

of the glacier cannot be obtained from optical images due to snow cover.

5. Discussion

ALOS optical and SAR data in 2007 and 2008 regarding the Keqikaer Baxi glacierwere used for the velocity map with feature tracking. Based on different imaging

mechanisms, different processes of pretreatment are illustrated. Optical images

require terrain corrections before co-registration, but this is not necessary for SAR

images. Orthorectification is difficult for SAR images in mountainous areas. It is

suggested that single-look SAR intensity images be first co-registered without terrain

correction to better maintain the raw glacier surface features. The flow field calculated

from single-look intensity data can later be projected onto a multilook georeferenced

image. Differences in imaging mechanisms ensure that the two kinds of data arehighly complementary in feature tracking. Optical images are good at expressing

visible surface features, while the advantage of SAR data is penetration of cloud

and snow.

Our research shows that different window sizes lead to different flow fields, but how

to find the best-sized window was not thoroughly addressed in previous studies. We

propose a new method using the AVG to solve this problem. AVG is defined to depict

variations in glacier velocities. AVG values fluctuate at high levels when the window

size is small and AVG values become stable as the window size increases. We havedemonstrated in this paper that the turning point of the AVG curve from fluctuation

to stability is selected as the best-sized window. This turning point is a balance

between the AVG and plane resolution of the flow field. In addition, the VGS is

used to eliminate error values. A window with a velocity that distinguishes it from

nearby windows is considered as error, according to the hypothesis of smooth varia-

tion of glacier velocity.

Cross-correlation can also be performed in the frequency domain by the conjugate

multiplication of fast Fourier transform (FFT) for the purpose of computationalefficiency (Zitova and Flusser 2003). In previous research, cross-correlation has

frequently been performed in the spatial domain for optical images that usually

contain high-quality image features; it has been performed in either the spatial

(Nakamura et al. 2007, Strozzi et al. 2008) or the frequency (Lange et al. 2007) domain


for SAR data. In our experiment, the cross-correlation calculation of SAR data was

performed in the spatial domain to correspond with the optical data. The AVG and

VGS methods are also applicable if cross-correlation is performed in the frequency

domain. These methods deal with windows containing velocity information, whether

the velocity is calculated in the spatial or the frequency domain.The flow field of the Keqikaer Baxi glacier was obtained from both optical and

SAR data. It has been shown on the flow field that the velocities increase with the rise

in elevation. The Keqikaer Baxi glacier is confirmed to be a dynamic and healthy

glacier. Although some differences exist between the two flow fields, our research

reveals that they display high levels of correlation and consistency.

Acknowledgements

Our research was supported by the National Basic Research Programme of China

(Grant No. 2009CB723901), the National Natural Science Foundation of China

(40671140) and the Chinese Academy of Sciences (kzcx2-yw-301). We are very grate-

ful to the Cold and Arid Regions Environmental and Engineering Research Institute,

the Chinese Academy of Sciences. With their help, we accomplished our fieldwork on

the Keqikaer Baxi glacier during June and July of 2008. Thanks also to the Japan

Aerospace Exploration Agency (JAXA) for providing ALOS study data.

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